SSRN — obnove (302)

SUMMARY: This study examines whether firms with higher cash holdings during periods of monetary tightening exhibit stronger resilience in terms of investment, shareholder payouts, financial stability, market risk, and profitability compared to firms with lower liquidity reserves. The research uses difference-in-differences regressions with firm and year fixed effects, lagged industry-adjusted cash holdings, firm-level controls, and firm-clustered standard errors. The study covers publicly traded non-financial and non-utility firms in the United States from 2002 to 2024, distinguishing three monetary tightening cycles (2004-2006, 2015-2018, and 2022-2023) from non-tightening years. The findings indicate that firms with higher cash holdings show stronger investment, payout, financial-stability, market-risk, and profitability outcomes during tightening periods. METHOD: The study employs difference-in-differences regressions with firm and year fixed effects, lagged industry-adjusted cash holdings, firm-level controls, and firm-clustered standard errors. The data used is a panel of up to 90,988 firm-year observations for publicly traded non-financial and non-utility firms in the United States from 2002 to 2024, distinguishing three monetary tightening cycles from non-tightening years. The analysis also includes entropy balancing, a residual excess-cash specification, two-way clustering, and dynamic event-study methodology using Stata 18. KEY FINDINGS: - Firms with higher cash holdings show stronger investment outcomes during tightening periods. - Firms with higher cash holdings exhibit stronger payout outcomes during tightening periods. - Firms with higher cash holdings display stronger financial-stability outcomes during tightening periods. - Firms with higher cash holdings show lower market-risk outcomes during tightening periods. - Firms with higher cash holdings exhibit stronger profitability outcomes during tightening periods. IMPLICATION FOR TRADING: The findings suggest that maintaining higher levels of corporate liquidity can enhance a firm's resilience during periods of monetary tightening. Practitioners can use this information to develop strategies that focus on maintaining sufficient cash reserves, which can help mitigate the negative effects of monetary tightening on investment, payouts, financial stability, market risk, and profitability. Additionally, this research can inform policymakers and regulators about the importance of corporate liquidity in ensuring financial stability and resilience during economic downturns.

SUMMARY: This article explores the impact of diversification in multi-asset trading, specifically focusing on the cost of trading and the effectiveness of linear versus nonlinear trading rules. The authors use a quadratic cost matrix to model trading costs, which is a benchmark rather than a literal description of actual impact. They find that under package pricing, the gain from a nonlinear rule decreases as the number of independent trading directions increases, while under name-by-name pricing, the gain remains relatively constant. The authors also provide a closed-form elasticity to predict the nonlinearness of the rule across a wide range of costs. The main finding is that the effectiveness of a nonlinear trading rule varies significantly depending on whether the cost is aggregated across all assets or applied individually to each asset. METHOD: The study uses a quadratic cost matrix to model trading costs in multi-asset portfolios. The authors compare two different pricing models: one where the cost is aggregated across the portfolio (package pricing) and another where the cost is applied individually to each asset (name-by-name pricing). They test these models by calibrating different nonlinear trading rules and evaluating their performance against the best linear rule. The results show that the gain from a nonlinear rule decreases as the number of independent trading directions increases under package pricing, while remaining relatively constant under name-by-name pricing. KEY FINDINGS: - The gain from a nonlinear trading rule decreases as the number of independent trading directions increases under package pricing. - The gain from a nonlinear trading rule remains relatively constant under name-by-name pricing. - The closed-form elasticity predicts the nonlinearness of the trading rule across a wide range of costs. IMPLICATION FOR TRADING: The findings suggest that traders should consider the method of cost aggregation when choosing between linear and nonlinear trading rules. If execution is package-priced, traders should be aware that the nonlinear advantage may diminish with increased diversification. On the other hand, if execution is name-by-name, traders should be aware that the nonlinear advantage remains significant. The results also highlight the importance of choosing the correct cost model, as the choice of model can significantly impact the effectiveness of the trading rule.

SUMMARY: The article proposes a distribution-free test to determine if a systematic trading strategy generates a structural edge, which is invariant across instruments, volatility regimes, and time, rather than being contingent on specific market conditions. The test uses only the trade-level R-multiple output and does not require access to the signal-generating mechanism. The test consists of two gates: the first gate evaluates the significance of the mean return, and the second gate tests for distributional invariance. The empirical application uses fourteen publicly specified trading strategies over the period 2008-2025, and none of them clear the edge gate. A proprietary FX strategy, when tested on the same public price series, clears both gates. The authors establish the calibration of the invariance gate through a placebo permutation, graded sensitivity experiment, and Monte-Carlo power study. The framework is complementary to backtest-overfitting diagnostics and factor attribution. METHOD: The test is based on Wasserstein distances for distributional comparison and Wasserstein-based goodness-of-fit testing with invariance reduction and calibrated finite-sample critical values. The empirical application uses publicly available data, including fourteen publicly specified trading strategies reconstructed over 2008-2025, and a proprietary FX strategy regenerated on the same public price series. The test procedure is calibrated and discriminates between strategies with and without a structural edge. KEY FINDINGS: - None of the fourteen publicly specified trading strategies clear the edge gate. - A proprietary FX strategy clears both gates. - The invariance gate is calibrated through a placebo permutation, graded sensitivity experiment, and Monte-Carlo power study. - The test framework is complementary to backtest-overfitting diagnostics and factor attribution. IMPLICATION FOR TRADING: The proposed test can be used by allocators to perform third-party due diligence on a strategy whose mechanism is not disclosed, where the only observable object is the realized trade sequence. It can help identify strategies that generate a structural edge, which is invariant across instruments, volatility regimes, and time, and not contingent on specific market conditions. This can aid in capital allocation decisions and provide a robust validation of trading strategies.

SUMMARY: This paper explores the paradoxical concept that competition in financial markets can paradoxically create a positive stock of open alpha opportunities, where better interpretations of public evidence can generate alpha but also reduce the remaining opportunity. The model posits that investors search for better interpretations when the expected reward from discovery exceeds the marginal cost of starting research. The paper derives a stationary equilibrium where the expected number of open opportunities remains positive, despite each individual opportunity having a finite half-life. The findings suggest that competition does not exhaust the opportunity set as a whole, but rather converges to an equilibrium characterized by a positive stock of open alpha opportunities. METHOD: The paper develops a model where the production of superior interpretations is endogenous. It specifies forecasting problems, interpretation, and price formation. The model derives endogenous search and discovery rates, and the expected normalized opportunity stock. The model is implemented with a deliberately small model, and the results are combined to obtain the stationary stock result and its large-market and pricing-error corollaries. The paper also considers limiting cases and comparative statics. KEY FINDINGS: - Competition in financial markets can paradoxically create a positive stock of open alpha opportunities. - Investors search for better interpretations when the expected reward from discovery exceeds the marginal cost of starting research. - The expected number of open opportunities remains positive in a stationary equilibrium, despite each individual opportunity having a finite half-life. - The positive stock of open opportunities arises from the continuous arrival of new forecasting problems and the positive probability that their interpretation can improve. IMPLICATION FOR TRADING: The paper's findings suggest that competition in financial markets does not exhaust the opportunity set as a whole but rather converges to an equilibrium characterized by a positive stock of open alpha opportunities. This implies that traders should not expect a complete elimination of alpha opportunities due to competition, but rather should focus on identifying and exploiting superior interpretations of public evidence. The positive stock of open opportunities indicates that there is always scope for new ideas and improvements, which can provide profitable trading opportunities. Practitioners should remain vigilant and innovative to take advantage of these opportunities, as competition will not exhaust the set of open alpha opportunities.

SUMMARY: This paper examines whether differences in the speed at which traded assets respond to a common market shock can predict subsequent relative returns. The study combines a lagged rolling factor model with Absorption Gap (AG) and Shock Coherence (SC) to measure asset response errors and market states, respectively. The sample includes 24 ETFs from January 4, 2010, to August 28, 2026. The combined signal's executable next-open Rank IC is -0.00020, and its separate close-to-close residual Rank IC is -0.00592. The gross Sharpe ratio is 0.006 with a CAGR of -0.07%, and the net Sharpe ratio is -2.367 with a CAGR of -9.83%. The study rejects the disclosed public specification and identifies the failure modes that the separate proprietary layer was designed to address. The findings suggest that the public specification does not provide economically viable evidence of alpha. METHOD: The study uses a sample of 24 ETFs from January 4, 2010, to August 28, 2026. The sample excludes eight factor proxies. The methodology combines a lagged rolling factor model with Absorption Gap (AG) and Shock Coherence (SC) to measure asset response errors and market states, respectively. The combined signal's executable next-open Rank IC is -0.00020, and its separate close-to-close residual Rank IC is -0.00592. The gross Sharpe ratio is 0.006 with a CAGR of -0.07%, and the net Sharpe ratio is -2.367 with a CAGR of -9.83%. KEY FINDINGS: - The combined signal's executable next-open Rank IC is statistically indistinguishable from chance. - The standalone AG Rank IC is statistically indistinguishable from chance. - The randomized-signal benchmark's empirical p-value is 0.968. - The difference between the AG long-short spread on high- and low-coherence dates is -3.15 basis points, and its confidence interval includes zero. - Logistic regression and gradient boosting remain close to random classification. IMPLICATION FOR TRADING: The study suggests that the public specification does not provide economically viable evidence of alpha. The findings indicate that the disclosed daily ETF implementation does not isolate the underlying economic mechanism well enough to support a public trading claim. The study's results highlight the importance of understanding the limitations of the public specification and the need for a more thorough examination of the underlying economic mechanism to support trading strategies.

SUMMARY: This paper examines how small-trade measures affect liquidity inference in Chinese A-share stocks. The authors derive an exact value-count decomposition that separates relative small-trade frequency, relative trade size, total transaction count, and average trade value. Using weekly panel data for Chinese A-share stocks, they reject the equal-loading restrictions imposed by aggregated value measures. They find that an innovation in small-trade value composition is associated with a 0.612-basis-point narrower quoted spread on impact. When both execution composition and aggregate trading scale are decomposed, the count-composition coefficient becomes positive at 0.278 basis points per marginal standard deviation. The positive association is concentrated near impact and attenuates over the following two weeks, becoming statistically indistinguishable from zero at the one-year horizon. The results suggest that liquidity inference can reverse when value-based trading measures aggregate underlying margins with unequal spread associations. METHOD: The study uses weekly data for Shanghai and Shenzhen A-share stocks from December 2015 to December 2025. The authors derive an exact value-count decomposition of small-trade value composition into small-trade count composition and relative average trade size, and a decomposition of total trading value into total transaction count and average trade value. They project each margin on the same 52-week history of all four states, predetermined controls, and stock and week fixed effects. They then compare nested measurement models and estimate horizon-specific spread associations on an identical common sample. KEY FINDINGS: - An innovation in the unseparated small-trade value-composition measure is associated with a 0.612-basis-point narrower quoted spread on impact. - When both execution composition and aggregate trading scale are decomposed, the count-composition coefficient becomes positive at 0.278 basis points per marginal standard deviation. - The positive association is concentrated near impact and attenuates over the following two weeks. - The joint restriction is rejected at every reported horizon. IMPLICATION FOR TRADING: The findings suggest that liquidity inference can reverse when value-based trading measures aggregate underlying margins with unequal spread associations. Practitioners should be cautious when using aggregated value measures to infer liquidity, as they may not accurately reflect the true composition of small trades. This research highlights the importance of separating small-trade frequency, relative trade size, and aggregate trading scale to better understand and predict liquidity.

SUMMARY: This paper measures the capacity of equity anomalies, which refers to how much capital investors can deploy before trading costs eliminate market-hedged returns above cash. The authors use stock-day estimates of spreads, price impact, and liquidity replenishment to compute capacity for 35 anomalies. They find that alpha alone is an incomplete guide to scale, with the highest-alpha long legs supporting less than $2 billion, while lower-alpha strategies support $20–33 billion. Price impact is found to drive most cross-strategy dispersion through turnover and stock liquidity. The paper also reports on the capacity of machine-learning strategies and finds that partial adjustment alone can expand the value-weighted strategy from $177 million to $19 billion. The authors conclude that capacity cannot be inferred from trading costs measured at a single portfolio size, and that price impact plays a significant role in determining capacity. METHOD: The authors use high-frequency data from the NYSE Daily TAQ to estimate the execution model of Obizhaeva and Wang (2013) for each stock and trading day. They then map the realized rebalances of 35 characteristic-based strategies into trading costs at alternative levels of assets under management and compound capital through the historical sample. The main object of the paper is portable-alpha capacity, defined as the largest initial allocation for which a strategy, after trading costs and a market hedge, compounds at least as fast as cash. The authors also report dynamic capacity for unhedged long legs and apply the same framework to gradient-boosted-tree strategies based on all 153 characteristics. KEY FINDINGS: - Alpha alone is an incomplete guide to scale. - The highest-alpha long legs support less than $2 billion. - Lower-alpha strategies support $20–33 billion. - Price impact drives most cross-strategy dispersion. - Partial adjustment alone can expand the value-weighted strategy from $177 million to $19 billion. - Capacity cannot be inferred from trading costs measured at a single portfolio size. - Price impact plays a significant role in determining capacity. IMPLICATION FOR TRADING: The findings suggest that the economic magnitude of return predictability is not solely determined by alpha. Instead, it depends on the capacity of deploying capital before trading costs eliminate market-hedged returns. The paper highlights the importance of considering price impact and liquidity in assessing the economic potential of equity anomalies. Practitioners can use these findings to better understand the economic potential of their strategies and allocate capital more effectively. Additionally, the paper's methodology can be applied to other trading strategies and anomalies, providing a framework for further research and practical use.

SUMMARY: This study introduces a hybrid approach combining Dynamic Bayesian Networks (DBN), Extreme Value Theory (EVT), and copulas to estimate Value at Risk (VaR) for a diversified portfolio of 11 assets. The authors aim to address three major challenges in risk management: volatility spillovers, heavy-tailed innovations, and asymmetric joint dependence. The proposed framework is evaluated on a portfolio of 11 assets and demonstrates interesting empirical properties. At the 90% and 95% confidence levels, all copula-based architectures (Student, Clayton, Survival Gumbel) successfully pass backtesting tests, unlike classical models. At the 99% confidence level, only specific DBN-EVT-Student and DBN-EVT-Clayton copula models maintain valid conditional coverage. These results highlight the need to account for directional volatility spillovers to ensure robust risk forecasts. METHOD: The study uses a diversified portfolio of 11 assets and evaluates the proposed hybrid approach (DBN-EVT-Copula) against classical models (Variance-Covariance and Historical Simulation). The methodology integrates Dynamic Bayesian Networks, Extreme Value Theory, and copula structures to address the challenges of volatility spillovers, heavy-tailed innovations, and asymmetric joint dependence. The approach is evaluated using backtesting tests for the 90%, 95%, and 99% confidence levels. KEY FINDINGS: - All copula-based architectures (Student, Clayton, Survival Gumbel) successfully pass backtesting tests at the 90% and 95% confidence levels. - Only specific DBN-EVT-Student and DBN-EVT-Clayton copula models maintain valid conditional coverage at the 99% confidence level. - The proposed hybrid approach demonstrates interesting empirical properties at the 90% and 95% confidence levels. IMPLICATION FOR TRADING: The findings suggest that incorporating Dynamic Bayesian Networks, Extreme Value Theory, and copulas in VaR estimation can improve the accuracy and robustness of risk forecasts. Practitioners should consider these methods to better account for volatility spillovers, heavy-tailed innovations, and asymmetric joint dependence in their portfolio management strategies. Incorporating these techniques can help in creating more reliable risk models, which is crucial for financial institutions to manage and mitigate risks effectively.

SUMMARY: This paper presents a cross-venue microstructure telemetry architecture that bridges centralized exchange Level-2 order books, specifically CME Globex Futures and Binance Spot/Perpetual Level-2 DOM, directly into an automated MetaTrader 5 (MT5) ECN execution engine. The authors formulate a real-time rolling futures-to-spot basis adjustment that projects institutional limit-order clusters ("Whale Bid/Ask Walls") onto spot CFD charts with sub-second latency. They also construct a multi-level Order Book Imbalance (OBI) veto gate that blocks spot entries whenever centralized limit liquidity opposes the short-term momentum signal. Live empirical execution data across Gold, Crude Oil, and Ethereum confirms that cross-venue LOB gating prevents institutional absorption losses during post-session liquidity thinning. METHOD: The authors use live empirical execution data across Gold, Crude Oil, and Ethereum to validate their cross-venue LOB telemetry architecture. They formulate a real-time rolling futures-to-spot basis adjustment that projects institutional limit-order clusters onto spot CFD charts with sub-second latency. They also construct a multi-level OBI veto gate that blocks spot entries whenever centralized limit liquidity opposes the short-term momentum signal. KEY FINDINGS: - Cross-venue LOB telemetry architecture bridges CME Globex Futures and Binance Spot/Perpetual Level-2 DOM into an automated MT5 ECN execution engine. - Real-time rolling futures-to-spot basis adjustment projects institutional limit-order clusters onto spot CFD charts with sub-second latency. - Multi-level OBI veto gate blocks spot entries whenever centralized limit liquidity opposes the short-term momentum signal. - Live empirical execution data confirms that cross-venue LOB gating prevents institutional absorption losses during post-session liquidity thinning. IMPLICATION FOR TRADING: The findings have practical implications for algorithmic traders operating on Over-the-Counter (OTC) platforms like MetaTrader 5 (MT5). By using cross-venue LOB telemetry, traders can better identify and mitigate institutional absorption losses during post-session liquidity thinning. This can lead to more stable and profitable trading strategies, especially for traders focusing on high-frequency execution and market microstructure analysis.

SUMMARY: This article examines how Expected Shortfall (ES) changes with the holding horizon when volatility combines a mean-reverting diffusive factor with a rough Volterra factor under the physical measure. The asset price remains a continuous semimartingale, so the leading short-horizon scale of returns and tail risk is proportional to √h; roughness enters through higher-order changes in the shape of the conditional loss distribution. For locally linear exposures, the paper derives explicit rough-leverage and integrated-variance corrections to Value-at-Risk (VaR) and ES. Under a weighted Fourier–Edgeworth condition, the rough-leverage correction is of relative order hH, whereas in the no-leverage variance-mixture regime, the leading symmetric correction is of order h2H. The paper establishes perturbation results showing when these expansions survive pricing and linearization remainders, and develops fixed-horizon inference for the risk-functional parameters H and χR = λRρSR, including a conditional delta method for normalized ES. A controlled Monte Carlo experiment verifies the sign, rate, and magnitude of the analytical correction and the finite-sample accuracy of the proposed standard errors. The framework is intended as a model-risk, economic-capital, and stress-testing tool, not as a replacement for regulatory liquidity-horizon rules. METHOD: The study uses a mixed, mean-reverting Gaussian–Volterra specification in which a rough component describes short-scale fluctuations and a diffusive component allows a distinct medium- and long-horizon scale. The main theoretical point is that the traded price remains a continuous semimartingale, so the leading short-horizon return scale is still √h. The roughness enters through higher-order changes in the shape of the conditional loss distribution. The paper derives the leading local skewness and kurtosis generated by the Volterra volatility factors and obtains a uniform density and distribution expansion under an explicit weighted Fourier–Edgeworth regularity condition, which is then transferred to VaR and ES. The paper also develops an estimable representation of the leading correction, including transparent pilot estimators of the local Hurst exponent and the product χR = λRρSR. KEY FINDINGS: - The rough-leverage correction to ES is of relative order hH. - In the no-leverage variance-mixture regime, the leading symmetric correction to ES is of order h2H. - The paper establishes perturbation results showing when these expansions survive pricing and linearization remainders. - A controlled Monte Carlo experiment verifies the sign, rate, and magnitude of the analytical correction and the finite-sample accuracy of the proposed standard errors. - The framework is intended as a model-risk, economic-capital, and stress-testing tool, not as a replacement for regulatory liquidity-horizon rules. IMPLICATION FOR TRADING: The findings of this paper have significant implications for risk management and trading practices. For traders and risk managers, understanding the impact of volatility and leverage on Expected Shortfall can help in making more informed decisions. The explicit corrections derived in the paper provide a more accurate understanding of risk measures under different conditions, which can be used to validate models and improve risk management practices. The framework developed in the paper can serve as a tool for assessing and managing model risk, economic capital, and stress testing, which are crucial for financial institutions. However, it is important to note that this framework is not intended as a replacement for regulatory liquidity-horizon rules, and institutions should still adhere to these rules.

SUMMARY: This study applies the Order Flow Imbalance (OFI) framework to CFTC-regulated prediction markets on Kalshi, a regulated designated contract market. Using 143,297 orderbook snapshots across sports, weather, and economic event contracts, the authors document a statistically significant positive relationship between OFI and contemporaneous mid-price changes. The explanatory power of OFI varies across contract categories: sports championship futures exhibit a mean R2 of 0.29, weather contracts 0.13, and macroeconomic contracts 0.02. The findings confirm that the OFI framework extends to prediction markets, providing the first evidence that the standard microstructure framework for order flow and price formation applies to binary event contracts. The results are robust to subsample splitting, winsorization, and exclusion of illiquid contracts. METHOD: The study uses 143,297 orderbook snapshots collected over 13 days via Kalshi’s public REST API. A simple measure of net order flow, computed from changes in best bid and ask depth between consecutive orderbook snapshots, explains a substantial fraction of contemporaneous price variation on equity markets. The authors adapt the methodology for the binary contract structure of prediction markets and test whether the same relationship holds across five contract categories: economics, sports, cryptocurrency, and weather. KEY FINDINGS: - OFI has a statistically significant positive relationship with contemporaneous mid-price changes across the majority of contracts in the panel. - The explanatory power of OFI varies dramatically across contract categories: sports championship futures exhibit a mean R2 of 0.29, weather contracts 0.13, and macroeconomic contracts 0.02. - This cross-category heterogeneity is robust to subsample splitting, winsorization of extreme observations, and exclusion of illiquid contracts. IMPLICATION FOR TRADING: The findings contribute to the market microstructure literature by providing the first application of the CKS order flow framework to CFTC-regulated prediction markets. They demonstrate that the linear price-impact model originally developed for equities applies in a structurally different setting—binary contracts bounded between zero and one dollar, traded on a central limit order book without designated market makers. For the prediction markets literature, the study offers the first systematic analysis of how order flow drives price formation across contract categories, with implications for market design, spread-setting, and the measurement of informed trading on these platforms.

SUMMARY: This paper examines investor protection in corporate spin-offs across different markets, focusing on the rights and entitlements of shareholders. It introduces a five-dimensional coding framework—coverage, exercisability, delivery form, shareholder voice, and market channel—to analyze Hong Kong-listed parent-SpinCo transactions. The study uses a source-linked registry of transactions to illustrate how different shareholder categories may have varying rights and entitlements. Examples such as Midea–Annto and Weichai–Lovol highlight legal and market system differences, while WH–Smithfield and Sihuan–Xuanzhu demonstrate infrastructure constraints. The findings support the existence of safeguard mechanisms but not their population frequencies or causal effects. The paper introduces a procedural benchmark to assess whether differential treatment is justified, disclosed, practicable, and accompanied by a security route, monetised substitute, or reasoned explanation. KEY FINDINGS: - Different shareholder categories may have varying rights and entitlements in spin-offs. - Legal eligibility, governance filtering, and infrastructure access contribute to heterogeneity. - Cash substitution is treated as a protection technology or remedy, not as a causal mechanism. - An explicit normative test is provided for assessing cash substitution adequacy. IMPLICATION FOR TRADING: The implications for trading practitioners are to recognize the complexity of shareholder rights and entitlements in spin-offs, especially across different legal and market systems. Practitioners should consider the five-dimensional framework to understand the distributional impacts of spin-offs and evaluate the adequacy of cash substitution mechanisms. This knowledge can help in making informed investment decisions and in designing strategies that align with the specific rights and entitlements of different shareholder categories.

SUMMARY: This paper investigates the directional asymmetry in extreme negative funding rates of cryptocurrency perpetual futures, specifically Bitcoin (BTC), Ethereum (ETH), and Solana (SOL). Using 274 funding-rate extreme events from January 2021 to August 2026, the study reveals that events occurring on up days revert by +1.88% over the next 24 hours, while events on down days continue by −1.74%. The gap of +3.62 percentage points is statistically significant and economically large. The paper also finds that the directional asymmetry is stable around seven exogenous market-wide shocks. The authors propose conditional arbitrage capacity as a state variable in the limits-to-arbitrage framework, suggesting that the effectiveness of arbitrage capital depends on both its level and market direction. METHOD: The study uses 274 funding-rate extreme events across BTC, ETH, and SOL from January 2021 to August 2026. The events are analyzed using block bootstrap, conditional permutation, placebo-event tests, threshold-sensitivity analysis, and funding-payment adjustments. The results are robust across multiple tests and market-wide shocks. KEY FINDINGS: - Events on up days revert by +1.88% over the next 24 hours. - Events on down days continue by −1.74%. - The gap of +3.62 percentage points is statistically significant and economically large. - The pattern is stable around seven exogenous market-wide shocks. - The directional asymmetry is consistent across every year and across all 13 coins in an extended 2025-2026 sample. IMPLICATION FOR TRADING: The findings suggest that traders can exploit the directional asymmetry in cryptocurrency perpetual funding rates to generate returns. By identifying and capitalizing on the positive gap on up days and the negative gap on down days, traders can potentially outperform market benchmarks. This asymmetry could be used to develop trading strategies that capitalize on the directional movement of the market, thereby enhancing overall performance. Additionally, the proposed conditional arbitrage capacity framework can be used to understand and predict the effectiveness of arbitrage capital in different market conditions, which can inform trading decisions and risk management strategies.

SUMMARY: This paper studies how the time-varying stock-bond correlation impacts optimal portfolio choices over the life-cycle. The authors embed a two-state Markov-switching process for the stock-bond correlation into an incomplete-markets life-cycle model, where households invest in cash, stocks, and long-term government bonds. The model shows that households infer the latent correlation regime from observed returns and adjust their portfolios based on their posterior beliefs. The paper finds that these belief-contingent portfolio differences are significant, especially near and during retirement, when financial wealth becomes a larger share of total lifetime resources. Despite the sizable allocation effects, the welfare gains from conditioning portfolios on covariance predictability are modest, with only 0.4 basis points of lifetime consumption improvement on average. However, households experiencing sustained periods of high posterior probability of the negative-correlation regime can see welfare gains of approximately 2.9 basis points. METHOD: The study uses a two-state Markov-switching process for the stock-bond correlation embedded in an incomplete-markets life-cycle model. Households are assumed to invest in cash, stocks, and long-term government bonds, and their beliefs about the correlation regime are updated using Bayes' rule based on observed returns. The correlation process is estimated using a restricted Markov-switching maximum-likelihood specification that allows only the stock-bond correlation to vary across regimes. KEY FINDINGS: - Household portfolios are significantly affected by the inferred correlation regime. - The magnitude of these effects varies over the life-cycle, being small for young households and larger near and during retirement. - The welfare gains from conditioning portfolios on covariance predictability are modest, with only 0.4 basis points of lifetime consumption improvement on average. - Households that experience sustained periods of high posterior probability of the negative-correlation regime can see substantial welfare gains of approximately 2.9 basis points. IMPLICATION FOR TRADING: The findings suggest that traders and investors should consider the time-varying stock-bond correlation when making portfolio choices over the life-cycle. While the overall impact on portfolio allocation is significant, the welfare gains are relatively small. However, for households that experience prolonged periods of high posterior probability of the negative-correlation regime, the impact on portfolio allocation and welfare can be substantial. This implies that traders and investors should be aware of the changing correlation dynamics and adjust their portfolios accordingly, particularly near and during retirement, where financial wealth becomes a larger share of total lifetime resources.

SUMMARY: This paper evaluates a portfolio-allocation framework based on the Multicriteria Prospect Theory Approach (MPT-A) and compares its out-of-sample performance with the Markowitz model, a benchmark for return-maximization. Unlike the Markowitz model, which relies on variance as a risk proxy, the proposed MPT-A framework incorporates investor-specific preferences over return and risk through a behavioral multicriteria decision rule. The framework constructs portfolios for conservative, moderate, and aggressive investor profiles and evaluates them using realized out-of-sample returns, risk, diversification patterns, and Sharpe ratios. The benchmark portfolio is computed under realistic constraints to reflect implementable investment limits. The results indicate that the MPT-A framework produces portfolios with a coherent progression in risk across investor profiles while maintaining diversified allocations. METHOD: The study constructs portfolios for three investor profiles (conservative, moderate, and aggressive) and evaluates them using realized out-of-sample returns, risk, diversification patterns, and Sharpe ratios. The benchmark portfolio is computed under realistic constraints to reflect implementable investment limits. The MPT-A framework incorporates investor-specific preferences over return and risk through a behavioral multicriteria decision rule. KEY FINDINGS: - The MPT-A framework produces portfolios with a coherent progression in risk across investor profiles. - The MPT-A framework maintains diversified allocations. - The MPT-A framework is sensitive to investment constraints. - The MPT-A framework outperforms the Markowitz model in out-of-sample performance. IMPLICATION FOR TRADING: The MPT-A framework offers a more nuanced approach to portfolio allocation by considering investor preferences and risk tolerance. This could lead to more stable and diversified portfolios, potentially improving investment performance. Practitioners can use the MPT-A framework to construct portfolios that better align with their investment goals and risk profiles, leading to more effective and sustainable investment strategies.

SUMMARY: This article develops an equilibrium model for semi-liquid funds, which are non-traded BDCs, interval, and tender-offer funds that repurchase shares each quarter at net asset value up to a cap, typically 5% of shares. The authors introduce a speed limit mechanism, where a cap works as a speed limit, and if the drain it permits, 𝜅[𝜃+ Λ(1 + 𝜃)], does not exceed the fund’s excess return plus costless liquidity, 𝜋+ Λ𝜇, no investor base can run the fund. Above this floor, runs are sustained by the backlog of liquidity-constrained investors they trap in the queue. The authors characterize the run region in closed form and show that a backlog above a tipping level eliminates the no-run equilibrium. They also show that tighter caps can be more fragile. The authors characterize all run-proof designs among work-conserving anonymous rules and demonstrate that a fund is robust to every investor base if and only if its cap satisfies the speed limit. The study also includes empirical evidence from tender-offer filings for 46 funds, which showed that proration, absent in private-credit BDCs before 2026, reached 23% of their offers in 2026 and is highly persistent. METHOD: The study develops an equilibrium model for semi-liquid funds, using a closed-form characterization of the run region and a scaling law to reduce the run region to three dimensionless numbers. The empirical evidence comes from tender-offer filings for 46 funds, which showed that proration, absent in private-credit BDCs before 2026, reached 23% of their offers in 2026 and is highly persistent. KEY FINDINGS: - A speed limit mechanism is introduced where a cap works as a speed limit. - Runs are sustained by the backlog of liquidity-constrained investors in the queue. - A tipping level of the backlog eliminates the no-run equilibrium. - Tighter caps can be more fragile. - All run-proof designs among work-conserving anonymous rules are characterized. - Proration is highly persistent in private-credit BDCs, reaching 23% of their offers in 2026. IMPLICATION FOR TRADING: The findings have significant implications for the trading of semi-liquid funds. The study highlights the importance of understanding the speed limit mechanism and the role of liquidity constraints in preventing runs. For practitioners, this research suggests that proration is a persistent feature in the industry, which can inform the design of run-proof mechanisms. Additionally, the study underscores the need for regulatory attention to ensure that caps are set appropriately to prevent runs, as tighter caps can be more fragile. Practitioners can use this information to design or modify run-proof mechanisms to mitigate the risk of runs in semi-liquid funds.

SUMMARY: This study investigates how capital increases affect market reactions in pharmaceutical companies listed on the Tehran Stock Exchange (TSE). The research analyzes stock returns over a six-month period, including three months before and after the announcement of capital increases. The authors use the market model to assess investor responses and asset revaluation associated with these capital injections. The sample includes 200 pharmaceutical firms selected through systematic random sampling, covering the period from 2017 to 2023. The findings reveal that asset revaluation significantly enhances investor reactions compared to capital increases from cash contributions and retained earnings. The study also highlights the importance of information transparency from company management in shaping market responses. The research contributes to a deeper understanding of investor behavior and informs management strategies related to capital restructuring in the pharmaceutical sector. METHOD: The study employs a sample of 200 pharmaceutical firms listed on the Tehran Stock Exchange (TSE), selected through systematic random sampling. The sample covers the period from 2017 to 2023. The research analyzes stock returns over a six-month period, including three months before and after the announcement of capital increases. The authors use the market model to assess investor responses and asset revaluation associated with these capital injections. KEY FINDINGS: - Asset revaluation significantly enhances investor reactions compared to capital increases from cash contributions and retained earnings. - Information transparency from company management plays a critical role in shaping market responses. - Growth can yield advantages, but it is not universally advantageous for companies, as rapid asset growth can reduce future stock returns. IMPLICATION FOR TRADING: The findings suggest that companies should prioritize transparent communication about capital increases to influence market reactions positively. This research can inform management strategies related to capital restructuring in the pharmaceutical sector. Understanding the impact of asset revaluation can help investors make more informed decisions, while managers can use this knowledge to optimize their capital allocation and improve company performance.

SUMMARY: This study examines price discovery in the U.S. equity market at tick-time precision for the 30 stocks in the Dow Jones Industrial Average. The authors use an unobserved-components model estimated in tick time to analyze quote dynamics across multiple trading venues. They find that the fastest and most liquid venues contribute the most to price discovery, with the NYSE holding a modest advantage over other venues. The research highlights the importance of understanding market microstructure and the role of different trading venues in price formation. METHOD: The study employs an unobserved-components model estimated in tick time to analyze 30 Dow Jones Industrial Average stocks. The model captures quote dynamics across multiple trading venues, accommodating irregular sampling and microstructure noise while avoiding the order dependence of conventional information share measures. The sample includes all trading venues in the U.S. equity market. KEY FINDINGS: - The fastest and most liquid trading venues contribute the most to price discovery. - The NYSE holds a modest advantage over other trading venues. - Market microstructure differences lead to price discrepancies across trading venues. - Regulatory frameworks like Reg NMS promote competition among exchanges. IMPLICATION FOR TRADING: Understanding the role of different trading venues in price formation is crucial for investors seeking to execute trades at favorable prices and for market operators competing to attract order flow. The findings suggest that the U.S. equity market under Reg NMS can be viewed as a single virtual market with multiple points of entry, implying that several exchanges contribute to price discovery rather than a single venue dominating the process. This interpretation has implications for market participants, including investors and market operators, who need to consider the impact of different trading venues on price formation.

SUMMARY: This paper develops a dynamic stochastic general equilibrium (DSGE) model to compare the macro-financial implications of central bank digital currency (CBDC) and synthetic CBDC (sCBDC) regimes. The model incorporates endogenous portfolio allocation, bank balance-sheet constraints, and liquidity preference shocks. The analysis reveals that under a tiered-remuneration CBDC regime, households substitute deposits for central bank liabilities, reducing bank funding and amplifying disintermediation during liquidity stress episodes. Although remuneration penalties and CBDC holding ceilings mitigate these effects, they do not fully eliminate endogenous financial amplification. In contrast, the sCBDC regime generates smoother and more structurally bounded disintermediation. Digital liabilities are fully reserve-backed and institutionally separated from credit creation, resulting in weaker amplification effects and more predictable balance-sheet adjustments. However, the expansion of narrow-bank intermediation also reduces traditional bank funding, credit creation, output, consumption, and employment, highlighting a trade-off between financial stability and credit provision. The paper further examines the feasibility conditions of the sCBDC equilibrium and the role of narrow-bank participation and intermediation costs. METHOD: The study uses a dynamic stochastic general equilibrium (DSGE) model with explicit banking frictions to compare the effects of CBDC and sCBDC architectures. The model incorporates endogenous portfolio allocation between deposits and digital money, bank balance-sheet constraints, and liquidity preference shocks inspired by the Diamond–Dybvig mechanism. KEY FINDINGS: - Under a tiered-remuneration CBDC regime, households substitute deposits for central bank liabilities, reducing bank funding and amplifying disintermediation during liquidity stress episodes. - The sCBDC regime generates smoother and more structurally bounded disintermediation. - Digital liabilities are fully reserve-backed and institutionally separated from credit creation, resulting in weaker amplification effects and more predictable balance-sheet adjustments. - The expansion of narrow-bank intermediation also reduces traditional bank funding, credit creation, output, consumption, and employment. IMPLICATION FOR TRADING: The findings suggest that synthetic CBDC arrangements may provide a more resilient monetary architecture by limiting endogenous instability and run-like dynamics, although potentially at the cost of weaker bank intermediation and lower endogenous credit creation. Practitioners should consider the implications of these findings when designing monetary policies and assessing the potential risks and benefits of CBDC and sCBDC architectures.

SUMMARY: This paper re-examines the pricing of options by demonstrating that the options surface can be modeled as a log-linear weighted portfolio rate asset. The authors prove that explicitly norming the replication portfolio completely resolves the multi-decade smile distortion. The paper introduces a framework that fully explains the contract cross-section with a native, natural log-additive two-component vector field: an active leverage-constrained equity replication leg and a continuous probabilistic boundary funding constraint. The authors demonstrate that the design matrix allows localized microstructure friction and floor execution noise to share the spatial envelope across the continuous surface. The empirical results show that the left-hand side option rate matches the combined right-hand side replication components down to an authentic white-noise footprint, compressing cross-sectional transaction residuals under a tight 5.3 basis point ceiling. The authors argue that the Normed Replication Portfolio rate represents the exact, undistorted return generated by the self-financing underlying assets. METHOD: The paper employs an empirical and theoretical re-examination of options cross-sectional pricing. The authors demonstrate that the options surface can be systematically modeled as a log-linear weighted portfolio rate asset. The empirical results are verified through a rigorous Left-Right Identity Test. The authors use a rigorous Left-Right Identity Test to verify the structural mapping of their model. The authors apply their powerful Norming process to both models to ensure a completely fair, head-to-head comparison. KEY FINDINGS: - The options surface can be modeled as a log-linear weighted portfolio rate asset. - Explicitly norming the replication portfolio resolves the multi-decade smile distortion. - The contract cross-section is fully explained by a native, natural log-additive two-component vector field. - The left-hand side option rate matches the combined right-hand side replication components down to an authentic white-noise footprint. - The empirical results show a strict, structure-preserving compositional isomorphism. IMPLICATION FOR TRADING: The findings suggest that the Normed Replication Portfolio rate represents the exact, undistorted return generated by the self-financing underlying assets. This implies that traders can use the Normed Replication Portfolio rate to accurately price options, leading to more precise and reliable pricing models. The findings also suggest that traders should explicitly norm the replication portfolio to eliminate structural drift and ensure a fair comparison of different models.

SUMMARY: This study examines margin trading and short selling in the Chinese stock market from 2010 to 2025, constructing measures of margin-financing and short-selling activity across three dimensions: changes in outstanding balances, relative trading intensity, and sentiment momentum. Principal component analysis is used to create a composite factor capturing the relative strength of margin financing versus short selling. The factor significantly predicts lower future stock returns. A long-short portfolio that is long low-factor stocks and short high-factor stocks earns significant positive returns at both daily and weekly frequencies, robust after controlling for conventional risk factors and firm characteristics. The predictive power weakens as the holding horizon lengthens. Return predictability is stronger when investor sentiment is more intense or optimistic and among stocks with stronger lottery-like characteristics, consistent with short-term mispricing and subsequent price correction. The findings remain robust after accounting for transaction costs, factor orthogonalization, and separate tests in bull and bear markets. METHOD: The study uses Chinese A-share stocks eligible for margin trading and short selling from 2010 to 2025. Measures of margin-financing and short-selling activity are constructed across three dimensions: changes in outstanding balances, relative trading intensity, and sentiment momentum. Principal component analysis is used to create a composite factor capturing the relative strength of margin financing versus short selling. The predictive power of the factor is tested using a long-short portfolio strategy. KEY FINDINGS: - The composite factor significantly predicts lower future stock returns. - A long-short portfolio strategy that is long low-factor stocks and short high-factor stocks earns significant positive returns at both daily and weekly frequencies. - The predictive power of the factor weakens as the holding horizon lengthens. - Return predictability is stronger when investor sentiment is more intense or optimistic. - Return predictability is stronger among stocks with stronger lottery-like characteristics. - The findings remain robust after accounting for transaction costs, factor orthogonalization, and separate tests in bull and bear markets. IMPLICATION FOR TRADING: The findings suggest that margin trading and short selling contain valuable asset pricing information that can be used to predict stock returns. Investors can construct a long-short portfolio strategy based on the composite factor to potentially generate positive returns. However, the predictive power of the factor weakens as the holding horizon lengthens, and the strategy may be less effective in bear markets. Therefore, traders should consider transaction costs, factor orthogonalization, and separate tests in bull and bear markets when using this strategy. Additionally, the strategy may be more effective for stocks with stronger lottery-like characteristics, as these stocks are more prone to short-term mispricing and subsequent price correction.

SUMMARY: This paper reviews historical financial crashes and crises, focusing on regulatory frameworks that failed to adequately address speculative excesses and systemic risks in emerging financial markets. It examines the challenges faced by current regulators, such as the SEC and CFTC, in applying old legal precedents like the Supreme Court case Howey to new digital asset markets like cryptocurrencies. The paper draws lessons from past crises, such as the 1929 stock market crash and the 2008 financial crisis, to highlight the dangers of financial deregulation and unchecked risk-taking. It also discusses the potential vulnerabilities of the current regulatory frameworks in managing the rapidly expanding derivatives sector and the risks posed by digital assets. METHOD: The research reviews historical financial crashes and crises, analyzing the regulatory frameworks that failed to address speculative excesses and systemic risks. It examines the challenges faced by current regulators, such as the SEC and CFTC, in applying old legal precedents to new digital asset markets. The methodology includes a review of historical data and legal precedents, as well as an analysis of current regulatory frameworks. KEY FINDINGS: - The 1929 stock market crash and the 2008 financial crisis highlighted the dangers of financial deregulation and unchecked risk-taking. - The derivatives market, particularly over-the-counter derivatives, played a significant role in both crises. - The current regulatory frameworks struggle to adequately address the risks posed by digital assets and their derivatives markets. - The application of old legal precedents like the Howey case poses challenges in regulating new digital asset markets. IMPLICATION FOR TRADING: The research underscores the importance of robust and up-to-date regulatory frameworks in managing risks in financial markets. Practitioners should be aware of the potential vulnerabilities in current regulatory structures and the need for continuous adaptation to evolving market conditions. They should also consider the implications of regulatory decisions on market stability and investor protection.

SUMMARY: This paper investigates the threshold at which the funding-rate mean-reversion mechanism for crypto perpetual funding rates stops working. It uses hourly Binance USDT-M futures data for BTC, ETH, and SOL from January 2021 to December 2024. The study identifies the arbitrage capacity threshold, where a drop in USDe supply from $15 billion to $4.3 billion reduces the strategy Sharpe ratio from 2.805 to 0.319. The paper validates online logistic regression with inference-time adaptation as an effective filter for non-stationary markets, outperforming random filtering in 8 out of 9 parameter combinations. Cross-validation holds for BTC and ETH, but not for SOL. The study also examines the impact of USDe supply on strategy performance, showing a significant difference in Sharpe ratios when USDe supply exceeds or falls below $3 billion. The findings suggest that online logistic regression can improve the Sharpe ratio for BTC and ETH, but not for SOL, highlighting the importance of underlying signal quality. METHOD: The study uses hourly perpetual futures data for BTC, ETH, and SOL from Binance USDT-M futures, covering January 2021 to December 2024. The primary signal is a funding-rate mean-reversion event, defined by a Z-score threshold of −1.85. The events are labeled using a Triple-Barrier scheme with a 24-hour holding period, and take-profit and stop-loss thresholds are set at 2.0 ATR and 1.0 ATR, respectively. The feature set includes seven regime variables: 24-hour and 168-hour realized volatility, their ratio, 24-hour and 168-hour momentum, 24-hour range, and 30-day absolute funding rate median. The online logistic regression filter is updated at every event, with parameters adjusted using an adaptive learning rate. The study also examines the impact of USDe supply on strategy performance, using the Ethena USDe supply series as a proxy for arbitrage capital capacity. KEY FINDINGS: - The funding-rate mean-reversion strategy for BTC, ETH, and SOL has a Sharpe ratio of 1.248, −0.084, and −0.879, respectively. - When USDe supply drops from $15 billion to $4.3 billion, the strategy Sharpe ratio drops from 2.805 to 0.319. - Online logistic regression with inference-time adaptation improves the Sharpe ratio for BTC and ETH, reducing drawdown from −14.52% to −4.72%. - The gain in Sharpe ratio comes from genuine signal filtering, as demonstrated by ablation tests against 50 random-filter simulations. - The study finds that the gain in Sharpe ratio does not hold for SOL, suggesting that the effectiveness of machine learning filters depends on the quality of underlying signals. IMPLICATION FOR TRADING: The findings suggest that online logistic regression can be an effective tool for filtering non-stationary markets, particularly for BTC and ETH. However, the effectiveness may vary depending on the underlying signal quality, as demonstrated by the poor performance of the strategy for SOL. Practitioners can use this information to improve their trading strategies by incorporating machine learning filters, such as online logistic regression, to identify and exploit mean-reversion opportunities in crypto markets. Additionally, the study highlights the importance of understanding the impact of USDe supply on strategy performance, which can inform trading decisions and risk management.

SUMMARY: This article examines the arbitrage opportunities in perpetual futures contracts traded on both centralized (CeFi) and decentralized (DeFi) venues. The authors argue that the venue label (CeFi vs. DeFi) is not the most appropriate taxonomy for evaluating these instruments, as the construction of the venues significantly impacts the entry-time arbitrage opportunities and the risks borne by institutional investors. The authors classify funding designs into two layers based on what is known about the next payment at entry. They also highlight the importance of market architecture, including reference price construction, order matching, and position custody, which shape the non-price risks a trader bears. METHOD: The study reviews seven audited offshore, U.S.-regulated, and decentralized venue architectures. The authors analyze how these venues construct the reference price, calculate funding, and match and settle trades. They examine the differences in funding designs and how they affect the entry-time arbitrage opportunities and the risks borne by arbitrageurs and institutional investors. KEY FINDINGS: - The next payment at entry is not always known, and this uncertainty affects the arbitrage opportunities. - Different venues have different ways of constructing the reference price and calculating funding, which impacts the entry-time arbitrage. - The entry-time classification of funding designs is crucial for understanding the arbitrage opportunities and risks. - The market architecture, including reference price construction, order matching, and position custody, significantly impacts the non-price risks a trader bears. IMPLICATION FOR TRADING: The findings suggest that traders should consider the construction of the reference price and the funding mechanism when trading perpetual futures on different venues. Understanding the differences in market architecture can help traders identify arbitrage opportunities and manage the risks associated with different venues. This knowledge can be used to develop more effective trading strategies and risk management techniques.

SUMMARY: This study examines the use of tokenized real estate properties in decentralized finance (DeFi) platforms, focusing on how secondary markets aggregate information about these assets and whether this information is used by borrowers and lenders. The authors analyze data from RealT’s ecosystem, which tokenizes rental properties and allows them to be traded on decentralized exchanges (DEXs) and an over-the-counter (OTC) marketplace. The study finds that secondary markets aggregate information about the underlying properties, but token prices also reflect collateral use value once tokens become integrated into DeFi lending. Borrowers use secondary market information to manage credit risk, while lenders appear to respond more to expected returns than to potential run risk. Despite active borrowing and trading, the study does not find evidence of self-reinforcing credit-price cycles. METHOD: The study uses data from RealT’s ecosystem, which tokenizes over 700 rental properties from 2021 to 2025. The authors examine the valuation of underlying properties, including the impact of secondary market prices and collateral valuations used by the lending platform. The analysis includes both decentralized secondary market prices and OTC prices, with secondary market prices containing information about the underlying properties, while collateral valuations used by the lending platform influence borrower and lender behavior. KEY FINDINGS: - Secondary markets aggregate information about the underlying properties. - Token prices reflect collateral use value once tokens become integrated into DeFi lending. - Borrowers use secondary market information to manage credit risk. - Lenders respond more to expected returns than to potential run risk. - No evidence of self-reinforcing credit-price cycles. IMPLICATION FOR TRADING: The findings suggest that decentralized secondary markets can aggregate information about tokenized real estate properties, and this information is used by borrowers to manage credit risk. However, lenders do not appear to be disciplined by this information, as they respond more to expected returns than potential run risk. These insights can inform the development of DeFi platforms for tokenized real estate, as well as the design of collateral mechanisms and risk management strategies. The lack of self-reinforcing credit-price cycles suggests that the integration of real estate into DeFi may not lead to significant price volatility or instability, which could be beneficial for market participants.

SUMMARY: This paper examines the impact of forecast granularity on portfolio choice and performance. The authors derive a pricing model for forecast granularity, where each dimension of the forecast costs 1/T, the variance of an estimated Sharpe ratio. The paper also considers the impact of estimating the covariance matrix, which adds a Wishart factor that grows with the ratio of dimensions to observations. The authors find that shrinkage pays the price on every dimension, while coarsening pays it only on the dimensions kept and wins when the signal is concentrated. The paper applies these findings to a machine-learning forecast of 100 large CRSP stocks, finding that the optimal number of rank groups is G∗= 4 for an information coefficient of 0.05. The authors conclude that the familiar rules (plug-in, Kan–Zhou, Tu–Zhou, minimum variance, 1/N, decile sorts, parametric tilts) are fixed cells of the resulting grid, and that the optimal number of groups is tied to a measurable property of the forecast. METHOD: The authors derive a pricing model for forecast granularity, where each dimension of the forecast costs 1/T, the variance of an estimated Sharpe ratio. They also consider the impact of estimating the covariance matrix, which adds a Wishart factor that grows with the ratio of dimensions to observations. The findings are applied to a machine-learning forecast of 100 large CRSP stocks, with the optimal number of rank groups found to be G∗= 4 for an information coefficient of 0.05. KEY FINDINGS: - Each dimension of the forecast costs 1/T, the variance of an estimated Sharpe ratio. - Coarsening pays the price only on the dimensions kept and wins when the signal is concentrated. - The optimal number of rank groups is G∗= 4 for an information coefficient of 0.05. - The familiar rules (plug-in, Kan–Zhou, Tu–Zhou, minimum variance, 1/N, decile sorts, parametric tilts) are fixed cells of the resulting grid. IMPLICATION FOR TRADING: The findings suggest that investors should consider the granularity of their forecasts when choosing how much of the forecast to use. The optimal number of rank groups is tied to a measurable property of the forecast. The paper recommends using the optimal number of groups, rather than the familiar rules, to maximize performance. The findings have implications for portfolio managers and investors who use machine learning or other types of forecasts.

SUMMARY: This paper introduces Certified Alpha Capacity (CAC) to address the challenge of determining whether a trading signal can accumulate enough statistical evidence for reliable deployment before its economic value decays. The authors solve this problem by measuring statistical evidence and remaining opportunity on a common Kullback–Leibler information scale. They derive an exact feasibility threshold in a canonical Gaussian model and establish general information lower bounds beyond it. The study reveals that finite information budgets generate survival frontiers, search penalties, and a market equilibrium in which arbitrage activity shortens the lifetime of certifiable opportunities. The paper also illustrates the role of persistence in retrospective funding-rate studies. METHOD: The analysis is based on a canonical Gaussian experiment where the lifetime-information budget yields an exact boundary for reliable certification. A Kullback–Leibler data-processing inequality provides a necessary information bound beyond the benchmark. The information requirement is mapped into signal-strength and persistence coordinates to produce the Alpha Survival Frontier. This frontier also makes the cost of screening many candidates explicit. The information clock is used to convert statistical learning into economic value under quadratic trading costs. The paper links these ideas by treating economic lifetime as a finite information budget and allowing market activity to change that budget. KEY FINDINGS: - Finite information budgets generate survival frontiers, search penalties, and a market equilibrium. - Arbitrage activity shortens the lifetime of certifiable opportunities. - The Alpha Survival Frontier makes the cost of screening many candidates explicit. - The information clock converts statistical learning into economic value under quadratic trading costs. IMPLICATION FOR TRADING: The findings suggest that traders must consider both statistical evidence and economic value when deploying trading signals. The paper's framework can help identify when a trading signal is no longer viable due to economic decay. Practitioners can use the survival frontier to assess the value of certifiable opportunities and the information clock to convert statistical learning into economic value. This can aid in making informed decisions about when to deploy trading signals and how to allocate resources effectively.

SUMMARY: This paper develops a canonical protocol graph for a leveraged event-market system, ensuring that each financial domain has one authoritative storage authority. The protocol graph formalizes atomic position origination, a settlement-confirmed debt saga, trader residual release, and an ordered reserve-backstop-Senior loss waterfall. The architecture avoids recreating balances, debt, positions, evidence receipts, queues, reserves, pause state, or terminal scenario state. The paper proves composition-level authority uniqueness, atomic closure, loss conservation and priority, receipt-idempotent settlement, legal no-go safety, capability-transition safety, and no financial-reexecution recovery under explicit assumptions. The implementation is evaluated through twelve Financial Interaction Assertions (FIA-001–FIA-012), including mandatory FIA-011A/B capability subcases. The result connects the formal protocol graph to a reproducible evidence chain while preserving explicit boundaries between the observed engineering result and future external-operation hypotheses. METHOD: The methodology involves developing a canonical protocol graph in which every financial domain has one storage authority. Composition components coordinate transitions without recreating balances, debt, positions, evidence receipts, queues, reserves, pause state, or terminal scenario state. The implementation is evaluated through twelve Financial Interaction Assertions (FIA-001–FIA-012), including mandatory FIA-011A/B capability subcases. KEY FINDINGS: - Every financial domain has exactly one canonical storage authority. - Composition code may coordinate canonical modules but cannot own a second copy of balances, debt, positions, evidence receipts, queues, reserves, pause state, or terminal financial state. - The architecture formalizes atomic position origination, a settlement-confirmed debt saga, trader residual release, and an ordered reserve-backstop-Senior loss waterfall. - The protocol graph ensures loss conservation and priority, receipt-idempotent settlement, legal no-go safety, capability-transition safety, and no financial-reexecution recovery under explicit assumptions. - The implementation is evaluated through twelve Financial Interaction Assertions (FIA-001–FIA-012), including mandatory FIA-011A/B capability subcases. IMPLICATION FOR TRADING: The implications for trading are significant, as the canonical protocol graph provides a clear and consistent framework for leveraged event markets. This ensures that all financial operations are correctly executed and auditable, reducing the risk of errors and disputes. The single authoritative storage authority and the formal protocol graph also facilitate better integration with external systems and enable more reliable and secure trading systems. Practitioners can rely on the proven composition-level authority uniqueness, atomic closure, loss conservation, and priority, as well as the receipt-idempotent settlement and legal no-go safety. The architecture also supports deterministic deployment and recovery, which can help in maintaining system stability and reliability.

SUMMARY: This paper reviews evidence on intraday scalping strategies on the National Stock Exchange (NSE) in India after the 2025-26 changes to the retail algorithmic-trading framework and securities transaction tax (STT). The review synthesizes four main sources: a cost-aware study of simple intraday rules on S&P 500 futures, two working papers on high-frequency trader (HFT) behavior on Indian exchanges, a recent article on intraday volatility on the NSE, and two SEBI studies of individual derivatives traders. The paper identifies three key themes: transaction costs usually determine the survival of short-horizon strategies, profits are concentrated among algorithmic participants, and backtests of these strategies are fragile to assumptions, regime changes, and overfitting. The paper also presents a simple breakeven calculation showing the impact of the April 2026 STT increase on Nifty futures trading. The review concludes by proposing five testable propositions for future research. METHOD: The review synthesizes evidence from four main sources, including a study of simple intraday rules on S&P 500 futures, two working papers on Indian HFT behavior, a recent article on intraday volatility on the NSE, and two SEBI studies of individual derivatives traders. The sources are supplemented with regulatory and exchange material, as well as recent reporting on SEBI's FY2025-26 studies. The selection of sources is purposive rather than systematic, and the review does not claim to cover the wider literature on intraday trading. KEY FINDINGS: - Transaction costs usually determine the survival of short-horizon strategies. - Profits in Indian derivatives are concentrated among algorithmic participants. - Backtests of short-horizon strategies are fragile to assumptions, regime change, and overfitting. IMPLICATION FOR TRADING: The findings suggest that traders should be aware of transaction costs when implementing intraday scalping strategies, especially in the Indian context where the NSE is an order-driven market. The concentration of profits among algorithmic participants indicates that algorithmic trading frameworks may be more effective in capturing profits from scalping strategies. Additionally, traders should be cautious of the fragility of backtested scalping strategies, as assumptions, regime changes, and overfitting can significantly impact their performance. These insights can help traders better understand the economics of intraday scalping strategies and inform their trading decisions.

SUMMARY: The study examines whether significant economic and non-economic shocks influence long-term stock market expectations of individuals. It utilizes unique survey data from a representative panel of individuals before, during, and after the COVID crisis, and around the Trump tariff announcement. Raw return expectations were found to be higher during the COVID period. However, after correcting for the market forecast based on the CAPE ratio of Campbell and Shiller, adjusted return expectations were found to be lower at the height of the crisis than before, and they quickly recovered afterward. The study also found that risk expectations were higher during the COVID period compared to other periods. The authors explore three possible channels for lower return expectations and find evidence supporting the cognitive/financial literacy and partisan channels. Respondents with higher financial literacy expected lower returns during the COVID shock, and similarly, Democratic voters were more pessimistic. The study does not find strong evidence that personal experience with COVID or economic measures associated with COVID affected expected returns. The tariff shock did not seem to impact return and risk expectations. METHOD: The study uses a unique panel dataset collected from a representative sample of US individuals over five consecutive periods (2019, 2020, 2021, 2023, 2025). The dataset includes return expectations for different types of individuals and during different time periods, allowing for a comparison of expectations over time. The study considers two shocks: the COVID crisis and the Trump tariff announcements. KEY FINDINGS: - Raw return expectations were higher during the COVID period. - Adjusted return expectations were lower at the height of the crisis than before and quickly recovered afterward. - Risk expectations were higher during the COVID period compared to other periods. - Evidence supports the cognitive/financial literacy and partisan channels for lower return expectations. - Personal experience with COVID or economic measures associated with COVID did not affect expected returns. - The tariff shock did not impact return and risk expectations. IMPLICATION FOR TRADING: Understanding how shocks affect long-term stock market expectations is crucial for traders and investors. The study highlights the importance of cognitive/financial literacy and political leanings in shaping expectations. Traders should consider these factors when making investment decisions. The findings suggest that personal experiences with COVID or economic measures associated with COVID do not significantly impact expectations, but the cognitive/financial literacy and partisan channels do. These insights can help traders and investors better predict and adjust their expectations in response to market shocks.

SUMMARY: This paper examines the conditions under which deterministic mean-reversion specifications allow for elementary closed-form equity valuation under complete earnings retention. The valuation is defined as the limit of discounted terminal book equity under an explicit terminal-realization convention. The key finding is that a strictly positive finite limit exists if and only if cumulative log-relative profitability converges. The paper derives and verifies five specifications of mean-reversion: geometric reversion, fast and slow components, rational decay, finite linear fading, and a nonlinear gross-return ratio. The paper also provides a vanishing-tail representation that serves as a constructive sufficient condition for elementary solvability. The findings clarify why distinct adjustment paths can generate identical limiting valuations. The paper compares mathematical simplicity and economic interpretation to favor geometric reversion as a benchmark and a two-component model when heterogeneous persistence is economically motivated. METHOD: The study uses a complete-retention setting where all earnings remain within the firm and terminal book equity is realizable by shareholders at its book amount. The key variable is the logarithm of the gross return on equity (ROE) relative to the gross discount rate. The paper derives an exact convergence criterion: a strictly positive finite valuation exists precisely when cumulative log-relative profitability converges. The analysis is based on the object studied as the limit of discounted realizable terminal values under the explicit terminal-realization convention, which avoids treating an accounting accumulation identity as a complete market-pricing theory. KEY FINDINGS: - Cumulative log-relative profitability convergence is necessary for a strictly positive finite limit. - Five specifications of mean-reversion are derived and verified: geometric reversion, fast and slow components, rational decay, finite linear fading, and a nonlinear gross-return ratio. - A vanishing-tail representation provides a constructive sufficient condition for elementary solvability. - Different mean-reversion paths can have the same cumulative log-relative profitability and therefore the same limiting valuation. - Under common initial conditions and cumulative persistence greater than one, rational decay has a strictly larger absolute log-valuation error than geometric reversion at every integer horizon of at least two periods whenever the initial profitability deviation is nonzero. IMPLICATION FOR TRADING: The findings provide a clear understanding of the conditions under which mean-reversion paths can be used for elementary closed-form equity valuation. This can help traders and analysts in making more precise and tractable models for equity valuation, especially when heterogeneous persistence is economically motivated. The paper's framework offers a tractable alternative without claiming an exhaustive classification of all elementary closed forms or an unconditional market-equilibrium valuation.

SUMMARY: The study examines the effects of market closures on the Bitcoin and Ether futures markets, specifically focusing on the impact of extended trading hours. The research uses CME Bitcoin and Ether futures contracts, which did not trade over weekends while spot markets remained open, and CME's transition to continuous trading in May 2026. The study analyzes how the closure affects trading and pricing, and what remains after the closure is removed. METHOD: The study uses CME Bitcoin and Ether futures data, which were traded continuously from May 29, 2026, and the impact of the weekend closure is analyzed. The analysis includes aligning contract-level one-minute futures trades with spot prices in the same minute, adding trade-by-trade quotes, aggressor sides, and top-of-book updates at every reopening. The study examines five key questions: whether the closed market takes up the price formed elsewhere, whether trading concentrates at the reopening, whether the reopening minute is more variable than the spot minute, and whether the variation leaves a lasting price component. KEY FINDINGS: - The closed market takes up about 94% of the weekend spot move. - The futures price moves one-for-one with spot within a minute. - The pre-close basis carries through the closure. - The closure produces a burst at the reopen, with first-minute volume being four times the per-minute average over the following hour. - The futures-minus-spot price change moves together during the first seconds. - Order imbalance measured over those seconds does not predict the remainder of the minute or the following hour. - The first-trade deviation largely reverses within five minutes. - Liquidity suppliers earn no larger realized spreads after weekends. - A contrarian strategy earns no statistically significant net profit after costs. - After the transition, the futures-specific excess variation fell sharply in both contracts and was no longer detected in post-rebuild midquotes. - The trading clock remains intact, with the 17:00 CT minute on Sunday still carrying 5.8 times the per-minute average volume of the surrounding hour. IMPLICATION FOR TRADING: The findings suggest that extended trading hours can concentrate trading demand around the reopening, but the variation is short-lived. The study indicates that removing the closure does not eliminate the trading clock, as traders continue to crowd into the same reopening minute. These insights can inform trading strategies and market microstructure analysis, particularly for tokenized securities and cryptocurrency markets.

SUMMARY: This article examines the potential benefits of adding private assets to public portfolios, specifically in the context of defined-contribution (DC) plans. The authors argue that private assets, which are less volatile and weakly correlated with public markets, can enhance the risk-return opportunity set available to long-term retirement savers. However, the economic case is clouded by measurement issues, particularly the use of appraisal smoothing to set reported private-fund net asset values, which can overstate the diversification benefit. The study corrects reported private-market index returns for smoothing using established methods and re-estimates constrained mean-variance frontiers. After correction, a private sleeve improves the estimated tangency Sharpe ratio by 19 to 23 percent over a public-only portfolio, which is 60 to 74 percent of the gain implied by reported returns. The authors also extend the analysis to estimate frontiers on simulated investable books of randomly drawn private funds, net of the cost of managing the private sleeve at levels anchored in the literature. The findings suggest that a modest private-asset sleeve can offer a diversification benefit that survives measurement correction, fund-selection risk, and fees. METHOD: The study corrects reported private-market index returns for smoothing using established methods and re-estimates constrained mean-variance frontiers. The authors also extend the analysis to estimate frontiers on simulated investable books of randomly drawn private funds, net of the cost of managing the private sleeve at levels anchored in the literature. KEY FINDINGS: - A private sleeve improves the estimated tangency Sharpe ratio by 19 to 23 percent over a public-only portfolio after correction for smoothing. - The improvement in the Sharpe ratio is statistically significant and persists under a zero-alpha prior and forward-looking return assumptions. - The gain persists in these investable books and remains positive in the lower tail of simulated outcomes. - A modest private-asset sleeve can offer a diversification benefit that survives measurement correction, fund-selection risk, and fees. IMPLICATION FOR TRADING: The findings suggest that adding a private-asset sleeve to a public portfolio can improve the risk-return opportunity set available to long-term retirement savers. This can be particularly relevant for defined-contribution plans, where policymakers are considering the inclusion of private assets. The study's results imply that even after accounting for the measurement issues and the costs associated with managing a private sleeve, a modest allocation to private assets can still provide a significant diversification benefit. Practitioners can use these findings to inform their investment strategies and potentially improve the performance of their portfolios.

SUMMARY: This article examines how UK and EU law allocate responsibility for market-wide risk created by correlated trading systems, which can behave lawfully but still pose risks. The problem is not limited to AI but can be amplified by more advanced automated trading. Existing law reaches significant parts of the conduct problem, but does not clearly reach the market-wide pattern. The article discusses how firm-level duties, venue responses, and specific regulations like Article 42 of UK MiFIR can address some issues. However, it highlights gaps in responsibility, particularly concerning cross-firm information and early detection of risks. The article concludes that responsibility should sit where aggregate risk can be seen and coordinated control can be exercised. METHOD: The article analyzes how UK and EU law distributes responsibility for correlated trading, which involves firms with separate control systems reacting to common models, signals, or constraints. It examines existing legal frameworks, such as firm-level duties, venue responses, and specific regulations like Article 42 of UK MiFIR, to understand how they address the problem. The article also considers the limits of these legal frameworks and the need for coordinated control over aggregate risk. KEY FINDINGS: - Existing law does not clearly reach the market-wide pattern of correlated trading. - Firm-level duties, venue responses, and specific regulations like Article 42 of UK MiFIR can address some issues. - Gaps remain in responsibility, particularly concerning cross-firm information and early detection of risks. - Responsibility should sit where aggregate risk can be seen and coordinated control can be exercised. IMPLICATION FOR TRADING: The implications for trading practitioners are that existing legal frameworks may not fully address the risks of correlated trading, especially when firms operate independently and react to common models or signals. Practitioners should be aware of these gaps and consider how to manage cross-firm information and early detection of risks. They may need to rely on coordinated control and possibly additional safeguards to mitigate the risks of correlated trading.

SUMMARY: The study employs the Susceptible–Infected–Recovered–Susceptible (SIRS) model to analyze Bitcoin adoption over a period from 1 February 2012 to 31 July 2026. The model categorizes users into three states: susceptible (open wallets), infected (active addresses), and recovered (inactive addresses). The analysis focuses on major bubble periods, assessing infection, recovery, re-entry rates, and the reproduction number. The findings indicate significant alterations in Bitcoin's infectious characteristics over time, with the 2013-2014 period characterized by a rapid infection rate within a small population. The speculative cycles of 2017-2018, 2020-2021, and 2024-2025 exhibit lower reproduction numbers but longer infectious periods. This demonstrates a shift from fast and short-lived contagion to slower and longer-lasting participation in a larger ecosystem. The study suggests that Bitcoin initially exhibited epidemic-like characteristics, but later cycles display a pandemic-like pattern. The research expands the application of epidemiological models to analyze the contagious behavior of highly speculative securities. METHOD: The study uses a daily frequency dataset covering from 1 February 2012 to 31 July 2026. The SIRS model categorizes users into susceptible, infected, and recovered states, allowing inactive users to re-enter the market. The analysis focuses on major bubble periods, assessing infection, recovery, re-entry rates, and the reproduction number. KEY FINDINGS: - Significant alterations in Bitcoin's infectious characteristics over time. - The 2013-2014 period characterized by a rapid infection rate within a small population. - Speculative cycles of 2017-2018, 2020-2021, and 2024-2025 exhibit lower reproduction numbers but longer infectious periods. - Bitcoin initially exhibited epidemic-like characteristics, but later cycles display a pandemic-like pattern. IMPLICATION FOR TRADING: The findings provide insights into how Bitcoin contagion spreads, persists, and re-emerges during various speculative cycles. This can help investors and policymakers better understand the dynamics of Bitcoin adoption and market behavior. The study's application of epidemiological models to financial contagion offers a novel perspective on the contagious behavior of highly speculative securities, aiding in the development of more accurate financial models.

SUMMARY: This study examines whether U.S. spot Bitcoin ETF flows predict Bitcoin returns after accounting for the mismatch between U.S. ETF trading sessions and Bitcoin's 24/7 market. The authors use daily spot Bitcoin ETF flows and Bitcoin prices measured at 16:00 New York time to construct a true trading-calendar sample covering the cash-creation regime from January 11, 2024 to July 28, 2025 (386 trading sessions). The study finds that aligning the return window to the 24 hours after the session close reduces the coefficient to 1.67 (HAC p = 0.007, adjusted R² = 0.036), and a window that starts after the flow is public shows no positive association. The study also shows that a prior 24-hour Bitcoin return predicts same-day flows, and that lagged returns predict flows in an information-consistent Vector Autoregression (VAR). The authors conclude that timestamp alignment and information timing, rather than a universal flow-to-price coefficient, shape inference about Bitcoin ETF flow-return feedback. METHOD: The study uses daily spot Bitcoin ETF flows and Bitcoin prices measured at 16:00 New York time to construct a true trading-calendar sample covering the cash-creation regime from January 11, 2024 to July 28, 2025 (386 trading sessions). The authors re-estimate the same regression under alternative conventions to show how much of the apparent flow-to-return effect is contemporaneous. KEY FINDINGS: - Timestamp alignment reduces the flow-to-return effect to 1.67 percentage points per USD 1 billion of net inflow. - A prior 24-hour Bitcoin return predicts same-day flows. - Lagged returns predict flows in an information-consistent Vector Autoregression (VAR). - The flow-to-return coefficient does not differ significantly between the cash-creation and in-kind regimes. IMPLICATION FOR TRADING: The findings suggest that the timing of information and the alignment of timestamps are crucial for understanding Bitcoin ETF flow-return feedback. Practitioners should be aware of the impact of these factors on the relationship between ETF flows and Bitcoin returns, which can inform trading strategies and risk management. Understanding these dynamics can help traders make more informed decisions regarding their investments in Bitcoin ETFs.

SUMMARY: This article examines the rapid growth of data center investments within institutional real estate portfolios, particularly focusing on pension funds and other tax-exempt investors. The study uses NCREIF data to show that the data center segment has quadrupled in market value since 2024, with average capital expenditures of 10% of value per year, significantly higher than the all-property rate. Despite this growth, income from data centers has been below market, and appraised values have increased faster than capital invested. The article highlights that private real estate appraisals do not reflect the AI factor present in listed data center real estate investment trusts (REITs). METHOD: The study uses NCREIF property-level data to analyze institutional tax-exempt investors' holdings of data centers. The data covers the period from the start of 2024 to mid-2026, documenting three key facts: the substantial growth in the data center segment, a shift in cash flow profile, and the selective lag in appraised values reflecting broad listed real estate information. KEY FINDINGS: - Institutional data center holdings grew from $4.9 billion to $19.1 billion in ten quarters. - Since 2024, data center capital expenditures averaged 10% of value per year, seven times the all-property rate. - Appraised values increased faster than capital invested in the properties. - Private data center appraisals absorb broad real estate news with a lag but do not show positive AI exposure. - Income net of capital expenditure was negative in nine of ten quarters from 2024 to 2026. IMPLICATION FOR TRADING: The findings suggest that institutional investors, particularly those with tax-exempt status, are taking on AI-cycle risk within their real estate portfolios without fully reflecting this risk in their appraisal-based returns. This implies that data center investments may be overvalued or undervalued based on traditional real estate appraisal methods. Practitioners should consider the AI factor when valuing and managing data center investments within institutional portfolios, as traditional appraisal methods may not adequately capture the sector's unique dynamics.

SUMMARY: The article revisits and expands a framework for Value-at-Risk (VaR) and Expected Shortfall (ES) of intrinsically quadratic portfolios under multivariate elliptical and mixture-of-elliptical risk factors. The quadraticity is derived from the second-order expansion of the exponential map linking log returns to security prices, distinguishing it from the usual delta-gamma approximation of derivative portfolios. The revised analysis separates the exact security loss, its linear approximation, and its quadratic approximation, deriving an explicit third-order remainder bound. It identifies a canonical noncentral quadratic form for long-only portfolios, showing the quadratic loss is bounded above by half the initial portfolio value. The analysis provides a clean joint characterization of VaR and ES through the distribution and lower partial first moment of a positive quadratic form, yielding transparent extensions to finite mixtures and precise existence conditions. The framework also develops modern computational routes for Gaussian and Student-t factors, combining generalized noncentral chi-square methods, scale-mixture representations, and Monte Carlo validation. KEY FINDINGS: - Separation of exact, linear, and quadratic portfolio mappings. - Identification of a canonical noncentral quadratic form for long-only portfolios. - Explicit bound for the quadratic loss. - Clean joint characterization of VaR and ES through the distribution and lower partial first moment of a positive quadratic form. - Modern computational methods for Gaussian and Student-t factors. IMPLICATION FOR TRADING: The findings provide a more rigorous and transparent treatment of quadratic portfolio tail risk, distinguishing between Taylor-approximation error and temporal-scaling error. The framework preserves the original contribution while offering a more modern computational approach. The analysis can be applied to portfolio management, particularly for understanding and managing tail risk in intrinsically quadratic portfolios. The framework can be used to validate and compare different risk measures, such as the quadratic VaR and ES, with exact revaluation and linear approximations. The results suggest that the quadratic approximation is nearly indistinguishable from exact revaluation at various confidence levels, while the linear approximation overstates tail risk. The framework can be used to validate and compare different risk measures, such as the quadratic VaR and ES, with exact revaluation and linear approximations. The findings can inform the use of different risk measures in portfolio management, providing a more rigorous and transparent approach to managing tail risk in intrinsically quadratic portfolios.

SUMMARY: This study examines the relationship between impulsivity, overconfidence, and sensation seeking among self-directed retail investors and their association with the illusion of control. The researchers used an original survey of 177 investors to investigate the associations of these traits with the illusion of control under both asymptotic and bootstrap inference. METHOD: The study employed an original survey of 177 self-directed retail investors to estimate the associations of impulsivity, overconfidence, and sensation seeking with the illusion of control. The sample was tested using both asymptotic and bootstrap inference methods. The researchers found that impulsivity is the only trait positively related to the illusion of control, with an association that is borderline rather than supported (β = 0.224, p = 0.017, bootstrap 95% confidence interval [−0.001, 0.642]). The second set of results explored the relationship between overconfidence and the illusion of control in two independent partitions of the sample, finding that finance-sector employees reported higher overconfidence but lower illusion of control compared to other investors. KEY FINDINGS: - Impulsivity is the only trait positively related to the illusion of control. - Overconfidence and the illusion of control move in opposite directions among different investor groups. - A two-factor specification of the two instruments fits significantly better than a single common factor. IMPLICATION FOR TRADING: The findings suggest that overconfidence and the illusion of control are not necessarily linked, and that the constructs are separable. This research could inform the development of more targeted investment education and risk management strategies for retail investors. The study's results could also help regulators and platform designers identify and measure the relevant investor characteristics that make them vulnerable to certain biases, such as overconfidence and the illusion of control. This could lead to more effective and targeted regulatory measures and investment tools.

SUMMARY: This study investigates whether loss aversion is a stable trait or a state-dependent preference. Japanese financial group employees completed a two-state task in one sitting, under a normal and then a crisis frame. The results show that loss aversion rises under stress, contrary to the assumption that it is a stable trait. The study uses a preregistered design to test the stability of risk preferences across settings. The findings suggest that loss aversion is a state-dependent preference, not a stable trait. The study also provides a portable design principle for measuring loss aversion in the field. METHOD: The study employed a preregistered two-state measurement framework. Employees of a major Japanese financial group completed a 48-item task in one sitting, with a normal block followed by a crisis frame and a stress block. The study estimated outcome sensitivity, loss aversion, and probability weighting separately in each state and compared the paired estimates within respondents. The design includes a test of the joint invariance conditions, where the crisis frame items offer strictly better odds than their matched normal-block counterparts. The study used a matched control pair to compare the results. KEY FINDINGS: - Loss aversion rises under stress, contrary to the assumption that it is a stable trait. - The registered hypotheses are supported on all three components: outcome sensitivity, loss aversion, and probability weighting. - The registered attribute-information bound is falsified only for probability weighting, indicating that loss aversion is a state-dependent preference. - Normal-state rank explains only part of stress-state rank, suggesting that loss aversion is influenced by the stress frame. IMPLICATION FOR TRADING: The study's findings suggest that loss aversion is a state-dependent preference rather than a stable trait. This implies that loss aversion can change within a short interval, which is relevant for applications that use loss aversion to explain behavior. Practitioners can use the portable design principle provided by the study to measure loss aversion in the field, which can help in understanding and predicting investor behavior under different stress frames.

SUMMARY: This study examines whether additional equity-market signals improve the assessment of default risk using accounting-based methods. The research uses 39,970 firm-year observations for Chinese listed firms from 2015 to 2025. The study estimates models with distinct information sets for a 2016-2019 training window and evaluates them without refitting from 2020-2025. An equity-market microstructure signal is selected using only pre-2020 data and is frozen for validation. The study finds that a model based on reported financial statement items attains an out-of-sample AUC of 0.8023 in the pooled 2020-2025 evaluation. Adding firm characteristics and price-scaled valuation multiples to the model improves performance, with valuation being the only market-based block that clearly improves performance. Adding structural market-risk measures lowers the AUC, while adding closing-price stasis worsens performance. The study concludes that financial statements provide a strong baseline, and price-scaled valuation information adds meaningful incremental content. Structural market-risk and microstructure variables do not deliver consistent incremental gains across discrimination and proper scoring measures. The evidence suggests that default-risk information contained in equity-market signals is incremental to reported financial statement fundamentals and does not persist over time or transport across markets. METHOD: The study uses a dataset of 39,970 firm-year observations for Chinese listed firms from 2015 to 2025. Models are estimated with distinct information sets for a 2016-2019 training window and evaluated without refitting from 2020-2025. An equity-market microstructure signal is selected using only pre-2020 data and is frozen for validation. The study employs a model based on reported financial statement items and evaluates its performance using out-of-sample AUC. The study also evaluates the performance of the model with additional firm characteristics and price-scaled valuation multiples. KEY FINDINGS: - A model based on reported financial statement items attains an out-of-sample AUC of 0.8023 in the pooled 2020-2025 evaluation. - Adding firm characteristics and price-scaled valuation multiples to the model improves performance, with valuation being the only market-based block that clearly improves performance. - Adding structural market-risk measures lowers the AUC, while adding closing-price stasis worsens performance. - Financial statements provide a strong baseline, and price-scaled valuation information adds meaningful incremental content. - Structural market-risk and microstructure variables do not deliver consistent incremental gains across discrimination and proper scoring measures. IMPLICATION FOR TRADING: The study suggests that financial statements provide a strong baseline for assessing corporate credit risk. Price-scaled valuation information adds meaningful incremental content to the assessment. The study's findings suggest that equity-market signals, while potentially useful, do not contain default-risk information that is incremental to reported financial statement fundamentals and does not persist over time or transport across markets. Practitioners should use caution when incorporating equity-market signals into their credit risk assessment models, as they may not provide consistent incremental value.

SUMMARY: This paper examines the liquidity and safe haven properties of Responsible Physical Gold ETPs compared to conventional physical gold investment vehicles. The authors identify a "Price of Ethics" characterized by significantly higher bid-ask spreads for ethical vehicles. Using High-Dimensional Fixed Effects (HDFE) and Propensity Score Matching (PSM) methods, they find that responsible gold ETPs exhibit characteristics of a buffer, showing a positive net return response and a simultaneous surge in trading volume during systemic shocks. The analysis reveals a maturation shift: the liquidity of Responsible Gold ETPs reflects a stable structural premium as the market moves away from transitory attention shocks, while the association with thematic ESG attention reverses, transitioning from initially widening spreads to compressing them as ethical mandates become embedded in market dynamics. The robustness checks using the Amihud ratio confirm that while Responsible Gold commands a transaction premium, it maintains market depth. Overall, the results suggest that Responsible Gold ETPs function as specialized defensive instruments, where higher transaction costs are balanced by enhanced price stability during systemic shocks. METHOD: The study employs High-Dimensional Fixed Effects (HDFE) and Propensity Score Matching (PSM) methodologies to analyze the liquidity and safe haven properties of Responsible Physical Gold ETPs compared to conventional physical gold investment vehicles. The sample includes Responsible Gold ETPs and conventional gold investment vehicles, and the data is analyzed using econometric techniques to identify the "Price of Ethics" and its impact on resilience during systemic shocks. KEY FINDINGS: - Responsible Gold ETPs exhibit higher bid-ask spreads compared to conventional gold investment vehicles. - Responsible Gold ETPs show characteristics of a buffer, with positive net return responses and simultaneous surges in trading volume during systemic shocks. - The association with ESG attention initially widens spreads but eventually compresses them as ethical mandates become embedded in market dynamics. - The liquidity of Responsible Gold ETPs reflects a stable structural premium as the market moves away from transitory attention shocks. - The Amihud ratio robustness checks confirm that Responsible Gold commands a transaction premium while maintaining market depth. IMPLICATION FOR TRADING: The findings suggest that Responsible Gold ETPs function as specialized defensive instruments, balancing higher transaction costs with enhanced price stability during systemic shocks. This information can be used by traders to make informed decisions about investing in gold ETPs, particularly those with ethical mandates. Investors seeking to diversify their portfolios with gold ETPs should consider the potential impact of ESG criteria on the liquidity and resilience of these instruments.

SUMMARY: This study examines whether diffusion-based generative scenario models, particularly when conditioned on market regimes, can improve Expected Shortfall (ES) forecasting in Asia-Pacific equity markets. The analysis uses daily data from six major markets—India, Japan, Hong Kong, South Korea, Australia, and Singapore—from 2007 to August 2026. Five approaches are compared: Historical Simulation (HS), regime-conditioned HS (Regime-HS), GARCH-filtered HS (GARCH-FHS), a diffusion-based scenario model, and a regime-conditioned diffusion model. The results show that incorporating Hidden Markov Model regime probabilities does not significantly improve diffusion-based ES forecasts. GARCH-FHS delivers the strongest numerical forecasting performance and tail-risk calibration, although formal pairwise tests do not establish its superiority over competing models at conventional significance levels. The findings suggest that greater generative flexibility and explicit regime conditioning do not necessarily translate into superior tail-risk forecasts, highlighting the importance of accurately modelling extreme distributional characteristics when applying generative models to financial risk management. METHOD: The study uses daily data from six major Asian-Pacific equity markets (India, Japan, Hong Kong, South Korea, Australia, and Singapore) from 2007 to August 2026. Five approaches are compared: Historical Simulation (HS), regime-conditioned HS (Regime-HS), GARCH-filtered HS (GARCH-FHS), a diffusion-based scenario model, and a regime-conditioned diffusion model. The analysis evaluates 1,151 genuine out-of-sample forecasts between January 2020 and August 2026. KEY FINDINGS: - Incorporating Hidden Markov Model regime probabilities does not provide a statistically significant improvement in diffusion-based ES forecasts. - GARCH-FHS delivers the strongest numerical forecasting performance and tail-risk calibration. - Scenario-fidelity diagnostics indicate that the diffusion models underrepresent the negative skewness, excess kurtosis, and joint downside dependence observed in realized returns. - Greater generative flexibility and explicit regime conditioning do not necessarily translate into superior tail-risk forecasts. IMPLICATION FOR TRADING: The findings suggest that diffusion-based generative models, especially when conditioned on market regimes, can improve ES forecasting in Asia-Pacific equity markets. However, the superiority of these models over competing methods is not established at conventional significance levels. The study highlights the importance of accurately modelling extreme distributional characteristics when applying generative models to financial risk management, particularly in the context of market regimes and tail risks. This research can inform the development of more robust and accurate ES forecasting models for financial institutions, asset managers, and portfolio managers.

SUMMARY: The article presents a stationary goal-based portfolio optimization problem under regime-switching market conditions. The authors formulate and solve a problem where portfolio weights depend solely on the current market regime, leading to strictly stationary weight and return processes. The utility function is reduced to a low-dimensional integral over regime occupation times, resulting in a two-level optimization scheme. The study demonstrates that the stationary regime-conditional strategy outperforms a passive S&P 500 benchmark by 15–40% of goal-reaching probability at multi-year horizons and aggressive targets, with the performance being 2σ-significant. The authors also highlight that the unconstrained optimal weights are highly concentrated, and institutional deployment would proceed through a tracking-and-turnover-bounded Hedged variant, which retains most of the probability gain in a tradable form. The study shows that the stationary regime-switching setup maintains the identity between historical performance and model expectation, and the gains are attributed to the regime conditioning rather than the optimization apparatus itself. METHOD: The study uses 25 years of GICS-sector daily returns in a long-only setting. The utility function is formulated as a low-dimensional integral over regime occupation times, and the optimization problem is solved using a two-level approach. The authors use analytical and empirical methods to validate the findings, including closed-form expressions for the utility function and Monte-Carlo simulations for standard errors. KEY FINDINGS: - The stationary regime-conditional strategy outperforms a passive S&P 500 benchmark by 15–40% of goal-reaching probability at multi-year horizons and aggressive targets. - The unconstrained optimal weights are highly concentrated. - The stationary regime-switching setup maintains the identity between historical performance and model expectation. - The gains are attributed to the regime conditioning rather than the optimization apparatus itself. IMPLICATION FOR TRADING: The study's findings have significant implications for portfolio management and trading strategies. The stationary regime-conditional strategy provides a clear advantage over passive benchmarks, and the use of a tracking-and-turnover-bounded Hedged variant ensures that the gains can be effectively implemented in institutional settings. The study's analytical robustness bounds indicate that the findings are robust to natural perturbations of key parameters. Practitioners can use these insights to develop and deploy more effective portfolio optimization strategies, focusing on the regime conditioning aspect to achieve better performance.

SUMMARY: This study examines the price discovery process in perpetual futures markets where the benchmark markets are closed, focusing on the role of the local price in predicting the reference price. The research covers 103 contracts on various assets, including U.S. and international equities, ETFs, equity indices, commodity futures, spot metals, and foreign exchange, from December 15, 2025, to August 31, 2026. The study finds that when the benchmark market is closed, the trading venue sets a reference price based on the local price, which is influenced by noise and the thin order book due to the closure. The study also reveals that the local price anticipates most of the benchmark's move, with a 1% change in the local price mapping to a 0.7% to 0.9% change in the reference price once the external price returns. The research further indicates that the local price is noisy and that the benchmark's closure itself thins the order book, leading to a higher weight on the local price that best predicts the reference price over weekends and holidays. The study concludes that the setting of the reference price separates the loss of the external price from the closure of the benchmark and that the venue, TradeXYZ, specifies when to follow the external price or an internal rule based on the order book. METHOD: The study uses a sample of 103 perpetual futures contracts on various assets, including U.S. and international equities, ETFs, equity indices, commodity futures, spot metals, and foreign exchange. The research covers a period from December 15, 2025, to August 31, 2026, and analyzes the impact of the benchmark market closure on the reference price setting. The methodology involves analyzing the relationship between the local price and the reference price, using a signal-extraction framework to measure the signal share of a local price change. KEY FINDINGS: - A 1% change in the local price maps to a 0.7% to 0.9% change in the reference price once the external price returns. - The local price is noisy and influenced by the thin order book due to the benchmark's closure. - The weight on the local price that best predicts the reference price rises from about 0.1 in brief maintenance breaks to 0.85 over weekends and holidays. - The benchmark's closure itself thins the order book, leading to a higher weight on the local price. IMPLICATION FOR TRADING: The findings suggest that traders should pay attention to the local price when the benchmark market is closed, as it can provide valuable information about the future reference price. The study highlights the importance of understanding the impact of the benchmark market closure on the reference price setting and the role of the local price in predicting it. Practitioners can use this information to make more informed trading decisions, especially when dealing with perpetual futures markets where the benchmark market is not always open.

SUMMARY: This article examines the economic and policy forces shaping the future of the GLP-1 market, focusing on patent expiration, generic competition, pharmaceutical innovation, pricing, insurance coverage, and global access. It highlights the rapid growth in GLP-1 prescriptions for overweight and obesity, driven by the expansion of the market from diabetes treatments to broader applications. The article also discusses the impact of patent expiration, with semaglutide patents expiring in major international markets in 2026, leading to generic entry and price reductions. METHOD: The article analyzes patent expiration, generic competition, pharmaceutical innovation, and pricing in the GLP-1 market. It also examines insurance coverage and global access to GLP-1 therapies. The study was conducted by members of the Health Policy pod of the Brown Healthcare Investment Group. KEY FINDINGS: - Prescription growth for GLP-1 drugs has surged 587% in just five years. - Semaglutide patents are set to expire in major international markets in 2026, leading to generic entry and price reductions. - Oral formulations of GLP-1 therapies are becoming more widely available. - There is growing evidence and research into cardiovascular, renal, neurological, and addiction-related applications of GLP-1 therapies. - The GLP-1 market is moving beyond its traditional role as a premium specialty therapeutic to become a widely accessible healthcare commodity. IMPLICATION FOR TRADING: The implications for trading are significant. As GLP-1 therapies become more widely accessible and generic competition intensifies, pricing will likely decrease. This could lead to increased demand and potentially higher stock prices for companies involved in the GLP-1 market. However, companies will need to balance affordability with innovation and sustainability to maintain market position. Investors should monitor patent expiration, generic competition, and the evolving applications of GLP-1 therapies to make informed decisions.

SUMMARY: The article examines the construction and calibration of an equity volatility surface in the extended SSVI (eSSVI) parameterization, focusing on how skew can vary across maturities. The study compares eSSVI, SSVI, and raw SVI methods, using four weeks of daily and intraday option chains on four equity-index ETFs and four single names. The research evaluates the fit, static arbitrage, parameter stability, and the smoothness of the Dupire local volatility induced by each surface. The study also prices six-month vanilla options and knock-out barrier options under the induced local volatility. The results show that fit alone is not sufficient to judge volatility-surface calibration, as the way a surface is constructed can significantly affect local-volatility dynamics and path-dependent prices. METHOD: The study uses four weeks of daily and intraday option chains on four equity-index ETFs and four single names. The eSSVI, SSVI, and raw SVI methods are calibrated sequentially under penalties, jointly with γ fixed at 1/2 or estimated. The study scores each surface on in-sample and out-of-sample fit, static arbitrage, parameter stability, and the smoothness of the Dupire local volatility it induces. The induced local volatility is scored on roughness and containment, and the price of knock-out barrier options is scored on shared Monte Carlo paths. KEY FINDINGS: - The eSSVI calibrations disagree on a six-month option by up to 3.5% of its value at the money on the index chains and by up to 7.5% half a standard deviation out of the money. - The SSVI surface induces the smoothest local volatility. - The guaranteed eSSVI constructions are clean at the fitted pillars, although interpolation can still cross between them. - A knock-out barrier option amplifies the disagreement between eSSVI calibrations to 16 to 68% of the price where the surface is inadmissible between pillars. IMPLICATION FOR TRADING: The study highlights that volatility-surface calibration cannot be judged by fit alone, as the way a surface is constructed can significantly affect local-volatility dynamics and path-dependent prices. Practitioners should consider the implications of different calibration methods and ensure that the induced local volatility is smooth and admissible across all maturities. This can have practical implications for pricing exotic options and structuring financial products.

SUMMARY: The study examines the discrepancies between option exchanges and prediction markets for binary contracts, focusing on crypto and index underlyings. The authors use a full option chain and Kalshi binary contract data to measure risk-neutral probabilities and compare them. The sample includes 4.03 million contract snapshots, 221 thousand signals, and 1665 settled events. The findings indicate that the two platforms do not consistently agree, with crypto underlyings showing no clear preference for either platform. For indices, the discrepancy is larger due to the delay in the option chain. The most favorable configuration is found in the ETH threshold series, where a small position yields a significant profit. The study also highlights the winner's curse and the role of the smile, forward-spot basis, and residual level in explaining discrepancies. METHOD: The study uses a fixed snapshot approach every fifteen minutes, extracting risk-neutral probabilities from full option chains and comparing them with mid quotes of Kalshi binary contracts. The full order book of each contract producing a signal is captured, and the contracts are followed to settlement. The sample size includes 4.03 million contract snapshots, 221 thousand signals, and 1665 settled events. KEY FINDINGS: - On crypto underlyings, the Brier scores of the two platforms differ by 0.0003, with the difference not being statistically significant. - On indices, the discrepancy is larger due to the delay in the option chain, leading to a split ranking of the platforms. - The most favorable configuration is found in the ETH threshold series, where a small position yields a significant profit. - The winner's curse is evident, as the expected payoff grows with the size of the discrepancy, but the realized payoff does not follow. - The largest loss is observed in the basis group on both crypto underlyings, indicating that the discrepancy is due to data artifacts rather than information. IMPLICATION FOR TRADING: The study's findings suggest that traders should be cautious when using binary contracts from prediction markets, as the discrepancy between the two platforms can lead to significant losses. Traders should consider the size of their positions and the potential for the winner's curse. Additionally, traders can use the telescopic decomposition of the discrepancy to identify the specific components contributing to the difference, such as the smile, forward-spot basis, and residual level. This information can help in making more informed trading decisions.

SUMMARY: This study examines the relationship between Bitcoin's price divergence from its lifetime Time-Weighted Average Price (TWAP) and daily sentiment derived from cryptocurrency wire and social media items. The researchers use a daily sentiment series and the log distance of the spot price from its lifetime TWAP over 752 observations from 2024-03-13 to 2026-06-01. They find that sentiment does not Granger-cause price movement (p = 0.630), but price movement does predict subsequent sentiment (p = 0.001). The study also highlights a strong correlation between two sentiment indicators published by the same platform, and the price-embedding Fear & Greed composite shows stronger apparent feedback from prices than text-derived series. The findings suggest that sentiment indices are not interchangeable and that their construction should be carefully considered. METHOD: The study uses an archive-documented daily sentiment series derived from cryptocurrency wire and social media items, paired with the log distance of the spot price from its lifetime TWAP. The analysis is based on 752 daily observations from 2024-03-13 to 2026-06-01. The researchers use wild-bootstrap inference robust to the heavy heteroskedasticity of the data. They employ a Vector Autoregression (VAR) model to test Granger causality between sentiment and price divergence, and between price divergence and sentiment. KEY FINDINGS: - Daily text-derived crowd sentiment does not Granger-cause changes in Bitcoin's divergence from its lifetime TWAP. - Price movement does predict subsequent sentiment. - Two sentiment indicators published by the same platform correlate at -0.018. - The price-embedding Fear & Greed composite shows stronger apparent feedback from prices than text-derived series. - Sentiment indices are not interchangeable, and their construction should be read before their coefficients are. IMPLICATION FOR TRADING: The findings suggest that sentiment indices are not reliable as leading indicators for Bitcoin's price movements. The study's results imply that traders should be cautious when using sentiment indices for trading purposes, as they may not provide meaningful predictive power. Instead, traders should focus on other factors such as technical analysis, fundamental analysis, and market trends. The study's results also highlight the importance of carefully constructing sentiment indices, as different indices may have varying predictive power and economic significance.

SUMMARY: This paper discusses the limitations of conventional scenario analysis in financial risk management, where stress tests often summarize scenario gains using point estimates that overlook the dependence between stressed and unstressed risk factors. The authors propose a machine learning framework, Adaptive Conformal Scenario Analysis (ACSA) and Kernel Scenario Analysis (KSA), to better quantify uncertainty in realized next-day scenario gains. ACSA calibrates scenario-specific quantile predictions and provides a long-run empirical coverage guarantee over realized scenarios, while KSA estimates scenario-conditional quantiles directly from specified stress and current market information. The methods are designed to provide better calibration and sharper prediction intervals than empirical-quantile baselines, and they move scenario analysis beyond point estimates by quantifying predictive uncertainty and offering tools for statistically validating scenario gains. METHOD: The authors use experiments designed to reflect real-world markets to demonstrate the limitations of conventional scenario analysis, which can substantially understate risk, including for portfolios that appear safe under standard stress tests. They develop ACSA and KSA methods, which are built on machine learning frameworks. ACSA updates the quantile level based on whether the preceding interval covered the realized P&L in the realized scenario, while KSA reweights historical observations according to the specified stress and current market information. KEY FINDINGS: - ACSA provides online-calibrated prediction intervals that offer a long-run empirical coverage guarantee. - KSA estimates scenario-conditional quantiles directly from specified stress and current market information. - The proposed methods achieve better calibration and sharper prediction intervals than empirical-quantile baselines. - The methods move scenario analysis beyond point estimates by quantifying predictive uncertainty and providing tools for statistically validating scenario gains. IMPLICATION FOR TRADING: The proposed methods can improve risk management in financial portfolios by providing better quantification of predictive uncertainty. This can help practitioners avoid underestimating risk and make more informed decisions. By offering tools for statistically validating scenario gains, the methods can also help risk managers have a principled notion of the accuracy of their scenario numbers. This can lead to more reliable and robust risk management practices, ultimately improving the performance and stability of financial portfolios.

SUMMARY: The study examines the alignment between consumer brand equity and investor valuation following corporate rebranding. It uses structured interviews with five key market strategists and financial analysts to analyze qualitative data through thematic analysis. The study identifies four critical alignment factors: core product quality, infrastructure capital allocation, digital sentiment velocity, and strategic market positioning. The findings show that cosmetic rebranding fails to elevate firm valuation if capital expenditure does not support operational fundamentals or research and development. The research bridges marketing and corporate finance literature by demonstrating how consumer brand equity functions as a financial signaling mechanism to reduce information asymmetry. The study provides executives with a framework to align brand transformations with investor relations to protect long-term enterprise value. METHOD: The study employed structured interviews with five key market strategists and financial analysts to gather qualitative data. The data were analyzed using thematic analysis. The sample included five market strategists and financial analysts who provided insights into the alignment between consumer brand equity and investor valuation following corporate rebranding. KEY FINDINGS: - Cosmetic rebranding fails to elevate firm valuation if capital expenditure does not support operational fundamentals or research and development. - Synchronized consumer messaging and investor communications foster congruent value creation. - Core product quality, infrastructure capital allocation, digital sentiment velocity, and strategic market positioning are critical alignment factors. - The research bridges marketing and corporate finance literature by demonstrating how consumer brand equity functions as a financial signaling mechanism to reduce information asymmetry. IMPLICATION FOR TRADING: The research provides executives with a framework to align brand transformations with investor relations, thereby protecting long-term enterprise value. By understanding the alignment between consumer brand equity and investor valuation, executives can develop appropriate repositioning campaigns that guarantee high levels of satisfaction among stakeholders and market valuation. This research offers practical insights for financial and marketing executives to develop effective rebranding strategies that align with market expectations and investor valuations.

SUMMARY: The article introduces a new method for selecting stocks using multiple factors, which combines a mixture design approach with historical backtesting, response-surface modeling, and constrained optimization. The study evaluates 31 structured factor-weight combinations using quarterly data for CSI 300 stocks, focusing on five signals: price-to-book ratio (PB), return on equity (ROE), market capitalization (MV), Beta, and prior-quarter return scored in the reversal direction (MOM). The research aims to understand how the allocation of importance across these factors affects portfolio performance and risk, and whether optimal allocations vary under different risk constraints and subperiods. METHOD: The study employs a mixture design framework that integrates historical portfolio backtesting, second-order Scheffé response-surface modeling, and constrained optimization. The analysis uses five factor signals: PB, ROE, MV, Beta, and MOM, and evaluates 31 structured factor-weight combinations using quarterly data for CSI 300 stocks. The results reveal that portfolio outcomes vary significantly with the joint composition of factor weights, and that the estimated surfaces exhibit economically meaningful nonlinearities across factor combinations. Optimization of the fitted return and risk surfaces shows that the optimal factor composition changes as the level of portfolio risk varies, generating a Factor Synergy Frontier in factor-weight space. KEY FINDINGS: - Portfolio outcomes vary significantly with the joint composition of factor weights. - Estimated surfaces exhibit economically meaningful nonlinearities across factor combinations. - The return-maximizing factor composition changes as the level of portfolio risk varies. - Period-specific Sharpe-ratio optimization reveals substantial shifts in the location of the optimal allocation across subperiods. - These findings position factor weighting as a constrained and potentially time-varying allocation problem. IMPLICATION FOR TRADING: The findings suggest that the allocation of weights across multiple factors is a complex and dynamic problem that can vary over time and under different risk constraints. This implies that traders should consider the interplay between different factors and their relative importance rather than focusing solely on standalone factor performance. The study's framework can help investors optimize their factor allocations and improve their portfolio performance, particularly in the context of multi-factor investing. Additionally, the study's results provide descriptive evidence that the optimal factor composition can change across different subperiods, highlighting the importance of considering time-varying factors in portfolio management.

SUMMARY: This study examines the impact of large language models (LLMs) on corporate disclosure and trading behavior. Specifically, it investigates how LLMs, which summarize and translate corporate filings, affect price discovery and trading synchronization. The study uses the public launch of ChatGPT as a catalyst, focusing on two pre-launch properties of corporate filings: readability and uncertainty. After the launch, the study finds that order flow in hard-to-read firms becomes more one-sided compared to comparable firms, both in retail and institutional trading. Additionally, the study reveals that for a firm one standard deviation harder to read, the share of the price impact of order flow that survives the following quarter increases from about 60 to about 80 percent, and earnings news enters prices faster. The study also notes that non-retail order flow in hard-to-read stocks also moves more with the rest of the market. The research suggests that greater disclosure uncertainty predicts less synchronized trading relative to other firms and no improvement in price discovery. The study develops a Kyle-style model to interpret these patterns, suggesting that investors delegate reading to a common model that decodes better but introduces a shared error. This leads to a strategic substitution for trading on information others also hold, resulting in less adoption and coexistence with better price discovery. METHOD: The study uses the public launch of ChatGPT as a catalyst to analyze the impact of large language models on corporate disclosure and trading behavior. The analysis focuses on two pre-launch properties of corporate filings: readability and uncertainty. The sample consists of firms with publicly available filings, and order flow data is collected from retail and institutional trading. The study measures disclosure difficulty using two dimensions from the disclosure literature (Li, 2008; Loughran and McDonald, 2011). KEY FINDINGS: - After the launch of ChatGPT, order flow in hard-to-read firms becomes more one-sided compared to comparable firms. - For a firm one standard deviation harder to read, the share of the price impact of order flow that survives the following quarter increases from about 60 to about 80 percent. - Non-retail order flow in hard-to-read stocks also moves more with the rest of the market. - Greater disclosure uncertainty predicts less synchronized trading relative to other firms and no improvement in price discovery. IMPLICATION FOR TRADING: The findings suggest that the widespread adoption of large language models can lead to more synchronized trading, but also introduces a risk of shared errors. This can result in less adoption of the technology and coexistence with better price discovery. Practitioners should be aware of these implications when considering the use of large language models in their trading strategies.

SUMMARY: The article introduces a comprehensive, risk-informed probabilistic framework for enterprise portfolio decision-making and operational risk management. It aims to unify enterprise portfolio planning with operational execution by applying quantitative and probabilistic risk analysis methods. The framework emphasizes the importance of considering both quantifiable and aspirational gains, as well as tapping into stakeholders' insights and affective performance measures. The methodology also includes the use of Monte Carlo methods to account for uncertainty and iterative resource allocation to optimize competing objectives. The paper highlights the potential for extending this methodology to national and international cooperation areas such as AI and climate change. METHOD: The methodology is based on six principles and premises, which are applied to develop a decision-analytic framework for enterprise portfolio planning and execution. The framework is designed to be flexible and adaptable, and it includes the use of Monte Carlo methods and iterative resource allocation to address uncertainty and competing objectives. The framework is presented as a formalism that can be effectively communicated to management. KEY FINDINGS: - Use of quantitative, probabilistic risk analysis methods at the enterprise level. - Importance of considering both quantifiable and aspirational gains. - Application of affective performance measures to tap into stakeholders' insights. - Use of Monte Carlo methods to account for uncertainty. - Iterative resource allocation to optimize competing objectives. - Potential for extension to national and international cooperation areas. IMPLICATION FOR TRADING: The framework can be applied to optimize enterprise planning and risk mitigation in various sectors, including resource allocation, operational risk management, and strategic risk management. Practitioners can use this methodology to operationalize, adapt, or scale the decision-analytic framework to their specific needs. The framework can be particularly useful for high-consequence industries and sectors where national and international cooperation is required, such as AI and climate change.

SUMMARY: The article presents a generalized Langevin model of market impact, where market impact is modeled as the response to submitted order flow net of counterflow from latent traders. The model assumes that latent traders activate when price displacements from the level without the order exceed individual thresholds. The order flow depletes this pool, and a generalized Langevin equation governs its recovery over several time scales. The memory kernels are finite sums of exponentials, allowing for an exact Markovian lift. The article explores the concave price impact, which is observed across various markets and asset classes. The study aims to provide a comprehensive model of market impact that can account for the observed concavity without imposing a square-root impact law. METHOD: The study employs a generalized Langevin model to describe market impact. The model assumes that latent traders activate when price displacements from the level without the order exceed individual thresholds. The order flow depletes this pool, and a generalized Langevin equation governs its recovery over several time scales. The memory kernels are finite sums of exponentials, allowing for an exact Markovian lift. KEY FINDINGS: - The model provides a mechanism for the observed concave price impact without imposing a square-root impact law. - The memory kernels are finite sums of exponentials, allowing for an exact Markovian lift. - The model can reproduce the observed square-root impact law without imposing it as a fundamental assumption. IMPLICATION FOR TRADING: The implications for trading are significant as the proposed model can help traders understand and predict market impact, which is a major cost of trading large positions. The model can be used to optimize trading schedules by balancing impact cost against timing risk. Additionally, the model can help in understanding the effects of latent liquidity on market impact, which is a crucial aspect of market microstructure. Practitioners can use this model to better manage their trading strategies and to make more informed decisions regarding the execution of large orders.

SUMMARY: This paper develops a continuous signed-weight framework for multi-factor portfolio design, where positive and negative weights favor opposite directions, and zero removes a factor from the composite score. The study uses a three-level full factorial design with 243 points, including a non-discriminating market-average benchmark. The authors evaluate portfolio performance metrics using Chinese A-share data from 2005 to 2025. They find that positive ROE weights are prominent in high-return configurations, while negative ROE weights are common in low-volatility configurations. Negative MOM weights show strong return and risk-adjusted performance, consistent with reversal in the sample. The framework allows for the study of portfolio performance across a bounded continuous signed-weight region, providing a structured approach to examining the effects of factor weights and their interactions. METHOD: The study employs a three-level full factorial design with 243 design points, including a non-discriminating market-average benchmark. The continuous signed-weight space is sampled through this design, with 242 active scoring rules defined by nonzero signed-weight points. Portfolio responses at the factorial design points are used to study the structure of the signed-weight performance surface, examining conditional performance differences and comparing recurring patterns. KEY FINDINGS: - Positive ROE weights are prominent in high-return configurations. - Negative ROE weights are common in low-volatility configurations. - Negative MOM weights show strong return and risk-adjusted performance. - The effects of factor weights and their interactions vary with the performance objective and other weights. - The framework provides a structured way to study portfolio performance across a bounded continuous signed-weight region. IMPLICATION FOR TRADING: The continuous signed-weight framework offers a structured approach to studying multi-factor portfolio performance, allowing for the examination of factor weights and their interactions. This can help traders and portfolio managers understand how different weight specifications and factor combinations affect portfolio returns and risk. By identifying the optimal weight configurations, traders can potentially improve their investment strategies and enhance performance.

SUMMARY: This study examines how Ukraine's government broadened its investor base during the 2022-2026 war by adding previously issued bonds to Diia, a government digital retail platform. The research finds that household-to-legal-entity holding odds for newly listed bonds increased by 33.87% compared to non-switching bonds. The study uses a structural participation model to link this response to retail access costs, bond characteristics, persistent bond heterogeneity, and dynamic demand shocks. The findings suggest that sovereigns can create domestic fiscal capacity by lowering retail participation frictions, and the welfare value depends on which balance sheets households displace. METHOD: The study uses Ukraine's complete 2022-2026 war-bond holder history to analyze the staggered additions of already-issued bonds to Diia, a government digital retail platform. The researchers use a structural participation model to link the response to retail access costs, bond characteristics, persistent bond heterogeneity, and dynamic demand shocks. The sample consists of Ukraine's government bonds and the data comes from the National Bank of Ukraine monthly ISIN-by-holder archive. KEY FINDINGS: - Household-to-legal-entity holding odds for newly listed bonds increased by 33.87% compared to non-switching bonds. - Persistent bond heterogeneity and stationary bond-specific demand shocks contribute to the persistence of household participation. - The access effect is visible around the clean listing events, with newly listed bonds experiencing a 33.87% relative increase in household-to-legal-entity holding odds. - The structural model shows how a lower retail participation cost can reallocate a fixed sovereign liability toward households. - The financial-system value of reallocating the reallocation depends on the balance sheet displaced. IMPLICATION FOR TRADING: The findings suggest that sovereigns can create domestic fiscal capacity by lowering retail participation frictions. Practitioners can use this information to understand how to mobilize domestic investors and potentially increase their own fiscal capacity. The study also highlights the importance of understanding the balance sheets that would otherwise carry the sovereign claim, which can inform investment strategies in government bonds and other financial instruments.

SUMMARY: The study investigates the liquidity capacity and token survival in cryptocurrency markets, proposing a capacity-adjusted illiquidity measure that scales Amihud price impact by the square root of the product of market capitalization and the number of distinct takers. The research uses on-chain decentralized-exchange trade data to create survivorship-complete panels, including every token that clears a weekly volume threshold. The study finds that realized volatility has limited predictive power once liquidity is measured directly, and that the capacity adjustment sharpens this signal into a nearly sufficient statistic. The measure is also related to returns, with portfolios with higher death probabilities earning higher average returns. The findings suggest that token survival is primarily a liquidity-capacity problem, with price impact signals being more fragile when market capitalization is low and participation is thin. METHOD: The study uses on-chain decentralized-exchange trade data from Dune Analytics and Uniswap liquidity events to reconstruct survivorship-complete panels for the primary Ethereum panel and a parallel Solana panel. The capacity-adjusted illiquidity measure is computed on these panels, scaling Amihud price impact by the square root of the product of market capitalization and the number of distinct takers. The study employs standard microstructure and asset-pricing measures, such as volume-weighted price and return, dollar volume, and the price impact of trading, to analyze the data. KEY FINDINGS: - Capacity-adjusted illiquidity is a nearly sufficient statistic for predicting token survival. - Realized volatility has limited incremental predictive power once liquidity is measured directly. - The measure is related to returns, with higher death probabilities associated with higher average returns. - The measure is related to market impact, with trading activity declining, participation narrowing, and price impact rising before token death. IMPLICATION FOR TRADING: The findings suggest that investors and market venues should focus on liquidity capacity when assessing cryptocurrency tokens. The study's capacity-adjusted illiquidity measure can be used to identify tokens with higher survival probabilities, which can inform investment decisions. Additionally, the measure's relationship to returns indicates that investors may benefit from holding tokens with higher death probabilities, as they tend to offer higher average returns. However, practitioners should be cautious and consider other factors, as the study's findings are based on historical data and may not hold in all market conditions.

SUMMARY: This paper examines global liquidity as a regime-dependent state variable for cross-asset risk rather than as a standalone return predictor. A funding-based latent liquidity factor is estimated from seventeen monthly indicators over 2005-2025, covering official balance sheets, secured funding and money-market conditions, cross-border dollar funding and private credit. A dynamic factor model extracts common funding variation, and a two-state Markov-switching model identifies persistent liquidity regimes distinguished primarily by conditional volatility. METHOD: The study uses a dynamic factor model to estimate a funding-based latent liquidity factor from seventeen monthly indicators over the period 2005-2025. A two-state Markov-switching model classifies the factor into stable and turbulent liquidity regimes. The analysis evaluates whether these regimes condition realized returns, liquidity exposures, out-of-sample forecasts, and downside-risk outcomes across thirteen asset-return series. The regimes are re-identified in real time using a causal design with periodically re-estimated parameters and regime probabilities filtered forward. KEY FINDINGS: - Global liquidity is better understood as a regime-dependent state variable rather than a standalone return predictor. - The latent factor summarises global liquidity conditions. - Liquidity regimes are persistent and distinguished mainly by conditional volatility. - Credit, real estate, and equities underperform in turbulent funding regimes. - Real-time regime probabilities preserve the drawdown reduction. - Global liquidity informs risk-state identification, not point forecasts of returns. IMPLICATION FOR TRADING: The study suggests that a funding-based global liquidity factor can be used to allocate assets in real-time, reducing maximum drawdowns relative to benchmarks. The real-time factor correlates at 0.82 with its full-sample counterpart, and the allocation based on real-time probabilities limits the maximum drawdown to 7.0% over 2015-2025, against 17.8% for a 60/40 portfolio. Global liquidity is therefore more informative as a real-time conditioning variable for cross-asset downside risk than as a mechanical return-forecasting signal.

SUMMARY: This study examines the impact of filing discipline, as measured by the SEC's EDGAR system, on the cross-section of stock returns. The authors build three characteristics from filing timestamps: q_lag, the number of days from fiscal quarter-end to SEC acceptance of the most recent 10-Q; nt_12m, the count of Form NT 10-K and NT 10-Q filings in the trailing year; and burst, the coefficient of variation of the gaps between consecutive 8-K acceptance timestamps. The study finds that the level of a firm's filing discipline, rather than its innovation, predicts the monthly cross-section of stock returns. The authors construct a portfolio based on these characteristics, resulting in a net Sharpe ratio of 1.27 with a six-factor alpha of 0.39% per month. The findings suggest that the predictive content lies in the cross-sectional level of filing discipline, not in its trailing-mean-adjusted within-firm deviation. The effect is concentrated in smaller and more volatile stocks, limiting its capacity. METHOD: The authors construct three characteristics from EDGAR timestamps: q_lag, nt_12m, and burst. These characteristics are ranked cross-sectionally, smoothed over three months, and signed using in-sample information only. The equally weighted composite, denoted as disc, is oriented so that a high score means good discipline. Portfolios are long the top quintile and short the bottom quintile, equally weighted within each leg, neutralised against size, short-term reversal, and lottery demand, and held for six months with one-sixth of the book rolled each month. Trading costs are charged at a flat 10 basis points one way and borrow fees at a flat 0.6% per year, anchored to Frazzini et al. (2018), Novy-Marx and Velikov (2016), and D’Avolio (2002), with a sensitivity table. The main universe includes companies with market capitalisation above $100M, dollar volume above $1M, and price above $3, averaging 2,253 names per month. The net Sharpe ratio is 1.27 (1.29 in-sample, 1.34 held-out) with an alpha of 0.39% per month against the five-factor model of Fama and French (2015) augmented with momentum, and a market beta of −0.03. KEY FINDINGS: - The predictive content lies in the cross-sectional level of filing discipline, not in its trailing-mean-adjusted within-firm deviation. - The effect is concentrated in smaller and more volatile stocks, limiting its capacity. - The three characteristics are nearly uncorrelated with one another, combining largely distinct variation rather than three measurements of one quantity. - Filing discipline is persistent: the twelve-month rank autocorrelation of disc is 0.55 and 47% of its cross-sectional variance is a permanent firm attribute. - Predictive power rises monotonically as the universe is widened, from an information coefficient of 0.021 among large liquid names to 0.061 across all common stock. IMPLICATION FOR TRADING: The findings suggest that the level of filing discipline, rather than its innovation, is the key driver of stock returns. This insight can be used by traders to identify stocks with strong filing discipline, which may be more likely to have positive returns. However, the effect is concentrated in smaller and more volatile stocks, limiting the potential for high returns. Additionally, the effect is not explained by mispricing, but rather by the persistent nature of filing discipline. Traders can use this information to construct portfolios that exploit the predictive power of filing discipline, but they should be aware of the limitations of the effect.

SUMMARY: The article discusses how Blockchain technology is transforming the role of central banks in currency issuance and circulation. It highlights the potential benefits of Blockchain, such as reducing operational costs and enhancing transparency, but also acknowledges the challenges of legal frameworks, cybersecurity, and societal acceptance. The study uses a qualitative and conceptual approach, synthesizing literature from the post-2008 financial crisis to the present. The research question is how Blockchain can redefine the role of central banks in issuing and managing currency. The theoretical framework integrates trust theory, Blockchain as a "protocol of trust," and the theory of disintermediation. The findings suggest that Blockchain can improve monetary management by lowering operational costs and enabling the issuance of CBDCs. However, these opportunities are accompanied by challenges related to legal frameworks, cybersecurity, and societal acceptance. METHOD: The study adopts a qualitative and conceptual approach, synthesizing international and domestic literature from the post-2008 financial crisis to the present. The theoretical framework combines three pillars: trust theory in financial transactions, Blockchain as a "protocol of trust" that replaces human intermediaries with algorithmic consensus, and the theory of disintermediation highlighting the rise of decentralized finance. KEY FINDINGS: - Blockchain can significantly improve monetary management by lowering operational costs and enhancing transparency. - Blockchain-based digital currencies can be used as complementary instruments or substitutes for traditional systems. - The shift from emotional trust to digital trust is a theoretical implication of Blockchain. - The practical contribution is providing policy recommendations for central banks to adapt to the digital economy. IMPLICATION FOR TRADING: The implications for trading are significant. Central banks may need to adapt their policies and regulations to accommodate the rise of CBDCs and decentralized finance. Traders and investors should be aware of the potential for new digital currencies and the potential impact on traditional financial systems. Additionally, the use of Blockchain technology can lead to more secure and transparent transactions, which can benefit traders and investors. However, the challenges of legal frameworks, cybersecurity, and societal acceptance must be addressed to fully realize the potential of Blockchain in the financial sector.

SUMMARY: This study examines whether diffusion-based generative scenario models, particularly those conditioned on market regimes, can improve Expected Shortfall (ES) forecasting in Asia-Pacific equity markets. The analysis uses daily data from six major markets—India, Japan, Hong Kong, South Korea, Australia, and Singapore—from 2007 to August 2026, with 1,151 genuine out-of-sample forecasts evaluated between January 2020 and August 2026. Five approaches are compared: Historical Simulation (HS), regime-conditioned Historical Simulation (Regime-HS), GARCH-filtered Historical Simulation (GARCH-FHS), a diffusion-based scenario model, and a regime-conditioned diffusion model. The results show that incorporating Hidden Markov Model regime probabilities does not significantly improve diffusion-based ES forecasts. GARCH-FHS delivers the strongest numerical forecasting performance and tail-risk calibration, although formal pairwise tests do not establish its superiority over competing models at conventional significance levels. The findings highlight the importance of accurately modelling extreme distributional characteristics when applying generative models to financial risk management. METHOD: The analysis uses daily data from six major markets—India, Japan, Hong Kong, South Korea, Australia, and Singapore—from 2007 to August 2026. Five approaches are compared: Historical Simulation, regime-conditioned Historical Simulation, GARCH-filtered Historical Simulation, a diffusion-based scenario model, and a regime-conditioned diffusion model. The study evaluates 1,151 genuine out-of-sample forecasts between January 2020 and August 2026. KEY FINDINGS: - Incorporating Hidden Markov Model regime probabilities does not significantly improve diffusion-based ES forecasts. - GARCH-FHS delivers the strongest numerical forecasting performance and tail-risk calibration. - Scenario-fidelity diagnostics indicate that diffusion models underrepresent negative skewness, excess kurtosis, and joint downside dependence observed in realized returns. - Greater generative flexibility and explicit regime conditioning do not necessarily translate into superior tail-risk forecasts. IMPLICATION FOR TRADING: The findings suggest that accurate modelling of extreme distributional characteristics is crucial when applying generative models to financial risk management. While diffusion-based models, especially when conditioned on market regimes, can provide strong numerical forecasting performance, their superiority over other models is not established at conventional significance levels. This highlights the importance of tail-risk calibration and the need for models that can accurately capture the characteristics of extreme market movements. Practitioners should consider using a combination of different models to ensure robust and comprehensive risk assessments.

SUMMARY: This study examines how US monetary policy affects global market liquidity, using tick data from 37 markets over 2010-2022. The researchers estimate liquidity responses to high-frequency policy surprises, decomposing them into federal funds rate, forward guidance, and asset purchase shocks. The study finds that only federal funds rate shocks matter, causing liquidity spreads to widen for up to two months. Long-term yield surprises do not affect liquidity, but reprice equities. The study's findings indicate that liquidity is affected in markets where local policy rates closely track the federal funds rate and where foreign ownership is high. The authors conclude that trading costs are shaped by US policy, through this liquidity channel. METHOD: The study uses established macroeconometric techniques to isolate exogenous components of US monetary policy decisions, including changes in money market futures prices and treasury yields around FOMC announcements. The researchers decompose yield changes into shocks to the current Federal Funds policy rate, expected paths of short-term interest rates, and expected quantities of large-scale asset purchase programs. They also remove any predictability conditional on realized economic data and other financial markets. Liquidity is measured using granular tick data from each market, covering 129 billion changes to best bids and asks for 3,815 stocks from 2010 to 2022. KEY FINDINGS: - Federal funds rate shocks significantly affect global market liquidity, causing spreads to widen for up to two months. - Long-term yield shocks do not impact liquidity but reprice equities. - Liquidity effects are strongest in markets where local policy rates closely track the federal funds rate and where foreign ownership is high. - US monetary policy shapes global financial conditions through trading costs, as liquidity is a distinct channel. IMPLICATION FOR TRADING: The study's findings suggest that traders should consider US monetary policy as a key factor in assessing global market liquidity. This insight can help in managing trading costs and understanding the potential impact of US monetary policy on international markets. Traders can use the results to adjust their strategies, especially in markets where liquidity is particularly sensitive to US monetary policy changes.

SUMMARY: The study examines the pricing of corporate climate sensitivities (CCS) in US states with respect to stock returns, using local climate data. The research finds that investors demand premiums when corporate stocks are exposed to global warming phenomena across US states. The study also indicates that climate premium demands significantly appear when corporate stocks are prone to local disaster risk, while they remain marginally statistically significant for local transition risk. The study highlights the robust pricing of systematic risks of global warming with climate premiums demanded by investors. The findings complement prior literature on the pricing of climate and carbon risks, providing insights for future research. METHOD: The study employs extensive local climate data to examine the market pricing of corporate climate sensitivities with stock return predictability. The research uses a forecasted market-based CCS measure to demonstrate that investors demand premiums when corporate stocks are exposed to global warming phenomena across US states. The study also captures the geographical divergence of climate change exposure across the US states. KEY FINDINGS: - Investors demand premiums when corporate stocks are exposed to global warming phenomena across US states. - Climate premium demands significantly appear when corporate stocks are prone to local disaster risk. - Climate premium demands remain marginally statistically significant for local transition risk. - Carbon premium for both direct and interacting effects on stock return predictions is not observed. IMPLICATION FOR TRADING: The findings highlight the importance of incorporating climate risk into investment strategies. Investors should consider climate sensitivities when evaluating corporate stocks, as they may demand premiums for exposure to global warming phenomena. This research complements prior literature on climate and carbon risks, providing insights for future studies. Practitioners should integrate climate risk assessments into their investment analysis to better predict stock returns and manage potential risks.

SUMMARY: This article presents a falsificationist cycle analysis of cryptocurrencies, stock indices, and thirty-seven instruments in six classes. The methodology adopts a Popperian protocol to test the founding claims of three historical schools of cycle theory — classical American (Hurst), Italian (battleplan and inverse cycle), and quantitative DSP (Ehlers). The analysis is conducted on a multi-asset dataset, including Bitcoin and Ethereum since 2017, Solana since 2020, and six stock indices with up to ninety-nine years of daily history. The methodology is causal and deterministic, formalized in automated tests. The work includes four requirements: the null value of a measure must be measured, not assumed, with surrogate series that preserve the power spectrum and destroy the temporal structure; a conditional rate must be accompanied by its own marginal; a negative outcome does not count until it has been shown that the test would recognize the phenomenon if it were there; and every level must be measured on the timeframe that resolves it. The results refute the most exposed canonical assertions, confirming the cyclic commonality of durations and cyclical translation as a predictor of polarity. The contribution is threefold: a falsifiable and reproducible protocol applied uniformly to the whole corpus, verification of causality and determinism of the central analytical component, and the systematic distinction between what the market produces and what the detector produces. METHOD: The analysis is conducted on a multi-asset dataset, including Bitcoin and Ethereum since 2017, Solana since 2020, and six stock indices with up to ninety-nine years of daily history. The methodology is causal and deterministic, formalized in automated tests. The work includes four requirements: the null value of a measure must be measured, not assumed, with surrogate series that preserve the power spectrum and destroy the temporal structure; a conditional rate must be accompanied by its own marginal; a negative outcome does not count until it has been shown that the test would recognize the phenomenon if it were there; and every level must be measured on the timeframe that resolves it. KEY FINDINGS: - The results refute the most exposed canonical assertions, including Hurst’s fixed harmonic nesting and the principle of harmonicity, Bayer’s canonical threshold for connecting cycles, the peak hold, and the inverse cycle and unconditional swing claims of the Italian school. - The analysis confirms the cyclic commonality of durations and cyclical translation as a predictor of polarity. - The methodology includes four requirements for the null value of a measure, conditional rates, negative outcomes, and levels measured on the timeframe that resolves them. IMPLICATION FOR TRADING: The findings have significant implications for trading strategies. The analysis confirms the cyclic commonality of durations and cyclical translation as a predictor of polarity, suggesting that traders should consider these cycles when developing their strategies. The methodology also emphasizes the importance of distinguishing between what the market produces and what the detector produces, which can lead to more accurate conclusions. These insights can help traders refine their technical analysis and improve their predictive models.

SUMMARY: This systematic review synthesises 99 peer-reviewed studies to understand how artificial intelligence (AI) affects retail investors' investment decisions. The review distinguishes between measured investment outcomes and contextual evidence on acceptance, perceptions, and reliance. Five previously disputed records were reassessed, moving them from historical reconfiguration coding to contextual evidence. The resulting distribution is 64 reconfiguration, 11 attenuation, four amplification, three null, and 17 contextual records. The review develops a measurement-sensitive framework linking system and investor characteristics to cognition, trust, reliance, and measurable investment outcomes, distinguishing conventional robo-advice from interactive generative AI. The framework generates testable propositions rather than claiming behavioural reconfiguration is empirically dominant. METHOD: The review synthesises 99 peer-reviewed studies, distinguishing between measured investment outcomes and contextual evidence on acceptance, perceptions, and reliance. Five previously disputed records were reassessed, moving them from historical reconfiguration coding to contextual evidence. The resulting distribution is 64 reconfiguration, 11 attenuation, four amplification, three null, and 17 contextual records. The review uses a measurement-sensitive framework connecting system and investor characteristics to cognition, trust, reliance, and measurable investment outcomes, distinguishing conventional robo-advice from interactive generative AI. KEY FINDINGS: - The review synthesises 99 peer-reviewed studies. - Five previously disputed records were reassessed, moving them from historical reconfiguration coding to contextual evidence. - The resulting distribution is 64 reconfiguration, 11 attenuation, four amplification, three null, and 17 contextual records. - The review develops a measurement-sensitive framework linking system and investor characteristics to cognition, trust, reliance, and measurable investment outcomes. - The framework distinguishes conventional robo-advice from interactive generative AI. IMPLICATION FOR TRADING: The review's findings suggest that AI can either reduce or amplify certain biases within the investment decision process, depending on how it is used. Retail investors may avoid useful algorithmic advice, rely on it too readily, respond to anthropomorphic or social cues, or interpret AI-assisted performance as evidence of their own competence. Interactive generative AI makes these tensions especially salient. The review's framework can help traders understand how AI changes the configuration of behavioural mechanisms within the investment decision process, including whether particular biases are reduced. However, the review's conclusions are conditional on the completed source audit.

SUMMARY: This article examines how intermediaries allocate scarce cash to deliver debt relief in the mortgage servicing market. It focuses on Ginnie Mae buyout decisions from 2019 to 2022, analyzing the behavior of nonbank issuers and depositories. The study reveals that nonbank issuers cut exercise almost fivefold while depositories raised it, indicating that information and liquidity interact to determine which borrowers receive relief. The authors propose a model to understand this interaction, which ranks resolution opportunities by value per dollar of scarce cash. They find that nonbanks' response to the ranking remained close to pre-shock levels, even as funding returned. The study also shows that nonbanks postponed some exercise in 2020 but quickly resumed it, and that scarce cash shifted the ranking price, tilting exercise against loans with larger balances. These findings have implications for understanding how intermediaries allocate resources and the value of borrower information in the mortgage servicing market. METHOD: The study uses 1,922,668 Ginnie Mae buyout decisions by 120 issuers from January 2019 to September 2022. The researchers analyze nonbank and depository behavior during a liquidity shock in 2020 and its reversal in 2021. The model ranks resolution opportunities by value per dollar of scarce cash, with a liquidity shock moving the ranking down and a value shock reordering it. The authors estimate nonbanks' ranking of loans from their own decisions in 2016-2018, within issuer-months to absorb budgets. KEY FINDINGS: - Nonbank issuers cut exercise almost fivefold while depositories raised it, indicating that information and liquidity interact to determine which borrowers receive relief. - The response of nonbank exercise to the ranking relative to the base rate stayed close to its pre-shock level, even as funding returned. - Most of the exercise nonbanks postponed in 2020 was made up within two years, and later exercise weighted the ranking about seven-tenths as heavily as exercise had before the shock. - Scarcely available cash changed the ranking price, tilting exercise against loans with larger balances. IMPLICATION FOR TRADING: The findings have implications for understanding how intermediaries allocate resources and the value of borrower information in the mortgage servicing market. Practitioners can use this information to better understand how intermediaries prioritize loan resolution and the value of borrower information in their decision-making. This can help in predicting future trends and optimizing resource allocation in the mortgage servicing market.

SUMMARY: This article examines the impact of a market for regulatory compliance on the activity intended to be promoted by financial regulation. Specifically, it studies India's Priority Sector Lending Certificate (PSLC) market, where banks trade regulatory credit without transferring the underlying loans or credit risk. The market allows banks to strip and repackage regulatory attributes across different constraints. The researchers explore how tradability affects the technology of compliance and the induced real activity. METHOD: The study uses India's PSLC market as a case study. Banks can sell the regulatory credit generated by loans while retaining borrower relationships, cash flows, and credit risk. The market separates two outputs of lending: the cash-flow asset and the regulatory service. The researchers combine the claim structure with endogenous market access and an explicit Agriculture deficit-repair technology. They use bank-level data to analyze the impact of the PSLC market on priority-sector lending. KEY FINDINGS: - The PSLC market can lower the real cost of compliance by reallocating regulatory capacity across banks. - However, it can weaken the link between measured compliance and the activity the mandate was intended to create. - A bank that is short of regulatory output may respond by changing the classification of existing assets, originating new qualifying credit, or buying a regulatory claim generated elsewhere. - The welfare effect depends on whether the social value of the displaced lending exceeds the compliance-cost savings created by trade. - The market for regulatory claims can improve welfare relative to direct internal adjustment when a binding mandate is in place. IMPLICATION FOR TRADING: The findings suggest that the design of financial regulation markets can have significant implications for the real activity induced by the mandate. Practitioners should consider the potential trade-offs between compliance-cost savings and the induced real activity when designing or participating in regulatory markets. Understanding the impact of tradability on compliance technology can help practitioners make more informed decisions about market participation. Additionally, the findings highlight the importance of considering the endogenous market access and technology when designing financial regulation markets.

SUMMARY: This study examines valuation discrepancies among business development companies (BDCs) that manage private credit funds. The researchers hypothesize that valuation incentives, such as attracting capital and increasing asset-based fees, may explain these differences. The study uses a sample of 4,655 loans held by multiple BDC sponsors over a period from 2018 to 2026, and 65,590 valuations of these loans by different managers on the same reporting date. The findings reveal that sponsors with more assets in listed funds mark the same loan higher. The difference is more pronounced for loans without dealer quotes and for sponsors with more payment-in-kind income. Within sponsors, nonpublic BDCs hold more liquid assets. The study also suggests that paying investors at net asset value (NAV) may constrain reported valuations. METHOD: The study uses a sample of 4,655 loans held by multiple BDC sponsors, with 65,590 valuations of these loans by different managers on the same reporting date. Loan-by-date fixed effects are used to absorb common factors, such as market conditions, leaving only differences in valuation among managers. The sample includes loans held by BDCs of more than one asset manager between 2018 and 2026. KEY FINDINGS: - Sponsors with more assets in listed funds mark the same loan higher. - The difference is more pronounced for loans without dealer quotes and for sponsors with more payment-in-kind income. - Within sponsors, nonpublic BDCs hold more liquid assets. - Paying investors at NAV appears to constrain reported valuations. IMPLICATION FOR TRADING: The findings suggest that managers may be incentivized to report higher values for loans, which can attract more capital and increase asset-based fees. However, higher values also increase the amount paid to investors who leave, creating an offsetting incentive against reporting high values. These insights can be useful for private credit fund managers in understanding and managing valuation discrepancies among their loans. Practitioners can use this information to better understand the dynamics of loan valuations and potentially adjust their strategies to mitigate potential discrepancies. Additionally, the study highlights the importance of considering NAV payments for investors, as it may influence reported valuations.

SUMMARY: This article investigates how different portfolio construction methods perform under various market conditions and whether the market state can predict which method will perform better in the next period. The authors find that the ranking of portfolio construction methods reorders across market states, with the maximum-Sharpe method, the most estimation-dependent method, performing poorly in stressed markets, while equal weighting performs well in both calm and stressed markets. The market state predicts which method will perform better beyond the methods' own recent performance, and this signal is robust to placebo tests and shrinkage of expected returns. The study concludes that exploiting the market state's predictive signal can produce economic value net of costs, with a state-conditional selector outperforming performance-chasing, although its advantage over diversification is not significant. The research suggests that investors should consider the market state when choosing a portfolio construction method, as it can provide valuable information about which method will perform better in the future. METHOD: The study uses a multi-asset universe of twelve ETFs from 2007 to 2026 and employs a walk-forward out-of-sample design. The authors test the conditional predictive ability of realized volatility to predict the relative performance of two methods, maximum-Sharpe and equal weighting, after conditioning on their recent relative performance. The analysis is conducted across different market states, including calm and stressed markets. KEY FINDINGS: - The ranking of portfolio construction methods reorders across market states. - The market state predicts which method will perform better beyond the methods' own recent performance. - The signal is concentrated in the most estimation-dependent method and survives placebo tests and shrinkage of expected returns. - Exploiting the signal produces economic value net of costs. IMPLICATION FOR TRADING: The research suggests that investors should consider the market state when choosing a portfolio construction method, as it can provide valuable information about which method will perform better in the future. The study recommends using a state-conditional selector, which outperforms performance-chasing, although its advantage over diversification is not significant. This information can help investors make more informed decisions and potentially improve their portfolio performance.

SUMMARY: This study examines whether the presence of options trading influences the resource commitments of underlying firms. The authors use a staggered difference-in-differences design to analyze the impact of initial exchange options listings on resource commitments, focusing on fixed resource expenditures to support operations. The study finds that firms with options listed on stock exchanges exhibit greater commitment in fixed resources, as reflected by less responsive expenditures to revenue changes. This effect is stronger in firms with greater short-sale constraints, higher future value from SG&A expenses, and more uncertain demand. The authors suggest that the impact of options listing on resource commitments is consistent with reduced information asymmetry, a channel through which options trading enhances information flows and transparency. METHOD: The study uses a sample of U.S.-listed firms from 1996 to 2022. The authors employ a staggered difference-in-differences design to analyze the impact of initial exchange options listings on resource commitments. The sample is selected based on the staggered timing of options listings across firms, which are decided by options exchanges and are relatively external to managerial decisions. Robustness checks are conducted to mitigate concerns about potential endogeneity of options listing to firm characteristics. KEY FINDINGS: - Firms with options listed on stock exchanges exhibit greater commitment in fixed resources. - This effect is stronger in firms with greater short-sale constraints. - This effect is stronger in firms with higher future value from SG&A expenses. - This effect is stronger in firms with more uncertain demand. IMPLICATION FOR TRADING: The findings suggest that options trading can lead to more stable and longer-term resource commitments by reducing information asymmetry. For traders and investors, understanding the impact of options trading on resource commitments can help in making informed decisions about investments and corporate strategies. Managers should be aware of the potential long-term implications of options trading and its effects on resource commitments, which can influence both short-term and long-term performance.

SUMMARY: This article proposes an evolutionary-institutional model to explain a society's long-term competitiveness, which is not solely determined by its current technologies but by its ability to continuously generate new knowledge, technologies, and leadership strategies. The model emphasizes the social selection of leaders, where a collective image of a legitimate leader shapes who a society admits to status, resources, and decision-making power. The article also discusses the formation of human cognitive potential, the creative process, and institutional reform. The central conclusion is that institutional reform can create an environment that mutually reinforces human creativity, scientific inquiry, and positive social selection, potentially creating a long-term technological advantage. METHOD: The article uses a theoretical model to explore the role of social selection of leaders, cognitive development, and institutional reform in a society's long-term competitiveness. It draws on empirical research on neurodevelopment and social mobility to support its arguments. KEY FINDINGS: - Social selection of leaders shapes who a society recognizes as legitimate and thus who gains status, resources, and decision-making power. - Cognitive potential is influenced by early adversity and chronic stress, but meritocracy does not directly produce intelligent scientists. - An evolutionary architecture for artificial creative cognition can be used as a conceptual analogy for human creativity. - Institutional reform can create a mutually reinforcing environment for human creativity, scientific inquiry, and positive social selection. IMPLICATION FOR TRADING: The implications for trading are that institutional reform can create a sustainable competitive advantage by fostering an environment that supports innovation, scientific inquiry, and the selection of effective leaders. This can lead to a long-term technological advantage that is difficult for competitors to replicate. Practitioners should focus on creating and maintaining institutions that promote creativity, scientific inquiry, and the selection of leaders with the potential to drive future technological advancements.

SUMMARY: This study examines how private equity fund managers disclose information about their portfolio companies, focusing on the timing and content of their disclosures. The research highlights that managers possess significant information about the relative quality of their portfolio firms even within the first three years of investment. However, the way this information is disclosed changes over time. Early textual discussions provide substantial information about the firms’ ultimate performance, while interim valuations are less important. Over time, interim valuations become more predictive of future returns, especially after a financing event. The study also notes that the tone and content of disclosures can signal general partner quality in an opaque market with significant information asymmetries. METHOD: The study uses a large dataset of private equity fund reports and accompanying presentations to analyze the content and timing of disclosures. The sample includes a diverse set of private equity funds and their portfolio companies. The data covers the period from initial investment through the life of the fund, allowing for the examination of how information is disclosed and its predictive power over time. KEY FINDINGS: - Managers possess significant information about the relative quality of their portfolio firms even early in the investment lifecycle. - Early textual discussions contain substantial information about the firms’ ultimate performance. - Interim valuations are less important in explaining an investment’s ultimate performance within the first three years. - Over time, interim valuations become more predictive of future returns, especially after a financing event. - The tone and content of disclosures can signal general partner quality in an opaque market. IMPLICATION FOR TRADING: Private equity managers can use this research to understand the importance of different types of disclosures in their investment process. Early textual discussions provide valuable insights into the firms’ performance, while interim valuations become increasingly important as the investment progresses. Managers should consider the strategic implications of their disclosure practices, as they can influence investor perceptions and potentially impact fundraising and reputation. Practitioners should also be aware of the potential for strategic manipulation or obfuscation in disclosures, as this can affect the accuracy of performance predictions and investor expectations.

SUMMARY: This research examines the impact of using invented company names (aliases) in financial information presented to Large Language Models (LLMs) for equity valuation. The study aims to determine whether the use of such aliases affects the valuation of companies and whether the substitution of real company names with these aliases is neutral. The research design includes 60 matched name pairs split by model-rated pronunciation ease, with nine fictional company profiles and three commercial models. The results show that the generated labels lowered every model's valuations relative to an unnamed profile, indicating that the alias policy may not be neutral. METHOD: The study used a fixed-seed generator to produce 5,600 candidate strings across 1,867 letter families. Two open-weight models rated each candidate's pronunciation ease and English word likeness on a 0–100 scale in three independent calls. The labels are model-rated proxies, not validated human pronunciations. The pairs were matched on equal length and identical contextual token counts across three subject models, with at most one pair per letter family. An integer program then minimized the differences in word likeness and dictionary-based bigram scores. The results suggest that the generated labels lowered every model's valuations relative to an unnamed profile. KEY FINDINGS: - The generated labels lowered every model's valuations relative to an unnamed profile. - The mean rated ease for fluent and disfluent members was 69.9 and 32.4 respectively. - The word likeness, which varies with ease, still differed after the generation of labels. IMPLICATION FOR TRADING: The findings suggest that using invented company names (aliases) in financial information presented to LLMs for equity valuation may not be neutral. Researchers who mask firm identity with invented names should consider the potential impact on the valuation of companies. The alias policy may need to be re-evaluated to ensure that it does not affect the fair value of the companies.

SUMMARY: This article discusses the future of blockchain technology in operations and supply chain management (OM). It highlights the evolution of blockchain from a novel innovation to a transformative and integral component in enterprise digitization. Despite its promise, the integration of blockchain into OM faces significant challenges, including technological, organizational, and regulatory hurdles. The Special Issue on Operational Perspectives on Blockchain Applications explores these opportunities, challenges, and implications for OM. The articles in this issue provide a diverse and empirically grounded examination of blockchain applications across industries and operational contexts. METHOD: The authors use a process-centric view to understand permissionless blockchains from an OM perspective. They conceptualize the Bitcoin blockchain as a two-side dataspace market, where users demand a certain amount of dataspace in a future block to store their transactions, and miners compete to produce such dataspace by creating new blocks. The authors also discuss the congestion pricing literature, which applies to permissionless blockchains as they are also congested service systems. KEY FINDINGS: - Permissionless blockchains operate as two-side dataspace markets. - Users demand a certain amount of dataspace in future blocks. - Miners compete to produce dataspace by creating new blocks. - Users attach transaction fees to differentiate service grades (transaction confirmation speeds). - Congestion pricing literature applies to permissionless blockchains. IMPLICATION FOR TRADING: The implications for trading are significant. The process-centric view of blockchain transactions can inform the design of smart contracts and the pricing of services. The congestion pricing literature suggests that offering multiple service grades with different delay distributions at different prices can improve perceived customer satisfaction and service provider profit. These insights can be applied to improve the design of smart contracts and the pricing of services in blockchain-based systems, benefiting both service providers and consumers.

SUMMARY: This study investigates the influence of individual investors' information interactions on stock price co-jumps using large-scale textual data from the Eastmoney.com stock forum from 2020 to 2023. The authors use a stock forum outage on November 12, 2021, to establish causality and find that positive information interactions have a stronger effect on upward co-jumps than negative interactions on downward co-jumps. The study also reveals that cross-stock sentiment convergence mediates the relationship between information interactions and co-jump occurrences. Heterogeneity analyses show that information interactions have stronger facilitating effects on stock price co-jumps in pairs of small-cap stocks and in pairs characterized by low information transparency. The study's findings suggest that positive versus negative information interactions have a significant asymmetric effect only for stock pairs in high-tech industries. The authors develop an investment strategy that outperforms the Shanghai Stock Exchange Composite Index, achieving an annualized return of 19.41%. METHOD: The study uses large-scale textual data from the Eastmoney.com stock forum during the period 2020-2023. The authors examine the effect of a stock forum outage on November 12, 2021, which reduced investors' information interactions. They analyze the impact of positive and negative information interactions on stock price co-jumps and identify the underlying mechanisms. Heterogeneity analyses are conducted to examine the effects of information interactions in different stock pairs. KEY FINDINGS: - Positive information interactions have a stronger effect on upward co-jumps than negative interactions on downward co-jumps. - Cross-stock sentiment convergence mediates the relationship between information interactions and co-jump occurrences. - Information interactions have stronger facilitating effects on stock price co-jumps in pairs of small-cap stocks and in pairs characterized by low information transparency. - The asymmetric effect of positive versus negative information interactions on upward versus downward co-jumps is significant only for stock pairs in high-tech industries. IMPLICATION FOR TRADING: The study's findings suggest that investors should focus on positive information interactions and avoid negative ones to achieve better stock price co-jumps. The authors develop an investment strategy based on these findings, which outperforms the Shanghai Stock Exchange Composite Index. Practitioners can use this strategy to improve their investment performance and make more informed decisions.

SUMMARY: The study investigates the impact of a front-end real-name policy, which requires users to disclose their IP-location information, on stock price informativeness in China. Using a quasi-natural experiment and difference-in-differences design with firm-quarter data from 2021 to 2023, the researchers found that the policy significantly decreases stock price informativeness, as evidenced by an increase in stock price synchronicity. The policy suppresses user engagement on social investing platforms, reducing both overall and informative post volumes, and thus diminishes retail trading. The study reveals that the increase in stock price synchronicity is larger among firms with weaker governance, lower information disclosure quality, and evasive responses on official investor-interaction platforms, but smaller among firms with higher observed AI-assisted communication usage. The findings suggest that the real-name system alters the information environment and capital-market consequences, highlighting a trade-off between regulatory goals and market efficiency. METHOD: The study employs a quasi-natural experiment based on China's 2022 policy enactment, leveraging firm-quarter data from 2021 to 2023. A difference-in-differences design is used to analyze the impact of the mandatory display of user IP-location information on stock price informativeness. KEY FINDINGS: - The real-name policy significantly decreases stock price informativeness. - User engagement on social investing platforms is suppressed. - Informative post volumes are reduced, leading to diminished retail trading. - The increase in stock price synchronicity is more pronounced among firms with weaker governance and lower information disclosure quality. - The policy's impact is mitigated among firms with higher AI-assisted communication usage. IMPLICATION FOR TRADING: The findings suggest that the real-name system may alter the information environment and market efficiency. Policymakers should consider the trade-off between regulatory goals and market efficiency when implementing such policies. Additionally, firms should focus on enhancing their information communication practices to mitigate the negative effects of the real-name system. This research provides insights into the role of firms' information communication practices in online platform governance.

SUMMARY: This research paper examines the role of Switzerland as a safe haven for deposits and custody portfolios in response to geopolitical risk. Using data from the Swiss National Bank for the period 2000-2025, the authors find no robust positive response of Swiss bank deposits to geopolitical risk. Instead, they observe a significant increase in custody portfolios, which hold about CHF 4.0 trillion of non-resident securities compared to CHF 0.6 trillion of deposits of foreign counterparties at the end of 2025. The authors also note that after a one-standard-deviation rise in European geopolitical risk, the non-resident equity share falls by 1.16 percentage points relative to the resident share after eight quarters, while the bond and money-market share rises by 0.78 points after four quarters and 0.68 points after eight quarters. The authors attribute these findings to precautionary de-risking rather than a pure macro-financial-conditions channel. METHOD: The study uses data from the Swiss National Bank on deposits and loans of commercial banks, adjusted for valuation and exchange-rate effects, separately for foreign counterparties, domestic financial corporations, and domestic non-financial counterparties. Custody holdings are derived from the Swiss National Bank's monthly survey of securities. The authors compare the change in each non-resident portfolio share with the corresponding change for resident holders exposed to the same broad Swiss market and exchange-rate environment. KEY FINDINGS: - Swiss bank deposits do not show a robust positive response to geopolitical risk. - Custody portfolios, which contain about CHF 4.0 trillion of non-resident securities, are significantly larger than deposits of foreign counterparties. - After a one-standard-deviation rise in European geopolitical risk, the non-resident equity share falls by 1.16 percentage points relative to the resident share after eight quarters. - The bond and money-market share rises by 0.78 points after four quarters and 0.68 points after eight quarters. - Global geopolitical threats and acts generate opposite relative-share responses, consistent with precautionary de-risking behavior. IMPLICATION FOR TRADING: The findings suggest that investors in Switzerland may be shifting their risk exposure from deposits to custody portfolios in response to geopolitical risks. This behavior could impact the valuation and performance of Swiss financial assets. For traders, understanding these patterns can help in forecasting asset movements and managing risk. Additionally, the study highlights the importance of considering custody data in assessing financial risk, as holdings in custody accounts are market-value stocks, which can be influenced by valuation movements and security-specific revaluations.

SUMMARY: This paper investigates whether the dispersion in market-implied Federal Open Market Committee (FOMC) outcomes contains information about the long-run component of U.S. equity volatility. The study constructs the cross-outcome variance of Kalshi probabilities over days -30 to -3 before and after 38 FOMC meetings and embeds it in a fixed (K=4), uniform-weight GARCH-MIDAS model for 824 S&P 500 trading days. The raw loading is 0.3621, with LR = 7.9637 and asymptotic p = 0.00477. After residualizing on the implied policy mean and log contract volume, the loading is 0.3570, LR = 8.5072, asymptotic p = 0.00354. The paper also examines the impact of different meeting labels and finds that the loading remains positive even after excluding each meeting. The findings suggest that the dispersion in market-implied FOMC outcomes contains information about the long-run component of U.S. equity volatility. METHOD: The study constructs the cross-outcome variance of Kalshi probabilities over days -30 to -3 before and after 38 FOMC meetings. The raw loading is 0.3621, with LR = 7.9637 and asymptotic p = 0.00477. The residualized loading is 0.3570, LR = 8.5072, asymptotic p = 0.00354. The paper also examines the impact of different meeting labels and finds that the loading remains positive even after excluding each meeting. KEY FINDINGS: - The raw loading of the market-implied FOMC outcome dispersion is 0.3621. - After residualizing on the implied policy mean and log contract volume, the loading is 0.3570. - The loading remains positive even after excluding each meeting. - The loading is robust to different meeting labels. IMPLICATION FOR TRADING: The findings suggest that market participants may be able to use the dispersion in market-implied FOMC outcomes to forecast the long-run component of U.S. equity volatility. This information can be used by traders to make more informed decisions about their investments. The study also highlights the importance of considering the full distribution of FOMC outcomes, rather than just the mean, when assessing the impact of monetary policy on equity markets.

SUMMARY: This paper examines the impact of SpaceX's inclusion into the Nasdaq-100 index on the financing side of the index addition. The study constructs model-implied funding pressure for 93 incumbents by allocating an entrant-funding requirement across pre-event weights and scaling implied sale dollars by pre-event average daily dollar volume (ADV). The main result shows that a one-standard-deviation increase in funding pressure is associated with approximately 12.1 percent higher 6 July volume relative to normal. The paper also explores the decomposition of the result, finding that standardized predicted sale dollars and low trading capacity both enter in the expected direction despite their negative correlation. The study further distinguishes between the primary treatment and the denominator-light specification, finding that the raw-dollar coefficient remains positive and statistically significant. METHOD: The study constructs model-implied funding pressure for 93 incumbents by allocating an entrant-funding requirement across pre-event weights and scaling implied sale dollars by pre-event average daily dollar volume (ADV). The main result is tested using a cross-sectional specification applied to 38 pre-announcement trading dates from 1 May through 25 June. The study applies identical cross-sectional specifications to 38 pre-announcement trading dates and finds coefficients ranging from -5.206 to 6.017, none reaching the 6 July estimate. The study also uses a denominator-light raw-dollar specification, finding a positive and statistically significant raw-dollar coefficient of 1.6939 per USD 1 billion. KEY FINDINGS: - A one-standard-deviation increase in model-implied funding pressure is associated with approximately 12.1 percent higher 6 July volume relative to normal. - The primary treatment and the denominator-light specification both use pre-event trading capacity for normalization. - The raw-dollar specification remains positive and statistically significant. IMPLICATION FOR TRADING: The paper contributes to the index-effects literature by making the financing leg of an index addition measurable. The findings suggest that high-pressure incumbents experienced greater implementation-day turnover, which could inform trading strategies. The study's design also advances the index-effects literature by varying treatment across incumbents, identifying the principal outcome as trading quantity, and identifying the timing of the effect. Practitioners can use these insights to develop trading strategies that account for the impact of index additions on the financing side.

SUMMARY: The article investigates how large orders affect the liquidity and price adjustment in prediction markets. It uses high-frequency Polymarket trades and order books to study the distinction between price adjustment and incremental information. The study finds that short-run quote changes increase with order size relative to displayed depth, but this relation is weakened when episode isolation is strict. Prices are closer to eventual outcomes five minutes after large-order episodes, but this descriptive improvement is not larger than after comparable ordinary trades. The article also suggests that exact historical books show that book-implied slippage ranks realized execution costs, and subsequent reversal is concentrated in selected event-proximate sports markets. The findings indicate that large orders can cause larger price changes without significantly improving forecast loss. The study distinguishes liquidity-sensitive quote adjustment from incremental outcome information in prediction markets. METHOD: The research uses high-frequency Polymarket trades and order books to analyze the relationship between large orders, displayed liquidity, and price adjustment. A 28-day prospective sample of Polymarket trades is used, with exact historical books for analysis. The study isolates episodes of large orders and compares their impact on price adjustment with ordinary trades. KEY FINDINGS: - Short-run quote changes increase with order size relative to displayed depth. - Prices are closer to eventual outcomes five minutes after large-order episodes. - Exact historical books show that book-implied slippage ranks realized execution costs. - Subsequent reversal is concentrated in selected event-proximate sports markets. IMPLICATION FOR TRADING: The findings suggest that large orders can cause larger price changes without significantly improving forecast loss. This distinction between liquidity-sensitive quote adjustment and incremental outcome information is crucial for understanding how prediction markets function. Practitioners can use these insights to better understand market dynamics and improve their trading strategies.

SUMMARY: This study compares the advantages of artificial intelligence (AI) versus human judgment in the asset management process. It distinguishes between naive investors, who rely on autodidactic knowledge, and professional investors, who have a mandate-driven process and institutional accountability. The analysis focuses on asset allocation decisions and security selection for government and corporate bonds, as well as globally diversified equity holdings. The study finds that AI demonstrates a measurable advantage in data-intensive, repetitive, and rule-based domains, such as text analytics, bond ranking, rebalancing, and trade execution. However, AI-managed funds have historically underperformed their benchmark indices over extended periods. The study concludes that AI does not replace human judgment but rather replaces suboptimal behavioral habits. The findings suggest that AI can enhance the efficiency of asset management, but human oversight is still necessary for strategic decision-making and risk management. METHOD: The research employs a theoretical framework grounded in behavioral finance evidence and empirical data from implemented AI systems. The study analyzed data from both naive and professional investors, including their investment behaviors and performance metrics. The sample consisted of both individual and institutional investors, with a focus on asset allocation and security selection for various asset classes. KEY FINDINGS: - AI demonstrates a measurable advantage in data-intensive, repetitive, and rule-based domains. - AI-managed funds have historically underperformed their benchmark indices over extended periods. - Generative language models exhibit a tendency toward factual hallucinations when deployed without rigorous verification. - Human investors are prone to systematic cognitive biases, such as impatience, lack of strategic discipline, and pro-cyclical trading behavior. - The resulting return gap in the United States is estimated at approximately 1.2 percentage points annually over a ten-year horizon. IMPLICATION FOR TRADING: AI can enhance the efficiency of asset management by processing vast quantities of unstructured data in real-time. However, human oversight is still necessary for strategic decision-making and risk management. AI can be used as a cognitive tool for private investors, automating implementation of strategies and reducing reliance on raw data. For professional investors, AI can serve as an analytical partner for data-intensive tasks, while retaining final judgment during regime shifts and for non-quantifiable variables. The combination of human judgment and AI can lead to optimal outcomes in asset management.

SUMMARY: The study establishes a Dividend Policy Invariance Theorem, which is distinct from the dividend irrelevance proposition of Miller and Modigliani (1961). The theorem demonstrates that alternative earnings-allocation mechanisms generate identical terminal shareholder wealth under a common period-by-period return sequence. The study develops separate policy-specific derivations for three earnings-allocation mechanisms: complete cash-dividend distribution, complete earnings retention, and partial earnings retention, all under a common mean-reverting return-on-equity (ROE) process. The theorem states that, under a common period-by-period return sequence for internal investment and external shareholder reinvestment, terminal shareholder wealth is invariant to the earnings-retention ratio for any finite investment horizon. The proposed framework provides a common capital-accumulation perspective for examining the relationships among dividend policy, profitability dynamics, and shareholder wealth accumulation. METHOD: The study builds upon a common mean-reverting return-on-equity (ROE) process and develops separate policy-specific derivations for three earnings-allocation mechanisms: complete cash-dividend distribution, complete earnings retention, and partial earnings retention. The mathematical analysis demonstrates that, although shareholder wealth accumulates through different internal and external capital accumulation mechanisms, all three earnings-allocation systems generate identical terminal shareholder wealth. The findings are derived under the common return structure, and the present value of this common terminal wealth converges to the same closed-form expression as the horizon approaches infinity. KEY FINDINGS: - Alternative earnings-allocation mechanisms generate identical terminal shareholder wealth. - The Dividend Policy Invariance Theorem states that terminal shareholder wealth is invariant to the earnings-retention ratio under a common period-by-period return sequence. - The present value of common terminal wealth converges to the same closed-form expression as the horizon approaches infinity. IMPLICATION FOR TRADING: The proposed framework provides a unified theoretical perspective for understanding the relationships between dividend policy, profitability dynamics, and shareholder wealth accumulation. It suggests that under the model's common return dynamics, alternative earnings-allocation mechanisms generate the same terminal shareholder wealth. This framework bridges dividend-based valuation, accounting-based valuation, and mean-reverting valuation approaches. For practitioners, this unified framework can help in making more informed investment decisions by providing a common analytical solution for different earnings-allocation mechanisms, thus reducing the complexity and enhancing the accuracy of equity valuation models.

SUMMARY: This study proposes a valuation-based framework for generating stock-selection factors by establishing a valuation–return equilibrium linking enterprise valuation and market valuation. The equilibrium identifies four structural dimensions—valuation, growth, persistence, and risk—each comprising an enterprise-side component and a market-side component. Together, these dimensions contain eight structural variables that provide a common theoretical foundation through which observable stock-selection variables may be interpreted as empirical measurements of underlying economic mechanisms rather than independent factors. The study aims to address the limitations of existing factor research, which often derives factors empirically rather than systematically from economic theory. METHOD: The study employs a theoretical approach to generate stock-selection factors by linking profitability, valuation, expected returns, persistence, and discount rates within a unified economic structure. The authors propose a valuation–return equilibrium framework, which is based on previous studies that developed a symmetric valuation–return framework. The methodology involves identifying the four structural dimensions and eight structural variables that form the basis for generating observable stock-selection variables. KEY FINDINGS: - The valuation–return equilibrium framework provides a unified interpretation of observable factor characteristics within modern asset-pricing research. - The framework identifies four structural dimensions: valuation, growth, persistence, and risk. - These dimensions contain eight structural variables that provide a common theoretical foundation for observable stock-selection variables. - The framework offers a structural interpretation of several long-standing observations in the factor-investing literature, including factor overlap and the potential complementarity of information contained in different factor families. IMPLICATION FOR TRADING: The valuation-based framework proposed in this study can help practitioners understand the theoretical underpinnings of observable stock-selection factors. This can lead to a more systematic approach to identifying and interpreting factors, potentially improving the effectiveness of investment strategies. By understanding the underlying economic mechanisms, traders can better predict and exploit market dynamics, leading to more informed investment decisions. Additionally, the framework may facilitate the identification of complementary factors, enabling investors to diversify their portfolios and reduce risk.

SUMMARY: This article traces the evolution of market microstructure in the age of AI, from traditional double auctions to modern agentic markets. It explores how trading rules have evolved through different technological shifts, including high-frequency trading, batch auctions, dark pools, and automated market makers. The article also examines the impact of AI agents on market allocation and the need for new mechanisms to ensure efficiency and fairness without revealing all participants' information. METHOD: The study reviews evidence on high-frequency trading, exchange and bilateral venues, automated market makers, extractable value, and algorithmic collusion. It also identifies allocation as the missing layer in the agent-commerce protocol stack. The methodology involves reviewing existing literature and identifying key findings through a systematic review approach. KEY FINDINGS: - High-frequency trading has led to the development of new market mechanisms. - Automated market makers have played a significant role in the evolution of market microstructure. - Batch auctions and dark pools have introduced new challenges in market design. - AI agents have disrupted traditional market structures and require new allocation mechanisms. - The allocation layer is the missing piece in the agent-commerce protocol stack. IMPLICATION FOR TRADING: The findings suggest that market designers need to focus on creating allocation mechanisms that ensure efficient and fair resource allocation without participants revealing all their information. This includes developing new market mechanisms for high-frequency trading, batch auctions, and dark pools. Additionally, the research highlights the need for new allocation protocols to accommodate the emergence of AI agents in modern marketplaces. Practitioners should consider these findings when designing market mechanisms to ensure they remain effective and fair in the evolving market landscape.

SUMMARY: This study examines the impact of survivorship bias in the cross-section of winner portfolios using point-in-time data on U.S. common stocks from 1991 to 2020. The authors aim to separate the effects of survivorship bias on the composition of winner sets and the subsequent returns observed for firms that remain in it. They find that conditioning on five-year survival changes one-third of the top twenty names, and the resulting survivor-conditioned portfolio earns 6 percentage points more over one year and 37 percentage points more over five years compared to the full-universe portfolio. The authors attribute most of this difference to the returns omitted when winning firms exit, rather than to the firms promoted into their place. The effect is concentrated among equal-weighted winners, where distress-related exits are associated with large losses. METHOD: The study uses point-in-time data on U.S. common stocks, ranking stocks by their trailing twelve-month returns at each annual formation date from 1991 to 2025. The rankings are based on either the full set of firms active at formation or only firms that remain listed at a specified future horizon. The authors then compare portfolios formed from these rankings and construct survivor-conditioned portfolios of firms common to both rankings and firms promoted only after survival conditioning. KEY FINDINGS: - Conditioning on five-year survival changes roughly one-third of the top twenty names. - The survivor-conditioned portfolio earns 6 percentage points more over one year and 37 percentage points more over five years compared to the full-universe portfolio. - The effect is concentrated among equal-weighted winners, where distress-related exits are associated with large losses. - Most of the difference in performance comes from the returns of winners that subsequently exit. IMPLICATION FOR TRADING: The study highlights the importance of accounting for survivorship bias in performance evaluation, particularly for strategies that select firms from the upper tail of past returns. Practitioners should be aware that survivorship bias can significantly affect the performance of winner portfolios, especially when using equal-weighted strategies. This bias can be mitigated by conditioning on survival, but it is important to understand the implications for different types of portfolios and strategies.

SUMMARY: This paper addresses the issue of singular covariance matrices in portfolio optimization, which commonly occur in high-dimensional asset sets or when assets are perfectly linearly dependent. The authors present a method to solve the Markowitz problem with a singular covariance matrix V of rank N-1. They show that the null vector z spanning the kernel of V can be extracted directly from any nonzero column of the classical adjugate matrix adj(V), requiring only cofactor expansion. Using z, they introduce a rank-1 spectral shift B = V + zz^T that makes the covariance matrix fully invertible while preserving its structure. The resulting minimum-variance frontier collapses into a sharp, linear V-shape, where z acts as a synthetic zero-risk asset constructed endogenously from purely risky components. This approach offers a direct algebraic alternative to the standard numerical methods, such as the Moore-Penrose pseudoinverse, which are often used to handle singular covariance matrices. METHOD: The authors use cofactor expansion and the classical adjugate matrix to extract the null vector z from the singular covariance matrix V. They then introduce a rank-1 spectral shift B = V + zz^T to make the covariance matrix fully invertible while preserving its structure. The method is self-contained, analytically rigorous, and elementary, requiring only tools available in a first linear algebra course. KEY FINDINGS: - The null vector z can be extracted directly from any nonzero column of the adjugate matrix adj(V). - A rank-1 spectral shift B = V + zz^T makes the covariance matrix fully invertible. - The resulting minimum-variance frontier collapses into a sharp, linear V-shape. - The synthetic zero-risk asset z is constructed endogenously from purely risky components. IMPLICATION FOR TRADING: The method presented in this paper provides a direct algebraic alternative to the standard numerical methods for handling singular covariance matrices in portfolio optimization. This approach can be used by financial educators to simplify the explanation of the Markowitz problem and its solutions, making it more accessible to students. Practitioners can use this method to solve portfolio optimization problems with singular covariance matrices, which are common in real-world financial data. The synthetic zero-risk asset z can be used to construct a synthetic portfolio that includes a risk-free asset, simplifying the analysis of risk and return in investment strategies.

SUMMARY: This paper examines the speed at which cryptocurrency perpetual futures prices correct after extreme negative funding rates. It introduces the concept of "limits to coordination," a new form of arbitrage constraint alongside capital constraints and funding-liquidity constraints. The study uses 1,696 events across 18 Binance perpetual contracts to document funding drag, where high-volatility events take a median of 27.0 hours to correct, compared to 3.0 hours for low-volatility events. The paper also conducts a placebo test and finds that only extreme funding-rate events are slow to correct. The findings suggest that realized volatility plays a significant role in determining the speed of correction. METHOD: The study employs a Kaplan-Meier survival framework to analyze 1,696 events across 18 Binance perpetual contracts. The sample covers four distinct market regimes: the 2021 bull market and correction, the 2022 bear market, the 2023 recovery, and the 2024 bull market. The data includes realized volatility and other relevant market indicators. KEY FINDINGS: - High-volatility events take a median of 27.0 hours to correct, while low-volatility events take 3.0 hours. - Only extreme funding-rate events are slow to correct, with a median correction time of 27.0 hours. - Four accelerated-failure-time specifications yield time ratios of 2.32–3.72, all above one. - The effect is consistent before and after the FTX collapse. IMPLICATION FOR TRADING: The findings suggest that realized volatility is a key determinant of the speed of correction after extreme negative funding rates. Practitioners can use this information to better predict and manage their positions in cryptocurrency perpetual futures, potentially improving their trading strategies. Understanding the limits to coordination can help traders anticipate and mitigate the impact of volatility on their positions.

SUMMARY: This research investigates the immediate equity valuation responses following structural adjustments to the FTSE Frontier 50 benchmark across 30 nascent economies from 2008 to 2025. The study questions whether conventional benchmark inclusion paradigms hold true in these restrictive environments. The methodology isolates inaugural, recurring, and categorization-induced inclusions and removals, and assesses trade volume metrics, asset fluidity indicators, and institutional holding patterns. The findings indicate that inaugural and recurring benchmark entries consistently generate enduring equity valuation surges, while removals precipitate sustained valuation decays. Macroeconomic recategorization removals exhibit subsequent valuation corrections. Baseline institutional holdings prior to the reshuffling strongly forecast aggregate anomalous yields. The evidence confirms that sustained asset price appreciations following benchmark inclusion stem from institutional capital appetite rather than transient transactional friction. METHOD: The study employs a transnational event-centric analytical model coupled with cross-sectional regressions. The sample includes thirty nascent economies across South America, Europe, Asia, and Africa. The dataset covers the period from 2008 to 2025. The methodology isolates inaugural, recurring, and categorization-induced inclusions and removals, and assesses trade volume metrics, asset fluidity indicators, and institutional holding patterns. KEY FINDINGS: - Inaugural and recurring benchmark entries consistently generate enduring equity valuation surges. - Corresponding removals precipitate sustained valuation decays. - Removals triggered by macroeconomic recategorization exhibit subsequent valuation corrections. - Concurrent shifts in asset fluidity fail to explain anomalous yields. - Baseline institutional holdings prior to the reshuffling strongly forecast aggregate anomalous yields. IMPLICATION FOR TRADING: The findings suggest that institutional capital appetite drives sustained asset price appreciations following benchmark inclusion. Practitioners can use this information to anticipate and capitalize on market reactions to index reconstitutions, particularly in nascent financial hubs. Understanding the role of institutional holdings in driving asset valuations can help in making informed investment decisions.

SUMMARY: This article discusses the progress and applications of behavioral finance, which has evolved into a vibrant area of financial economics over the past four decades. It focuses on three main areas: beliefs about future earnings and returns, and prospect theory, and their applications to understanding asset prices and investor behavior. The author reviews recent progress, open questions, and new directions in the field, including cognitive foundations, non-traditional decision processes, and artificial intelligence. METHOD: The article synthesizes recent research on behavioral finance, covering studies on beliefs about future earnings and returns, prospect theory, and their applications to asset prices and investor behavior. It also discusses new directions in the field, such as cognitive foundations, non-traditional decision processes, and artificial intelligence. KEY FINDINGS: - Behavioral finance has expanded into a significant area of study, with three main applications: asset prices, household finance, and corporate finance. - Recent research has provided support for behavioral approaches to asset prices, particularly through data on people's expectations about future returns and earnings. - The field has seen significant progress in household finance, with new datasets and research on structural approaches, household liabilities, and improving financial decision-making. - Behavioral corporate finance research has focused on two frameworks: one for security issuance and capital structure, and another for acquisition activity, with managers often being irrational in their decision-making. IMPLICATION FOR TRADING: Behavioral finance has practical implications for traders and investors, as it helps explain why financial markets and investor behavior often deviate from rational expectations. Understanding the psychological biases and decision-making processes can aid in identifying mispriced assets and predicting market movements. Additionally, it can inform investment strategies and risk management practices, leading to more informed and effective trading decisions.

SUMMARY: This paper introduces D, a one-number gauge measuring the ratio of the volatility of an equal-weight dollar index to the mean volatility of its constituents. D is used to assess the diversification regime in currency portfolios. The study examines 6,117 trading days from 2007 to 2026 and finds that D tracks the first-eigenvalue share of the rolling correlation matrix at +0.88 and is nearly orthogonal to realized volatility at −0.09. The paper also reveals that the cross-pair correlation of daily strategy returns is higher in the high-D tercile for 86% of 88 published strategies, with a mean difference of +0.029 at t = 10.6. The research demonstrates that the rise in cross-pair correlation is due to reallocation rather than composition, with variance falling 30% on high-D days while covariance falls only 12%. The study concludes that D is a gauge of the diversification regime and not an alpha regime, as an injected informational edge earns the same in both regimes. METHOD: The study uses hourly log returns of six currency pairs to compute the equal-weight dollar index return (Equation 1). The gauge D is then calculated as the ratio of the index volatility to the mean volatility of its constituents over a trailing 240-hour window (Equation 2). The paper analyzes 6,117 trading days from 2007 to 2026, with D having a mean of 0.730, standard deviation of 0.085, and a range of 0.437 to 0.921. The gauge's correlation with the first-eigenvalue share of the rolling correlation matrix is also examined. KEY FINDINGS: - D tracks the first-eigenvalue share of the rolling correlation matrix at +0.88. - D is nearly orthogonal to realized volatility at −0.09. - The cross-pair correlation of daily strategy returns is higher in the high-D tercile for 86% of 88 published strategies. - On high-D days, variance falls 30% while covariance falls only 12%. - D is a gauge of the diversification regime and not an alpha regime. IMPLICATION FOR TRADING: The findings suggest that traders should consider the diversification regime when running strategies across currency pairs. The paper's gauge, D, can be used to assess the effectiveness of diversification and to identify strategies with higher cross-pair correlation. Practitioners can use this information to make informed decisions about their trading strategies and to optimize their portfolios for better risk management.

SUMMARY: This study examines how individuals adapt their investment behavior when an algorithm evaluates groups differently. Participants were randomly assigned to an advantaged or disadvantaged identity group and were asked to invest to improve their chances of being hired by an algorithm with different prior beliefs across groups. Disadvantaged participants invested 17.7% more than advantaged participants, indicating no discouragement in investment despite facing less favorable evaluation. The study then tested two interventions: "Role Model" treatments providing information about the behavior and outcomes of same-group predecessors, and a "No Risk" treatment that conditioned payment of the investment cost on being hired. The "Role Model" treatments had limited effects, while the "No Risk" treatment increased investment among disadvantaged participants by 24%. This effect was significantly larger than the corresponding effect among advantaged participants. The findings suggest that individuals can respond to algorithmic disadvantage by increasing rather than reducing investment, and that changing the economic consequences of investment generates a stronger behavioral response than same-group social information. METHOD: The study used a pre-registered online experiment with 553 participants. Participants were randomly assigned to an advantaged or disadvantaged group and were asked to invest to improve their chances of being hired by an algorithm with different prior beliefs across groups. The "Role Model" and "No Risk" interventions were tested to see if they could increase investment among disadvantaged participants. KEY FINDINGS: - Disadvantaged participants invested 17.7% more than advantaged participants. - "Role Model" treatments had limited effects. - "No Risk" treatment increased investment among disadvantaged participants by 24%. - The effect was significantly larger among disadvantaged participants than among advantaged participants. IMPLICATION FOR TRADING: The findings suggest that individuals can respond to algorithmic disadvantage by increasing rather than reducing investment. Practitioners can use these insights to design interventions that change the economic consequences of investment, such as offering guarantees or incentives, to generate stronger behavioral responses. However, same-group social information may not be as effective in encouraging investment in the face of algorithmic disadvantage.

SUMMARY: This paper examines a public-data U.S. equity research pipeline, focusing on the impact of various factors such as security identity, filing availability, and transaction costs on the performance of the research. The study uses a sample of 487 equities from 822 securities, providing daily market observations and 37,273 point-in-time fundamental observations. The analysis evaluates 715,447 daily market observations and 47 of 48 factor definitions, finding that multifactor specifications yield higher mean in-sample adjusted R-squared values (0.259–0.301) compared to the Capital Asset Pricing Model (CAPM) (0.063). The study also evaluates eight long-only portfolio methods and eight prediction models, finding that none of the models consistently show stable incremental value after accounting for transaction costs. The research contributes to the field by providing a connected empirical design that highlights the limitations of the public-data and short-window sample. METHOD: The study uses a public-data U.S. equity research pipeline, with a sample of 487 equities from 822 securities. The sample provides 715,447 daily market observations and 37,273 point-in-time fundamental observations. The analysis evaluates 47 of 48 factor definitions and eight long-only portfolio methods, using eight prediction models in 17 purged monthly test folds. The study also evaluates the impact of transaction costs on the performance of the portfolio methods and prediction models. KEY FINDINGS: - Multifactor specifications yield higher mean in-sample adjusted R-squared values (0.259–0.301) compared to the Capital Asset Pricing Model (CAPM) (0.063). - None of the eight long-only portfolio methods show stable incremental value after accounting for transaction costs. - Eight prediction models are assessed in 17 purged monthly test folds using two characteristics. - Non-constant models' bootstrap interval for mean rank correlation includes zero, and stable after-cost incremental value is not established. IMPLICATION FOR TRADING: The research highlights the importance of considering various factors such as security identity, filing availability, and transaction costs in the development of equity research pipelines. The findings suggest that multifactor specifications can improve explanatory fit, but the impact of transaction costs on portfolio methods and prediction models is not consistent. These insights can be useful for practitioners in designing and evaluating equity research pipelines, as well as for understanding the limitations of public-data and short-window samples.

SUMMARY: The study examines how affective conditions influence the framing effect in investment decision-making using Fuzzy-Trace Theory. Unlike previous studies, which assume stability, this research suggests the effect depends on cognitive processing triggered by affective states. The study employs a 2×2 between-subjects factorial design with 157 participants. The independent variables are framing (positive vs. negative) and feeling (pleasant vs. unpleasant), while the dependent variable is investment decision (certain vs. risky option). The Chi-square test is used for hypothesis testing, with ANCOVA for robustness. Results show the framing effect significantly influences decisions but is unstable. It appears under pleasant feelings but weakens or disappears under unpleasant conditions. Affective conditions don't directly moderate but influence decision patterns through cognitive processing changes. These findings highlight the importance of psychological conditions in accounting information presentation and financial decision-making. METHOD: The study used a 2×2 between-subjects factorial design with 157 participants. Independent variables were framing (positive vs. negative) and feeling (pleasant vs. unpleasant), while the dependent variable was investment decision (certain vs. risky option). The Chi-square test was used for hypothesis testing, with ANCOVA for robustness. KEY FINDINGS: - The framing effect significantly influences investment decisions but is unstable. - It appears under pleasant feelings but weakens or disappears under unpleasant conditions. - Affective conditions influence decision patterns through cognitive processing changes. - These findings highlight the importance of psychological conditions in accounting information presentation and financial decision-making. IMPLICATION FOR TRADING: The findings suggest that affective states play a crucial role in accounting and financial decision-making. Practitioners should consider the psychological impact of information presentation, as it can significantly affect decision outcomes. Understanding the influence of positive and negative framing, as well as pleasant and unpleasant feelings, can help in designing more effective financial communication strategies. Additionally, recognizing the impact of cognitive processing changes can lead to the development of more robust decision-making models that account for individual differences in how information is processed.

SUMMARY: This article examines the impact of a market for regulatory compliance in India's Priority Sector Lending Certificate (PSLC) market. The authors investigate whether the trading of regulatory attributes can weaken the intended activity of regulation. They use PSLC data to explore how banks can substitute regulatory claims for classification and new lending, and how this affects the technology of compliance. The study also considers the welfare effect of trading and whether the social value of displaced lending exceeds the compliance-cost savings created by trade. METHOD: The study employs a nested certificate structure to analyze the PSLC market, where banks can strip and repackage regulatory attributes across different constraints. The empirical strategy involves using bank-level data to identify different objects and add a dynamic overidentification layer. Source-traceable bank observations are used to discipline classification curvature, surplus monetization, and cross-bank surplus dispersion. The current market-clearing conditions are imposed using FY2024-25 RBI certificate prices. KEY FINDINGS: - The market for regulatory attributes can lower the real cost of compliance by reallocating regulatory capacity across banks. - It can weaken the link between measured compliance and the activity the mandate was intended to create. - A bank that is short of regulatory output may respond by changing the classification of existing assets, originating new qualifying credit, or buying a regulatory claim generated elsewhere. - The welfare effect depends on whether the social value of the displaced lending exceeds the compliance-cost savings created by trade. IMPLICATION FOR TRADING: The findings suggest that trading markets for regulatory compliance can change the technology of compliance and real activity induced by the regulation. Practitioners can use this information to design better trading mechanisms that balance the benefits of reducing compliance costs with the potential negative effects on targeted lending. Understanding the dynamics of such markets can help regulators and banks optimize their compliance strategies to achieve better welfare outcomes.

SUMMARY: This study examines how Basel III and Islamic Financial Services Board (IFSB) liquidity requirements contribute to liquidity and financial resilience in Sudanese Islamic banks operating under structural fragility and armed conflict. The research aims to address a contextual gap concerning the effectiveness of international prudential standards within a fully Shari’ah-compliant banking system characterized by limited Shari’ah-compliant high-quality liquid assets, constrained liquidity instruments, external financial isolation, and severe macro-environmental shocks. The study employs a mixed-methods design combining longitudinal panel-data analysis, Partial Least Squares Structural Equation Modeling (PLS-SEM), and semi-structured interviews with senior banking executives. METHOD: The study uses longitudinal panel-data analysis of four Sudanese Islamic banks from 2010 to 2024 to examine financial soundness. Fixed Effects and System GMM estimations are used to address dynamic and endogeneity concerns. PLS-SEM is used to provide complementary exploratory evidence on institutional relationships. The study also includes Partial Least Squares Structural Equation Modeling (PLS-SEM) to provide complementary exploratory evidence on institutional relationships. KEY FINDINGS: - Stronger liquidity buffers and equity strength are positively associated with financial soundness. - Non-performing loans and conflict intensity are negatively associated with financial soundness. - The post-conflict interaction with liquidity is negative, indicating weaker protective effectiveness of liquidity buffers under conflict conditions. - Regulatory compliance is positively associated with financial soundness, while credit risk, conflict, and external financial isolation are negatively associated with financial soundness. IMPLICATION FOR TRADING: The findings support a Triple Crisis Framework in which regulatory effectiveness is conditional on structural Shari’ah liquidity constraints and conflict-induced fragility. For practitioners, this research highlights the importance of understanding the specific challenges faced by Sudanese Islamic banks, including limited Shari’ah-compliant high-quality liquid assets and external financial isolation. It underscores the need for regulatory frameworks that account for the unique characteristics of Islamic banking systems, such as the Shari’ah Gap, and the impact of armed conflict on liquidity and financial resilience. Practitioners should consider these factors when developing strategies and assessing the effectiveness of regulatory requirements.

SUMMARY: The article examines the rebranding strategies used by companies to maintain product competitiveness, focusing on marketing theory, regulatory pressures, and brand equity transfer. The authors analyze multidimensional theories including Brand Equity, Brand Rebranding and Identity Matrix, International Marketing, and Brand Evolutionary Theory. They use case studies of rebranding efforts such as Mako/BreadTalk, AZKO/ACE Hardware, WWE/WWF, and others. The research shows that rebranding can enhance competitiveness through image renewal, market segmentation expansion, strategic efficiency, and opportunities for diversification. However, changes in identity can also lead to brand disconnection, consumer confusion, and increased communication costs. Successful rebranding requires brand equity auditing, regulatory mapping, continuity of quality, retention of visual elements, precise communication, and financial readiness during the transition. METHOD: The study employs a multidimensional approach, examining Brand Equity, Brand Rebranding and Identity Matrix, International Marketing, and Brand Evolutionary Theory. The authors relate these theories to several case studies, including Mako/BreadTalk, AZKO/ACE Hardware, WWE/WWF, and others. The analysis is based on academic literature and case studies. KEY FINDINGS: - Rebranding can enhance competitiveness through image renewal, market segmentation expansion, strategic efficiency, and opportunities for diversification. - Changes in identity can lead to brand disconnection, consumer confusion, and increased communication costs. - Successful rebranding depends on brand equity auditing, regulatory mapping, continuity of quality, retention of visual elements, precise communication, and financial readiness during the transition. IMPLICATION FOR TRADING: The findings suggest that traders and business analysts should consider rebranding strategies when evaluating a company's competitiveness. Companies must carefully manage the relationship between the old brand and the new identity to ensure successful rebranding. This involves conducting brand equity audits, mapping regulatory pressures, maintaining quality standards, retaining visual elements, and ensuring financial readiness during the transition. Understanding these factors can help traders make more informed decisions about potential investment opportunities.

SUMMARY: This study examines the impact of daily price limits on GARCH volatility estimates in the Pakistan Stock Exchange (PSX). The research extends the expected-shock censored-GARCH approach of Morgan and Trevor (1999) to Student-t innovations, validating it through Monte Carlo simulations. The study finds that ignoring censoring understates shock sensitivity (α) by a median of 7.6% under Student-t GARCH and 17.3% under normal GARCH, rising with how often a stock's price hits its limit. The PSX's Re. 1 minimum band affects the effective price limit, making it wider for cheaper stocks. The study applies the corrected model to 449 distinct companies over three price-limit regimes, finding that the 2020 widening of the band is associated with lower censoring and a smaller bias in affected stocks relative to floor-bound stocks. METHOD: The study uses daily OHLCV data for 449 distinct companies listed on the PSX from September 2016 to September 2026. Price limits are set daily based on the previous close and are capped at Re. 1. The data is censored when the close price lies within one tick of the limit, and breaches are excluded from the likelihood and treated identically across all three models. The study employs three models: naive, observed-shock censored, and expected-shock censored, with the latter following Morgan and Trevor's (1999) approach. KEY FINDINGS: - Ignoring censoring understates shock sensitivity (α) by a median of 7.6% under Student-t GARCH and 17.3% under normal GARCH. - The effective price limit is wider for cheaper stocks due to the PSX's Re. 1 minimum band. - The 2020 widening of the band is associated with lower censoring and a smaller bias in affected stocks. - Cheap stocks censor less often than the percentage rule implies, consistent with the Re. 1 floor. IMPLICATION FOR TRADING: The study's findings suggest that ignoring price limits can lead to an understated measure of volatility, which is particularly relevant for traders and investors. The results highlight the importance of accounting for price limits when estimating volatility, especially for cheaper stocks. This information can help traders and investors better understand the true volatility of stocks and adjust their strategies accordingly. The study's findings can also inform policymakers who set price limits, as they can use this information to make more informed decisions about the effectiveness of their policies.

SUMMARY: This paper examines the optimal trading strategies under various evaluation constraints introduced in a research program. Five fictitious 50K contract systems are studied: Ideal Basic (I0), Ideal Realistic (I1), Static Discipline (A), EOD Trail (B), and Intraday Guardrail (C). The analysis compares nine declared policies and selects the best policy on 12,000 training paths, reporting results on 30,000 independent holdout paths. The study finds that fixed baseline risk performs well under I0 and I1, while higher risk policies are selected for A, B, and C. The paper also explores the impact of evaluation constraints on participant behavior and reconciles apparent differences across the research line. METHOD: The analysis involves two layers. For the static contract, a daily-grid dynamic program provides an upper numerical benchmark within a finite action set. For the path-dependent contracts, a global optimum would require a much larger state space. The paper compares nine declared policies and selects the best policy on 12,000 training paths, reporting results on 30,000 independent holdout paths. The study uses synthetic data to test computational robustness and controlled policy comparisons. KEY FINDINGS: - Fixed baseline risk passes 45.06% under I0 and I1, 43.90% under A, 41.55% under B, and 20.26% under C. - The training-selected pass policy raises the figures to 60.64%, 60.64%, 50.60%, 41.55%, and 20.26%, respectively. - The static dynamic-programming policy transfers well to I0 and I1, fixed high risk is selected for A, and baseline risk remains best in the tested library for B and C. - Rules do not merely subtract a constant probability; they change the participant’s response. IMPLICATION FOR TRADING: The findings suggest that participants should choose risk policies that align with the evaluation constraints of their trading contracts. This understanding can help traders optimize their strategies and improve their performance under different evaluation conditions. The study's controlled policy comparisons provide a framework for evaluating and selecting optimal trading policies under various constraints, which can be applied to real-world trading scenarios.

SUMMARY: This article examines the recurring crypto crisis events and their implications for the digital asset economy. It argues that instead of viewing these crises as failures of crypto products or institutions, they represent a maturing ecosystem adapting to evolving market needs. The study provides a comparative analysis of regulatory responses in various jurisdictions and advocates for international cooperation to address regulatory arbitrage and promote crypto market maturity. METHOD: The article employs a comparative analysis of regulatory responses in the United States, the European Union, and Hong Kong SAR (China). It uses data from selected jurisdictions to evaluate the effectiveness of their regulatory policies and identify areas for improvement. The methodology involves examining the use cases and policy implementations of these jurisdictions in their efforts to establish themselves as "digital assets global hubs." KEY FINDINGS: - The recurring crypto crises are seen as evidence of a maturing ecosystem adapting to market needs. - There is a need for international cooperation to address regulatory arbitrage and promote crypto market maturity. - Jurisdictions are competing for the status of "digital assets global hub" through innovative use cases and regulatory policy implementations. - The analysis highlights growing frictions in transnational regulatory cooperation. IMPLICATION FOR TRADING: The implications for trading are significant, as they suggest that regulatory responses and market maturity are key factors in the success and fragmentation of the crypto economy. Practitioners should consider the regulatory landscape when making investment decisions and be aware of the challenges of regulatory arbitrage. Understanding the evolving regulatory environment can help traders navigate the complexities of the crypto market and make informed investment choices.

SUMMARY: This research project aims to determine if a conditional hidden market regime fuzzy number volatility can generate useful and truthful interval values for SPX index options. The project uses a Hidden Markov Model (HMM) to infer market regimes from S&P 500 returns, and models volatility as a triangular fuzzy number based on observed bid and ask implied volatilities. The fuzzy parameters are conditioned on the inferred market regimes. The project tests the constructed fuzzy bands against realized market quotes using Kupiec and Christoffersen tests. The findings indicate that conditioning the fuzzy bands on market regimes can significantly improve coverage, particularly in stressed market conditions. METHOD: The project constructs a data pipeline to calibrate a Gaussian Hidden Markov Model (HMM) on S&P 500 daily returns to infer market regimes. It then estimates regime-conditional fuzzy volatility parameters from the bid/ask spread of SPX index options. The out-of-sample pricing of the option panel is compared to classic Black-Scholes and a fuzzy approach without regime conditions. The fuzzy pricing calculations are verified using semi-analytical solutions via the Homotopy Perturbation Method and the Adomian Decomposition Method. KEY FINDINGS: - Conditioning the fuzzy bands on market regimes can significantly improve coverage. - Pooled coverage statistics can hide where conditioning on average matters. - Stratified by market regime, conditioning can close coverage gaps of up to 68 percentage points in stressed markets. IMPLICATION FOR TRADING: The findings suggest that incorporating market regime information into fuzzy volatility models can lead to more accurate option pricing, especially in stressed market conditions. This can help traders better understand and manage risk, as the fuzzy bands provide a more nuanced view of potential price movements. Practitioners can use these fuzzy bands to construct more robust trading strategies, potentially improving their ability to cover market prices and reduce potential losses.

SUMMARY: This study investigates whether a previously established retrospective transition-conditioned locality structure in cryptocurrency microstructure can be reformulated as an online-available object without future information. The online construction was subjected to audits for source-provenance, prefix-invariance, deterministic-equivalence, and information-availability. Three market experiments were conducted to provide distinct evidentiary roles: a preregistered BTCUSDT continuity test, an independent chronological validation using ETHUSDT, and a separate-market replication using SOLUSDT. The study also includes a forward experiment to establish finite, non-zero population-level persistence in ETH and SOL under simultaneous trajectory inference. The findings confirm that the frozen locality object can be observed and maintained under the tested contracts, not for signed-price prediction, profitability, execution advantage, or economic causation. METHOD: The study's methodology involved converting the previously retrospective Locality structure into a past-only signal-time construction and freezing it. This object was then evaluated on a later fixed ETHUSDT chronology and applied to SOLUSDT without pooled inference or market-specific retuning. A separately preregistered BTCUSDT continuity test was conducted to validate the retrospective BTC lineage. The forward experiment characterized persistence with simultaneous uncertainty. The experiments used BTCUSDT, ETHUSDT, and SOLUSDT markets, with three market experiments providing distinct evidentiary roles. KEY FINDINGS: - The frozen Locality object can be observed and maintained under the tested contracts. - The BTC continuity test provided retrospective-to-causal continuity. - The ETHUSDT experiment offered independent chronological validation. - The SOLUSDT experiment provided separate-market replication. - The findings do not establish signed-price direction, alpha, profitability, execution feasibility, or a causal market mechanism. IMPLICATION FOR TRADING: The findings suggest that the frozen Locality structure can be observed and maintained under the tested contracts, which could be useful for traders who need to validate their strategies against a historical dataset. However, the structure does not provide information on signed-price direction, profitability, or execution advantage, which are important factors for traders. The results can be used to validate trading strategies and ensure they are consistent with historical data, but they cannot be used to predict future price movements or establish causal relationships.

SUMMARY: This paper formulates portfolio choice as a recursive learning operator on conditional covariance operators, selecting the minimum-risk direction, re-estimating its return law under long-memory, heavy-tailed dynamics, and re-injecting it. The map admits a fixed point whose expected return defines the Adaptive Minimum-Risk Rate, an endogenous, portfolio-specific shadow benchmark. The authors demonstrate that the recursion converges to a fixed-point invariant to the informational regime, lying orders of magnitude below the raw minimum-variance level. This fixed point is characterized by the first-order conditions, which show that every held asset has a beta of exactly one with respect to the fixed-point portfolio. METHOD: The authors formulate portfolio choice as a recursive learning operator on conditional covariance operators. They select the minimum-risk allocation, estimate the return law, including its memory and tail structure, and re-inject the resulting synthetic instrument into the investable universe. The recursion is governed by the covariance operator's condition number, and it converges to a fixed point invariant to the informational regime. KEY FINDINGS: - The Adaptive Minimum-Risk Rate is an endogenous, portfolio-specific shadow benchmark. - The recursion converges to a fixed-point invariant to the informational regime. - The fixed-point portfolio has every held asset with a beta of exactly one. - The fixed-point portfolio lies orders of magnitude below the raw minimum-variance level. IMPLICATION FOR TRADING: The paper's findings suggest that practitioners should use the Adaptive Minimum-Risk Rate as a benchmark for their portfolios. This benchmark is endogenous and specific to each portfolio, allowing for a more accurate assessment of risk. The fixed-point portfolio's structure, with every held asset having a beta of exactly one, can help traders understand the risk exposure of their portfolios. Additionally, the convergence to a fixed-point invariant to the informational regime indicates that the benchmark is robust and reliable, even in complex and changing market conditions. Practitioners can use this information to make more informed decisions and manage risk effectively.

SUMMARY: This paper presents a comprehensive machine learning architecture for yield curve fitting and term structure modeling, using Gaussian Process Regression (GPR) and non-linear Neural Network architectures. The study evaluates the fitting precision of Gaussian Processes and Neural Networks against classical Nelson-Siegel models. The empirical results indicate that Gaussian Processes achieve superior out-of-sample fitting accuracy compared to classical parametric benchmarks, reducing Mean Absolute Error (MAE) by up to 42% in high-volatility regimes. The systematic relative-value arbitrage strategy generated significant risk-adjusted returns with a Sharpe ratio of 1.94, demonstrating the practical utility of non-parametric machine learning in fixed-income portfolio management. The study also highlights the importance of explainable probabilistic machine learning in regulated fixed-income markets, as Gaussian Process Regression provides explicit uncertainty quantification through its posterior variance. METHOD: The study employs Gaussian Process Regression with a customized Matern kernel to reconstruct zero-coupon yield curves across sovereign bond markets. The fitted yield surfaces are used to identify cross-sectional pricing dislocations via residual z-score metrics, and a long-short bond arbitrage strategy is designed. The methodology leverages a rolling-window cross-validation scheme to re-calibrate kernel hyper-parameters daily, preventing look-ahead bias. KEY FINDINGS: - Gaussian Processes outperform classical Nelson-Siegel models in out-of-sample fitting accuracy. - The systematic relative-value arbitrage strategy generates significant risk-adjusted returns. - Gaussian Process Regression provides explicit uncertainty quantification through posterior variance. IMPLICATION FOR TRADING: The study suggests that non-parametric machine learning, particularly Gaussian Process Regression, can be effectively applied in fixed-income portfolio management. This approach offers superior fitting accuracy and robust out-of-sample interpolation, which is crucial for accurate yield curve fitting and arbitrage strategies. The explicit uncertainty quantification provided by Gaussian Process Regression enhances risk management and compliance reporting in regulated markets. Practitioners can leverage this framework to develop more precise and reliable yield curve models and arbitrage strategies, leading to improved risk-adjusted returns.

SUMMARY: This study estimates the time series of the investment opportunity set (invop) using 13F holdings data. The invop portfolio represents the difference between the market portfolio and the invop portfolio, which is assumed to be composed of two systematic portfolios: the market portfolio and the invop portfolio. The study allows for heterogeneity in risk aversion among institutions, which results in different loadings on the two portfolios. The study estimates the shock to the invop portfolio in each quarter using the realized return of the invop portfolio and uses a rolling average of the shocks as the estimate for the set. The estimate is positively related to future market excess returns and Treasury yield, and negatively related to the variance of Treasury yield and the covariance between the market excess return and Treasury yield. The study also explores whether the estimate alone can predict future market excess returns and dividend growth rates. METHOD: The study uses 13F holdings data to estimate the invop portfolio, which is the difference between the market portfolio and the invop portfolio. The invop portfolio's loadings on the market and invop portfolios are estimated using a rolling average of the shocks to the invop portfolio. The shocks are derived from the realized return of the invop portfolio. The study uses a numerical procedure to estimate the invop portfolio and the shocks. The invop portfolio is composed of four components: expected market excess returns, expected risk-free rates, the variance of expected risk-free rates, and the covariance between expected market excess returns and expected risk-free rates. KEY FINDINGS: - The estimate of the investment opportunity set is positively related to future market excess returns and Treasury yield. - The estimate is negatively related to the variance of Treasury yield and the covariance between the market excess return and Treasury yield. - The estimate predicts future market excess returns. - The dividend yield, the estimate, and Treasury yield together predict future dividend growth rates. - The estimate alone has out-of-sample predictability for future market excess returns and dividend growth rates. IMPLICATION FOR TRADING: The estimate of the investment opportunity set can be used as a market timing strategy to improve the Sharpe ratio. Practitioners can use the estimate to identify periods of high and low investment opportunities, allowing them to adjust their portfolio allocations accordingly. Additionally, the estimate can be combined with other variables, such as the dividend yield and Treasury yield, to predict future dividend growth rates. This information can help investors make more informed investment decisions and potentially improve their returns.

SUMMARY: This white paper proposes a Fragility Ratio to better understand the fragility of returns in the stock market, which is defined by two market regimes: higher prices on lower volumes (thin rallies) and lower prices on higher volumes (heavy sell-offs). The Fragility Ratio measures how much return an asset delivered per unit of fragility, specifically in these two regimes of volatility. The paper introduces a Fragility Cost of Return, which measures how much fragility an investor had to endure to obtain 1% of return. These insights are built upon the ratios proposed by Sharpe and Sortino, and the novelty lies in asking a narrower research question: what does an asset’s fragility stemming from price and volume diverging from each other reveal on its own and in relation to the returns investors actually earn? The Fragility Ratio indicator is created and made available for use by financial analysts, investors, and journalists on TradingView. METHOD: The Fragility Ratio is proposed as a new metric to evaluate the fragility of returns in the stock market. It is based on the Sharpe and Sortino ratios, and the Fragility Cost of Return is introduced to measure the cost of fragility for obtaining 1% of return. The Fragility Ratio indicator is created using code from Claude AI and made publicly available on TradingView for financial professionals to use in their reports and articles. KEY FINDINGS: - The Fragility Ratio measures the fragility of returns in terms of price and volume divergence. - The Fragility Cost of Return quantifies the cost of fragility for obtaining 1% of return. - The Fragility Ratio and Fragility Cost of Return provide insights into how much return an asset delivered per unit of fragility, specifically in the two divergent regimes of volatility. IMPLICATION FOR TRADING: The Fragility Ratio and Fragility Cost of Return can help traders and investors better understand the fragility of their returns and the costs associated with it. By identifying and managing fragility, traders can potentially improve their investment decisions and reduce the risk of losses. These metrics can be used to evaluate the performance of different investment strategies and identify opportunities where returns are obtained with less fragility. Additionally, the Fragility Ratio indicator can be used by financial professionals to provide insights to their clients and improve their reporting on investment performance.

SUMMARY: This study examines the trading behavior on Kalshi, a CFTC-regulated perpetual futures exchange, focusing on the impact of fees and incentives on trading volume. The analysis covers the first six months of trading, from June to December 2026, and uses Kalshi's public trade data and CFTC filings to document the presence of a fixed-size flow in BTC and ETH contracts. The study reveals that 39% of BTC and 48% of ETH notional volume is executed in resting quotes of a fixed dollar size, which are fully absorbed by orders of the same size. The paper also documents the changes in the fee schedule and incentive program, which have no apparent effect on the fixed-size flow. The study concludes that the fixed-size flow is not driven by trading activity, but rather by the cost-effectiveness of producing volume. METHOD: The study reconstructs the fee and incentive history from Kalshi's CFTC filings and uses the public trade data to document the fixed-size flow in BTC and ETH contracts over the first six months of trading. The methodology includes analyzing the raw trade data to compute the volume and dollar size of the fixed-size flow, as well as testing the flow against the changes in the fee schedule and incentive program. KEY FINDINGS: - 39% of BTC and 48% of ETH notional volume is executed in resting quotes of a fixed dollar size. - The fixed-size flow is present from the first week of trading and has changed its dollar size, intensity, and quote-replenishment cycle. - The fixed-size flow is insensitive to changes in the fee schedule, as its gross edge remains stable. - The fixed-size flow is not influenced by identifiable latency arbitrageurs, as volume from this source does not change in response to fee changes. - The gold and silver contracts listed on Kalshi show no fixed-size flow, indicating that the fixed-size flow is specific to BTC and ETH contracts. IMPLICATION FOR TRADING: The findings suggest that the fixed-size flow on Kalshi is not driven by trading activity, but rather by the cost-effectiveness of producing volume. This implies that traders are not engaging in wash trading or exploiting market inefficiencies, but rather are executing trades in a cost-effective manner. For practitioners, this suggests that the fixed-size flow may not be a significant factor in trading decisions and that other factors, such as market conditions and liquidity, may be more important. Additionally, the lack of fixed-size flow in gold and silver contracts suggests that the fixed-size flow is specific to BTC and ETH contracts and may not be applicable to other markets.

SUMMARY: This study examines whether board co-option, which refers to the appointment of directors during the incumbent CEO's tenure, is associated with future stock price crash risk. The research also investigates whether CEO-director appointment ties matter beyond directors' formal independence status. The study uses a sample of U.S. firms from 1996 to 2019 and finds that board co-option is positively associated with future stock price crash risk. Co-opted non-independent directors exhibit a stronger positive association with crash risk than co-opted independent directors. Non-co-opted independent directors are associated with lower crash risk. The findings suggest that formal independence may moderate the monitoring implications of CEO-director appointment ties without eliminating them. METHOD: The study uses a sample of U.S. firms from 1996 to 2019 and examines the association between board co-option and subsequent crash risk. Additional analyses distinguish directors by co-option and formal independence status and account for CEO tenure, CEO and firm characteristics, and external governance. The study accounts for CEO tenure, additional CEO and firm characteristics, dedicated institutional ownership, and the market for corporate control. KEY FINDINGS: - Board co-option is positively associated with future stock price crash risk. - Co-opted non-independent directors exhibit a stronger positive association with crash risk than co-opted independent directors. - Non-co-opted independent directors are associated with lower crash risk. - The association between board co-option and crash risk persists after accounting for CEO tenure, additional CEO and firm characteristics, dedicated institutional ownership, and the market for corporate control. IMPLICATION FOR TRADING: The findings suggest that formal independence may moderate the monitoring implications of CEO-director appointment ties without eliminating them. This research has implications for corporate governance and stock price stability. For practitioners, these findings highlight the importance of monitoring and oversight within the board, particularly for co-opted directors who may be less willing to challenge the CEO. Understanding the impact of board co-option can help in managing potential risks and enhancing stock price stability.

SUMMARY: This post-consultation position paper discusses the need for an infrastructure to attribute decisions made by autonomous financial agents (AFAs). The paper identifies a gap in the Financial Stability Board's (FSB) June 2026 consultation on Sound Practices for Responsible AI Adoption, which requires that AFAs have defined accountability structures, integrated risk management, and human-in-command oversight. However, the paper notes that the consultation does not specify the technical substrate that would make these requirements auditable. The authors propose a three-component technical framework, grounded in W3C open standards, to address the gap in infrastructure. The framework includes W3C Decentralized Identifier and Verifiable Credential specifications for agent identity and delegation-chain attestation, and a 13-field Calibration Reporting Standard (CRS) for privacy-preserving verification of calibration governance disclosures. The authors argue that without a protocol for how the record is structured, signed, and transmitted, "human-in-command" governance is merely a policy intention rather than an operational control. METHOD: The authors analyzed thirteen regulatory documents from six geographic jurisdictions to confirm that Multi-Agent Attribution, the specification of accountability for delegated authorization chains, scores 0/12 across all Category A governance frameworks. The proposed framework, AIF v0.1, addresses authorization-chain attribution through these three components. The authors also grounded the framework in practitioner evidence from three operational contexts: autonomous content policy enforcement at Meta, e-invoicing attribution infrastructure at a digital asset infrastructure organization in Abu Dhabi, and autonomous fraud and Sybil detection at OKX. An implementation path for FSB member jurisdictions is proposed, with a proportionality mechanism tied to the materiality assessment framework in Sound Practice 5. KEY FINDINGS: - The FSF's June 2026 consultation on Sound Practices for Responsible AI Adoption requires that AFAs have defined accountability structures, integrated risk management, and human-in-command oversight, but does not specify the technical substrate that would make these requirements auditable. - A proposed three-component technical framework, grounded in W3C open standards, addresses the gap in infrastructure. - The framework includes W3C Decentralized Identifier and Verifiable Credential specifications for agent identity and delegation-chain attestation, and a 13-field Calibration Reporting Standard (CRS) for privacy-preserving verification of calibration governance disclosures. - The proposed framework scores 0/12 across all Category A governance frameworks for Multi-Agent Attribution. IMPLICATION FOR TRADING: The proposed framework addresses the gap in infrastructure for attributing decisions made by autonomous financial agents, which is critical for ensuring accountability in financial regulation. The framework is grounded in practitioner evidence and is proposed for implementation in FSB member jurisdictions. The authors suggest a proportionality mechanism tied to the materiality assessment framework in Sound Practice 5. This framework could be used by financial institutions and regulators to ensure that AI agents are operating within their authorized scope and to maintain transparency and accountability in financial transactions.

SUMMARY: This paper develops a forecasting framework for realized volatility (RV) using the fractional Ornstein-Uhlenbeck (fOU) process, which is a continuous-time model. The authors estimate the fOU process by approximate Whittle maximum likelihood (AWML) with a modified Paxson spectral kernel, and couple the estimates with a truncated causal best linear predictor (fOU-BLUP) for direct multi-step means. The study compares fOU-BLUP against HAR, free-d ARFIMA, and GARCH models on a panel of liquid high-frequency equity assets. The results show that fOU-BLUP enters the 90% model confidence set against HAR, free-d ARFIMA, and GARCH at the horizons studied, with the clearest MSE gains away from the most heavily mined mega-cap names. The authors also provide an out-of-sample map from residual-scale forecasts to level-scale variance, so that MSE and Patton QLIKE can be read jointly while accounting for loss asymmetry and exponential transformation curvature. METHOD: The authors use a panel of liquid high-frequency equity assets as the empirical test bed. They estimate the fOU process by approximate Whittle maximum likelihood (AWML) with a modified Paxson spectral kernel. The estimates are coupled with a truncated causal best linear predictor (fOU-BLUP) for direct multi-step means. The study uses a rolling causal horse race design to compare fOU-BLUP against HAR, free-d ARFIMA, and GARCH models. KEY FINDINGS: - fOU-BLUP enters the 90% model confidence set against HAR, free-d ARFIMA, and GARCH at the horizons studied. - fOU-BLUP provides the clearest MSE gains away from the most heavily mined mega-cap names. - The residual-to-level map links linear L2 optimality on log-scale residuals to Patton QLIKE evaluation on the original realized-volatility level. - The study provides an out-of-sample map from residual-scale forecasts to level-scale variance. IMPLICATION FOR TRADING: The findings suggest that encoding spectral roughness in a discrete linear predictor can improve the accuracy of real-time volatility forecasting. This approach could be used by execution desks to schedule child orders and by risk management desks to construct return-density and Value-at-Risk calculations. The study also highlights the importance of using a fully causal out-of-sample design for forecasting, as opposed to regional case studies.

SUMMARY: This study examines the effectiveness of traditional econometric models like ARMA, GARCH, and EGARCH in forecasting the implied volatility of crypto derivatives when historical data is limited. The research compares these models with machine learning approaches, including LASSO and Gradient Boosting, which utilize additional features like returns and rolling volatility. The findings show that simpler models like ARMA and LASSO outperform GARCH-based models, which struggle with limited data. Machine learning models demonstrate competitive performance, highlighting their adaptability to the unique characteristics of cryptocurrency markets. METHOD: The study uses a sample of crypto derivatives and compares traditional econometric models with machine learning approaches, including LASSO and Gradient Boosting, which utilize additional features like returns and rolling volatility. The data used for the analysis includes historical data on crypto derivatives and market returns. KEY FINDINGS: - Traditional econometric models like GARCH struggle with limited historical data. - Machine learning models, such as LASSO and Gradient Boosting, outperform GARCH-based models. - Machine learning approaches demonstrate competitive performance in forecasting crypto derivatives volatility. - The findings suggest that specialized models are necessary to handle the unique and highly volatile nature of cryptocurrency markets. IMPLICATION FOR TRADING: The findings suggest that traditional econometric models, such as GARCH, may not be reliable for forecasting the volatility of crypto derivatives, especially when historical data is limited. Practitioners should consider using machine learning approaches, such as LASSO and Gradient Boosting, which can adapt to the unique characteristics of cryptocurrency markets. This can help improve the accuracy of volatility forecasting for crypto derivatives, which is crucial for managing risks and making informed investment decisions.

SUMMARY: This paper applies Value at Risk (VaR) and Expected Shortfall (CVaR) to a portfolio of four large-cap technology stocks: Apple (AAPL), Alphabet/Google (GOOGL), Microsoft (MSFT), and NVIDIA (NVDA). The actual daily price data from Yahoo Finance was used, corrected for stock splits and dividends. The data covers 751 trading days from January 2, 2022, to December 31, 2024. The study uses historical simulation to estimate VaR and CVaR, with an equally weighted portfolio of $25,000 in each stock. The 1-day VaR at 95% confidence is $3,139.14, and the CVaR is $4,101.54. The paper highlights that VaR only indicates the loss threshold, while CVaR provides insight into the severity of losses beyond that threshold. The study also examines the diversification benefits of holding all four stocks together, finding a 10% reduction in risk. The findings are discussed in the context of real market conditions, which are often volatile and non-normal. METHOD: The study uses historical simulation to estimate VaR and CVaR. The data consists of actual daily price returns for the four technology stocks, corrected for stock splits and dividends. An equally weighted portfolio of $25,000 in each stock is used. The VaR and CVaR are calculated over a period of 751 trading days from January 2, 2022, to December 31, 2024. KEY FINDINGS: - The 1-day VaR at 95% confidence is $3,139.14. - The 1-day CVaR at 95% confidence is $4,101.54. - NVDA carries nearly twice the tail risk of AAPL or MSFT. - Holding all four stocks together provides a 10% reduction in risk. - The 10-day VaR at 95% confidence is $9,926.83. IMPLICATION FOR TRADING: The study's findings provide valuable insights for risk management in technology equity portfolios. The gap between VaR and CVaR highlights the importance of understanding both the threshold of potential losses and the severity of those losses. Practitioners can use this information to better manage risk, particularly in volatile and non-normal market conditions. The diversification benefits of holding a portfolio of multiple stocks are also important to consider, as they can significantly reduce overall risk.

SUMMARY: This research paper examines the role of chonsei, a popular rental lease arrangement in Korea, from the perspective of landlords as "gap investors." The paper argues that chonsei promotes investment by incorporating a landlord default option into the lease. The authors use cointegration models to show that housing starts in Korea depend on rental lease market conditions. The paper suggests that landlords can use the default option to delay investment decisions, bringing forward the optimal investment timing. The study contributes to the understanding of how rental lease arrangements can be used as a financing vehicle for investments, particularly in the context of housing. METHOD: The study employs cointegration models for housing starts in Korea from 2005 to 2023. The authors treat a rental lease contract as a financing vehicle for investments, incorporating a landlord default option into the lease. The model is derived from backward induction strategy, where the landlord first earns the right to default at contract maturity and then determines the optimal timing for investing in a housing unit through the defaultable lease. The study supports the theoretical model with empirical evidence from housing starts data. KEY FINDINGS: - Chonsei, a popular rental lease arrangement in Korea, can be viewed as a financing vehicle for investments. - The presence of a landlord default option in the lease structure can expedite investment timing for landlords. - Housing starts in Korea are influenced by rental lease market conditions. - The study provides empirical support for the theoretical model using cointegration models for housing starts data. IMPLICATION FOR TRADING: The findings suggest that rental lease arrangements can be used as a financing vehicle for investments, particularly in the housing market. This can be useful for investors who are looking for alternative financing options. The implication for traders is that they may consider using rental lease arrangements as a way to finance investments in real property, such as housing. However, traders should be aware of the potential risks associated with defaulting on the lease, as this can affect their investment strategy.

SUMMARY: The article investigates the relative predictive role of idiosyncratic and systematic jump risks in forecasting realized volatility across major U.S. equity market sectors. The study compares linear benchmark models with a broad set of machine learning methods, finding that selected machine learning models improve forecasting accuracy relative to the High-Frequency ARCH (HAR) model. Feature importance analysis indicates that systematic jump risk contributes more strongly to volatility predictability than idiosyncratic jump risk. The authors propose novel regime-dependent predictors derived from a Markov regime-switching framework and incorporate them into the learning process, leading to a marked improvement in forecasting performance, particularly for volatility direction prediction. The findings suggest that integrating jump-based feature engineering and regime-dependent information within machine learning frameworks yields more accurate and robust volatility forecasts. METHOD: The study conducts a comprehensive out-of-sample comparison of linear benchmark models and a broad set of machine learning methods. Feature importance analysis is used to assess the relative predictive importance of systematic co-jump and idiosyncratic-jump measures in volatility forecasting. Novel regime-dependent predictors derived from a Markov regime-switching framework are incorporated into the learning process. KEY FINDINGS: - Machine learning models improve forecasting accuracy relative to the HAR model. - Systematic jump risk contributes more strongly to volatility predictability than idiosyncratic jump risk. - Novel regime-dependent predictors lead to improved forecasting performance. - The integration of jump-based feature engineering and regime-dependent information yields more accurate and robust volatility forecasts. IMPLICATION FOR TRADING: The findings suggest that practitioners can improve their volatility forecasting models by incorporating jump-based features and regime-dependent information into machine learning frameworks. This can lead to more accurate and robust forecasts, which are essential for risk management, portfolio allocation, and derivative pricing in financial markets. By understanding the distinct sources of risk and their predictive effects, traders can make more informed decisions and potentially enhance their trading strategies.

SUMMARY: This research article examines how households form and assess their inflation expectations following major macroeconomic disruptions in twelve euro-area economies from 2004 to 2025. The study uses a large household-level dataset to analyze the accuracy and formation of inflation expectations in response to adverse events. The key finding is that inflation expectations tend to overestimate inflation, with households overestimating inflation following adverse events. The authors attribute this overestimation to households' tendency to overreact to inflationary news and to behavioral biases in response to adverse non-inflationary shocks. METHOD: The researchers use a large household-level dataset for twelve euro-area economies. They identify major macroeconomic disruptions using stock market volatility, which serves as a mirror reflecting large adverse events. The study focuses on how these disruptions affect inflation expectations, with a particular interest in the accuracy and formation of these expectations. The researchers capture several major adverse events, such as the Global Financial Crisis, the Covid Pandemic, and the Russian Invasion of Ukraine, and analyze their impact on inflation expectations. KEY FINDINGS: - Following adverse events, the degree of inaccuracy in inflation expectations typically increases. - Inflation expectations tend to rise relative to 12-month ahead inflation realizations, suggesting households overestimate inflation. - The study finds evidence of households overreacting to inflationary news and exhibiting behavioral biases in response to adverse non-inflationary shocks. IMPLICATION FOR TRADING: The findings have significant implications for monetary policy, particularly in a monetary union. The updating of inflation expectations during disruptive episodes could lead to cross-country heterogeneity, potentially impairing the effectiveness of monetary policy. Practitioners can use these insights to better understand and predict inflation expectations, which can inform their trading strategies. For example, traders might adjust their positions based on the expected impact of adverse events on inflation expectations, potentially leading to more accurate and profitable trading decisions. Additionally, the study's findings can help central banks design more effective communication strategies to manage inflation expectations and maintain monetary policy effectiveness.

SUMMARY: This paper investigates whether carbon allowance prices (EUA) and energy futures (TTF gas, German power, Brent, and WTI) co-jump at high frequency around the 2026 Strait of Hormuz closure. The authors use univariate jump detection, co-exceedance and common-jump tests, and bivariate Hawkes models to analyze one- and five-minute futures data from July 2025 to July 2026. The key finding is that carbon almost never co-jumps with gas or power, but its only reliable discontinuous link is through crude oil, which is stable across regimes. The authors suggest that the widely discussed 2026 decoupling was real but due to a diffusive and compositional channel rather than a jump channel. This study has implications for hedging carbon jump risk and the design and surveillance of the EU ETS. METHOD: The study combines univariate jump detection, co-exceedance and common-jump tests, and bivariate Hawkes models applied to one- and five-minute futures on EUA allowances, TTF gas, German power, Brent, and WTI. The sample covers July 2025 to July 2026, which brackets the 2026 Strait of Hormuz closure. KEY FINDINGS: - Carbon almost never co-jumps with gas or power. - Its only reliable discontinuous link runs through crude oil. - Cross-excitation from oil is stable across regimes. - Carbon's self-excitation triples and its diffusive correlation with oil turns negative. IMPLICATION FOR TRADING: The findings suggest that carbon and energy markets do not co-jump at high frequency, indicating that the carbon market is less sensitive to energy shocks than previously thought. This has implications for carbon traders and risk managers who need to understand the nature of the relationship between carbon and energy markets. The study also highlights the importance of understanding the mechanisms through which carbon prices are affected by energy shocks, which can inform the design and surveillance of the EU ETS.

SUMMARY: This preprint research paper examines whether overconfidence in the ability to detect financial fraud is related to self-reported financial fraud victimization. The study uses data from a nationally representative survey in the U.S. and finds that male, younger, and higher-income respondents are more likely to exhibit fraud detection overconfidence. Respondents who are overconfident in their fraud detection ability are less likely to report losing money to financial fraud. The authors suggest that fraud detection overconfidence may be a new construct to explore the relationship between overconfidence and financial fraud victimization. METHOD: Data were collected from a sample of 1,004 respondents using a mixed-mode, probability-based panel representative of the U.S. adult population. Financial fraud victimization was measured by a question asking whether respondents had lost money due to a financial fraud or scam in the past year. Fraud detection overconfidence was measured using two questions: one subjective and one objective, assessing respondents' confidence in their ability to detect fraud and their likelihood of investing in a hypothetical investment opportunity that mimics investment fraud. KEY FINDINGS: - Male respondents are more likely to exhibit fraud detection overconfidence. - Younger respondents are more likely to exhibit fraud detection overconfidence. - Higher-income respondents are more likely to exhibit fraud detection overconfidence. - Overconfident respondents are less likely to report losing money to financial fraud. - Fraud detection overconfidence may be a new construct to explore the relationship between overconfidence and financial fraud victimization. IMPLICATION FOR TRADING: The findings suggest that overconfidence in financial fraud detection may be a significant factor in financial fraud victimization. This implies that traders and investors who are overconfident in their ability to detect fraud may be more susceptible to falling victim to financial scams. Financial education programs could focus on reducing overconfidence in fraud detection to mitigate the risk of financial fraud victimization.

SUMMARY: This study investigates the stock resilience premium and its underlying determinants in China's capital market. The authors construct a comprehensive resilience index using 32 macro- and micro-level indicators and estimate the resilience betas of individual stocks via rolling-window regressions. The study reveals a significant and economically meaningful resilience premium in the Chinese stock market, robust after controlling for risk factors in both time-series and cross-sectional regressions. The authors conduct conditional portfolio analyses based on firm size, volatility, liquidity, momentum, and valuation to uncover the drivers of the premium. They also examine the state-dependence of the resilience premium, finding that it is predominantly concentrated during periods of high market volatility, elevated investor sentiment, and economic expansion. The study concludes that economic policy uncertainty acts as a critical amplifier, with high-resilience firms outperforming low-resilience firms in high-EPU regimes. The cross-sectional pricing of resilience is found to be highly state-contingent, with markets rewarding idiosyncratic shock-buffering capabilities during escalation regimes. METHOD: The study uses a comprehensive resilience index constructed from 32 macro- and micro-level indicators. Resilience betas of individual stocks are estimated via rolling-window regressions. Conditional portfolio analyses are conducted based on firm size, volatility, liquidity, momentum, and valuation. The resilience premium is examined for state-dependence, and cross-sectional pricing is analyzed for state-contingency. KEY FINDINGS: - Significant and economically meaningful resilience premium in the Chinese stock market. - Resilience premium remains robust after controlling for risk factors in both time-series and cross-sectional regressions. - High-resilience firms outperform low-resilience firms in high-EPU regimes. - Cross-sectional pricing of resilience is highly state-contingent, with robust premium during escalation regimes. IMPLICATION FOR TRADING: The findings suggest that investors should focus on identifying high-resilience firms, as they are likely to outperform low-resilience firms during periods of high market volatility, elevated investor sentiment, and economic expansion. This information can be used to construct portfolios that capitalize on the resilience premium, potentially leading to higher returns. However, investors should also be aware that the resilience premium may become indistinguishable from noise during easing or stable periods.

SUMMARY: This paper investigates how hedge funds' leveraged positions affect the sensitivity of sovereign bond yields to market shocks. By combining granular regulatory data on repo positions with high-frequency monetary policy surprises, the author studies this amplification at the individual bond level. Four main findings emerge: hedge funds add excess volatility to shock days, this additional sensitivity accounts for more than a quarter of the total yield movement, the amplification is two-sided, and it is concentrated among bonds held in directional books. The results suggest that hedge funds' leverage can significantly amplify the response of sovereign bond yields to market shocks. METHOD: The study uses granular regulatory data on repo positions and combines it with high-frequency monetary policy surprises. The author tracks the daily positioning of hedge funds across individual sovereign bonds and uses this data to estimate the additional yield sensitivity that arises when hedge funds are trading a given bond. The micro data is complemented with bond market data to provide a comprehensive view of the effects. KEY FINDINGS: - Hedge funds add excess volatility to sovereign bond yields on shock days. - This additional sensitivity accounts for more than a quarter of the total yield movement. - The amplification is two-sided and operates regardless of whether the shock moves prices in favor of a position or against it. - The amplification is concentrated among bonds held in directional books. IMPLICATION FOR TRADING: The findings suggest that hedge funds' leverage can significantly amplify the response of sovereign bond yields to market shocks. This implies that traders should be aware of the potential for increased volatility in bond yields when hedge funds are active in the market. Practitioners should consider the directional nature of hedge funds' positions when analyzing sovereign bond yields, as this can lead to significant deviations from fundamental value. Additionally, the study highlights the importance of granular data in understanding the impact of hedge funds on bond markets, which can inform trading strategies and risk management practices.

SUMMARY: This paper uses Market Prism's proprietary point-in-time equity panel to examine high-confidence episodes of narrative exhaustion, which are defined as a state transition rather than a directional trading signal. The study examines episodes from March 2, 2026, and conditions subsequent returns on fair-value divergence, narrative verdict transitions, narrative-energy trajectories, and thematic context. The paper finds that deeply undervalued securities exhibit positive subsequent drift, while overvalued securities do not display the same behavior. The study also identifies a narrower overvalued state characterized by persistent narrative risk and collapsing narrative energy, which produces weaker return distributions. The paper concludes that narrative exhaustion marks depletion of an informational state, and subsequent price direction depends on valuation displacement and the structure of what follows. METHOD: The study uses Market Prism’s proprietary point-in-time equity panel to examine high-confidence episodes of narrative exhaustion from March 2, 2026. Subsequent returns are conditioned on fair-value divergence, narrative verdict transitions, narrative-energy trajectories, and thematic context. Episodes are de-duplicated into distinct episodes to ensure uniqueness. The study also isolates different states of overvaluation and undervaluation to analyze their impact on subsequent returns. KEY FINDINGS: - Deeply undervalued securities exhibit positive subsequent drift. - Overvalued securities do not display the same behavior. - A narrower overvalued state characterized by persistent narrative risk and collapsing narrative energy produces weaker return distributions. - Undervalued securities transitioning toward renewed structural support exhibit positive returns. IMPLICATION FOR TRADING: The findings suggest that traders should consider the state of the market when evaluating the potential for undervalued securities to reprice upward. Overvalued securities do not provide reliable short-term trading signals. The study also highlights the importance of understanding the structure of the market, including the narrative and valuation dynamics, to make informed trading decisions. Traders should be cautious when interpreting overvalued exhaustion states, as they may not indicate a bearish market.

SUMMARY: The study aims to explore the relationship between contingent determinants and the adoption of Enterprise Risk Management (ERM) in US banks, with a focus on understanding the dynamics of the ERM-value relationship. The authors aim to assess the correspondence between ERM adoption and five contingent determinants (environmental uncertainty, competition in the sector, company size, company complexity, and board monitoring). The study employs an ERM adoption/maturity score to gather information on risk management and compliance policies from 350 US banks. The findings suggest that the relationship between contingency and ERM adoption is a critical factor in determining value congruence. The implications of these results underscore the necessity for companies to consider contingency factors in ERM adoption and framework as drivers of enhanced opportunities to achieve greater value. METHOD: The study uses an ERM adoption/maturity score to gather information on risk management and compliance policies from 350 US banks. The contingent determinants of environmental uncertainty, competition in the sector, company size, company complexity, and board monitoring are assessed to understand their influence on ERM adoption. KEY FINDINGS: - The relationship between contingency and ERM adoption is a critical factor in determining value congruence. - ERM adoption is influenced by a combination of environmental, competitive, and internal factors. - The ERM-value relationship is contingent upon the presence of certain factors. - Companies should consider contingency factors in ERM adoption and framework to achieve greater value. IMPLICATION FOR TRADING: The findings suggest that incorporating contingency factors into the ERM framework can enhance value creation for financial institutions. Practitioners should consider these factors when implementing ERM strategies to improve their risk management and potentially increase their firm value. Understanding the dynamics of ERM adoption and its relationship to value can help banks and financial institutions make more informed decisions and improve their risk management processes.

SUMMARY: This paper examines the prediction market PredictIt, which is a CFTC-regulated US political prediction market. The authors found that during the 2016-22 period, PredictIt's prices diverged significantly from probabilities, allowing for predictable trading profits even after fees. The market exhibited two well-known biases: short aversion, leading to overpricing of securities, and a longshot bias, where low-probability events were overpriced and high-probability events were underpriced. The authors attribute these biases to regulatory limits on downside risk, particularly the limit of $850 of downside risk per outcome. These limits, which bound the most popular contracts on the 2020 Presidential election, caused traders to take larger positions on low-probability outcomes, contributing to overpricing and longshot biases. The authors argue that these biases undermine the primary goal of prediction markets, which is to aggregate beliefs to yield minimally biased predictions. METHOD: The study uses data from PredictIt, a CFTC-regulated US prediction market focused on political outcomes. The sample includes all available data from 2014 to 2022, and the data covers multi-outcome markets. The authors analyze the prices of securities on PredictIt and compare them to probabilities, as well as to prices in other prediction markets. They also examine the behavior of traders, particularly their tendency to take larger positions on low-probability outcomes due to the $850 downside risk limit. KEY FINDINGS: - PredictIt's prices diverged significantly from probabilities, allowing predictable trading profits even after fees. - PredictIt exhibited short aversion, leading to overpricing of securities. - PredictIt exhibited a longshot bias, where low-probability events were overpriced and high-probability events were underpriced. - These biases were likely the result of regulatory limits on downside risk, particularly the $850 limit per outcome. - The most active markets provided the largest opportunities to profit from overpricing, as they were not offset by other traders using the same strategy. IMPLICATION FOR TRADING: The findings suggest that regulatory limits on downside risk can undermine the ability of prediction markets to accurately price event probabilities. This has implications for traders who rely on prediction markets for arbitrage or other trading strategies. The results also highlight the importance of understanding the regulatory environment in which prediction markets operate, as it can significantly impact their efficiency and effectiveness. For practitioners, the findings suggest the need to be aware of such regulatory constraints and to consider their potential impact on market performance.

SUMMARY: This paper introduces Emotional Regret Expected Utility (EREU) within the framework of Relative Rationality Theory to explain the Allais paradox, a challenge to Expected Utility Theory. The Allais paradox involves individuals preferring a certain gain in the first experiment but choosing a risky lottery in the second, despite the Independence axiom requiring consistent preference rankings. The paper proposes that lottery outcomes are not treated as isolated monetary payoffs but as emotionally adjusted consequences, evaluated through Decision-EAW. Emotional weights are attached to these comparisons, capturing anticipated regret, relief, rejoicing, certainty protection, and life goals. Unlike Rank-Dependent Utility, EREU preserves linear probabilities and uses the alternative lottery as the counterfactual reference for emotionally weighted comparison. The paper suggests that the preference reversal can be explained by the strength of emotionally weighted certainty-zero comparisons relative to high-payoff comparisons. METHOD: The study employs Relative Rationality Theory, which treats lottery outcomes as emotionally adjusted consequences (Decision-EAW) rather than isolated monetary payoffs. The model maintains linear probabilities and uses the alternative lottery as the counterfactual reference for emotionally weighted comparison. The analysis is based on the Allais paradox, where individuals prefer a certain gain in the first experiment but choose a risky lottery in the second, despite the Independence axiom requiring consistent preference rankings. KEY FINDINGS: - Lottery outcomes are evaluated as emotionally adjusted consequences. - Emotional weights are attached to directional counterfactual comparisons. - The preference reversal can be explained by the strength of emotionally weighted certainty-zero comparisons relative to high-payoff comparisons. - EREU preserves linear probabilities and uses the alternative lottery as the counterfactual reference for emotionally weighted comparison. IMPLICATION FOR TRADING: The implications of EREU for trading suggest that traders should consider the emotional impact of outcomes, rather than just the monetary value. This approach can help in understanding and predicting decision-making patterns, particularly in situations where certainty and risk are involved. By incorporating emotional weights into decision-making, traders can better account for the psychological factors that influence choices, leading to more accurate predictions and strategies. Additionally, understanding the role of emotional adjustment can aid in designing more effective risk management and hedging strategies.

SUMMARY: This study examines the persistence and impact of market fragmentation and liquidity in U.S. equity markets, focusing on the decade from 2005 to 2015. The research uses high-frequency data for 1,758 highly traded stocks to document that both the allocation of a stock's trading across venues and its liquidity exhibit long memory processes with common components. Previous econometric methods do not accurately capture the relationship between fragmentation and liquidity due to these properties. The study finds that competition among lit exchanges is associated with tighter spreads at short horizons and wider spreads at longer horizons, with deeper books at every horizon. A stock's order flow migration off-exchange is associated with wider lit spreads and thinner lit depth at every horizon, while market-wide off-exchange migration is associated with deeper books. METHOD: The research utilizes a decade of high-frequency data for 1,758 highly traded stocks to document the persistence of market fragmentation and liquidity. The study employs statistical methods to identify the long memory processes in the allocation of trading across venues and liquidity. The findings are based on the analysis of competition among lit exchanges and the impact of off-exchange trading on liquidity. KEY FINDINGS: - Fragmentation and liquidity allocation exhibit long memory processes with common components. - Competition among lit exchanges is associated with tighter spreads at short horizons and wider spreads at longer horizons. - Off-exchange order flow migration is associated with wider lit spreads and thinner lit depth at every horizon. - Market-wide off-exchange migration is associated with deeper books. IMPLICATION FOR TRADING: The findings suggest that market fragmentation and the persistence of liquidity are critical factors in understanding market microstructure. Practitioners can use these insights to develop strategies that take into account the competitive dynamics between lit exchanges and the impact of off-exchange trading on liquidity. Understanding these relationships can help in optimizing trading strategies and improving market efficiency.

SUMMARY: The article investigates how quickly the ability to predict a volatility regime decays when a forecaster's information is delayed by δ days. Using nine years of daily S&P 500 data, the study evaluates four forecasters against a binary target: whether the realized variance five days hence exceeds its trailing 252-day median. A simple threshold rule, "is volatility above its median now," remains above a constant forecast out to three days of delay against the more demanding of the two constants a forecaster could actually have run. Neither the Yang-Zhang estimator nor a walk-forward HAR classifier is distinguishable from the threshold rule at any delay. The article explains that for a median-split target, under approximate joint lognormality, the threshold rule is the Bayes rule among functions of the delayed state alone. The study concludes that the threshold rule is the best cut on the delayed state, and the negative result is attributed to the target, not the models. METHOD: The study uses daily open, high, low, and close data for the S&P 500 index over nine years. Two volatility proxies are computed over rolling five-day windows: the close-to-close proxy and the Yang-Zhang proxy. The target is defined as whether the close-to-close proxy exceeds its trailing 252-day median. Four forecasters are evaluated, and the forecasters' predictions are compared against the threshold rule. The study avoids look-ahead errors by comparing a delayed observation against a contemporaneous threshold. KEY FINDINGS: - The simple threshold rule remains above a constant forecast out to three days of delay. - Neither the Yang-Zhang estimator nor the walk-forward HAR classifier is distinguishable from the threshold rule at any delay. - For a median-split target, under approximate joint lognormality, the threshold rule is the Bayes rule among functions of the delayed state alone. IMPLICATION FOR TRADING: The study's findings suggest that the threshold rule is the best cut on the delayed state, and the negative result is attributed to the target, not the models. Practitioners should consider the threshold rule as a robust baseline for volatility regime prediction under information delay, as the threshold rule outperforms more sophisticated models. However, practitioners should also be aware that the threshold rule does not provide a ceiling, as it is an approximation rather than a benchmark.

SUMMARY: The article examines the use of breadth (correlations with other markets) and implied volatility from the options market as substitutes for delayed market data in forecasting. The study uses S&P 500 realised variance as a case study, withholding the forecaster's own data for varying periods and measuring the performance of other sources. The findings indicate that a current foreign cross-section returns 71% [59, 81] of what a delay of eleven weeks would have cost, and that an options market on the asset itself makes the cross-section redundant. The study also finds that the substitution rate can be measured before any purchases are made, and that the effect of extra staleness on housing prices is significant. METHOD: The study uses S&P 500 realised variance as a case study, with the forecaster's own data withheld for varying periods (δ days). Other sources are left untouched, and the performance of each is measured. The experiment is conducted where the truth is observable, using a housing-price index panel with similar characteristics to the private equity portfolio described in the introduction. The results are measured on the same set of days as a previous section. KEY FINDINGS: - A current foreign cross-section returns 71% [59, 81] of what a delay of eleven weeks would have cost. - An options market on the asset itself makes the cross-section redundant, but only on the asset itself. - The substitution rate can be measured before any purchases are made. - Extra staleness on housing prices gives 67.8% [45, 92] on the next month's return and 19.0% [6, 31] on a backward-looking variance. IMPLICATION FOR TRADING: The findings suggest that using breadth and implied volatility from the options market can be a useful substitute for delayed market data. Practitioners can use the substitution rate to determine which sources to use and how much they can rely on them. The study's results can inform trading strategies by providing insights into the effectiveness of different substitutes for delayed market data.

SUMMARY: This research revalues U.S. stock market performance and economic growth in ounces of gold, a monetary unit used for five millennia but largely abandoned over the past 50 years. Systematic differences in stock market performance, economic growth, and risk compensation are found when these quantities are revalued in gold. The authors find that when stock market performance and economic growth are revalued in gold, both appear substantially weaker over the past 100 years. The differences are evident in the duration and severity of economic and market downturns, as well as in their subsequent recoveries. The authors use S&P 500 data from 1926 to 2024 to estimate latent expected returns and volatility using a particle filter and maximum likelihood, and measure risk compensation using the price of risk, defined as expected return per unit of risk. The results indicate that the decline in dollar-denominated risk compensation under nominal distortion is consistent with money illusion. The authors decompose inflation to examine the role of unanticipated inflation and find that M2 growth does not directly generate the money-illusion channel. Instead, incorporating money expansion reveals an asymmetric response of risk compensation to inflation surprises. The authors also find that revaluation in gold has implications for the risk-return relationship, with the positive gold-denominated risk-return relationship strengthening under nominal distortion. METHOD: The authors revalue U.S. stock market performance and economic growth in ounces of gold, a monetary unit used for five millennia but largely abandoned over the past 50 years. They use S&P 500 data from 1926 to 2024 to estimate latent expected returns and volatility using a particle filter and maximum likelihood, and measure risk compensation using the price of risk, defined as expected return per unit of risk. The authors decompose inflation to examine the role of unanticipated inflation and find that M2 growth does not directly generate the money-illusion channel. Instead, incorporating money expansion reveals an asymmetric response of risk compensation to inflation surprises. KEY FINDINGS: - The revaluation of U.S. stock market performance and economic growth in gold reveals systematic differences in performance and growth. - The price of risk, defined as expected return per unit of risk, declines under nominal distortion when measured in dollars. - The price of risk remains fairly stable when inflation surprises increase when measured in gold. - The positive gold-denominated risk-return relationship strengthens under nominal distortion. IMPLICATION FOR TRADING: The findings suggest that the decline in dollar-denominated risk compensation under nominal distortion is consistent with money illusion. Practitioners can use this revaluation in gold to better understand the relationship between risk and return, particularly in environments of inflation and monetary expansion. This can help in making more informed investment decisions and potentially improve risk management strategies.

SUMMARY: The paper examines a 147-day period where two large USDC/USDT spot venues, Venue A and Venue B, had different tick sizes. Venue A reduced its tick size from 0.0001 to 0.00001 on April 14, 2026, while Venue B maintained the coarser grid until September 8. The paper measures the cross-venue price difference and its impact on traders. METHOD: The study uses trade tapes from both venues to measure the cross-venue price difference. It also employs a queue-realistic replay strategy to quantify the value of the asymmetry. The data is analyzed over 91 coarse-grid days, with a passive quote on the coarse venue and hedging on the fine venue. KEY FINDINGS: - The cross-venue price difference reached half a basis point 41.5% of the time during the window. - After the window closed, the difference was 0.10% of the time. - The gap was real and large, but untradeable due to the queue rent. - The coarse grid's one-basis-point effective spread was split between makers and price impact. - The larger venue's tick size reduction led to a fall in its realised spread by about twenty times. IMPLICATION FOR TRADING: The study highlights the importance of understanding market microstructure, particularly tick size differences. Traders should be aware of the queue rent and the adverse selection effect on the coarse venue. The findings suggest that even large price differences can be untradeable due to the queue mechanism, which can lead to significant losses. Practitioners should consider the impact of market latency and order book structure when evaluating cross-venue opportunities.

SUMMARY: The article examines a dynamic model of shareholder trading and voting in a population of small investors and a large fund with heterogeneous preferences over Environmental, Social, and Governance (ESG) policies. The study characterizes the term structure and limiting distributions of shareholdings and corporate policy. The findings suggest that it may be difficult to learn about a fund's preference for pro-social policy from static snapshots of its trading and voting. The dynamic term structure must be examined. Moderately pro-social investors tend to hold shares in more pro-social firms and can prevent a highly demanding fund from moving a firm's policy as far as it would prefer. The paper provides a dynamic framework for understanding how trading, voting, and heterogeneous ESG preferences jointly shape corporate policy. METHOD: The study employs a dynamic model where a pool of small investors and a large fund exhibit heterogeneity in balancing firm profit and pro-social concerns. In each period, investors can buy or sell shares in a collection of firms and vote on governance decisions for the firms in their portfolio. The results are analyzed to study the stable, or absorbing, limiting distributions of shareholdings and policy, as well as the equilibrium dynamics (term structure) of the model. KEY FINDINGS: - It may be difficult to learn about a fund's preference for pro-social policy from static snapshots of its trading and voting. - Moderately pro-social investors tend to hold shares in more pro-social firms and can prevent a highly demanding fund from moving a firm's policy as far as it would prefer. - The term structure shows a form of investing in profit when the fund has relatively low assets to maximize its impact later, and then increased consumption of greener policies at the expense of financial return as the fund's wealth rises. - Characterizing how the fund optimally balances these cross pressures allows us to overturn the simple view that responsible investors should always buy green firms and divest from brown firms. IMPLICATION FOR TRADING: The findings imply that inferences about an investor's commitment to pro-social behavior based on a short time series of trading and voting behavior may be biased. The study suggests that the term structure of shareholdings and corporate policy should be examined. Practitioners should consider the dynamic nature of ESG preferences and the potential for large, high-demand funds to influence corporate policy. This can help in understanding the optimal balance between pro-social and financial concerns, and in making more informed investment decisions.

SUMMARY: The study applies the Futureproofing Ratio (FR) framework to 25 ASX-listed non-financial companies in Australia, using inaugural FY2025 AASB S2 climate-related disclosures and sustainability reports. The FR is calculated as 1 + TOR - TRR, where TOR represents monetised positive social and environmental value flows, and TRR represents negative flows. The study introduces a tiered Scope 3 imputation procedure and a four-stream mining-waste classification to price distinct waste streams. The value-weighted FR is -2.029, indicating gross negative externalities amounting to approximately 4.05 times the aggregate financial enterprise value. The study finds a positive correlation between TRR and implied cost of equity, but no specification clears all three pre-stated thresholds. The findings suggest that negative externalities are concentrated in sectors like coal, iron ore, and coal-fired generation, while the healthcare/technology group has positive externalities. KEY FINDINGS: - The Futureproofing Ratio (FR) is calculated as 1 + TOR - TRR, where TOR represents monetised positive social and environmental value flows, and TRR represents negative flows. - The value-weighted FR is -2.029, indicating gross negative externalities amounting to approximately 4.05 times the aggregate financial enterprise value. - The study introduces a tiered Scope 3 imputation procedure and a four-stream mining-waste classification to price distinct waste streams. - The study finds a positive correlation between TRR and implied cost of equity, but no specification clears all three pre-stated thresholds. IMPLICATION FOR TRADING: The study's findings suggest that negative externalities are concentrated in sectors like coal, iron ore, and coal-fired generation, while the healthcare/technology group has positive externalities. This information can be used by investors, superannuation trustees, and policymakers to make more informed decisions about the risks and opportunities associated with different sectors. For instance, investors could use the FR to identify companies with significant negative externalities and avoid them, while positive externalities could be seen as a potential opportunity for investment. The study's FR framework could also be used to inform the development of more robust climate-related disclosures and improve the accuracy of cost of equity estimates.

SUMMARY: This article examines the phenomenon of algorithmic collusion in financial markets using reinforcement learning, specifically Q-learning, to simulate pricing agents in a Bertrand oligopoly. The study reports an independent simulation of Q-learning agents in a Bertrand oligopoly with logit demand and connects the results to competition policy for financial markets. Across 32 duopoly sessions, the mean collusion index is 0.778, with a 95 per cent confidence interval of [0.735, 0.821], and per-firm profits are 40 per cent above the Bertrand-Nash benchmark. Not one session ended at the competitive outcome. The article also conducts a separate experiment on a common price grid, showing that the index falls from 0.807 with two agents to 0.577 with three and 0.471 with four, while the relative profit gain over the competitive benchmark rises from 40 to 69 per cent. The findings suggest that conduct of this kind is not covered by existing competition law, such as Section 1 of the Sherman Act or Article 101 of the Treaty on the Functioning of the European Union. The article concludes by proposing a treatment that isolates the mechanism and points to a remedy a regulator could write down and verify, using the RealPage proposed final judgment of November 2025 as the outer edge of enforcement practice. METHOD: The study used an independent simulation of Q-learning agents in a Bertrand oligopoly with logit demand. The simulation was conducted across 32 duopoly sessions, and the results were analyzed to determine the mean collusion index and per-firm profits. A separate experiment on a common price grid was also conducted to further investigate the phenomenon. KEY FINDINGS: - Across 32 duopoly sessions, the mean collusion index was 0.778 with a 95 per cent confidence interval of [0.735, 0.821]. - Per-firm profits were 40 per cent above the Bertrand-Nash benchmark. - The index fell from 0.807 with two agents to 0.577 with three and 0.471 with four. - Conduct of this kind is not covered by existing competition law, such as Section 1 of the Sherman Act or Article 101 of the Treaty on the Functioning of the European Union. IMPLICATION FOR TRADING: The findings suggest that algorithmic collusion in financial markets is prevalent and can lead to significant profit gains. Existing competition law does not adequately address this phenomenon. The study recommends isolating the mechanism and proposing a remedy that a regulator could write down and verify, using the RealPage proposed final judgment of November 2025 as the outer edge of enforcement practice.

SUMMARY: This study examines whether the timing of quote revisions in financial markets contains information about liquidity stress that is not captured by the magnitude of price movements. The authors develop a subordinated-Lévy framework that separates return variation into a diffusion component and a component associated with irregular business time. They find that clock dispersion, a measure of how unevenly price revisions occur over calendar time, is related to the dispersion of mid-quote revisions and rises sharply around FOMC statements. The response is concentrated in the statement window, while changes are not observed around CPI and employment releases. The study suggests that the timing of quote revisions can provide information about the within-event evolution of market stress that is not conveyed by a single volatility measure. METHOD: The study uses S&P 500 index quotes around stress and policy events and SPY National Best Bid and Offer data from 2007 to 2025. The empirical analysis examines one-minute S&P 500 index bid and ask quotes over 45 trading days surrounding major stress and policy episodes, and uses SPY National Best Bid and Offer data for 779 trading days from 2007 through 2025. The clock-dispersion parameter, v, is used as the central empirical object, and the empirical contribution is to document that clock dispersion is associated with the dispersion of mid-quote revisions and rises sharply during the statement window of FOMC announcements. KEY FINDINGS: - Clock dispersion is related to the dispersion of mid-quote revisions. - Clock dispersion rises sharply around FOMC statements. - Changes are not observed around CPI and employment releases. - Clock dispersion does not forecast liquidity conditions. IMPLICATION FOR TRADING: The findings suggest that the timing of quote revisions can provide information about the within-event evolution of market stress that is not conveyed by a single volatility measure. This information could be useful for traders to understand short-lived episodes of market liquidity stress, particularly around scheduled announcements. However, the forecasting role of clock dispersion remains limited.

SUMMARY: This paper examines the performance of liquidity pools on decentralized exchanges, specifically focusing on the Uniswap v3 protocol. The authors use per-minute swap flows valued against a centralised-exchange reference price to decompose passive returns into fees, realized loss-versus-rebalancing (LVR), and gas costs for 62 Uniswap v3 pools on Ethereum, Arbitrum, and Base over a period of 37 weeks in 2026. The study finds that the fee APR, a commonly used metric for ranking pools, does not accurately reflect the pools' realized net profitability. The authors demonstrate that pools with higher fee APRs tend to have lower realized net returns, indicating that the metric orders pools in the opposite direction of their profitability. The study also reveals that realized LVR and gas costs play significant roles in determining passive returns. The authors conclude that the spatial structure of liquidity does not improve aggregate LVR performance, and that just-in-time liquidity and gas costs are important factors in passive returns. METHOD: The study uses per-minute swap flows for 62 Uniswap v3 liquidity pools on Ethereum, Arbitrum, and Base over 37 weeks in 2026. The data is valued against a centralised-exchange reference price. The passive returns are decomposed into fees, realized loss-versus-rebalancing (LVR), and gas costs. The results are analyzed using statistical methods to test the impact of these factors on passive returns. KEY FINDINGS: - Fee APR does not accurately reflect the realized net profitability of liquidity pools. - Pools with higher fee APRs tend to have lower realized net returns. - Realized LVR and gas costs are significant factors in determining passive returns. - The spatial structure of liquidity does not improve aggregate LVR performance. - Just-in-time liquidity and gas costs are important factors in passive returns. IMPLICATION FOR TRADING: The findings suggest that liquidity providers should consider more comprehensive metrics than just fee APR when evaluating the performance of their liquidity pools. The study highlights the importance of accounting for realized LVR and gas costs in performance measurement. Practitioners can use this information to make more informed decisions about where to allocate liquidity, potentially leading to better performance and reduced losses. Additionally, the results suggest that just-in-time liquidity and gas costs are significant factors that should be taken into account when evaluating the performance of decentralized exchanges.

SUMMARY: This article surveys recent generative-diffusion models of the limit order book and interprets them through a geometric lens. The order book is seen as a spatially-indexed density of resting volume over price, with depth acting as a diffusion coefficient. The article argues that a liquidity collapse is the approach to a degeneration set, where the induced geometry pinches, and the recovery of liquidity is a reverse-time diffusion. The cost of trading across the collapse is the control energy, which diverges as the degenerate direction is crossed. The article collects empirical evidence from the literature on the 2010 Flash Crash, stylized facts about order-book depth and resilience, and recent diffusion-based order-book simulators. It introduces no new pricing models or trading strategies but offers a vocabulary to connect the order-book literature to the degeneration set. METHOD: The article uses the geometric lens of degenerate diffusion to interpret empirical data from the order book literature. It reuses notation and results from companion surveys, including the forward SDE, diffusion matrix, induced metric, degeneration set, and generative diffusion representations. The order book is modeled as a spatially-indexed density over price, with depth acting as the diffusion coefficient. KEY FINDINGS: - A liquidity collapse is the approach to the degeneration set. - The recovery of liquidity is a reverse-time diffusion. - The cost of trading across the collapse is the control energy, which diverges as the degenerate direction is crossed. IMPLICATION FOR TRADING: The article does not offer new trading strategies or pricing models. Instead, it provides a geometric framework to connect the order book literature to the degeneration set. Practitioners can use this framework to understand the geometric implications of liquidity collapse and recovery, and to analyze the cost of trading across such events. This can inform the design of trading strategies and the interpretation of market data.

SUMMARY: This paper explores the concept of shared authority and its role in organizing financial positions, particularly in the context of tokenized financial assets. The authors argue that while tokenized assets can be natively transferable and programmable, the relationships around them are typically coordinated through provider-controlled accounts. The paper investigates the conditions under which a financial position can persist as the common object of accepted claims and service mandates as its uses or operating providers change. The authors define shared authority as bounded powers held by separately identified actors over a continuing position, with relevant state transitions coupled to accepted commitments. The study identifies three consequences of the mechanism: first, in a sequential financing economy with finite dated resources, the authors bound owner value across all policies subject to a specified repayment-source restriction and construct a fully funded arrangement that strictly exceeds the bound under stated conditions. Second, the paper characterizes compatible specialist entry, voluntary formation, and transferable service value around inherited claims. Third, a stock-lending application connects advance acceptance to executable opportunities and derives quantities, rents, and valuation in an established equilibrium. The results identify when authority design expands the useful financial relationships supported by existing capital and how the resulting benefits are divided. METHOD: The study uses a theoretical approach to analyze the concept of shared authority and its implications for financial organization. The authors define shared authority and examine its role in organizing financial positions. The study also includes a theoretical framework for understanding the mechanisms of shared authority and its effects on financial relationships. The authors use a theoretical model to identify the conditions under which shared authority can persist and the resulting benefits. KEY FINDINGS: - Shared authority is defined as bounded powers held by separately identified actors over a continuing position, with relevant state transitions coupled to accepted commitments. - In a sequential financing economy with finite dated resources, the authors bound owner value across all policies subject to a specified repayment-source restriction and construct a fully funded arrangement that strictly exceeds the bound under stated conditions. - The paper characterizes compatible specialist entry, voluntary formation, and transferable service value around inherited claims. - A stock-lending application connects advance acceptance to executable opportunities and derives quantities, rents, and valuation in an established equilibrium. IMPLICATION FOR TRADING: The implications for trading are significant. The study identifies conditions under which shared authority can persist and the resulting benefits, which can expand the useful financial relationships supported by existing capital. This can lead to more efficient and flexible financial arrangements, potentially reducing the need for reconstruction of positions and their inherited terms inside every provider's private perimeter. The findings can inform the design of financial products and the organization of financial markets, leading to more robust and adaptable systems for managing financial positions.

SUMMARY: This research paper explores how large language models (LLMs) can be taught to account for transaction costs in the search for trading signals. The study builds a closed-loop system where an LLM proposes daily equity factor expressions in an AST-validated language, which are backtested and priced with an explicit transaction-cost model. The research aims to identify the effect of including transaction costs in the reward function of the LLM agent. The study uses 21 five-year training/one-year test walk-forward folds, with four seed alphas from distinct predictor categories and three LLM seeds. The findings show that rewarding the agent on gross return alone leads to high turnover and negative cumulative returns, while rewarding the agent on net return (net of transaction costs) reduces turnover and improves the Sharpe ratio. The study concludes that the specification of the reward function is a critical research choice and that including transaction costs in the reward function can significantly impact the agent's findings and the resulting strategy. METHOD: The study employs a closed-loop system where an LLM proposes daily equity factor expressions in an AST-validated language. These expressions are backtested on a reconstructed S&P universe (1999-2026) and priced with an explicit transaction-cost model. The research uses 21 five-year training/one-year test walk-forward folds, with four seed alphas from distinct predictor categories and three LLM seeds. The findings are compared between a baseline agent rewarded on gross return quality alone and an execution-aware agent rewarded net of an explicit turnover and cost penalty. KEY FINDINGS: - Agents rewarded purely on gross return converge to high turnover and produce negative cumulative returns despite positive average gross Sharpe. - Rewarding the agent net of transaction costs reduces turnover and improves the Sharpe ratio. - The specification of the reward function is a critical research choice. - Including transaction costs in the reward function changes what the agent finds and how well it survives cost. IMPLICATION FOR TRADING: The study's findings suggest that including transaction costs in the reward function can significantly impact the agent's findings and the resulting strategy. Practitioners should consider the impact of transaction costs when designing trading strategies based on LLM-generated signals. Incorporating transaction costs into the reward function can lead to more stable and profitable strategies, as evidenced by the improved Sharpe ratio and reduced turnover observed in the study. Practitioners should also be aware that the choice of reward function can lead to different outcomes, and they should carefully consider which reward function best aligns with their trading objectives.

SUMMARY: The study investigates abnormal stock returns (ARs) of five U.S. firms with outstanding arbitration claims against Venezuela, specifically those related to the covert U.S. operation "Operation Absolute Resolve" in 2026. The ARs are analyzed on the day before the public disclosure of the operation, which involved the U.S. capture of then-Venezuelan President Nicolás Maduro. The research finds that these firms exhibited positive ARs, suggesting informed trading. The study uses a four-factor model to compute ARs over a 224-day window, with the exposure-weighted average AR of 3.05% and an equal-weighted average AR of 2.25% being statistically significant at the 5% level. The positive AR–exposure relationship is explored, and the study compares these findings to other research on informed trading around classified government operations. METHOD: The study examines all U.S.-traded firms with documented outstanding Venezuelan arbitration claims, including Halliburton, ConocoPhillips, ExxonMobil, Williams Companies, and Smurfit Westrock. The exposure of each firm is calculated as its outstanding claim value divided by its 2025 market capitalization. ARs are computed using the Carhart four-factor model. The study uses a pre-event cumulative abnormal return to examine the timing of informed trading. The sample is restricted to firms with outstanding claims and excludes those that have been dismissed, divested, or delisted. The study also includes a separate analysis of Chevron, which is examined as an active-operations case. KEY FINDINGS: - Five U.S. firms with outstanding arbitration claims against Venezuela exhibited positive abnormal returns on the day before the public disclosure of Operation Absolute Resolve. - The exposure-weighted average abnormal return (AR) of these firms is 3.05%, and the equal-weighted average AR is 2.25%, both statistically significant at the 5% level. - The positive AR–exposure relationship is suggestive but not statistically significant. - The ARs of these firms are comparable to those on insider-trading days, as reported by the SEC. IMPLICATION FOR TRADING: The findings suggest that informed trading may be detectable from publicly observable events, such as the day before a covert government operation. This could be useful for traders to identify potential informed trading signals in the stock market. However, the findings are based on a small sample size, and further research is needed to confirm the robustness of these results.

SUMMARY: This paper investigates the importance of the loss function in machine learning option pricing, specifically whether a model should be trained on pricing errors or implied-volatility errors. The authors use 8.67 million S&P 500 index-option observations from 1997 to 2025 to estimate the same machine learning model under two loss functions that differ only in the space where the error is measured. The price-space loss function lowers pooled pricing RMSE by 25.3%, nearly twice what the network itself contributes over the parametric benchmark, and attains a lower error in every sample year and on the implied-volatility criterion that its rival optimizes. The study finds that the advantage varies with contract vega but not with market volatility, indicating that the loss function rather than the sample period is the key factor. The authors conclude that the training space is a first-order design choice and that researchers who price options with machine learning are better off measuring the error where the model will be judged. METHOD: The study used 8.67 million S&P 500 index-option observations priced out of sample from 1997 through 2025 to estimate the same machine learning model under two loss functions that differ only in the space where the error is measured. The price-space loss function was used to estimate the model, which lowered pooled pricing RMSE by 25.3% compared to the parametric benchmark. The study found that the advantage of the price-space loss function varied with contract vega but not with market volatility. KEY FINDINGS: - The price-space loss function lowers pooled pricing RMSE by 25.3% compared to the parametric benchmark. - The price-space loss function attains a lower error in every sample year and on the implied-volatility criterion that its rival optimizes. - The advantage of the price-space loss function varies with contract vega but not with market volatility. IMPLICATION FOR TRADING: The study suggests that researchers who price options with machine learning should measure the error where the model will be judged, which is in the price-space. This finding implies that the choice of loss function is crucial for the accuracy of machine learning models in option pricing, and practitioners should consider this when applying machine learning techniques in trading.

SUMMARY: The article discusses the risk-neutral crash frontier, a concept used to characterize the set of probability and loss pairs that one distribution can generate while pricing every quote inside its spread, with depth as their ratio. The authors define 𝑝𝐾and 𝑠𝐾as the probability of a crash and the normalized loss of a put price, respectively. They show that the market prices the product of likelihood and depth, not the factors themselves. The authors characterize the set of probability and loss pairs that one distribution can generate and call its boundary the risk-neutral crash frontier. They use linear programming to trace the frontier and price any portfolio of digital and put payoffs sharply, each bound certified by a static super-replicating portfolio of cash, forward, and quoted options. The authors apply their method to SPX quotes sampled weekly from January 2013 through August 2023. METHOD: The study uses weekly SPX cross sections from 2013 to 2023 to analyze the risk-neutral crash frontier. The authors define the probability and loss pairs based on the risk-neutral measure Q and the terminal index level divided by its forward. They use linear programming to trace the frontier and price any portfolio of digital and put payoffs sharply, each bound certified by a static super-replicating portfolio of cash, forward, and quoted options. They apply their method to SPX quotes sampled weekly from January 2013 through August 2023. KEY FINDINGS: - The risk-neutral crash frontier characterizes the set of probability and loss pairs that one distribution can generate. - The frontier can be traced by linear programming without discretizing the state space. - The depth frontier and its area follow in closed form. - The method can be applied to analyze the risk-neutral crash frontier using SPX quotes sampled weekly from January 2013 through August 2023. IMPLICATION FOR TRADING: The risk-neutral crash frontier provides a framework for understanding the relationship between crash probability and loss, which can be used by risk committees to stress test particular scenarios. Practitioners can use the method to price and hedge claims that pay only in a crash, such as put options. The method can also be applied to analyze the risk-neutral crash frontier using SPX quotes sampled weekly from January 2013 through August 2023. This can help traders and risk committees better understand the market's perception of crash risk and make more informed decisions.

SUMMARY: This paper examines how the physical crash frontier is revealed by observable bid and ask quotes in option markets. The authors characterize the pairs of physical crash probability and expected loss below the crash threshold that these quotes admit. They find that the quotes admit a set of pairs that is convex and can be computed exactly using second-order cone programs at a calibrated risk aversion of two. The authors also note that the quotes beyond the two nearest puts to a 10 percent decline shrink the range of admissible crash probabilities by about 80 percent, yet the upper end remains two to three times its lower end. The paper concludes that a positive floor is a joint statement about prices and a tail restriction, and anything tighter than the frontier is an assumption. METHOD: The authors collect all risk-neutral distributions that price each quote inside its spread, have the forward as their mean, and are supported on a bounded interval. They characterize the sets of crash probability and crash depth that these distributions generate under the risk-neutral measure and call their boundary the risk-neutral crash frontier. They then pass each admissible distribution through the pricing kernel of a power utility investor with relative risk aversion γ, who holds the market. The resulting physical distribution is then used to define the physical crash frontier of the cross section, which separates the crash scenarios the quotes admit from those they rule out. The set is compact and convex, and its support function in any direction is the value of a finite second-order cone program. The frontier is sharp, meaning that every point in it is generated by an admissible distribution, and every point outside it lies at a positive distance from all of them. KEY FINDINGS: - The quotes admit a convex set of pairs (crash probability, expected loss) below the crash threshold. - The quotes beyond the two nearest puts to a 10 percent decline shrink the range of admissible crash probabilities by about 80 percent. - The quotes admit a physical crash frontier that is convex and can be computed exactly using second-order cone programs. - The quotes admit a positive floor, which is a joint statement about prices and a tail restriction. IMPLICATION FOR TRADING: The physical crash frontier provides a clear boundary of physical crash risk that can be inferred from observable bid and ask quotes. Practitioners can use this information to better understand and manage physical crash risk, especially in times of stress. The frontier can help identify the most extreme scenarios that are plausible given the available quotes, and it can be used to evaluate the robustness of different risk management strategies. However, practitioners should be aware that the frontier is not the only possible boundary, and assumptions may be necessary to further refine the understanding of physical crash risk.

SUMMARY: This paper introduces MartingaleONet, a physics-constrained neural operator for real-time stochastic volatility option pricing and volatility calibration. It addresses the computational bottlenecks in traditional methods by mapping PDE parameter functions directly to option surfaces, achieving significant inference speedups over finite difference solvers. The central contribution is a differentiable Martingale Drift Correction Layer integrated into TimeGAN, which ensures the risk-neutral martingale condition and mitigates statistical arbitrage. The paper also derives C1-smooth sensitivity operators using PyTorch Automatic Differentiation, demonstrating that these Autograd Greeks reduce portfolio PnL variance by 59.5% under proportional transaction costs. These contributions enable accurate and efficient real-time option pricing and hedging, with empirical validation on US market ETF data. METHOD: The methodology leverages the Heston stochastic volatility model and employs physics-constrained operator learning to map PDE parameter functions directly to option surfaces. A differentiable Martingale Drift Correction Layer is integrated into TimeGAN to ensure the risk-neutral martingale condition. PyTorch Automatic Differentiation is used to derive C1-smooth sensitivity operators for exact Autograd Greeks. KEY FINDINGS: - MartingaleONet achieves significant inference speedups over finite difference solvers. - A differentiable Martingale Drift Correction Layer ensures the risk-neutral martingale condition. - C1-smooth sensitivity operators derived via PyTorch Automatic Differentiation reduce portfolio PnL variance by 59.5% under proportional transaction costs. - Empirical validation confirms state-of-the-art accuracy and arbitrage-free guarantees. IMPLICATION FOR TRADING: MartingaleONet enables accurate and efficient real-time option pricing and hedging, with applications in dynamic deep hedging and volatility calibration. The use of physics-constrained operator learning and differentiable layers ensures robustness and accuracy, making it suitable for modern high-frequency quantitative trading environments. The derived Autograd Greeks provide precise sensitivity estimates, reducing portfolio PnL variance under transaction costs, enhancing the effectiveness of deep hedging strategies.

SUMMARY: This study examines the market maker's performance on the Derive platform, which matches orders off-chain and settles them on-chain. The analysis focuses on 603,940 trades in BTC, ETH, and HYPE options between January 2024 and September 2026. The study reveals that the market maker loses, on average, 13.05 USDC per contract 30 minutes after a trade, with adverse selection accounting for a significant portion of this loss. The research finds that the maker's loss is concentrated among a few taker wallets, with 10 wallets accounting for 90.5% of the maker's aggregate loss. The study also identifies the importance of counterparty identity in understanding market maker performance, as the maker's loss is not influenced by trade size or sweeps. The findings suggest that the literature has underestimated the importance of adverse selection and counterparty identity in options trading. METHOD: The study uses a large dataset of 603,940 trades in BTC, ETH, and HYPE options on the Derive platform. The dataset includes information on the wallets and subaccounts of both parties, the transaction hash, and the realized profit of each side. The research measures the market maker's performance by calculating the mean markout 30 minutes after a trade, and splits the results by counterparty class. The study also includes fixed effects for instrument-by-day to control for instrument-specific effects. KEY FINDINGS: - The market maker loses, on average, 13.05 USDC per contract 30 minutes after a trade. - Adverse selection accounts for a significant portion of the market maker's loss. - The loss is concentrated among a few taker wallets, with 10 wallets accounting for 90.5% of the maker's aggregate loss. - Trade size and sweeps do not explain the market maker's loss. - The counterparty identity is crucial in understanding market maker performance. IMPLICATION FOR TRADING: The findings suggest that market makers should be aware of adverse selection and the importance of counterparty identity in options trading. The study highlights the need for more research on adverse selection and the impact of counterparty identity on market maker performance. Practitioners can use this information to make more informed decisions about their trading strategies and to identify potential counterparty risks.

SUMMARY: This paper identifies a fundamental error in the foundation of modern option pricing theory, specifically in the derivation of the Black and Scholes (1972, 1973) model. The authors argue that by enforcing a constant risk-free rate on the cross-sectional drift, the model incorrectly treats a random variable as a deterministic constant. The paper demonstrates that this mis-specification leads to severe relative pricing errors, particularly in the tails of the implied volatility distribution. The authors propose a corrected model that treats the hedge as a dynamic random variable, using the Fokker-Planck equation with a variable drift rate that is dynamically derived from the analytical solution. This new model maintains the classical Black-Scholes solution architecture while systematically resolving the cross-sectional strike gradient through structural identity. METHOD: The authors evaluate the foundational derivations of the Black and Scholes model and expose the error in their specification of the cross-sectional drift. They use the Fokker-Planck equation with a constant drift rate equal to the risk-free rate as a starting point, but demonstrate that this approach is incorrect. The authors then introduce the concept of a dynamic hedge as a random variable expectation, and derive a new option pricing model that uses a variable drift rate derived from the analytical solution. This new model maintains the classical Black-Scholes solution architecture while systematically resolving the cross-sectional strike gradient. KEY FINDINGS: - The Black and Scholes model incorrectly treats a random variable as a deterministic constant. - This mis-specification leads to severe relative pricing errors, particularly in the tails of the implied volatility distribution. - The hedge in the Fokker-Planck solution is a random variable expectation. - The new option pricing model uses a variable drift rate derived from the analytical solution, which is dynamically derived from the hedge. IMPLICATION FOR TRADING: The implications for trading are significant, as the new option pricing model provides a more accurate and stable framework for valuing options. Practitioners can use this model to better understand the true risk and return profile of options, which can inform more informed trading decisions. The model's ability to systematically resolve the cross-sectional strike gradient can also help in hedging strategies, as it provides a clearer and more consistent view of the relationship between different strike prices. Additionally, the model's use of a time-varying drift rate can lead to more dynamic and adaptive hedging strategies, as the drift rate can adjust to changing market conditions.

SUMMARY: The study examines volatility retracement and mean reversion within NASDAQ futures markets, focusing on information asymmetry imbalances. The research uses minute-level data from 2025 to analyze three-bar sequences that form imbalances. The study finds that 96.1% of imbalances eventually revert, with a 95% confidence interval. The maximum adverse excursion (MAE) and time to mitigation are analyzed, revealing that 23.4% of imbalances drag through a 1% drawdown. The study also identifies a positive correlation between imbalance formation frequency and VIX levels. The findings suggest that imbalances revert regardless of trend direction, with a high rate of reversion, and that imbalance formation frequency is closely related to volatility. METHOD: The study employs minute-level data from 2025 to analyze three-bar sequences that form imbalances in NASDAQ futures markets. A detection algorithm is developed to identify these imbalances, and the study tracks their formation, characteristics, and eventual mitigation over a full year of data. The analysis focuses on the fraction of imbalances that experience mean reversion, the distribution of time-to-mitigation and maximum adverse excursion, and the relationship between imbalance formation frequency and VIX levels. KEY FINDINGS: - 96.1% of imbalances eventually revert, with a 95% confidence interval. - 23.4% of imbalances drag through a 1% drawdown. - There is a positive correlation between imbalance formation frequency and VIX levels. - Imbalances revert regardless of trend direction, with a high rate of reversion. - Imbalance formation frequency is closely related to volatility. IMPLICATION FOR TRADING: The findings suggest that traders can build a viable, risk-adjusted strategy around imbalances, as they revert regardless of trend direction. The study's results indicate that traders should be aware of the potential for imbalances to revert, and the associated drawdowns, when trading these patterns. The study also highlights the importance of understanding the relationship between imbalance formation frequency and volatility, as this can inform trading decisions.

SUMMARY: This paper examines how regulatory scrutiny affects institutional investors' ability to act as stewards of their portfolio firms. The authors propose "regulatory scrutiny" as a measure of institutional distraction, showing that institutional investors who are distracted by regulatory investigations exhibit lower portfolio trading activities. The paper finds that firms with a larger share of distracted institutions are more likely to increase CEO compensation and pursue value-destroying acquisitions, with underperformance persisting for up to four years. The distraction also carries costs for the institutions themselves, as they are less likely to exit firms after value-destroying actions. The authors argue that while regulatory oversight is crucial for market fairness and stability, it may inadvertently weaken institutions' capacity to monitor corporate behavior. METHOD: The study uses institutional investors' disciplinary interactions with regulatory bodies, specifically registered investment advisors, as a proxy for institutional distraction. The authors analyze data from Form ADV filings, which disclose information about the business operations of investment advisors, including any criminal, regulatory, or civil judicial actions. The sample consists of institutional investors who file Form ADV reports, representing more than 70% of institutional investors. KEY FINDINGS: - Institutional investors distracted by regulatory investigations exhibit lower portfolio trading activities. - Firms with a larger share of distracted institutions are more likely to increase CEO compensation and pursue value-destroying acquisitions. - Firms with distracted institutions exhibit underperformance that persists for up to four years. - Institutions distracted by regulatory scrutiny are less likely to exit firms after value-destroying actions. - Regulatory scrutiny diverts attention from value-relevant information, reducing monitoring effectiveness. IMPLICATION FOR TRADING: The findings suggest that regulatory oversight, while crucial for market integrity, may weaken institutional investors' ability to monitor corporate behavior. This implies that regulators must carefully consider the unintended consequences of oversight, particularly the potential for distracted institutions to engage in governance failures. Practitioners should be aware of the potential for regulatory scrutiny to divert institutional investors' attention, potentially leading to underperformance and value-destroying actions in firms. Regulatory bodies should also consider the importance of maintaining effective oversight while minimizing the negative spillovers on corporate governance.

SUMMARY: This article explores the importance of the prediction target, or label, in stock selection using machine learning techniques. It shows that the choice of labels is crucial and that the distribution of the label matters significantly. The authors decompose the label into location, scale, and shape operators, combine them in a factorial design, and attribute out-of-sample stock-selection performance to each operator. They demonstrate that reshaping the target alone can improve performance, raising the Sharpe ratio from 0.68 to 1.69 for the Gaussianised cross-sectional rank of the same return. The study concludes that the prediction target deserves the same engineering effort as features and models. METHOD: The study uses a fixed investable universe of 1,960 US stocks, 122 firm features, and a fixed model (gradient-boosted tree ensemble). The authors decompose the label into location, scale, and shape operators and evaluate fifteen curated labels, including sector-demeaned, PCA-residual, and Fama-French six-factor (FF6) residual targets. They confirm the conclusions do not depend on the learner by repeating the study under two alternative models (penalized linear regression and feed-forward neural network). The performance differences are attributed to specific axes using various correlation-correction procedures. KEY FINDINGS: - Reshaping the target alone can significantly improve out-of-sample stock-selection performance. - The label explains much more of the out-of-sample variation than the model does. - The label can be decomposed into location, scale, and shape operators. - Sector-demeaned, PCA-residual, and Fama-French six-factor (FF6) residual targets are introduced as new centered labels. IMPLICATION FOR TRADING: The findings suggest that the prediction target should be treated as a critical component in stock selection, similar to features and models. By engineering the target, practitioners can potentially improve their stock selection performance. This work highlights the importance of carefully selecting and normalizing the target variable, which can help mitigate the impact of regime shifts and overweighting towards risky stocks. Practitioners can use these insights to optimize their models and improve their stock selection strategies.

SUMMARY: This paper introduces a new method for valuing poison pills, specifically focusing on their impact on takeover options. The authors incorporate poison pills directly into the valuation of stock-for-stock takeover bids, addressing the dynamic nature of dilution when both firms' share prices are stochastic and the takeover agreement contains optionality, a walk-away barrier, or a collar. The paper connects shareholder rights plans to exchange-option valuation, providing a new dynamic framework that distinguishes the value of individual merger claims from the bidder's aggregate cost of obtaining control. The model covers both flip-in and flip-over poison pills, including NOL pills, and applies to ratio-barrier and collared takeover options. The authors derive closed-form formulas for these structures, ensuring transparency and practical applicability. METHOD: The study uses a combination of theoretical analysis and closed-form formulas to value poison pills within the context of takeover options. The authors consider both flip-in and flip-over poison pills, as well as NOL pills, and apply their models to ratio-barrier and collared takeover options. The models incorporate stochastic share prices and optionality, walk-away barriers, and collars. The valuation framework is based on the Margrabe option structure, which is extended to include knock-in and knock-out provisions written on the ratio of two asset prices. The authors derive formulas for both flip-in and flip-over poison pills, as well as NOL pills, and apply them to the specific takeover option structures. KEY FINDINGS: - The paper provides a new framework for valuing poison pills within the context of takeover options, distinguishing the value of individual merger claims from the bidder's aggregate cost of obtaining control. - The models incorporate stochastic share prices and optionality, walk-away barriers, and collars, providing a more comprehensive valuation of poison pills. - The paper derives closed-form formulas for both flip-in and flip-over poison pills, as well as NOL pills, and applies them to ratio-barrier and collared takeover options, ensuring transparency and practical applicability. IMPLICATION FOR TRADING: The findings of this paper have significant implications for traders and investors involved in mergers and acquisitions. By providing a more accurate valuation of poison pills, the paper offers a clearer understanding of the economic incidence of dilution in stock-for-stock takeover bids. This information can be used to make more informed decisions regarding the use of takeover options and poison pills in merger and acquisition strategies. Additionally, the closed-form solutions developed in the paper can be used to quickly and accurately value these takeover options, aiding in the decision-making process for both buyers and sellers in M&A transactions.

SUMMARY: This paper examines the nature and severity of belief distortions in market reactions to hundreds of economic news events. The authors use a combination of structural estimation and algorithmic machine learning to quantify bias. They find that investors systematically overreact to perceptions about multiple fundamental shocks, which often dampens market volatility rather than amplifying it. The study implies that the stock market can underreact to news, even when investors overreact to all shocks. The authors argue that traditional approaches to measuring overreaction are limited and propose a more comprehensive empirical approach to address these gaps in the literature. METHOD: The authors use a combination of structural estimation and algorithmic machine learning to measure the nature and severity of belief distortions in market reactions to hundreds of economic news events. They analyze a large dataset of news events and investor sentiment to quantify bias and estimate revisions in the representative investor's perceptions about multiple sources of risk. KEY FINDINGS: - Investors systematically overreact to perceptions about multiple fundamental shocks. - Overreaction often dampens rather than amplifies market volatility. - The stock market can underreact to news, even when investors overreact to all shocks. - Traditional regression approaches are limited and do not provide a reliable measure of overreaction. IMPLICATION FOR TRADING: The findings suggest that traditional approaches to measuring overreaction in the stock market may be flawed. Practitioners should consider using a more comprehensive empirical approach to better understand investor behavior and market dynamics. This could lead to more accurate predictions and improved trading strategies. Additionally, the study highlights the importance of considering multiple sources of risk and the potential for underreaction in the stock market, which could have implications for portfolio management and risk assessment.

SUMMARY: This research documents excess volatility in term structure prices that cannot be reconciled with standard asset pricing models, even after accounting for variations in discount rates. The authors compare prices of claims on the same cash flow stream but with different maturities and find that long maturity prices are significantly more variable than justified by the behavior of short maturity prices. The findings are pervasive across various asset classes including equity, currency, credit default swaps, commodities, and inflation. The authors define standard models as those driven by a vector autoregression under the risk-neutral pricing measure, a class of models that encompasses many leading asset pricing paradigms. METHOD: The study uses term structure data to evaluate the internal consistency of prices across different maturities. The authors compare prices of claims on the same cash flow stream but with different maturities, finding significant inconsistencies. They reject internal consistency conditions in all term structures studied, including equity options, currency options, credit default swaps, commodity futures, variance swaps, and inflation swaps. The authors use a vector autoregression under the risk-neutral pricing measure as a standard model. KEY FINDINGS: - Excess volatility is found in term structure prices that is irreconcilable with standard asset pricing models. - Long maturity prices are significantly more variable than justified by short maturity prices. - The inconsistency is pervasive across various asset classes, including equity, currency, credit default swaps, commodities, and inflation. - Standard models are driven by a vector autoregression under the risk-neutral pricing measure. IMPLICATION FOR TRADING: The findings suggest that existing asset pricing models may be inadequate in explaining term structure data, particularly for long maturity prices. Practitioners should be cautious when applying these models to predict future asset prices, as they may not accurately capture the underlying dynamics. Alternative models or adjustments to existing models may be necessary to better describe the term structure data.

TITLE: Validation-Calibrated Risk-Function Ambiguity for Minimax-Regret Portfolio Optimization AUTHORS: Zheqi Fan SUMMARY: This research addresses the issue of validating risk functions in portfolio optimization, particularly focusing on Expected Shortfall (ES). The study introduces a validation-calibrated minimax-regret framework to ensure that portfolio choices are robust against model discrepancies and validation errors. The key is to measure the decision-relevant discrepancy between risk functions, which is invariant to common additive level shifts, and to use a polyhedral ambiguity set over convex risk function aggregations. METHOD: The methodology involves fixed candidate ES risk engines, an independent validation sample, and a pre-specified audit library. The authors form a polyhedral ambiguity set over convex risk function aggregations, which is validated under bounded returns. This approach allows for a high-probability true ES regret certificate that separates model discrepancy, validation error, audit-coverage error, and optimization error. The procedure also permits model-class rejection and admits an exact linear-programming reformulation for moderate ambiguity systems. KEY FINDINGS: - A decision-relevant discrepancy is introduced, which measures distortions in pairwise risk differences and is invariant to common additive level shifts. - A polyhedral ambiguity set is constructed over convex risk function aggregations, providing finite-sample containment under bounded returns. - The framework allows for model-class rejection and admits an exact linear-programming reformulation. - The validation-calibrated minimax-regret framework ensures robust portfolio optimization by separating various sources of error. IMPLICATION FOR TRADING: This research provides a robust framework for validating risk functions in portfolio optimization, which can help traders and portfolio managers make more reliable investment decisions. By ensuring that the optimization process is not overly sensitive to model errors, practitioners can better manage risk and improve the overall performance of their portfolios. The exact linear-programming reformulation also simplifies the implementation of this validation process in real-world trading scenarios.

TITLE: Reading Tea Leaves: Benefits and Costs of Learning about Future Demand Shocks AUTHORS: Not stated SUMMARY: This article explores the effects of learning about future demand shocks in financial markets within the framework of noisy rational expectations equilibrium (NREE). It examines how such information acquisition impacts investor behavior and market outcomes, particularly focusing on the front-running channel and a novel future uncertainty channel. METHOD: The study proposes a model of a competitive CARA-normal economy with overlapping-generation investors. Investors in this model decide how to allocate precision to signals on current dividend innovations, current demand shocks, and future demand shocks, subject to a linear information capacity constraint. KEY FINDINGS: - Investors learn less about current dividend and demand shocks when they have access to information about future demand shocks. - The front-running channel causes investors to front-run future demand shocks, while the future uncertainty channel makes future demand shocks relevant to current market behavior. - Market-wide dividend information acquisition decreases, making dividends riskier and raising risk premiums. - Individual investors benefit ex ante by reducing the conditional variance of future excess payoffs. - Higher heterogeneity in private information increases price volatility and makes prices less informative contemporaneously but more informative in later periods. IMPLICATION FOR TRADING: Traders should be aware that learning about future demand shocks can lead to increased market volatility and less informative prices in the short term. However, it can also provide better ex ante utility by reducing the uncertainty of future payoffs. Practitioners should consider the trade-offs between current and future information when making investment decisions.

TITLE: A Volatility-Based Single Parameter LGD Model AUTHORS: Hank Z. Yang SUMMARY: This paper proposes a new loss given default (LGD) model that links LGD to the default rate through a single parameter, asset volatility. The model is designed for wholesale credit risk management and can be applied to LGD forecasting, stress-testing, and integration into the Basel Advanced Internal Rating-Based (AIRB) framework. It also has potential applications in market risk management, particularly for deriving market-based LGD for regulatory credit valuation adjustment (CVA) under the Basel Advanced CVA capital framework. METHOD: The model is based on a simplified Merton corporate debt model and uses a conditional lognormal distribution to derive LGD. The single parameter, asset volatility, is calibrated using data on probability of default (PD), expected loss (EL), and correlation factor (rho). The model is compared to existing LGD models, such as those proposed by Frye and Tasche. KEY FINDINGS: - The proposed LGD Mapping Function (LMF) is a closed-form equation that simplifies the calibration process. - The model is robust and can be extended to factor in debt seniorities. - The asset volatility parameter has intuitive economic meaning and correlates well with the credit cycle. - The model can be easily implemented in wholesale credit risk management and integrated into the Basel AIRB framework. IMPLICATION FOR TRADING: This model provides a straightforward and efficient way to estimate LGD, which is crucial for risk management and regulatory compliance. Practitioners can use this model to improve their LGD forecasts and stress-testing processes, ensuring better risk assessment and management. The model's simplicity and robustness make it particularly useful for integrating into existing risk management systems.

TITLE: When proxy agreement is not accuracy: Closing-price construction and range-based volatility AUTHORS: Anam Giri and Deeason Sitaula SUMMARY: This study investigates the accuracy of volatility estimators, specifically focusing on the Parkinson estimator and range-based volatility measures. The research aims to understand under what conditions closing-price construction can lead to inaccurate volatility comparisons. METHOD: The study uses a known-target experiment with 40,000 simulated sessions, each containing 195 trades, and a 20% closing window. The authors derive the exact expectation of a squared-return proxy and provide a model-specific correction for finite-grid sampling and Gamma-weighted log-price averaging. They also use Monte Carlo simulations to quantify the error in their estimations. KEY FINDINGS: - Parkinson’s standard-deviation-scale ratio is 1.013 against a squared-return proxy but 0.920 against the latent target. - Holding paths and trades fixed isolates the closing window’s effect. - The findings concern unconditional measurement levels, with Monte Carlo intervals quantifying simulation error. - Agreement between proxies does not necessarily establish the correct latent scale. IMPLICATION FOR TRADING: Practitioners should be cautious when using closing prices for volatility estimation, as the choice of closing window can significantly affect the accuracy of the results. The study highlights the importance of understanding the underlying assumptions and potential biases in volatility proxies. Traders should consider using range-based estimators and Monte Carlo simulations to better capture the true volatility levels.

TITLE: Adverse Commodity Futures Term Structure and Corporate Asset Impairment AUTHORS: Bingxuan Jianga, Jihong Tanga SUMMARY: This study investigates whether an adverse commodity futures term structure predicts asset impairment. Using data from 191 Chinese listed firms in the ferrous-metals supply chain from 2014Q1 to 2024Q4, the research examines the interaction between a one-sided contango measure and predetermined inventory exposure. Firms with greater inventory exposure are more likely to recognize impairment losses as contango intensifies, with this association persisting one quarter ahead and extending to the intensity of positive impairment losses. Lower gross margins and operating cash flows are consistent with an inventory-pressure channel, indicating that futures-curve information identifies reporting risk beyond price volatility. METHOD: The study uses quarterly accounting data combined with daily settlement prices for rebar, iron ore, coking coal, and coke. A directional term-structure measure is defined, and the analysis interacts this measure with predetermined inventory exposure. The sample includes 191 firms over 11 years, with data covering 7,984 firm-quarter observations. KEY FINDINGS: - Adverse contango is positively associated with impairment occurrence among firms with greater predetermined inventory exposure. - The association persists one quarter ahead and extends to positive impairment-loss intensity. - Lower gross margins and operating cash flow are consistent with an inventory-pressure channel. - Basis volatility yields no comparable interaction. - The adverse contango signal is linked to firm-level reporting risk, particularly among financially constrained firms and in rebar and iron ore. IMPLICATION FOR TRADING: Traders and investors should monitor adverse commodity futures term structures, especially in industries with significant inventory exposure, as these can signal potential asset impairment. Understanding these signals can help in making informed decisions regarding risk management and portfolio adjustments.

TITLE: Sentiment classifier disagreement and stock returns: Evidence from CEO earnings calls AUTHORS: Luke Ridewood-Thomson, Yizhi Wang SUMMARY: This study investigates whether disagreement between dictionary and contextual sentiment classifiers in CEO earnings call language is associated with subsequent stock returns. The research uses a disclosure ambiguity index (DAI) constructed from Loughran–McDonald and FinBERT classifications for 1,010 firm-quarter observations from 23 S&P 500 financial firms during 2014–2024. METHOD: The analysis employs Bloomberg earnings call transcripts covering 23 firms over 44 quarters. The DAI measures the proportion of sentences labeled negative by the Loughran–McDonald classifier that FinBERT labels neutral or positive. The study examines both a linear association and an exploratory tail specification. The buy-and-hold abnormal return (BHAR) is used as the dependent variable, calculated as the firm’s compounded price return minus the S&P 500 Financials index return over a 90-day period. KEY FINDINGS: - No significant linear association was found between DAI and 90-day abnormal returns. - The highest DAI decile had lower returns compared to the middle deciles. - After excluding low-denominator observations and redefining the top decile, the estimate became small and insignificant. - The exploratory tail association is sensitive to measurement and threshold choices. IMPLICATION FOR TRADING: The findings suggest that while disagreement between sentiment classifiers in earnings calls may indicate ambiguity, it does not consistently predict future stock returns. Practitioners should be cautious when using such disagreement as a predictive tool, as its effectiveness may vary significantly based on the specific measurement and threshold used.

TITLE: 7488382-real-contracts-under-common-trading-scenarios-a-comparison-framework-with-a-source-linked-pilot AUTHORS: Francisco Matilla Serrano SUMMARY: This paper introduces a framework for comparing real contracts under common trading scenarios, focusing on the stability of contract comparisons across different scenarios. It evaluates contracts based on pass probability, funded conditions, and actual payments, using a detailed contractual atlas and a set of synthetic P&L scenarios. METHOD: The study uses a comprehensive contractual atlas containing 177 product-route-size configurations across 22 providers, with 155 evaluation and 103 funded configurations meeting the necessary criteria. The application simulates end-of-day, intraday, and static loss floors, soft daily loss limits, consistency thresholds, minimum trading days, qualifying days, and coded first-request hurdles under three synthetic P&L scenarios, producing 774 stage-scenario estimates. KEY FINDINGS: - The framework decomposes the primary estimand into pass probability, funded conditions, and actual payments. - It provides an exact pairwise value decomposition, highlighting the impact of access probability, funded opportunity, and entry price. - The study reports 774 stage-scenario estimates, including paired paths, standard errors, and Wilson intervals. - It defines validation and multiplicity rules for named comparisons, ensuring the reliability of the comparison framework. IMPLICATION FOR TRADING: This framework offers traders a robust method to evaluate contract performance under various trading scenarios, aiding in the selection of more favorable contracts. Practitioners can use these insights to optimize their trading strategies and improve their overall financial outcomes.

TITLE: The Effect of AI Adoption on Earnings Quality: Evidence from S&P 500 Technology Firms AUTHORS: Julien Merheb SUMMARY: This study investigates whether the extent of AI-related disclosure in annual reports of S&P 500 technology firms is associated with the quality of their reported earnings. The research aims to understand if firms that extensively discuss AI in their filings tend to report higher-quality earnings. METHOD: The study constructs a balanced panel of S&P 500 technology firms, measuring AI disclosure intensity through keyword-frequency analysis of Form 10-K filings. Earnings quality is proxied by the absolute value of discretionary accruals estimated using the Modified Jones Model, while controlling for firm size, leverage, profitability, growth, and year effects. KEY FINDINGS: - A statistically significant negative association exists between AI disclosure intensity and absolute discretionary accruals. - Firms with more extensive AI-related disclosure tend to report higher-quality earnings. - The findings support the signaling channel explanation, suggesting that AI investment is associated with stronger internal controls and reporting processes. IMPLICATION FOR TRADING: The results imply that investors should consider firms' AI-related disclosures as a potential indicator of higher earnings quality. Auditors and regulators can use these findings to better evaluate the credibility of corporate reporting. Traders may benefit from incorporating AI disclosure intensity as a factor in their analysis, potentially leading to more informed investment decisions.

TITLE: Consumption-Portfolio Choice with Asset Tastes AUTHORS: Holger Kraft and Sirui Zhou SUMMARY: This paper investigates a continuous-time consumption-portfolio choice problem where investors exhibit preferences for certain risky assets, referred to as "asset tastes." The study models a scenario with a risk-free bond and two risky assets, one favored and one non-favored, with both diffusive and jump risk. The authors derive solutions for both finite and infinite horizons and establish the existence and uniqueness of these solutions. METHOD: The research employs a dynamic resource-allocation framework under uncertainty, incorporating preferences for particular assets. The model is solved using stochastic control techniques, and verification theorems are provided to ensure optimality. The study is based on U.S. data for green and brown stocks to illustrate theoretical results. KEY FINDINGS: - Preferences for a particular asset increase exposure to that asset, known as the preference effect. - Demand for the non-favored asset is crowded out, generating a substitution effect. - The sensitivity ("beta") of the favored asset to the non-favored asset determines the strength of the crowding-out effect. - The total risky share increases when this sensitivity is below one and decreases when it exceeds one. - Portfolio outcomes are jointly determined by preference-based and market-based features. IMPLICATION FOR TRADING: The findings suggest that investors' preferences for certain assets can significantly influence their portfolio composition. Traders should consider these preferences when formulating investment strategies, as they can lead to deviations from classical portfolio theory predictions. Understanding the interaction between asset preferences and market dynamics can help in better managing risk and optimizing portfolio returns.

TITLE: Beyond Compute Futures: The Economics of Computational Risk and Customized Derivatives AUTHORS: Peter C. Earle, Ph.D SUMMARY: This paper explores the economic implications of computational capacity as a critical production input, highlighting the increasing uncertainty firms face regarding its price, availability, and technological characteristics. The author argues that while standardized compute futures can address some benchmarkable risks, substantial computational risks remain too heterogeneous for effective exchange-traded hedging. By drawing on the evolution of various derivative markets, the paper develops a framework for understanding when computational risk favors customized bilateral contracts. METHOD: The study draws on the historical development of commodity, electricity, freight, and interest-rate derivatives to establish a framework for understanding computational risk. It examines the potential structures of compute swaps and the benchmarks and market infrastructure required to support them. KEY FINDINGS: - Standardized compute futures can hedge benchmarkable price exposures but fail to address the heterogeneous nature of computational risks. - The division between standardized futures and customized derivatives is determined by the underlying heterogeneity of computational risk. - Compute swaps are necessary for managing specific risks related to hardware, geography, workload, and contractual structure. - Financially settled H100 and B200 rental index futures and GPU compute futures are examples of standardized compute futures. IMPLICATION FOR TRADING: The paper suggests that traders and firms should consider the limitations of standardized compute futures and explore customized bilateral contracts for managing specific computational risks. This approach can help in reducing basis risk and ensuring more tailored risk management strategies.

TITLE: Data-Driven Minimax-Regret Portfolio Optimization under Tail-Risk Ambiguity AUTHORS: Zheqi Fan SUMMARY: This paper addresses the challenge of portfolio optimization under tail-risk ambiguity, where short training samples and competing risk engines can lead to suboptimal decisions. The study proposes a two-sample rule that involves scoring engines on the training fold, auditing relative tail-risk rankings on a held-out sample, and projecting the training mix onto admissible engine mixtures. The rule interpolates between the validated mix and worst-case admissible regret, with a parameter set in advance. METHOD: The methodology involves using a small engine polytope to solve the rule as a linear program by enumerating its extreme points. The authors do not claim a polynomial dual reformulation in general and avoid placing a Wasserstein ball around return laws. The approach aims to bound true Expected Shortfall regret in finite samples and treats an empty admissible set as a certified rejection of the engine library. KEY FINDINGS: - The rule interpolates between the validated mix and worst-case admissible regret. - A high-probability bound on true Expected Shortfall regret is provided, separating model tolerance, validation error, coverage error, and optimization error. - An empty admissible set results in a level-β rejection of the engine library. - The approach does not use a regime-switching Wasserstein program on return laws. IMPLICATION FOR TRADING: This research offers a practical framework for portfolio managers to handle tail-risk ambiguity by incorporating both training and validation steps. By bounding the regret and providing a clear rejection option for the engine library, practitioners can make more robust decisions. The linear programming approach ensures computational efficiency, making it suitable for real-world applications where data-driven optimization is critical.

TITLE: Data Availability Charges and Rollup Fee Expenditure in Ethereum AUTHORS: David Kermaani SUMMARY: This paper examines the impact of Ethereum's Dencun upgrade on data availability charges for rollups, focusing on Arbitrum, Base, Optimism, and zkSync Era. The study compares the observed blob burn against a calldata benchmark during a specific period, identifying significant differences in expenditure and transaction composition. METHOD: The research uses a comparative analysis of fee expenditure before and after the Dencun upgrade. It employs a historical calldata gas schedule to benchmark blob burn and decodes pre-adoption payload gas to identify post-adoption expenditure components. The study also accounts for the fixed-composition and pooled intensity of transactions. KEY FINDINGS: - Blob burn was 97.63% below a calldata benchmark for the observed period. - Nine congestion days accounted for 99.9986% of the observed burn. - Decoded payload calldata accounted for 83.78% of Layer 1 expenditure. - Excluding zkSync, pooled bytes per Layer 2 transaction fell 10.81%, and the fixed-composition index fell 6.91%. - Including zkSync under a minimum-packing assumption, the pooled increase ranged from 3.71% to 9.48%, and the fixed-composition decline ranged from 6.29% to 18.56%. IMPLICATION FOR TRADING: Understanding the specific fee structures and expenditure patterns of rollups can help traders and investors anticipate and manage costs associated with deploying and operating on Ethereum. This detailed analysis provides insights into the economic incidence of the Dencun upgrade, enabling more informed decision-making regarding the use of different rollups.

TITLE: Beta Recall, Alpha Recall, and a Contamination Detector That Needs No Labels∗ AUTHORS: Bach Nguyen, Imperial College Business School, London, United Kingdom SUMMARY: The study measures training-data leakage in large language models (LLMs) used for equity signals by comparing their performance inside and outside their training windows. The research uses a pre-registered monthly grid of 50 US large-cap stocks and finds a significant gap in information coefficient (IC) between the in-window and out-of-window performance of Llama 3.3 70B, indicating leakage. METHOD: The study employs a protocol to measure the leakage by comparing the IC of a recall probe inside and outside the training window. It also uses a dollar-neutral long-short book to assess the practical implications of the leakage. The research does not rely on labeled data, making it a label-free detector. KEY FINDINGS: - The cross-sectional IC of the recall probe is +0.166 inside the training window and -0.019 outside, resulting in a gap of +0.185 (moving-block bootstrap p=0.017). - A dollar-neutral long-short book shows an annualized Sharpe ratio of +2.14 inside the window and +0.40 outside. - The leakage is cross-sectional rather than directional, with the model’s month-level direction call being 0.708 accurate before the cutoff and 0.680 after. - Contamination is detectable through month-to-month repetition of the model’s own ranking, which rises from +0.052 in window to +0.411 out, with a changepoint at 2022-12 (p=0.001). IMPLICATION FOR TRADING: Practitioners should be aware of the potential for training-data leakage when using LLMs for equity signals. The findings suggest that models trained on historical data may retain information that can be exploited, leading to backtest results that may not hold out of sample. Implementing market-neutral strategies or using label-free detectors can help mitigate the risk of leakage, but further research is needed to generalize these findings across different models and datasets.

TITLE: Co-movement Without Leadership: Bitcoin’s Regime-Level Influence on Altcoins, and Why Its Predictability Is Not Profitability AUTHORS: Abdullah, COMSATS University Islamabad, Lahore Campus SUMMARY: This study investigates the relationship between Bitcoin and altcoins, challenging the conventional belief that Bitcoin leads and altcoins follow. Using hourly and 4-hour Binance data from 2024 to 2026, the research finds that while Bitcoin and altcoins exhibit strong contemporaneous co-movement (average hourly correlation of 0.772), Bitcoin's short-term predictive lead is negligible (one-hour-ahead correlation of 0.006). Leadership is instead observed at the trend-regime level, where altcoins that disagree with Bitcoin's regime converge to it within 24 hours at a rate 15.1 percentage points higher than a control group. METHOD: The study employs machine learning techniques, including LSTM, XGBoost, Transformer, and Kolmogorov–Arnold networks, to predict trend regimes. It uses a two-year dataset of Bitcoin and nine major altcoins, applying Holm-corrected Granger tests and walk-forward validation to ensure robustness. KEY FINDINGS: - Bitcoin and altcoins co-move strongly, with an average hourly correlation of 0.772. - Bitcoin’s one-hour-ahead predictive lead is essentially zero, with a correlation of 0.006. - Altcoins converge to Bitcoin’s regime within 24 hours after a regime flip, with a 15.1 percentage point higher follow rate. - The effect is consistent across different exchanges and quote currencies. - Trading on this regime convergence is not profitable, yielding a loss of 0.343% per event after transaction costs. IMPLICATION FOR TRADING: The study highlights that while Bitcoin’s trend regimes influence altcoins, the predictability of these regimes is not profitable due to the speed of market reactions and transaction costs. Practitioners should focus on other indicators or strategies that can capture the slower-moving regime changes, rather than attempting to trade on short-term correlations.

TITLE: Structural Imbalances in the Generative AI Market: Inference Unit Economics, the Aggregate CapEx–Revenue Gap, and a Stylized Liquidity-Depletion Model AUTHORS: Ilya A. Dushin SUMMARY: This working paper examines the financial health and sustainability of the generative artificial intelligence (GenAI) and large language model (LLM) industry as of 2026. The author develops a quantitative framework to assess the internal consistency of the capital-formation cycle by analyzing the cost-to-revenue ratio, the capital expenditure (CapEx) and revenue gap, and the efficiency-adjusted demand identity. The paper aims to determine if the observed expenditure trajectory is internally consistent with the observed revenue trajectory. METHOD: The study uses a bottom-up engineering cost model to estimate the cost-to-revenue ratio for inference of ultra-large models. It also employs a two-equation exponential divergence model to calibrate infrastructure CapEx and end-market AI revenue. Additionally, an efficiency-adjusted demand identity is derived to analyze the relationship between the growth rate of tokens-per-accelerator efficiency and token demand. The findings are combined with a Cash Flow-to-CapEx Sustainability Ratio and publicly disclosed intra-ecosystem investment and procurement commitments to characterize the conditions under which the observed expenditure trajectory ceases to be organically fundable. KEY FINDINGS: - The implied cost-to-revenue ratio for inference of ultra-large models is approximately 3,950× under unbatched single-stream execution, falling to a range of roughly 30×–100× when dynamic batching and low-precision quantisation are accounted for. - An annual funding gap in excess of USD 600 billion is implied by a two-equation exponential divergence model of infrastructure CapEx and end-market AI revenue. - When the growth rate of tokens-per-accelerator efficiency exceeds the growth rate of token demand, net physical accelerator demand contracts, a phenomenon labeled the inverted Jevons regime. IMPLICATION FOR TRADING: The findings suggest that the generative AI market is facing significant structural imbalances, with a large gap between capital expenditure and revenue. This could indicate potential liquidity issues and a need for external financing. Traders should be cautious of the high cost-to-revenue ratios and the potential for liquidity depletion, which could lead to market instability. Understanding these dynamics can help traders anticipate shifts in the market and adjust their strategies accordingly.

TITLE: A Corporate Bond Market in Transition: Trading Activity, Credit Risk, and Issuer Heterogeneity in India AUTHORS: Thadavillil Jithendranathan, University of St. Thomas SUMMARY: This study examines the evolution of the secondary market for corporate bonds in India from January 2023 to March 2026. The research finds that while the number of transactions increased significantly, typical transaction sizes decreased, with trading activity concentrated in specific segments, particularly A/BBB-rated Non-Banking Financial Companies (NBFCs). Credit ratings remain strongly associated with yield spreads, and issuer characteristics and business models provide additional insights into risk pricing. METHOD: The study analyzes exchange-traded Indian corporate bonds, combining transaction data with bond characteristics, credit ratings, issuer classifications, and government bond yields. The sample period was chosen to reflect a contemporary post-pandemic environment, focusing on changes in trading activity without attributing them to specific regulatory events. KEY FINDINGS: - Transaction counts increased sharply, but typical transaction sizes declined. - Trading activity was concentrated among A/BBB-rated bonds issued by NBFCs, particularly Investment and Credit Companies and Micro Finance Institutions. - Credit ratings remain strongly associated with yield spreads. - Previously dormant A/BBB-rated NBFC bonds became more active following large transactions, but this did not lead to systematic yield compression. IMPLICATION FOR TRADING: The findings suggest that increased trading activity and broader secondary-market trading do not necessarily imply uniform liquidity or lower required compensation for risk. Practitioners should consider the credit characteristics of securities, issuer attributes, and risk pricing when evaluating market developments. Understanding these dynamics is crucial for effective trading strategies in the Indian corporate bond market.

TITLE: Moral Inflation in Synthetic Investors: When LLM Personas Replace Heterogeneity with Normative Archetypes AUTHORS: Seung Ho Jeon, Dr. H. Steve Leslie, Dr. Hrishikesh Desai (not stated) SUMMARY: This study examines the validity of using large language models (LLMs) as synthetic investors in financial surveys and experiments, particularly in the context of Islamic finance. The researchers generated responses from seven LLM personas, totaling 176,229 cleaned records, and compared them with 31 human participants using an 11-item Likert instrument. METHOD: The study employed LLMs to simulate investor behavior, with responses from 30,000 personas, and compared them to 31 human participants. The LLMs were benchmarked against human responses on the same instrument, which covered financial ethics and risk. KEY FINDINGS: - Mean alignment between LLM and human responses is high (r=0.854), but there are substantial distributional and structural distortions. - LLM responses show a 0.53 Likert point average moral inflation. - AI response variance is approximately half that of human variance. - Religion effects are amplified, with eta²=0.795 for musharakah endorsement. IMPLICATION FOR TRADING: These findings suggest that while LLMs can provide useful aggregate data, they may systematically overrepresent normative behavior, leading to biased results. Practitioners should be cautious when using LLMs for detailed or individual-level analysis, as they may not accurately capture the full spectrum of human investor behavior.

TITLE: Risk Parity Portfolios with and without Pre-Selection under Different Degrees of Risk Aversion AUTHORS: Valentina Piantoni, Sergio Ortobelli Lozza SUMMARY: This paper investigates the impact of asset pre-selection in Risk Parity (RP) strategies with varying degrees of risk aversion. The authors first demonstrate that as the number of assets increases, Risk Parity portfolios converge towards equally weighted portfolios. This finding supports focusing on small portfolios where RP allocations remain distinct from the 1/N benchmark and are more suitable for investors with transaction costs, limited capital, and monitoring constraints. The study proposes RP portfolio strategies based on coherent Gini-Type risk measures, which incorporate investor preferences through a risk-aversion parameter. Empirical analysis compares random and Sharpe-ratio-based pre-selection methods to assess their impact on RP portfolio performance. METHOD: The research employs both theoretical and empirical methods. Theoretical analysis proves the convergence of Risk Parity weights towards uniform allocation as the number of assets increases. Empirical analysis involves comparing two pre-selection mechanisms: random selection from the Dow Jones Industrial Average and a Sharpe-ratio-based selection, across different degrees of risk aversion. KEY FINDINGS: - Risk Parity portfolios converge towards equally weighted portfolios as the number of assets increases. - Small portfolios are more suitable for Risk Parity strategies due to reduced transaction costs and monitoring constraints. - Gini-Type Risk Parity strategies allow investors to adjust downside-risk exposure based on their risk aversion. - Pre-selection methods, particularly those based on historical Sharpe ratios, can significantly improve RP portfolio performance. IMPLICATION FOR TRADING: Practitioners can use Risk Parity strategies in small portfolios, which are less sensitive to estimation risk and more practical for managing transaction costs. The Gini-Type Risk Parity approach provides a flexible framework for adjusting risk exposure according to investor preferences. Pre-selection based on historical performance can enhance portfolio performance, making Risk Parity a robust tool for risk management and diversification.

TITLE: Endogenous Market Structure: Over-the-Counter versus Exchange Trading AUTHORS: Ji Hee Yoon SUMMARY: This paper investigates the factors influencing traders' preferences for over-the-counter (OTC) or centralized markets, focusing on how trader heterogeneity and information shape market and counterparty choices. The study finds that OTC trading is more attractive when idiosyncratic asset values and private information dominate, as it allows for better learning and liquidity compared to centralized markets. OTC trading can remain advantageous even in competitive markets with lower price volatility. The paper also explores the feedback effects between market size and composition, leading to either single-venue or coexisting OTC and centralized markets. METHOD: The research employs a theoretical framework to model trader behavior and market dynamics. The sample includes various asset classes, and the data are derived from theoretical models and empirical evidence. KEY FINDINGS: - OTC trading is favored when idiosyncratic components dominate asset values and private information is imprecise. - OTC trading can remain attractive in competitive markets with lower price volatility. - Trader heterogeneity significantly influences market structure and counterparty choices. - Feedback effects between market size and composition can lead to single-venue or coexisting OTC and centralized markets. IMPLICATION FOR TRADING: Understanding the conditions under which OTC trading is more beneficial can help traders and market designers optimize their strategies. For practitioners, this research suggests that the choice between OTC and centralized markets depends on the nature of the asset and the information environment. Market regulators may also use these insights to design more effective trading venues and policies that balance the benefits of both OTC and centralized markets.

TITLE: Managerial Beliefs Under Uncertainty: CEO Overconfidence and the Transmission of Economic Policy Uncertainty to Corporate Leverage AUTHORS: Gilmarques Agapito Costa, Tarcísio Pedro da Silva, Zélia Maria da Silva Serrasqueiro Teixeira, Luciana Salles Barbosa SUMMARY: This study investigates the impact of economic policy uncertainty (EPU) on corporate leverage, specifically examining whether CEO overconfidence conditions this transmission. The research integrates macro-financial and behavioral finance perspectives, focusing on emerging markets due to their financial frictions and institutional volatility. METHOD: The study uses an unbalanced panel of 3,764 non-financial companies from BRICS economies over the period 2015-2024, totaling 22,760 firm-year observations. A System-GMM model is employed to address dynamic endogeneity and reverse causality. Overconfidence is measured through a new multidimensional index based on abnormal investment, acquisitions, and earnings optimism. KEY FINDINGS: - The impact of macroeconomic uncertainty on capital structure is heterogeneous and depends on managers' behavioral traits. - Contrary to the traditional amplification thesis, policy uncertainty acts as an external disciplining constraint, mitigating the economic expression of managerial overconfidence by narrowing access to credit. - The results suggest that increased policy uncertainty leads to more cautious financing behavior, particularly in firms with overconfident CEOs. IMPLICATION FOR TRADING: These findings imply that traders should consider the interplay between macroeconomic uncertainty and managerial overconfidence when making investment decisions. Specifically, during periods of high EPU, firms with overconfident CEOs are likely to reduce leverage, which could affect stock prices and credit markets. Traders should monitor EPU indicators and CEO behavior to anticipate changes in corporate financing strategies.

TITLE: Do Hard-to-Trade Stocks Actually Pay Off? AUTHORS: Yonatan Brunshtein SUMMARY: The article investigates whether stocks that are hard to trade perform better than those that are easy to trade, using data from the TSX Venture Exchange over nine years. The author tested five different measures of illiquidity on 1,127 companies and found that four of the five measures indicated that harder-to-trade stocks outperformed easier-to-trade ones, while one measure showed the opposite result. The author attributes this discrepancy to survivorship bias, a distortion that occurs when data only includes companies that are still active. METHOD: The author used three specific measures of illiquidity—Amihud ratio, zero-return days, and the Corwin-Schultz spread—on 1,127 companies over nine years. The data was split to ensure the results were not influenced by the author's biases. An out-of-sample test was conducted to validate the findings. KEY FINDINGS: - Four out of five illiquidity measures indicated that harder-to-trade stocks performed better. - One measure suggested the opposite, likely due to survivorship bias. - The "Q5 minus Q1" spread, which compares the returns of the most illiquid to the most liquid stocks, was consistently positive. - The results were robust, with strong statistical significance. IMPLICATION FOR TRADING: The findings suggest that traders might benefit from focusing on harder-to-trade stocks, as they tend to outperform. However, practitioners should be cautious due to the potential for survivorship bias and should consider using multiple measures of illiquidity to validate their strategies.

TITLE: Local Factors and Return Chasing: A New Phase in Emerging Equity Markets AUTHORS: Jongrim Ha, Daisoon Kim, Inhwan So SUMMARY: This paper investigates the shift in drivers of global equity flows to emerging market economies (EMEs) by analyzing investor-level data from 2004 to 2024. The study uses a dynamic factor model to decompose flows into global (push) and country-specific (pull) components, revealing that local factors have become the primary driver, especially post-COVID. METHOD: The research employs monthly equity fund flow data from the Emerging Portfolio Fund Research (EPFR) global database, covering three periods: pre-Global Financial Crisis, post-GFC, and post-COVID. The analysis focuses on understanding how investor flows respond to domestic return factors across different macro-financial environments. KEY FINDINGS: - Global factors historically dominated capital flows, but local factors have emerged as the primary driver post-COVID. - Local return chasing has increased, with investors becoming more sensitive to local excess returns. - The shift is linked to greater portfolio concentration among EMEs and improved macroeconomic resilience. - Global investors now differentiate more across EMEs based on local return factors. IMPLICATION FOR TRADING: Traders and investors should consider the growing importance of local factors in emerging markets, as global liquidity and risk sentiment may no longer be the primary drivers. This shift could lead to more volatile capital flows and increased opportunities for active return chasing, requiring more nuanced strategies that account for local market conditions.

TITLE: How Do Investment Behaviors Transmit Across Generations? The Effects of Parents and Neighborhoods AUTHORS: Rosa Kleinman, Clara Egehoved Tørsløv SUMMARY: This study investigates how investment behaviors are transmitted from parents to their children, using Danish administrative data and quasi-experimental designs. The research finds that childhood exposure to parents' investment behaviors significantly influences adult investment behaviors. Additionally, exposure to the investment behaviors of peers' parents also plays a role, suggesting that both direct and indirect influences are at work. METHOD: The study employs Danish registry data and two quasi-experimental designs. The first design uses plausibly exogenous variation in the timing of parental separations to estimate the causal effect of parental stock market participation on children's future participation. The second design examines the effect of exposure to peers' parents' investment behaviors through a movers design. KEY FINDINGS: - An extra year of exposure to a parent who participates in the stock market increases the child’s probability of stock market participation by 0.266 percentage points in adulthood. - Children who move between Danish municipalities converge to the average participation probability of their peers' parents, indicating the influence of peer-parents. - There is evidence of substitutability between exposure to own-parents and peer-parents, suggesting that the influence of one channel reduces the effect of the other. IMPLICATION FOR TRADING: Understanding the transmission of investment behaviors can help traders and investors recognize the importance of family and community influences on financial decisions. This knowledge can inform strategies to encourage positive financial behaviors among younger generations and potentially influence investment education programs.

TITLE: Do AI Trading Agents Herd? Experimental Evidence and Financial Stability Implications AUTHORS: Anne Artrip and Seung Jung Lee SUMMARY: This paper investigates whether AI-powered trading agents exhibit herd behavior, a phenomenon where traders follow the crowd rather than relying on their own information. The authors use laboratory-style experiments with large language models to replicate classic studies on herd behavior in trading decisions. Their findings suggest that AI agents rely more on private information than market trends, unlike human traders. However, AI can be guided to herd optimally when explicitly instructed to maximize profits. METHOD: The study employs experimental methods using large language models in a controlled environment, replicating classic studies on herd behavior. The sample includes both AI agents and human traders. Data are collected through simulated trading scenarios to observe behavior under different conditions. KEY FINDINGS: - AI agents rely less on market trends and more on private information compared to human traders. - AI can be induced to herd optimally when explicitly guided to make profit-maximizing decisions. - AI agents inherit some elements of human conditioning and bias. - The results highlight the potential for AI to amplify or mitigate financial instability. IMPLICATION FOR TRADING: The research suggests that while AI can reduce herd behavior by relying more on private information, it can also be manipulated to herd when profit maximization is the goal. This has significant implications for financial stability, as AI's behavior can either stabilize or exacerbate market volatility. Traders and regulators should be aware of these dynamics to better understand and manage the risks associated with AI in financial markets.

TITLE: From Knowledge to Action: Financial Literacy as a Driver of Consumer Financial Behavior in Rangpur City Corporation, Bangladesh AUTHORS: Hasan Maskawath Ahmmed Zidan, Graduate Student, Department of Public Administration, University of Dhaka, Bangladesh SUMMARY: This study investigates the impact of financial literacy on consumer financial behavior in Rangpur City Corporation, Bangladesh, using a mixed-methods approach. The research combines structured surveys, key informant interviews, and in-depth interviews to explore the relationship between financial literacy and financial behavior among residents. METHOD: The study employed a mixed-methods design, including a structured survey of 400 respondents, 5 key informant interviews, and 20 in-depth interviews. The theoretical framework is based on the Theory of Planned Behavior and the Financial Literacy Framework. KEY FINDINGS: - There is a significant awareness-access paradox, with less than 50% of respondents having participated in financial literacy classes. - Formal financial education significantly improves savings rates, comfort with financial risk, investment participation, and responsible debt management. - Financial literacy is influenced by educational qualifications and income levels, with higher educated and higher-income individuals exhibiting better financial behavior. - Structural barriers, such as limited institutional presence, normative behavior, low digital literacy, and language-medium mismatch, hinder the translation of positive financial attitudes into action. IMPLICATION FOR TRADING: Financial literacy programs tailored to local contexts, such as those targeting women and incorporating digital platforms, could enhance consumer financial behavior. Traders and financial institutions should consider integrating financial literacy into their strategies, particularly in secondary cities, to improve overall financial health and stability.

TITLE: Multivariate Stochastic Volatility with Machine Learning-Augmented Regime Detection for Cross-Market Financial Contagion: A Bayesian Nonparametric and Ensemble-Learning Comparative Framework AUTHORS: Joshua Christopher SUMMARY: This paper extends previous research by Christopher (2026) to investigate financial contagion between the S&P 500 and Nifty 50 indices. It introduces a multivariate stochastic volatility (MSV) model jointly estimated with an infinite Hidden Markov Model (iHMM) and an ensemble machine learning classifier, replacing the previous univariate GARCH model and fixed three-regime classification. The study uses a full Bayesian implementation, employing Gibbs/FFBS-estimated MSV and iHMM estimated via truncated Dirichlet-process approximations. METHOD: The research employs a comparative framework, reusing data from the S&P 500 and Nifty 50 from 2007 to 2024. It compares a fixed-threshold GARCH approach with a data-driven, multivariate stochastic volatility model combined with an ensemble machine learning classifier. The methodology includes detailed derivations and convergence diagnostics provided in the appendices. KEY FINDINGS: - A posterior mean correlation of 0.824 was found between the two markets' latent volatility innovations. - Contagion-intensification estimates ranged from -49.5% to +295.7% depending on the estimator, regime-partition choice, sample period, and bias-correction. - Beam sampling recovered 11-12 active regimes, compared to 14-15 under truncated alternatives. - The study documents the sensitivity of correlation-based estimates to methodology and highlights the reliability of bias correction. IMPLICATION FOR TRADING: The findings suggest that financial contagion estimates can be highly sensitive to the chosen methodology, making it crucial for traders to consider multiple approaches and robustness checks. Practitioners should be aware of the limitations of traditional GARCH models and consider integrating machine learning techniques for more accurate and reliable contagion analysis.

TITLE: The sword and the seal: Crypto asset recovery as quasi-legal mobilization under fragmented authority AUTHORS: Ruoran Lai and Zehan Lin SUMMARY: This article explores how legal intermediaries mobilize fragmented authority to recover stolen crypto assets post-hack. It introduces a four-task model of quasi-legal mobilization, focusing on the recovery process from 2016 to 2026, involving 468 major hacks. The study draws on 128 interviews and 4,775 public documents. METHOD: The research employs a mixed-methods approach, combining qualitative interviews with quantitative data analysis. The sample includes 468 major crypto hacks from 2016 to 2026, providing a comprehensive timeline of recovery efforts. KEY FINDINGS: - Legal intermediaries classify harm, translate technical evidence, connect claims to enforcement actors, and sequence forums to facilitate recovery. - Recovery processes are highly dependent on victims' access to institutional leverage and elite intermediaries. - Quasi-legal mobilization reduces legitimacy ambiguity by producing recognition across public and private sites. IMPLICATION FOR TRADING: Understanding the role of legal intermediaries in crypto asset recovery is crucial for traders and investors. This knowledge can help in formulating strategies to protect assets and navigate the complex legal landscape. Traders should be aware of the potential delays and dependencies on intermediaries, which can impact the speed and success of recovery efforts.

TITLE: Strategic Conformity in Financial Markets: Do Crisis Reassurance Statements Coincide with Heightened Market Volatility? AUTHORS: Yunus Emre Gültekin SUMMARY: This study investigates whether crisis reassurance statements issued by firms during acute financial stress paradoxically lead to increased market volatility, potentially signaling to investors that a crisis is imminent. The research applies Wegner et al.'s (1987) ironic process theory, which posits that deliberate suppression of a thought paradoxically increases its accessibility, to an organizational context. METHOD: The study analyzed twelve firms that issued public reassurance statements between 2020 and 2023, categorized into those that subsequently collapsed and those that survived. A volatility ratio was calculated for each firm, comparing the standard deviation of daily returns in the five trading days following the reassurance statement to the 30 trading days preceding it. The results showed a significantly higher post-reassurance volatility ratio for firms that collapsed compared to those that survived. KEY FINDINGS: - Firms that collapsed after issuing reassurance statements had a significantly higher post-reassurance volatility ratio (Mdn = 7.73) compared to surviving firms (Mdn = 2.09). - The effect was consistent across different post-event and baseline windows, with complete rank separation between groups. - Excluding the single cryptocurrency observation (LUNC) did not eliminate the effect, although it attenuated the difference. IMPLICATION FOR TRADING: These findings suggest that crisis reassurance statements may inadvertently signal to the market that a crisis is a live possibility, triggering herd behavior among investors. Traders should be cautious when interpreting such statements and consider the potential for increased volatility. This research underscores the importance of understanding psychological mechanisms in financial markets and highlights the need for firms to be mindful of the unintended consequences of their communications during crises.

TITLE: The Rules Are the Model: Path Dependence, Payout Frictions, and Selection in Synthetic Trading-Evaluation Contracts AUTHORS: Francisco Matilla Serrano SUMMARY: This paper investigates how different contract rules affect the outcomes of synthetic trading-evaluation contracts. The author uses a common-path rule engine to simulate profit-and-loss paths under various conditions, comparing outcomes with and without specific rules. The study extends previous work on participant-value analysis and selection analysis. METHOD: The research employs a counterfactual simulation approach, applying the same standardized profit-and-loss shocks to different contract configurations. The simulation uses six latent trader types, 30,000 antithetic paths per type and stage, and a 20-day horizon. The population includes 20% positive-drift types. The study evaluates two benchmarks and nine counterfactuals, including three synthetic contract bundles. KEY FINDINGS: - Different rules, such as minimum days, daily-loss limits, and trailing drawdowns, significantly alter passing and payout probabilities. - A strict intraday-trailing package reduces passing and payout-event rates. - Positive-drift traders constitute a higher share of passers and payout events in the strictest bundle. - The $1,000 daily-loss limit has the largest effect on pass probability and can reverse participant expected value. IMPLICATION FOR TRADING: Practitioners must consider the full range of contract rules when evaluating trading opportunities. Rules can dramatically change outcomes, even when the underlying market conditions and trader characteristics remain constant. This research highlights the importance of understanding and comparing the complete contract design, not just the nominal account size or target/drawdown ratios.

TITLE: Do Markets Discipline the Adoption of Dangerous Technology? AUTHORS: Roberto C. Gutierrez Jr. and Robert C. Ready SUMMARY: This study examines how financial markets influence the adoption of dangerous technologies, particularly those with increasing disaster risks as their capabilities grow. The research uses a model where disasters impose proportional losses, affecting the relative payoffs and marginal utility of adopting firms. METHOD: The authors develop a fixed-date adoption model based on P´astor and Veronesi (2009), allowing firms to choose when to adopt as they learn about the technology. Numerical results show that common survivable losses accelerate adoption, while adopter-only damage and extinction restrain it. The study also examines the co-movement of technology stock returns with option-implied tail probabilities. KEY FINDINGS: - Disasters that impose equal proportional losses on adopting and non-adopting firms do not directly discipline adoption. - Adopter-only disaster losses significantly discipline adoption by lowering the relative payoff in high-marginal-utility states. - Extinction-level disasters reduce adoption values but have a modest effect due to the use of physical survival probabilities. - Financial markets provide little discipline against adopting technologies with potential for extinction. IMPLICATION FOR TRADING: The findings suggest that traders should monitor option-implied tail risks, especially in sectors adopting potentially dangerous technologies. Increased tail risk may indicate higher adoption of risky technologies, which could impact stock prices and investment strategies. Traders should also consider the potential for market discipline to be weak in the face of high-risk technologies, as financial markets may not fully internalize the associated risks.

TITLE: Crowding in an Artificial World: AI and the Future of Investing AUTHORS: Ludwig Chincarini, Conor Falvey, Fabio Moneta SUMMARY: This study investigates whether independently operating AI agents, given the same data and instructions, would converge on similar investment strategies, which is a critical question for understanding the potential for crowded trades in the future of investing. Four AI agents, built on different platforms, were tasked with designing long-short equity strategies using a standardized library of 209 published return signals. METHOD: The agents were given identical data, instructions, objectives, and binding portfolio constraints. They independently implemented signal eligibility and portfolio construction in isolated workspaces. After a validation step, the strategies were frozen and evaluated over a twelve-year locked test period from January 2013 to December 2024. KEY FINDINGS: - The agents converged on five out of 126 eligible signals, with two signals appearing in every specification. - The average Jaccard overlap of the agents' signal sets was about seven standard deviations above a family-matched selection null. - Holdings and desired trades showed high correlation across agents, with similarity persisting over the twelve-year test period. - Trade synchronization was elevated only under the benchmark preserving each agent's own construction, indicating that implementation choices significantly affect measured crowding. - Gross average returns remained positive for all strategies, and net six-factor alphas were significant at 5% for two of six specifications. IMPLICATION FOR TRADING: The findings suggest that AI-driven strategies may exhibit significant convergence, leading to crowded trades. This could have implications for market efficiency and financial fragility. Practitioners should be aware of the potential for similar strategies to emerge independently, which could amplify market movements. Additionally, the study highlights the importance of understanding the underlying AI models and their implementation choices in shaping strategy outcomes.

TITLE: Fedspeak, LLM-Derived Signals, and High-Frequency Trading AUTHORS: Aihui Wei (Ph.D. Program in Economics, The Graduate Center, City University of New York) SUMMARY: This study investigates how financial markets process incremental information released across Federal Reserve communication channels, particularly focusing on the Communication Divergence Signal (CDS) derived from large language models (LLMs). The research aims to understand the impact of these divergences on intraday asset prices and trading volume. METHOD: The study uses LLMs to construct the CDS, which captures semantic, tonal, and framing shifts across sequential Federal Open Market Committee (FOMC) communications. High-frequency event-study methods are employed to analyze intraday asset price movements and abnormal trading volume in response to these communication divergences. KEY FINDINGS: - Communication divergence generates significant and persistent intraday movements in asset prices and trading volume. - Abnormal trading volume is measured relative to non-announcement benchmarks, isolating excess activity linked to information arrival. - Daily open interest shows systematic responses, indicating belief updating and position reallocation rather than transitory liquidity provision. IMPLICATION FOR TRADING: Traders can use the CDS to identify shifts in the Federal Reserve's communication, which can inform trading strategies. Understanding the impact of communication sequencing and framing can help in predicting market reactions to upcoming FOMC communications, thereby enhancing trading decisions.

TITLE: Do a legislator's trades anticipate legislative acts? Timing, disclosure and proximity tests on the Pelosi household, 2014–2026 AUTHORS: Marco Belardi SUMMARY: This study examines whether the trades disclosed by the household of Representative Nancy Pelosi, as required by the STOCK Act, anticipate legislative acts and generate abnormal returns. The research uses three event-study designs: timing, disclosure, and proximity, on a dataset of 227 transactions over 12 years. METHOD: The study employs event-study designs to analyze the Pelosi household's trades. It uses 65 Periodic Transaction Reports filed between 2014 and 2026, including 117 discretionary events after cleaning, and 68 involving long call options. The tests focus on abnormal returns, market reactions to disclosures, and clustering of trades before relevant acts. KEY FINDINGS: - Timing: Signed abnormal returns after trades are nil at 5, 20, and 60 sessions. Purchases beat the S&P 500 by 15.9% and the Nasdaq-100 by 12.2%, while sold stocks outperformed by 24.5%, but overall, no significant earnings against benchmarks. - Disclosure: No abnormal volume or return when filings become public, including in 2021-2026 when aggregators publish them within hours. - Proximity: Out of 88 act-ticker pairs from 42 federal acts, only 3 trades were preceded by acts within 60 days, compared to 4.4 expected by random dates, and 8 trades were in the opposite direction. IMPLICATION FOR TRADING: The findings suggest that Pelosi's household trades do not exhibit the signature of informed trading. For traders, this implies that legislative acts do not provide a reliable timing signal for market movements. Practitioners should focus on other indicators rather than relying on legislative calendars for trading decisions.

TITLE: Regime-Switching Asset Volatility and Optimal Capital Structure: A Simulation-Based Extension of the Leland Model AUTHORS: Peter Mutua Malonza SUMMARY: This paper extends the Leland model by incorporating regime-switching asset volatility, allowing asset volatility to follow a two-state Markov process. The study uses Monte Carlo simulations to solve for the value-maximizing coupon and compares the results to a constant-volatility benchmark. METHOD: The research employs a Monte Carlo simulation approach, using non-proprietary parameters to model the impact of regime-switching volatility on firm value and default probability. The sample includes a range of coupon levels, and the data is generated through simulations. KEY FINDINGS: - The value-maximizing coupon, and thus the target leverage, is essentially unchanged between the regime-switching and constant-volatility specifications for well-capitalized firms. - For highly leveraged firms already in financial distress, regime-switching volatility reduces firm value and increases default probability. - Regime-switching volatility modestly increases firm value and decreases default probability for coupon levels at or below the value-maximizing level. IMPLICATION FOR TRADING: Practitioners should consider the impact of volatility clustering on capital structure decisions, especially for firms that are already over-leveraged or in financial distress. This research suggests that volatility clustering could be a significant risk factor for such firms, potentially necessitating more frequent monitoring of volatility regime risk.

TITLE: 7479198-inflation-is-economic-reflection-of-portfolio-return-part-ii-laspeyres-index-adj AUTHORS: Victor Olkhov, Independent, Moscow, Russia SUMMARY: This research explores the relationship between portfolio returns and inflation, specifically focusing on the Laspeyres index and its adjustment. The study aims to address the inconsistencies in the current theory and practice of assessing the Laspeyres index and inflation by introducing adjusted inflation and deriving its successive approximations. It resolves the century-old Walsh’s problem regarding the appropriate trade weights in the basket and introduces market-based and basket-based variances of inflation. Additionally, the paper proposes the use of the Sharpe Ratio as a measure to evaluate the assurance of inflation forecasts. METHOD: The study employs mathematical models and economic theories, particularly those related to portfolio returns and price indices. It uses historical data and theoretical frameworks to derive equations for adjusted inflation and to address the Walsh’s problem. The research does not rely on specific empirical data but rather on theoretical constructs and equations. KEY FINDINGS: - The study provides an equation for adjusted inflation and its successive approximations. - It resolves the Walsh’s problem by converting trade weights to match those of the past or current period. - Market-based and basket-based variances of inflation are introduced to describe uncertainties in inflation forecasts. - The Sharpe Ratio is proposed as a measure of the assurance of inflation forecasts. IMPLICATION FOR TRADING: The findings suggest that traders and policymakers should consider both the expected inflation rate and its variances when making decisions. By incorporating these measures, they can better manage risks and uncertainties associated with inflation. This approach aligns with Markowitz’s advice on portfolio selection, emphasizing the importance of forecasting both the mean and the variability of returns.

TITLE: Equity Signals and Mortgage-Backed Security Returns: An AI-Agent-Assisted Systematic Meta-Analysis AUTHORS: Julian Atanassov, Zhipeng Jiang, Yao Ma, Yanan Zhu SUMMARY: This study aims to resolve inconsistencies in the relationship between equity market signals and mortgage-backed security (MBS) returns by conducting a systematic meta-analysis. The research employs an AI agent for literature retrieval, screening, and coefficient extraction, with human oversight ensuring the accuracy of the analysis. METHOD: The study uses an AI agent to gather and analyze data from relevant studies, focusing on coefficients related to equity volatility and MBS returns. The sample includes two studies, and the data spans from October 2003 to December 2022. The key findings are derived from a baseline regression using the VIX index as a measure of equity volatility. KEY FINDINGS: - A one-point increase in VIX raises the MBS convenience premium measured against AAA corporate bonds by 3.207 basis points and lowers the premium measured against Treasury securities by 1.257 basis points, both significant at the 1% level. - The sign of the volatility effect depends on the benchmark asset against which the MBS convenience premium is measured. - The apparent disagreement in previous studies is resolved by coding the benchmark asset as a moderator. - The findings support the safe-asset demand framework, indicating that a flight to safety raises demand for all safe assets, but Treasury securities gain more. IMPLICATION FOR TRADING: Practitioners can use these findings to better understand the impact of equity market volatility on MBS returns. By recognizing the role of benchmark assets, traders can develop more accurate models for hedging and forecasting in the MBS market. This can help in managing risk and improving investment strategies in response to changes in equity market conditions.

TITLE: Bitcoin, Decentralized Finance, and Frontier Markets: A Controlled Analysis of Portfolio Diversification (2020–2026) AUTHORS: Nawar Kanaan Al-Dabbagh, Omar Zuhair Ezlden, Ahmed Abdulkareem Ahmed SUMMARY: This study examines the relationship between Bitcoin returns, decentralized finance (DeFi) sector, and traditional stock market returns, focusing on the Iraq Stock Exchange (ISX) from January 4, 2020, to March 4, 2026. The research uses the autoregressive distributed lag (ARDL) methodology to analyze the impact of these digital assets on traditional markets. METHOD: The study employs the ARDL methodology to assess the short-run and long-run impacts of Bitcoin and DeFi on the ISX. The sample includes data from the ISX over the specified period, with a focus on the interconnectedness of digital assets with traditional markets. KEY FINDINGS: - Bitcoin returns have a statistically significant negative short-run impact on ISX returns, but no significant long-run impact. - DeFi has no significant impact on the ISX, attributed to the novelty of these assets, limited investor awareness, and inadequate digital infrastructure in frontier markets. - Bitcoin may serve as an indicator of global risk trends, given its influence in relatively isolated markets like Iraq. IMPLICATION FOR TRADING: The findings suggest that Bitcoin can act as an alternative asset and liquidity competitor rather than a safe haven or hedging tool in traditional markets. Traders should consider the potential for Bitcoin to influence market dynamics, especially in frontier markets. Additionally, the lack of significant DeFi impact indicates that traders in frontier markets may need to focus more on traditional investment strategies until DeFi becomes more accessible and widely understood.

TITLE: AI model news and relative equity repricing: Evidence from infrastructure and software stocks AUTHORS: not stated SUMMARY: The study investigates whether large returns around AI model news reflect common infrastructure-software repricing or individual constituent-specific movements. Using a frozen external database, the researchers identified 28 language-model records and found that seven two-session spreads exceeded a descriptive volatility threshold. Only two retained the threshold after every single-stock deletion, involving Claude models with opposite performance directions. Two large negative GPT spreads were strongly influenced by software firms reporting earnings. METHOD: The research employed event-study methods to benchmark abnormal performance against historical return variation. The sample included infrastructure and software stocks, with Yahoo Finance adjusted prices covering 1,399 trading sessions from 2021 to 2026. Compustat provided firm-report records, and the main cohort used Epoch AI's Frontier Models CSV, downloaded and frozen on September 6, 2026. KEY FINDINGS: - Seven two-session spreads exceeded a descriptive volatility threshold. - Only two retained the threshold after every single-stock deletion. - Claude 3 Opus and Claude 3.5 Sonnet showed opposite directions of relative repricing. - Two large negative GPT spreads were strongly influenced by software firms reporting earnings. IMPLICATION FOR TRADING: The findings suggest that interpreting AI equity responses requires careful consideration of timing, portfolio legs, and constituent influence. Practitioners should be aware that large returns around AI model news may not necessarily reflect common infrastructure-software repricing, and should consider the specific impact of individual constituents. This research highlights the importance of thorough analysis in making trading decisions related to AI model news.

TITLE: Rolling Shannon Entropy and the Structural Complexity of Bitcoin Market Dynamics AUTHORS: Markoulis S.*, University of Cyprus SUMMARY: This study investigates whether rolling Shannon entropy can capture structural complexity and provide predictive information in Bitcoin's return dynamics. Using normalized rolling Shannon entropy over 15-, 30-, and 60-day windows, the research finds a strong positive association between entropy and rolling volatility, with high-entropy regimes exhibiting higher risk and lower average returns. Correlation and regression analyses show that entropy significantly enhances volatility forecasting models, doubling the explanatory power compared to an AR(1) benchmark. Out-of-sample tests indicate that entropy-augmented models reduce mean squared forecast errors by 5% to 17%, supporting the use of complexity-based measures in financial market modeling. METHOD: The study employs rolling Shannon entropy calculated over different time windows and evaluates its predictive power in volatility forecasting. Correlation and regression analyses are used to assess the relationship between entropy and volatility, while out-of-sample tests are conducted using both recursive estimation and a fixed 2/3–1/3 split. KEY FINDINGS: - High-entropy regimes are associated with higher risk and lower average returns. - Entropy remains strongly associated with forward volatility measures. - Entropy inclusion in volatility regressions approximately doubles explanatory power. - Entropy-augmented models reduce mean squared forecast errors by 5% to 17%. - Tests of predictive accuracy reject the null hypothesis of equal forecast performance. IMPLICATION FOR TRADING: These findings suggest that rolling Shannon entropy can provide valuable insights into Bitcoin's structural complexity, offering traders a tool to better predict market volatility. By incorporating entropy measures into their models, traders can potentially improve their forecasting accuracy and risk management strategies, leading to more informed trading decisions.

TITLE: Market State Entropy: Order-Book Organization, Transition Uncertainty, and Evidence from Event-Driven Trading AUTHORS: William Robert McConnell SUMMARY: This paper extends the framework developed in Paper IX, focusing on the concept of market state entropy to analyze the organization of observable order-book activity and the uncertainty about the book's next state. It introduces a taxonomy of market-state entropy, covering various aspects such as event composition, queue allocation, and directional outcomes. METHOD: The research employs a documentary case series assembled from author-supplied order-book displays and broker transaction images from a manual event-driven trading study. Six instrument examples are examined, including timed sell-then-buy sequences and long trades. The empirical evidence is derived from specific trading records and does not include a complete signal census or estimates of entropy's predictive effect. KEY FINDINGS: - Market state entropy provides a representation for testing, rather than a universal measure of fragility. - The distributional summaries must be interpreted jointly with the geometry and support of the order book. - Equal spatial entropy can accompany very different depths at the touch, and low transition entropy can describe a nearly certain adverse move. - The framework offers a mathematically consistent public measurement system using standard finite differences and forecast-scoring comparisons. IMPLICATION FOR TRADING: Traders can use this framework to better understand the state-dependent response of markets and the uncertainty surrounding future price movements. By analyzing the entropy of the order book, traders can make more informed decisions regarding their trading strategies, particularly in event-driven scenarios. This can help in assessing the resilience of the order book and predicting potential market transitions.

TITLE: Retail investor preferences for green-objective funds with closer impact: role of high and low investment stakes AUTHORS: Gottlieb U.*, Aleo Orcas D., Edenbrandt A.K., Lagerkvist CJ. SUMMARY: This study investigates how retail investors' preferences for green-objective funds vary with the level of investment stakes. Using a discrete choice experiment with Swedish retail investors, the research examines the impact of different fund characteristics, including environmental objectives, expected impact, and management strategies. The findings indicate that investors show substantial interest in green funds, but there is no clear prioritization among environmental objectives or preference for a closer region of operations. Instead, the study finds that the proximity of expected impact matters conditionally, with higher stakes significantly increasing the required compensation for risk without reducing stated willingness to choose green funds. METHOD: The study employs a discrete choice experiment with a split-sample treatment, varying the portfolio share to be reallocated to proposed funds. The funds differ in EU Taxonomy-related environmental objectives, region of operations, expected impact, management strategy, environmental performance information, risk classification, and expected return. The sample consists of Swedish retail investors, and the data are analyzed using latent class analysis. KEY FINDINGS: - Substantial overall interest in the proposed green funds. - No clear prioritization among environmental objectives. - No general preference for a closer region of operations. - Proximity of expected impact matters conditionally, particularly for biodiversity. - Positive screening and active engagement are preferred over exclusion. - Higher investment stakes significantly increase required compensation for risk. - Anticipated regret, moral norms, and affective heuristic explain preference heterogeneity. IMPLICATION FOR TRADING: The findings suggest that green-fund demand is more influenced by risk, management strategy, and impact proximity than broad environmental labels. Traders and fund managers should focus on providing clear and credible information about the environmental impact and risk profile of green funds. Additionally, understanding that higher stakes increase the required compensation for risk can help in designing investment products that cater to a broader range of investor preferences.

TITLE: Bad Markets, Bad Advisors? Investor Scrutiny and Recorded Financial Advisor Misconduct AUTHORS: R. David McLean, Chi Wan, and Mengxin Zhao SUMMARY: This study investigates the relationship between poor stock market performance and recorded financial advisor misconduct. The research finds that misconduct rates rise significantly during market downturns, suggesting that increased investor scrutiny rather than higher actual misconduct is the primary driver. Poor market performance also correlates with higher rates of directly verifiable violations and complaints that yield no adverse findings or payments. METHOD: The authors use a combination of time-series and cross-sectional analyses, comparing misconduct rates in years with different market returns. They also examine the impact of geographic exposure to corporate fraud and the Madoff scandal, which are unrelated to the specific complaints studied. The data includes financial advisor misconduct records from the U.S. KEY FINDINGS: - Recorded misconduct rates are 28% higher in years with one standard deviation below average market returns and 65% higher in years two standard deviations below average. - Poor market performance increases the likelihood of complaints being filed, even if the underlying misconduct occurred earlier. - Exposure to corporate fraud and the Madoff scandal also increases recorded misconduct, indicating heightened investor vigilance. IMPLICATION FOR TRADING: Practitioners should be aware that market downturns may lead to increased scrutiny of their actions by clients and regulators. This heightened scrutiny can result in more complaints and recorded misconduct, even if the actual misconduct has not changed. Advisors should maintain high ethical standards and be prepared for increased regulatory attention during market downturns.

TITLE: Dimensional Transformation Theory: A Unified Framework of Cross-Level Volume Role Transition in the A-Share Market AUTHORS: Xumin Pan (co-first author), Ziyang Pan (co-first author), and not stated SUMMARY: This paper introduces the Dimensional Transformation Theory (DTT), a novel theoretical framework that reinterprets trading volume as a multi-level, role-shifting signal within financial markets. The study aims to understand how trading volume transitions between different hierarchical levels and its implications for market dynamics. The research is based on 77,825 daily observations of 28 Shenwan primary industries. METHOD: The empirical design includes data from 2020 to 2024, covering various market conditions such as bull-bear transitions, sector rotations, and macroeconomic dominance. The study constructs variables and employs a multi-frequency data framework to test four hypotheses related to hierarchy conduction, role transition, resonance returns, and cross-level synchronization. KEY FINDINGS: - Hierarchy conduction is observed, indicating that trading volume can shift between different hierarchical levels. - Role transition is evident, showing that volume roles change dynamically within the market. - Resonance returns are present, suggesting that volume transitions can lead to significant market returns. - Cross-level synchronization occurs, demonstrating that volume changes at one level can influence another. IMPLICATION FOR TRADING: The DTT provides a comprehensive framework for understanding volume dynamics and its role in market transitions. Traders can use this theory to identify hierarchical shifts and role transitions, potentially leading to better timing of trades and improved trading strategies. By recognizing the multi-level nature of volume, practitioners can develop more sophisticated trading models that account for these complex interactions.

TITLE: Loss Aversion, Ambiguity Aversion, and Equity Participation among Urban Indian Savers AUTHORS: Diveet Jain SUMMARY: This study investigates the reasons behind the low equity participation among Indian savers, focusing on loss aversion and ambiguity aversion. The research was conducted through a survey among urban savers in the Mumbai Metropolitan Region (including Pune), measuring these behavioral biases and controlling for risk tolerance. METHOD: The study used a survey to measure loss aversion and ambiguity aversion through hypothetical choice ladders. Risk tolerance was included as a control variable. Data were analyzed using logistic regression to determine the independent associations of these biases with equity participation. KEY FINDINGS: - Loss aversion was independently associated with non-participation across all robustness checks. - Ambiguity aversion showed a bivariate association but no independent association after controlling for loss aversion and risk tolerance. - The findings suggest a behavioral dimension to the puzzle of low equity participation rather than structural factors. IMPLICATION FOR TRADING: Practitioners should consider loss aversion and ambiguity aversion when designing investment strategies for Indian savers. Understanding these biases can help in creating more tailored investment products and educational programs to encourage greater participation in financial markets.

TITLE: Size Anomalies in U.S. Bank Stock Returns: A Fiscal Explanation AUTHORS: Priyank Gandhi and Hanno Lustig SUMMARY: This paper investigates the size anomaly in U.S. bank stock returns, where larger banks exhibit lower risk-adjusted returns compared to smaller banks, despite being more leveraged. The authors propose that government guarantees, which protect large banks during financial crises, contribute to this anomaly. METHOD: The study analyzes historical U.S. bank stock returns, using deciles of total book value to categorize banks. It employs principal component analysis to identify a size factor in the risk-adjusted returns of bank portfolios, which is orthogonal to standard risk factors. KEY FINDINGS: - A 100% increase in a bank’s book value lowers its annual return by 2.23% per annum. - The size factor in bank stock returns measures bank-specific tail risk, which is not captured by standard risk factors. - This size factor is orthogonal to stock and bond risk factors and accounts for most of the pricing anomaly. IMPLICATION FOR TRADING: The findings suggest that investors should be cautious about the risk-adjusted returns of larger banks, as they are more exposed to government guarantees that can distort market pricing. Traders should consider incorporating size factors in their risk models to better account for the unique characteristics of bank stocks.

NASLOV: The Impact of Policy Returns on Surrender Behavior in Investment-Type Unit-Linked Life Insurance AVTORJI: Jukka Johansson, Aalto University, Finland POVZETEK: Raziskava uporablja podatke iz dveh življenjskih ubezinskih kompanij, da izuči vpliv političnih vrhuncev na pustilno ponašanje v ubezkih politih za ustreznost. Natančno je najdeno, da politi z pozitivnimi vrhunci imajo pustilne stopnje, ki so približno 40% manjše, kot politi z negativnimi vrhunci. Rezultati niso v celoti objašnjeni s statičnimi političnimi in osebni stopnji vrhuncev ali hipotezo o vrhunecu (IRH) ali hipotezo o nesrečnem nalogu (EFH). Dodatne analize potvrdí, da kratkih obdobjnih vrhunce in prebivalni vrhunci znatno vplivajo na pustilne stopnje. Dodatno je najdeno podpora hipotezi, da je vpliv vrhuncev v politih za ustreznost zavisan od vpliva vrhunca: povečanje zasebnega vrhunca smanjuje vrhunce politi, kar poveča pustilne stopnje. METODA: Raziskava uporablja politične vrhunce na politih za ustreznost, da izuči pustilno ponašanje. Podatki so pridobljeni iz dveh življenjskih ubezinskih kompanij. Dodatne analize potrjujejo, da kratkih obdobjnih vrhunce in prebivalni vrhunci znatno vplivajo na pustilne stopnje. KLJUČNE UGOTOVITVE: - Politiki z pozitivnimi vrhunci imajo pustilne stopnje, ki so približno 40% manjše, kot politi z negativnimi vrhunci. - Statične politične in osebne stopnje vrhuncev, IRH in EFH ne morejo v celoti objašniti pustilne ponašanja. - Kratkih obdobjnih vrhunce in prebivalni vrhunci znatno vplivajo na pustilne stopnje. - Vpliv vrhuncev v politih za ustreznost je zavisan od vpliva vrhunca. POMEN ZA TRGOVANJE: Raziskava je pomembna za razumevanje in predviđanje pustilnega ponašanja v ubezkih politih za ustreznost, kar je ključno za učinkovito upravljanje rizikov in optimizacijo finančnih rezerv. Razumevanje tega ponašanja lahko pomaga ubezniškim kompanijam v bolj učinkovitem upravljanju rizikov in finančnih rezerv, kar je ključno za učinkovito trgovino.

NASLOV: 7476038-persistent-homology-in-an-emerging-equity-market-dependence-structure-regime-sta AVTORJI: Raziye Rafibakhsh, MohammadAmin Rezazadeh POVZETEK: Raziskava obravnava, ali je persistentna homologija informativna pri opisovanju zavisnosti in tržnih regij v rastujočem trgu akcij, ter ali jo to dodatno vlagajo v predviđanje prihodnje realizirane varijanc. Raziskovalci analizirajo 2.769 dnevnih opazovanj petih indeksov Tehreanskega trga akcij od 26. marca 2014 do 22. septembra 2025. Nastopajoči rezultati pokažejo, da tradicionalne indikatorji vlagajo značilno, vendar nekompletno v topološko raznolikost. Topologija se razlikuje, če se trenutno zavisnost odstrani, zato so Gaussova kovarijansa in Gaussova kopulna surrogatna metode boljši v reprodukciji topološkega razpada. Analiza regij je identificirala stabilen granicni čas v junu 2018 in manj stabilen v aprilu 2022. Topologija se najbolje opisuje z okno velikosti 50 dneh, pri katerih dodate topologije podatke zgoraj se vlagajo v predviđanje, vendar s podmnožico natančnosti. Nelinearna učenje zmanjša izbiro natančnosti, vendar je njeno interval vključno z nuljo in predviđa prihodnje realizirane varijanci le pod določenimi pogoji. METODA: Raziskovalci uporabljajo 2.769 dnevnih opazovanj petih Tehreanskega trga akcij indeksov, ki se razlikujejo v 50 dnevnih oknehu, z 30 in 100 dnevnim oknom za odvisnost. Uporabljajo četiri merila traje in konvencionalne indikatorje volatilnosti in zavisnosti. Surrogatne metode uporabljajo za ustrezno statistično analizo. Regij analizirajo brez uporabe topologije in ocenjujejo prihodnje realizirane varijanci z vloženim validacijo in nepotrdljivim ohranilcem. KLJUČNE UGOTOVITVE: - Tradicionalni indikatorji vlagajo značilno, vendar nekompletno v topološko raznolikost. - Odstranitev trenutne zavisnosti vpliva veliko na topologijo. - Gaussova kovarijansa in Gaussova kopulna surrogatna metode reprodukuje večjo distribucijo topologije. - Regija analiza identificira stabilen granicni čas v junu 2018 in manj stabilen v aprilu 2022. - Dodate topologije podatke zgoraj se vlagajo v predviđanje, vendar s podmnožico natančnosti. - Nelinearna učenje zmanjša izbiro natančnosti, vendar je njeno interval vključno z nuljo in predviđa prihodnje realizirane varijanci le pod določenimi pogoji. POMEN ZA TRGOVANJE: Topologija je najbolje opisana z oknom velikosti 50 dneh, pri katerih dodate topologije podatke zgoraj se vlagajo v predviđanje. Topologija je najbolj informativna kot kompaktne nelinearne predstave zavisnosti in regij. Vlaganje topologije v predviđanje zmanjša natančnost, vendar je njeno vlaganje v predviđanje prihodnje realizirane varijanci pod določenimi pogoji koristno.

NASLOV: 7475921-navigating-the-crypto-accounting-frontier-an-ifrs-reporting-framework AVTORJI: Hussain Baqir Challawala POVZETEK: Članek razvija praktičen okvir za klasifikacijo in izvajanje zveze z kriptovaluti pod IFRS. Vprašanje se nanaša na težave pri uvrstitev kriptovalut, ki so zelo volitvene in niso prilagodljive tradicionalnim konceptom novča, novčnih ekvivalentov ali finančnih instrumentov. Nauka ponuja določen okvir, ki je založen na temeljnih principih obstojnjih standardov in razprave večjih finančnih družb in standardov. Okvir omogoča finančnim odborom struktuirano razsudko in dokumentacijo odluk, dokler se standardi še uveljavljajo. METODA: Članek je razvijen na podlagi obstojnjih standardov IFRS in razprav, ki jih vodi večje finančne družbe in standardov. V članku je predstavljena osebna izkušnja avtorja in razprava o težavah, ki jih prinašajo kriptovalute pod IFRS. Pristop je založen na ekonomskega karaktera kriptovalut in namenu njihovega ustreznega uvrstitev. KLJUČNE UGOTOVITVE: - Kriptovalute so zelo volitvene in niso prilagodljive tradicionalnim konceptom novča, novčnih ekvivalentov ali finančnih instrumentov. - Finančni odbori morajo uporabljati znatno razsudilo za uvrstitev kriptovalut pod IFRS. - Nauka ponuja okvir, ki je založen na temeljnih principih obstojnjih standardov in razprave večjih finančnih družb in standardov. POMEN ZA TRGOVANJE: Ugotovitve so uporabne za praktiko, ker omogočajo finančnim odborom struktuirano razsudko in dokumentacijo odluk, dokler se standardi še uveljavljajo. To je pomembno, saj kriptovalute se pogosto pojavljajo na bilansnih listih podjetij, kar pomeni, da je jasen postopek uvrstitev pomemben za investitorje, auditorje in regulatorje.

NASLOV: Trading Gamification, Asset Prices, and Liquidity AVTORJI: Philipp Chapkovski, Debaleena Goswami, Arzu Işık, Marius Zoican POVZETEK: Raziskava je potekala v laboratoriju, kjer so udeleženci delovali na trgu, ki je bil ali ne gamificiran. Gamificirani trgi so podlagali 31% večjega obmeca, s povečanjem limitnih naravil in intradaynega prometa. Gamificiranje je vzbudilo strategije trgovca, ki so se izražale z zmanjšanjem razporedov oznake cene za 26% in povečanjem globine trga za 24%. Ta trgovska aktivnost pa je bila nespremnih, saj cene so reagirale manj na informacije v toku naravil. Tako je gamificiranje povečalo napačno cenjenje trgovine za 28% in je zmanjšalo cene trgovske efikasnosti. METODA: Raziskovalci so implementirali laboratorij, v katerem so udeleženci trgovale z eno dividendnega plačljivega vloge v večhodnih obdobjih. Trgi so bile razdeljene v negamificirane in gamificirane, kjer so udeleženci morda zmagali nagrade za trgovske aktivnosti in dobivali obaveze o tokih cen. Raziskovalci so ponavljali vsako trgovino dvečkrat s istimi udeleženci, da bi ugotovili učinke gamificiranja. KLJUČNE UGOTOVITVE: - Gamificirani trgi so podlagali 31% večjega obmeca. - Limitne naravile in intradayni promet so povečali. - Trgovske strategije so povečale globino trga za 24%. - Gamificiranje je povečalo napačno cenjenje trgovine za 28%. - Trgovska efikasnost je pomanjšala, saj cene so reagirale manj na informacije v toku naravil. POMEN ZA TRGOVANJE: Raziskava pokazuje, da gamificiranje trgovine lahko poveča obmec in trgovske aktivnosti, vendar tudi napačno cenjenje in manjša trgovska efikasnost. Trgovci, ki delajo na gamificiranih trgih, bi morali biti ogromno oprezni pri analizi cene, saj so manj senzitivne na informacije v toku naravil.

NASLOV: Skill or Variance? What Trading Evaluations Actually Select AVTORJI: Francisco Matilla Serrano POVZETEK: Raziskava razlikuje mešanje mehanskih in umetnih trgovcev pri preverjanju trgovinskih ocen. Trgovci s večjo varčevnostjo lahko zelo pravdepodobno prejmejo oceno, čeprav imajo negativno očekovano trgovinsko vrednost. Ocena je nekopletna različica, saj ne pravilno razlikuje trgovce z pozitivno očekovano trgovinsko vrednostjo. Tudi finančni trgovci ne zagotavljajo bilansiranega finančnega rezultata, saj bi potrebno povečati ocenno popustilo ali zmanjšati prvi neto izdajniški izid za bilansiranje. METODA: Raziskava uporabljajo stohastični model zmanjševanja in prihodkov, ki vključuje različne vrste trgovcev z različnimi očekovanimi danimskih zmanjševanj, varčevnostmi in rizikovimi množili. Model uporablja 40.000 antitetičnih Monte Carlo poti na diskretnem intradnevnem mrežu za ocenjevanje končnih horizontnih barier. KLJUČNE UGOTOVITVE: - Večja varčevnost lahko poveča pravdepodobnost, da trgovci z negativno očekovano trgovinsko vrednostjo prejmejo oceno. - Ocena je nekopletna različica, saj ne pravilno razlikuje trgovce z pozitivno očekovano trgovinsko vrednostjo. - Finančni trgovci ne zagotavljajo bilansiranega finančnega rezultata, saj bi potrebno povečati ocenno popustilo ali zmanjšati prvi neto izdajniški izid za bilansiranje. POMEN ZA TRGOVANJE: Raziskava pokazuje, da preverjanje trgovskih ocen ne zagotavlja izbire trgovcev s pozitivno očekovano trgovinsko vrednostjo. Trgovci s večjo varčevnostjo lahko prejmejo oceno, čeprav imajo negativno očekovano trgovinsko vrednost. To pomeni, da bi trgovci morali biti izbrani na drugi način, če je cilj izbirati trgovce s pozitivno očekovano trgovinsko vrednostjo.

NASLOV: Time-Varying U.S. Equity Market Integration AVTORJI: Filip Jedmo, Andrew J. Patton POVZETEK: Raziskava nudi nov merilnik za integracijo trga v U.S. akcijskem trgu, ki je opremljen z metodo k-means klasteriranja. Izračunana razdalja med klasteri značilnih faktorov uporablja kot merilo integracije ali segmentacije trga. Raziskava je zaznamala, da se integracija ali segmentacija U.S. akcijskega trga razlikuje zelo zelo pogosto. Proučevanje je pokazalo, da je trg več segmentiran v obdobjih, ki so značilna za tržno stres, kot je obdobje zmanjšane finansne likvidnosti. METODA: Raziskovalci uporabljajo k-means klasteriranje, da identificirajo skupine akcij, ki pridobivajo različne faktorske premijske cene. Razdalja med teh skupinami meri segmentacijo trga. Prva klasterizacija uporablja grupiranje cene faktorov, da ocenjujejo skriti skupine z različnimi faktorskimi premijskimi cimi. Nato uporabljajo nov bootstrap test, da odbijajo hipotezo o konstantni stopnji segmentacije v času. KLJUČNE UGOTOVITVE: - U.S. akcijski trg je značilno segmentiran v obdobjih zmanjšane finansne likvidnosti in obdobjih tržnega stresa. - Trg je več integriran v obdobjih, ko se faktorske premijske cene ne morejo razložiti. - Različne skupine akcij pridobivajo različne faktorske premijske cene, ki so merljive z izračunano razdaljo med klasteri. POMEN ZA TRGOVANJE: Raziskovalni rezultati pokažeta, da je integracija ali segmentacija U.S. akcijskega trga zelo zelo pogosto spreminjalna. Trgovci bi morali upoštevati, da je stopnja segmentacije v času, da lahko optimizirajo svoje investicije. V obdobjih, ko je segmentacija visoka, je mogoče dobiti večji Sharpe razmerje z uporabo skupinskih faktorskih premijskih cen, kot z uporabo skupnih faktorskih premijskih cen.

NASLOV: 7473158-regime-switching-asset-correlation-in-hybrid-structural-intensity-credit-risk-model AVTORJI: Peter Mutua Malonza POVZETEK: Članek razvija hybridni model strukturnih in intenzitetskih kreditnih rizikov, ki vključuje regim-spreminjajočo se sistemsko korelacijo. Model je implementiran s Monte Carlo simulacijo 100-širine portfelja, ki se razvija v pet let. Izračunani rezultati pokazujejo, da regim-spreminjajoča korelacija prinaša značljive večje rizike v okrajških delih izdajnega razdoblja, pri čemer je 99,9% kritična rizika (VaR) približno 23% višja in pričakovana izguba (ES) na 97,5% kritični ravni približno 15% višja, kot pri modelu z konstantno korelacijo. Te rezultate so zgodobne za prakso, saj podpirajo obavijesti iz Basel III in IFRS 9 o prokikulčarnosti in sicer podrazumevajo, da točno kalibrirane točkne kapitalne modele močno preocenjujejo okrajške kapitalne zahtevnosti. METODA: Model je implementiran s Monte Carlo simulacijo 100-širine portfelja, ki se razvija v pet let. Kolektivni korelacije je prikazana s dvostiščnega Markovovim regim-spreminjanjem, ki vključuje "normalno" in "stresno" regim. Različica modela z regim-spreminjanjem prinaša značljive večje okrajške rizike, kot je 99,9% kritična rizika (VaR) približno 23% višja in pričakovana izguba (ES) na 97,5% kritični ravni približno 15% višja, kot pri modelu z konstantno korelacijo. KLJUČNE UGOTOVITVE: - Model z regim-spreminjanjem korelacije prinaša značljive večje okrajške rizike, kot je 99,9% kritična rizika (VaR) približno 23% višja in pričakovana izguba (ES) na 97,5% kritični ravni približno 15% višja, kot pri modelu z konstantno korelacijo. - Te rezultate podpirajo obavijesti iz Basel III in IFRS 9 o prokikulčarnosti. - Točno kalibrirane točkne kapitalne modele močno preocenjujejo okrajške kapitalne zahtevnosti. POMEN ZA TRGOVANJE: Te ugotovitve so zgodobne za prakso, saj podpirajo obavijesti iz Basel III in IFRS 9 o prokikulčarnosti in sicer podrazumevajo, da točno kalibrirane točkne kapitalne modele močno preocenjujejo okrajške kapitalne zahtevnosti. To pomeni, da kreditne institucije in investitori morajo upoštevati regim-spreminjajočo korelacijo pri ocenjevanju okrajških kapitalnih zahtevnosti, da bi izogabenje preizkušenih okrajških rizikov.

NASLOV: 7473138-detectable-but-not-exploitable-predictability-and-trading-costs-on-the-casablanca AVTORJI: Ahmed Afachtal POVZETEK: Raziskava opisuje, da je na Casablančanski državni trgu (CDT) predikatibilnost, ki je merljiva, vendar ne je ekonomska vrednostna. Iz 73 varstev CDT, 21 in 26 varstev prikazujejo predikatibilnost, ki je merljiva z pogoji, ki so znatno večji kot greske. Vendar pa površinska prihodki, ki so merljivi, se ne morejo osnovati na publikiranih troškovih podlagah, ki se izražajo v vsaj 1.10% za pet sestavkov. Ni enega varstva, ki bi prikazovalo značljiv delovni zaslon, 32 pa prikazujejo značljivo manjšino. To pomeni, da je predikatibilnost realna in široko razsprende, vendar ne more biti ekonomsko koristna. METODA: Raziskava uporablja panel od 73 varstev CDT, ki je obnašal obdobje od 2010 do 2026. Podatki so zgoraj na CDT sejno kalendarsko vrstilo, kar omogoča nesmiselno merjenje ne-trading intervalov. Prispevki so merljivi z pogoji, ki so znatno večji kot greske. Prispevki se ne morejo osnovati na publikiranih troškovih podlagah, ki se izražajo v vsaj 1.10% za pet sestavkov. KLJUČNE UGOTOVITVE: - 21 in 26 od 60 varstev prikazujejo merljivo predikatibilnost, ki je znatno večja kot greske. - Površinska prihodki, ki so merljivi, se ne morejo osnovati na publikiranih troškovih podlagah, ki se izražajo v vsaj 1.10% za pet sestavkov. - Ni enega varstva, ki bi prikazovalo značljiv delovni zaslon. - 32 varstev prikazujejo značljivo manjšino. POMEN ZA TRGOVANJE: Raziskava poudarja, da je merljiva predikatibilnost na CDT vendar ne more biti ekonomska vrednostna. To pomeni, da je merljiva predikatibilnost, ki je merljiva, vendar ne more biti ekonomska vrednostna. Trgovci bi morali biti opremljeni s tem, da merljiva predikatibilnost ne more biti ekonomska vrednostna, če je trošek za prihodki večji od merljivih prihodkov.

NASLOV: Quasi-Explicit Recovery of Heston Parameters from SSVI-Parameterized Volatility Surfaces AVTORJI: Chris Bemis, University of Minnesota, Department of Mathematics POVZETEK: Članek predstavlja metodu za izračun Hestonovih parameterjev iz SSVI-parameteriziranih volatilnostnih površin. Metoda je zasnovana na asimptotskih rezultatih in elementarnih računih, in je konstruirana kot kvazijekstern mapiranje, kjer je vsak Hestonov parameter izrazen kot eksplikativna funkcija ustreznih volatilnostnih površin. Metoda je sekvencialna in ne zahteva multidimensionalnega iskanja. Ključne ugotovitve vključujejo identifikacijo parameterjev v dveh asimptotskih regijah in na-the-money termustrukturi. METODA: Metoda zasnovana je na kvazijekstern mapiranju, kjer je vsak Hestonov parameter izrazen kot funkcija SSVI-parameteriziranih volatilnostnih površin. Konstrukcija je založena na identifikaciji parameterjev v dveh asimptotskih regijah in na-the-money termustrukturi. Identifikacija parameterjev ni potrebna za več kot skalarne monotone račune. KLJUČNE UGOTOVITVE: - Metoda je sekvencialna in kvazijeksterna, z uporabo asimptotskih rezultatov in elementarnih računov. - Parameterjev je potrebno identificirati v dveh asimptotskih regijah in na-the-money termustrukturi. - Identifikacija parameterjev ni potrebna za več kot skalarne monotone račune. POMEN ZA TRGOVANJE: Metoda je uporabna za trgovce, saj omogoča stabilen in individuabilno interpretabilen izračun Hestonovih parameterjev iz SSVI-parameteriziranih volatilnostnih površin. To pomeni, da je optimizacija ni potrebna, saj je mapiranje kvazijeksterna in sekvencialna. Kritično, metoda omogoča izračun Hestonovih parameterjev brez uporabe multidimensionalnih iskanj, kar je koristno za prakse v finančni trgovini.

NASLOV: ENGINEERED LIQUIDITY: EXPLOITING MARKET MAKER HEDGING VIA OPTIONS AS A MECHANISM FOR INSTITUTIONAL POSITION ACCUMULATION AVTORJI: Vladimir Markevich POVZETEK: Raziskava opisuje teoretično strategijo, ki jo uporablja veliki institucionalni trgovci, da sestavijo pozicijo v podlagnem vložku, uporabljajo limitne narave in opcije. Glavni problem je nedovoljena naravna likvidnost, ki bi omogočila izvajanje narave na željenem cijenu in količini. Zamišljeni način je, da institucionalni trgovci kupujejo veliko količino opcij brez ekonomskega namena, kar običajno obvešča trgovce za opcije (trgovce za likvidnost) za odražanje delta in negativno gamma. To pomeni, da morajo izvajati agresivne prodaje, ki so potrebne za odražanje negativnega gamma. Trgovski narave to zamenjajo z limitnimi naravami, sestavljajo pozicijo na znatno nižji ceni, kot bi to omogočilo direktno trgovino. Po završitvi limitnih narav, institucionalni trgovci odpravljajo opcije, kar povračijo likvidnost in vrne ceno podlagnega vložka na podlagno vrednost. METODA: Raziskava je teoretična in se osredotoči na analizo obstoječih literatur o tržnem mekanizmu in likvidnosti. Avtorji se odvisijo od teorije kontinuiranega lekciranja, teorije manipulacije z trženjem in proračunov gamma in inventarizacije. Metodologija je založena na empirični podatki o vložkih opcij in likvidnosti, ki so jih omogočili raziskovalci v preteklosti. KLJUČNE UGOTOVITVE: - Institucionalni trgovci uporabljajo opcije, da obveščijo trgovce za opcije za odražanje delta in negativno gamma, kar pomeni agresivne prodaje. - Trgovski narave, ki so obveščeni, izvajajo agresivne prodaje, ki so potrebne za odražanje negativnega gamma. - Trgovski narave, ki so obveščeni, izvajajo agresivne prodaje, ki so potrebne za odražanje negativnega gamma. - Trgovski narave, ki so obveščeni, izvajajo agresivne prodaje, ki so potrebne za odražanje negativnega gamma. POMEN ZA TRGOVANJE: Ta strategija omogoča institucionalnim trgovcem, da sestavijo pozicije v podlagnem vložku na znatno nižji ceni, kot bi to omogočilo direktna trgovina. To je posebno koristan za trgovce, ki uporabljajo limitne narave, saj imajo več kontrole nad ceno, na kateri se narave izvajajo. Ta strategija je težko razpoznati kot manipulacijo, saj je zelo mehanizmov, ki so potrebni za njeno izvajanje.

NASLOV: Benchmark-Sensitive Cryptocurrency Diversification: Evidence from Thai REIT Portfolios AVTORJI: Chaiyathad Phutthadet, Ausawatap Akartwipart, Chainarong Kaewmuangmoon POVZETEK: Raziskava testira, ali dodajanje Bitcoina in Ethereuma do portfelja thaihskih REITov izboljša rezultate za thaihskega investitorja. Izpeljana je, da pričakovani izboljški rezultati ne so bili statistično značilni pod korekcijo množične značilnosti. Njihova najpomembnejša najdena rešitev je, da vsakodnevni 95% conditional value-at-risk značilno se pogosto poveča v vseh tri korekcije za pet od osmih kriptovalutno vključenih portfeljev. Tukaj pa ne obstaja značilna povezava med izboljškami Sharpe-ovega koeficienta, s katerimi se pa le strategija z rollingom (P9) značilno poveže pod korekcijo Romano-Wolfa. Natančno 300-330 besed. METODA: Raziskava uporablja 1562 ujemanih dnevnih observacij (od 16. januar 2020 do 30. junij 2026) za 13 thaihskih REITov, ki so bili izbrani v zasebni vzorec. Modelirani su naraščajoči stroški, ki se nanašajo le na kvartalne top-level reallokacije, ne pa na dnevno povezovanje portfelja REITov. Uporabljena je metoda z osoznanjem o zavisnosti (Bonferroni, Benjamini–Hochberg, Romano–Wolf) za inferenco. KLJUČNE UGOTOVITVE: - Njihova najpomembnejša najdena rešitev je, da vsakodnevni 95% conditional value-at-risk značilno se pogosto poveča v vseh tri korekcije za pet od osmih kriptovalutno vključenih portfeljev. - Njihova najpomembnejša najdena rešitev je, da ne obstaja značilna povezava med izboljškami Sharpe-ovega koeficienta. - Strategija z rollingom (P9) je značilno povezana pod korekcijo Romano-Wolf. POMEN ZA TRGOVANJE: Raziskava ukazuje, da dodajanje kriptovalut do portfelja thaihskih REITov ne vsebuje značilnih izboljškov v obliki izboljšk Sharpe-ovega koeficienta. Ta rezultat je pomemben za praktiko, saj poudarja, da dodajanje kriptovalut ne vsebuje značilnih izboljškov za thaihskih investitorje. Tukaj pa je pomembno, da vsakodnevni 95% conditional value-at-risk značilno se pogosto poveča, kar je značilno za vsa tri korekcije.

NASLOV: Cultural Anchoring and Investor Sentiment: Evidence from the Twenty-Four Solar Terms AVTORJI: Jaehee Jang, Jiayu Zuo, Lei Yan, Xiaoying Wu POVZETEK: Tega raziskava preučuje, ali tradicionalni kalendarski sistemi, kot so Twenty-Four Solar Terms, povezani so z dnevno investorovo sentimentom v finančnih trgovinah. Izvedli so analizo dnevne investorove sentimentne točk v Kitaju, Koreji in Združenih Državah. Natančno je najdeno, da investorovo sentiment v Kitaju in Koreji sledi statistično značljivemu okružju okoli prehodov skozi Twenty-Four Solar Terms, s podrazumevano padom v letnem obdobju. V Združenih Državah pa nista podobne vzorce nekajte. Rešetke za čas in panel regresije potrjujejo, da je učinek Twenty-Four Solar Terms preostal videti po kontroli za lokalno temperature in druga kalendarska anomaљe. Analize heterogenosti podjetij podpirajo, da je učinek značljiviji med podjetji, ki so več naražena na trgovino z značilnostmi sentimenta. Raziskava potrjuje, da je kulturno značljiva časovna oznaka povezana z trgovinsko investorovo sentimentom, izhaja iz standardnih temperature in kalendarskih učinkov. METODA: Raziskovalci uporabljajo Twenty-Four Solar Terms, tradicionalen kalendarski sistem v Evropi neznani, za preučevanje dnevne investorove sentimentne točke v Kitaju, Koreji in Združenih Državah. Rešetke za čas in panel regresije kontrolirajo za lokalne temperature in druga kalendarska anomaљe. Analize heterogenosti podjetij raziskujeta, ali je učinek značljiviji med podjetji, ki so več naražena na trgovino z značilnostmi sentimenta. KLJUČNE UGOTOVITVE: - Investorovo sentiment v Kitaju in Koreji sledi statistično značljivemu okružju okoli prehodov skozi Twenty-Four Solar Terms. - Podobne vzorce nekajte nista narejene v Združenih Državah, čeprav so sročeni do obdobjev. - Rešetke za čas in panel regresije potrjujejo, da je učinek Twenty-Four Solar Terms preostal videti po kontroli za lokalne temperature in druga kalendarska anomaљe. - Analize heterogenosti podjetij podpirajo, da je učinek značljiviji med podjetji, ki so več naražena na trgovino z značilnostmi sentimenta. POMEN ZA TRGOVANJE: Ta raziskava podpira idejo, da kulturno značljive časovne oznake so povezane z trgovinsko investorovo sentimentom, izhaja iz standardnih temperature in kalendarskih učinkov. To je pomembno za razumevanje in modeliranje investorovega sentimenta, ki je ključen za trgovino. Raziskava podpira uporabo kulture v analizi finančnih trgovin in je pomembna za razvoj novejših strategij za prepoznavanje in upravljanje s trgovinsko sentimentom.

NASLOV: Anchoring Options to an Incomplete Forward Curve: Identification and Out-of-Sample Evidence from Turkish Electricity AVTORJI: Onat Uzkana, Koray Ömercan Saçlı POVZETEK: Članek razvija nov model za oceno električnih opcij v trgu, kjer so dostopne le težave mesečnih naprednih cen. Model razdeljuje, kar trži identificira, od kar ne, in konstruirane je tak, da je napredna stopnja cen največje identificirana predmet. Cenovni sistem je preverjen s Monte Carlo simulacijami in pokazuje, da je tržna stopnja cen težava predpovedi realnih cijen, kar ograničava točnost ocene električnih opcij. METODA: - Razvijen je model, ki konstruirane je z mesečnimi srednji cenami, ki so določene kot najlakša stopnja, ki se ujemajo z observiranimi mesečnimi povprečnimi cenami. - Dodana je dva-odkrivščinska Ornstein–Uhlenbeck residual, ki je odvisna od residualne poti, in je uvedena v centrirani obliku, da je pričakovana vrednost cen v skladu s napredno stopnjo. - Cenovni sistem je ocenjen s povezanim moment-ODE in končnopravilnim sistemom, ki je preverjen s Monte Carlo simulacijami. - Model je preverjen na odseljenih cenah, ki so realizirane v prvi polovici 2026, in pokazuje, da je tržna stopnja cen težava predpovedi realnih cijen. KLJUČNE UGOTOVITVE: - Tržna stopnja cen je težava predpovedi realnih cijen. - Cenovni model reproducirje tržno stopnjo cen, kar pomeni, da gre za greško tržne cene. - Ocenjevanje električnih opcij z tem modelom je ograničeno, ker je tržna stopnja cen težava predpovedi realnih cijen. POMEN ZA TRGOVANJE: - Model je uporaben za ocenjevanje električnih opcij v trgu, kjer so dostopne le težave mesečnih naprednih cen, vendar je omejen, ker je tržna stopnja cen težava predpovedi realnih cijen. - Trgujevalci bi morali upoštevati težave tržne stopnje cen pri ocenjevanju električnih opcij, če želijo doseči točnejše ocene.

NASLOV: 7471715-on-bitcoin-volatility AVTORJI: ni navedeno POVZETEK: Raziskava obravnava, ali je povezljivost volatilnosti med Bitcoinom in S&P 500-om spremenila po maju 2023. Izpeljana je totalna povezljivost z uporabo vektorne avtomatske regresije in generaliziranih razloženih razlik v napovednih napakah. Povezljivost je narasla od približno 1.6% pred razdelitveno točko do 24.2% po njej, zato, da križne tržne šokle objašnjujejo približno eno četrto del napovedne napake v drugem trgu. Povezljivost je značilno povečala Bitcoinovo integracijo z volatilnostjo ameriških udeleženih trgov. METODA: Raziskava uporablja mesečno realizirano volatilnost, izpeljano iz dnevnih povratkov, za obeh trgov in ocenjuje statične povezljivostne merila z uporabo vektorne avtomatske regresije in generaliziranih razloženih razlik v napovednih napakah. Povezljivost je merila s pomočjo generaliziranih razloženih razlik v napovednih napakah v okviru povezljivostnega okvira Diebolda in Yilmazja. Povratki so izračunani kot logaritmi razmerja med dnevno ceno in prejšnjo dnevno ceno. KLJUČNE UGOTOVITVE: - Totalna povezljivost narasla je od približno 1.6% pred breakpointom do 24.2% po breakpointu. - Križne tržne šokle objašnjujejo približno eno četrto del napovedne napake v drugem trgu. - Povezljivost je značilno povečala Bitcoinovo integracijo z volatilnostjo ameriških udeleženih trgov. POMEN ZA TRGOVANJE: Ugotovitve, da je volatilnost Bitcoina povečala svojo povezljivost z volatilnostjo ameriških udeleženih trgov, so pomembne za trgovce, ki morajo raziskovati, kako Bitcoinov trženje vpliva na volatilnost drugih finančnih trgov. To značilno povečanje povezljivosti je pomembno za strategije, ki uporabljajo modeliranje volatilnosti za predviđanje rizikov in tržnih potez.

NASLOV: Beyond Monetary Incentives: The Role of Meaning in Professional Behavior and Performance AVTORJI: Li Lai, Chundan Lan, Joshua Madsen, Shan Wu POVZETEK: Raziskava obravnava, ali je povezava med delovnim pomenom in profesionalnim ponašanjem in produkcijo vrednostnega informacij v trgu kapitala. Izpeljana je, da se delavci bolj prilagajajo delovnim pomenom, preučevanjem letnih let, ki so končana z številom 9, in bolj prilagajajo svoje profesionalno ponašanje. Delavci, ki so letni leti, izdajajo bolj odločen, točnejši in hitrejši predviđanja, ki so bolj odpravljeni v trgu. Te rezultate kažejo, da delovni pomen vpliva ne samo na prenosenje človeškega kapitala med zaposlitvami, ampak tudi na izvedbo delovnega ponašanja, okolje informacij podjetij in distribucijo vrednostnega informacij v trgu. METODA: Raziskava uporabljala je letnine let, ki so končane z številom 9, kot predvidljivo spremembni faktor, ki vpliva na introspekcijo delavcev. Raziskovalci je prvič pokazali, da delavci bolj pravijo, preučujejo in prenosejo, brez prenosenja na višje plačila ali večjo stopnjo. Nato je raziskava obravnavala finančne analisti, ki so ključni za informacijsko okolje podjetij in trga. Analisti, ki so letnine leti, izdajajo bolj odločen, točnejši in hitrejši predviđanja, ki so bolj odpravljeni v trgu. KLJUČNE UGOTOVITVE: - Delavci bolj pravijo, preučujejo in prenosejo, brez prenosenja na višje plačila ali večjo stopnjo. - Finančni analisti, ki so letnine leti, izdajajo bolj odločen, točnejši in hitrejši predviđanja, ki so bolj odpravljeni v trgu. - Delovni pomen vpliva ne samo na prenosenje človeškega kapitala, ampak tudi na izvedbo delovnega ponašanja, okolje informacij podjetij in distribucijo vrednostnega informacij v trgu. POMEN ZA TRGOVANJE: Raziskovalni rezultati kažejo, da delovni pomen vpliva na profesionalno ponašanje in produkcijo informacij v trgu kapitala. To pomeni, da bi podjetja in finančni institucije morala razmisliti o vključevanju delovnega pomena v svoje strategije za zaposlitve in razvoj. Delavci, ki iskalijo delovni pomen, bi lahko bolj prilagajali svoje profesionalno ponašanje in izvedbo, kar bi lahko vplivalo na vrednost podjetij in trga kapitala.

NASLOV: 7471014-tail-aware-arbitrage-of-energy-storage-systems-in-volatile-electricity-markets-w AVTORJI: Mingli Chen, Hongxun Hui, Yonghua Song POVZETEK: Raziskava predstavlja nov model za arbitražo energijnih shranilnih sistemov (ESS) v volatilnih električnih trgovinah s negativnimi cijenama. Model uključuje inverzno hiperboliko sine transformacijo cijena kao općenitom metriku povratka, te asimetričnu volatilnost model u transformiranoj domeni. Pristup uključuje stabilizirani scenarijski generator s Jensenovim tipom odzivom i omeđenim tržnim cijenama. Ugotovljeni je pristup zaščito protiv negativnih gubitaka, čim se održava pozitivna arbitražna profitabilnost. METODA: Prva metoda uključuje inverznu hiperboliku sine transformaciju cijena (asinh) kao općenitom metriku povratka, koja je dobro definirana i podnosi negativne cijene i nule. Druga metoda je asimetrični volatilnost model u transformiranoj domeni s individualnim općenitimi Pareto distribucijama za gornje i donje krajnje dijelove. Tretja metoda uključuje rizik upravljanje na osnovi uslovnog rizika (CVaR) u stohastičkom planiranju modelu. KLJUČNE UGOTOVITVE: - Inverzna hiperbolika sine transformacija pomaže uključiti negativne cijene, kontrolirajući deformacije i komprimiraju ekstremne promjene. - Asimetrični volatilnost model generira stabilne scenarije koje uključuju asimetrične ekstremne cijene. - CVaR bazirano planiranje bilansira arbitražni profit i rizik. POMEN ZA TRGOVANJE: Raziskava ponuja pristup zaščito protiv negativnih gubitaka u volatilnim trgovinama s negativnim cijenama, čime se može učiniti stabilnije planiranje i operiranje energijnih shranilnih sistemata. To je posebno koristan za trgovce, koji moraju upravljati rizicem negativnih cijena u arbitražnim operacijama.

NASLOV: One Instrument, Two Technologies: Price Discovery Between Tokenized and Traditional Equity Markets AVTORJI: Kaitao Lin, Senior Financial Economist, World Federation of Exchanges POVZETEK: Tovornike, znane kot xStocks, so blockchain tokenji, ki se emitirajo proti uverljivim delom sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sodobnih sod

NASLOV: 7470102-high-volatility-is-the-key-a-joint-treatment-of-asset-pricing-puzzles AVTORJI: Errikos Melissinos POVZETEK: Raziskava pritega vprašanja, ki se pojavljajo v literaturi o ceneh ameriških varstev, in navaja, da se večina teh potez iz消去不必要的中文干扰词，保留英文部分 --- METODA: 研究基于一个标准的资产定价模型，该模型的关键特征是边际投资者的消费波动性较高且时间变化。作者通过调整边际投资者的假设，即边际投资者并非普通家庭，而是更可能参与资产市场的富裕家庭或金融中介，来解决这些资产定价难题。研究使用了扰动解法，并与现有文献中的独立研究进行了比较。 KLJUČNE UGOTOVITVE: - 高消费波动性是解决资产定价难题的关键。 - 边际投资者不是普通家庭，而是消费波动性更高的富裕家庭或金融中介。 - 在边际投资者具有高消费波动性的模型中，资产定价难题如股票溢价、无风险利率、债券溢价、超额波动性和股票回报可预测性消失。 - 模型中的大多数相关时刻与实际数据匹配。 POMEN ZA TRGOVANJE: 研究表明，边际投资者的高消费波动性可以解释资产定价中的多个难题。对于交易者和投资者而言，理解这一假设有助于更好地预测市场行为和资产价格。通过采用高消费波动性的模型，可以更准确地匹配实际市场数据，从而改进投资策略和风险评估方法。

NASLOV: 7469080-price-discovery-and-dark-pool-execution-under-deadline-constraints-an-option-pri AVTORJI: Soya Matsumoto POVZETEK: Raziskava opisuje vpliv temne trga na cene in raziskovanje cene v teoriji trgovine z opcijskim modelom. Trgovci, ki uporabljajo temne trge, imajo obveznost izvajanja z določenim meškom, ki je povezan s asimetričnimi stroški, če ne dosežejo ali presežejo cilj. Temni trgi zmanjšajo cene in raziskovanje cene v primeru pozitivnih rizikov izvajanja, in to zmanjšanje postane neskončno, če se riziko izvajanja zmanjša. Če je presežen strošek dovolj manjši kot strošek nedospeka in vrednost privatne informacije, trgovci presežejo predvzemno v temne trge, ki je to v linearnem izjave neskončno. METODA: Raziskava uporablja opcijski model, ki vključuje temen tržnem trgu in temen trgu, kjer je opcijska cena temne trga lognormalno porazdeljena. To omogoča reševanje izjave v zatvoreni obliki. Trgovci, ki uporabljajo temne trge, imajo obveznost izvajanja z določenim meškom, ki je povezan s asimetričnimi stroški, če ne dosežejo ali presežejo cilj. KLJUČNE UGOTOVITVE: - Temne trge zmanjšujejo cene in raziskovanje cene v primeru pozitivnih rizikov izvajanja. - Cene in raziskovanje cene postanejo neskončno manjši, če se riziko izvajanja zmanjša. - Če presežen strošek je dovolj manjši kot strošek nedospeka in vrednost privatne informacije, trgovci presežejo predvzemno v temne trge. POMEN ZA TRGOVANJE: Raziskava je uporabna za razumevanje vpliva temnih trgov na cene in raziskovanje cene. Trgovci, ki uporabljajo temne trge, morajo upoštevati asimetrične stroške, ki so povezani z obveznostjo izvajanja. To značilnost lahko vključuje v svoje strategije za izvajanje, da bi se izognili pretežnim stroškam.

NASLOV: Stock–Bond Diversification in an Inflation-Driven Rates Regime AVTORJI: Vatsal Maniar POVZETEK: Raziskava obravnava, kako se odnose naštevanih akcij in zavodov zavira v okviru inflacije vpliva na cene zavodov. V 2022 je odnosa med akcijami in dolgoročnimi zavodi spremenila, zato je 60/40 poročilo enega od najslabših let. Raziskava opisuje, da je ta odnos regime-zavisen in da je pravila-odvisna prilagajalna strategija, ki je testirana od leta 2007 do 2026, ne izpostavljena standardnemu 60/40 poročilu. Ugotovljeni razlog je, da je glavni vpliv na odnos realne stopnje oprostila, nisem inflacijskih breakevens. METODA: Raziskava je testirala pravila-odvisno prilagajalno poročilo, ki je uporabljala lagirane, vidljive signalje, podpisane z transakcijskimi stroški in omejitvami. Poročilo je preverjeno proti standardnim merilom od leta 2007 do 2026. KLJUČNE UGOTOVITVE: - Odnos med akcijami in zavodoma je regime-zavisen. - Pravila-odvisna prilagajalna strategija ne je izpostavljena standardnemu 60/40 poročilu. - Glavni vpliv na odnos je realna stopnja oprostila, nisem inflacijskih breakevens. POMEN ZA TRGOVANJE: Raziskava pokazuje, da je pravila-odvisna prilagajalna strategija težko izpostavljena standardnemu 60/40 poročilu, če je uporabljena z lagiranimi, vidljivimi signalji in omejitvami. To pomeni, da je ključno razumeti, kako se odnosi med akcijami in zavodoma spreminja v različnih ekonomskeh okolijih.

NASLOV: Market Edge versus Contract Edge: Negative-Expectancy Trading under an Idealized Evaluation Contract AVTORJI: Francisco Matilla Serrano POVZETEK: Raziskava se posveča vprašanju, da bi lahko trgovski postopek, ki v povprečju izgubi, bil koristan za trgovca, ki kupi trgovski ocenjevni sporok. V modelu, ki je razvijen v tem raziskovanju, je odgovor pozitiven. Trgovski postopek se ne izboljša, ampak sporok spremeni trgovčino odzivnost. V primernem dvojstvenem trgu, uspešno trgovsko izvedbo prinaša U, neuspešno pa izgubi D. Baselineski sporok je "50K" račun z U = $3,000 in D = $2,000. Trgovcu se pravi ploskva za ocenjevanje F, uspešnost ocenjevanja p, in izplačilo W le po drugi uspešni fazi s uspešnostjo q. Trgovski očekovani rezultat je pU - (1 - p)D, dokler je očekovani rezultat trgovca pq sW - F - pA. Pod pogojem, da je q = p, s = 1, in A = 0, sporok je pozitiven, če je p > √F / W, while the market strategy is negative expectancy when p < D / (U + D). Z U = $3,000, D = $2,000, F = $40, in W = $3,000, je interval približno 11.55% < p < 40%. Analiza pa razlikuje tri parametra: ploskvu za ocenjevanje F, ploskvu za aktivacijo A, in delilno sliščnost s; ilustrativna pogosto nesrečna specifikacija (F, A, s) = ($90, $100, 90%) poveča pravilnost pravilnosti strategije na 20.20% in daje pričakovano finančno prizorišče trgovca od - $2 pri p = q = 20%. Precizna enumeracija daje pravilnost strateškega pričakovanja 87.01% za 50 osnovnih ciklov in 40.37% za pogosto nesrečno specifikacijo. Raziskava ni tvrdnja o arbskem trgu in ne opisuje nobene odkriti družbe. To je začetni, preprost model širše raziskovalne programa o sporokih, trgovskih različnostih, strategičnih rizikih, demografskih dinamikah, upravljanju izplačil in odvisnosti po več računih. METODA: Raziskava je osnovana na teoriji trgovskih sporokov in modelu, ki je preprost in začetno. V modelu je določena uspešnost trgovske izvedbe in izplačilo, pri čemer je uspešnost ocenjevanja p, izplačilo W, in ploskva za aktivacijo A. Podobne pravila so preproste in niso opisane nobene odkriti družbe. Raziskava je podrobno raziskuje, kako se očekovani rezultati spreminjajo pri različnih vrednostih teh parametrov. KLJUČNE UGOTOVITVE: - Trgovski postopek, ki v povprečju izgubi, lahko za trgovca, ki kupi trgovski ocenjevni sporok, biti koristan. - Očekovani rezultat trgovca ni vedno enak očekovanemu rezultatu v trgu. - Ploskva za aktivacijo in ploskva za ocenjevanje lahko spremeničajo očekovani rezultat trgovca. - Pravilnost strateškega pričakovanja z odkritimi parametri je zelo visoka. POMEN ZA TRGOVANJE: Raziskava ponuja težave za trgovce, ki se odločajo o kupit trgovski ocenjevni sporok. Če je trgovski postopek negativno očekovan, lahko kupitev sporokov prinaša pozitiven rezultat za trgovca, če so pravilni parametri pravilno izbrani. Trgovci morajo razumeti, da očekovani rezultat v trgu ni vedno enak očekovanemu rezultatu v trgu, in izbrati parametre sporokov, ki jih lahko optimir

NASLOV: Cash Flow Pressure and Portfolio Adjustment in Defined Benefit Pension Plans: Evidence from Brazil AVTORJI: Roswelton Anjos de Paula POVZETEK: Tega raziskava obravnava, kako se povezava med finančno naporom in prilagajanje poročilnih poročil v razmerjih zaveznih odnosov (DB) v Bolžiji. Raziskava uporablja mesečne podatke iz 78 bolžišč med leti 2015 in 2025. Finančni napor meri odnos med odvajanjem in dodajanjem zaveznih odnosov, kar se ugotavlja z testi za strukturne spremembe, ki dovoljujejo spremembe v ravni in trendu. Raziskava raziskuje lastnosti povezane z glavnimi spremembami v razdelitvi vstopov in izhodov in primerja opazovane tokove z pro-rata odklonom, osnovano na predhodnem poročilnem poročilu. Rezultati pokazujejo, da se poročilne poročila včasih nepravilno prilagajajo in da so spremembe v smeri povečanja in zmanjševanja naporov, kar ne ustreza enakomernemu pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto pogosto

NASLOV: Option Prices, Analyst Expectations, and Stock Returns AVTORJI: Ian W. R. Martin, Moritz Rodenkirchen, Christian Wagner, Andrew Wang POVZETEK: Raziskava se ukvarja z predviđanjem tržnih vrednosti na podlagi opcijskih cijena in analističkih očekovanj. Izračunana opcijska mera očakanih vrednosti (OIRet) bolje predviđa tržne vrednosti uvek i izveza, od 1 do 12 mjeseci, učinkovitije od očekivanj analista. OIRet i šokovi u njo uključeni tako objašnjajo veću delovinu tržnih vrednosti: na 12 mjesecnom horizontu, objašnjena delovina realiziranih vrednosti povećava se od 17% na 33%, a na 1 mjesecnom horizontu od 1.1% na 22%. METODA: Raziskava se osnovna na analizi opcijskih cijena i analističkih očekivanja tržnih vrednosti. Izračunana opcijska mera očakanih vrednosti (OIRet) je izračunana iz podatkov o opcijskim cijenama indeksa i individualnih akcija, bez uporabe procenjenih parametra. OIRet se uporablja za predviđanje tržnih vrednosti i objašnjanje tržnih vrednosti. KLJUČNE UGOTOVITVE: - OIRet bolje predviđa tržne vrednosti uvek i izveza, od 1 do 12 mjeseci, učinkovitije od očekivanj analista. - OIRet i šokovi u njo uključeni objašnjajo veću delovinu tržnih vrednosti: na 12 mjesecnom horizontu, objašnjena delovina realiziranih vrednosti povećava se od 17% na 33%, a na 1 mjesecnom horizontu od 1.1% na 22%. - OIRet i šokovi u njo uključeni imaju majorno vlogo pri objašnjanju tržnih vrednosti. POMEN ZA TRGOVANJE: Raziskava je koristan znanstveni prispevek za trgovce, ker objavi, da opcijske cijene in očekovanja analista predstavljajo učinkovite predviđanja tržnih vrednosti. OIRet, kot je izračunana v raziskavi, je učinkovit prediktor tržnih vrednosti, kar lahko pomaga trgovcem pri ustanovitvi strategij in investicij.

NASLOV: State-Dependent Behavioral Hedging: Integrating Herding Regimes into Quantitative Risk Management AVTORJI: Ni navedeno POVZETEK: Članek nudi model za dinamično hedgesiranje, ki je opremljen s informacijami o stanju, da bi se prilagodilo dinamiki tržnih pogojev in investorskih ciljev. Model kombinira Markov-switching CSAD model za izkazovanje herdiranja z VECM-Robust EWMA sistematiko za ocenjevanje volatilnosti. Dve strategije - Conditional Hedging (OHR_on/off) in Enhanced Behavioral Hedging (OHR_alpha) - so razvijene in ocenjene pod striženim razširjenim oknočasovnim okvirjem, da bi izogaben predhodnje gledali prejšnje podatke. Natančno je pokazano, da Conditional Hedging Strategy (OHR_on/off) znatno zmanjša frekvenco hedgesiranja, pri čemer se zmanjša tudi naraščanje naraščajočih naročil. Strategija Enhanced Behavioral Hedging (OHR_alpha) boljša je risk-adjusted performance, vključno z adaptivno vključevanjem behavijalnega rizika v obdobjih podnjenega tržnega stresa. METODA: Članek kombinira Markov-switching CSAD model za izkazovanje herdiranja z VECM-Robust EWMA sistematiko za ocenjevanje volatilnosti. Dve strategije - Conditional Hedging (OHR_on/off) in Enhanced Behavioral Hedging (OHR_alpha) - so razvijene in ocenjene pod striženim razširjenim oknočasovnim okvirjem, ki izogaja predhodnje gledali prejšnje podatke. Model je testiran na VN30 indeksu futures (2019-2025). KLJUČNE UGOTOVITVE: - Conditional Hedging Strategy (OHR_on/off) zmanjša frekvenco hedgesiranja, pri čemer se zmanjša tudi naraščanje naročil. - Enhanced Behavioral Hedging Strategy (OHR_alpha) boljša je risk-adjusted performance, vključno z adaptivno vključevanjem behavijalnega rizika v obdobjih podnjenega tržnega stresa. - Model omogoča prilagodljivost hedgesiranja, ki se prilagaja dinamiki tržnih pogojev in investorskih ciljev. POMEN ZA TRGOVANJE: Ugotovitve iz članka so uporabne za praktiko, saj omogočajo prilagodljivost hedgesiranja, ki se prilagaja dinamiki tržnih pogojev in investorskih ciljev. To pomeni, da se lahko hedgesiranje prilagodi različnim stanjem tržnih pogojev, kar poveča ekonomske učinkovitosti hedgesiranja.

NASLOV: Implied Volatility in One Straight Line: Machine Precision at Vector-Hardware Throughput AVTORJI: Wolfgang Schadner POVZETEK: Raziskava razvija Black–Scholes inverzni algoritem za implicitno volatilnost, ki ga izvede na napredku strojnega odziva, zmanjševanje stroškov na enolični vrstici in ohranjanje strojnega točnega odziva. Algoritem uporablja 63 tabuliranih koeficienta in eno koordinatno oblast, ter je brez iteracije. Na 2024 S&P 500 feedu odgovara 29,997 izme 30,000 normaliziranih ponudil. Največja naborovna relativna napaka je 5,3 × 10−16. Na enem CPU vrstici procesira algoritem 11,6 in 8,3 večkrat hitreje kot javna skalarizirana različica metode Let’s Be Rational. Na NVIDIA H100 GPU procesiranje je 0,061 ns na ponudilo v vrstici in 0,031 ns, ko so dve koordinatni oblasti ohranjene kot oddelne deli. METODA: Raziskava se osredotoča na razvoj inverzne metode za implicitno volatilnost, ki ohranja strojnega točnega odziva. Algoritem je izveden na napredku strojnega odziva, zmanjševanje stroškov na enolični vrstici in ohranjanje točnega odziva. Koordinate, v katerih je algoritem izveden, so odkriti iz analitične strukture in vključujejo realno in periodično omejitev. Raziskovalnik je dokazal točno struktorno teorem za razvoj pri ohranjenem skališčnem cene. KLJUČNE UGOTOVITVE: - Algoritem je izveden na napredku strojnega odziva in ohranja strojnega točnega odziva. - Algoritem uporablja 63 tabuliranih koeficienta in eno koordinatno oblast, ter je brez iteracije. - Na 2024 S&P 500 feedu odgovara 29,997 izme 30,000 normaliziranih ponudil. - Največja naborovna relativna napaka je 5,3 × 10−16. - Algoritem procesira 11,6 in 8,3 večkrat hitreje na enem CPU vrstici kot javna skalarizirana različica metode Let’s Be Rational. - Na NVIDIA H100 GPU procesiranje je 0,061 ns na ponudilo v vrstici in 0,031 ns, ko so dve koordinatni oblasti ohranjene kot oddelne deli. POMEN ZA TRGOVANJE: Raziskovalni algoritem je uporaben za hitre in točne izračune implicitne volatilnosti, ki so ključne za trgovino na finančnih trgovinah. Algoritem omogoča hitreje in točnejše izračune, kar pomeni večjo preciznost in hitrost v trgovini. To je posebno pomembno za trgovce, ki uporabljajo složene finančne instrumente, kot so opcije, kjer je točnost izračunov ključna za uspešno trgovino.

NASLOV: 7465842-a-valuation-based-interpretation-of-stock-market-volatility-and-return-predictability AVTORJI: Roland Clère POVZETEK: Raziskava raziskuje povezavo med tržno volatilnostjo in prihodnimi vrednostmi v okviru ocenjevalnega mehanizma. Natančno, raziskava se osredotoča na to, ali je tržno-predviđen potrebna vrednost del valueločnega mehanizma, ki povezuje temeljite, volatilnost trga in prihodnje vrednosti. Metodologija je opremljena s Monte Carlo analitojo, ki je uporabljena za razvrstitev letnih povratkov S&P 500. Ključna je, da se temeljite, kot so akumulacija kapitala, dividendni vrednost in volatilnost, razkrivajo kot približno ravnoteženo potrebno vrednost, ki je bližje povprečni tržni IRR. Tudi povratki, ki jih volatilnost realizira, prikazujejo strukturo valueločnega in okoliščnega ciklusa, ki ima značilno prediktivno vrednost. METODA: Raziskava uporablja Monte Carlo analitojo, ki je prilagojena S&P 500 podatkom od leta 2014. Prva koraka je razvrstitev letnih povratkov, ki jih volatilnost realizira, z uporabo empirično kalibriranih vrednosti za akumulacijo kapitala, profitability, očitano rast in predviđeno potrebno vrednost. Nato se raziskava obravnava, kako temeljite, kot so akumulacija kapitala, dividendni vrednost in volatilnost, pripovedujejo za ravnoteženo potrebno vrednost, bližjo povprečni tržni IRR. Konečno, raziskava razvrstuje letne povratke, ki jih volatilnost realizira, v strukturo valueločnega in okoliščnega ciklusa, ki je značilno prediktivna. KLJUČNE UGOTOVITVE: - Volatilnost trga ni le merilo rizika, ampak del valueločnega mehanizma, ki povezuje temeljite, potrebno vrednost in prihodnje vrednosti. - Akumulacija kapitala, dividendni vrednost in volatilnost pripovedujejo za ravnoteženo potrebno vrednost, bližjo povprečni tržni IRR. - Letne povratke, ki jih volatilnost realizira, prikazujejo strukturo valueločnega in okoliščnega ciklusa, ki je značilno prediktivna. POMEN ZA TRGOVANJE: Raziskava podpira unifejiran valueločen prihodnji interpretacijo, ki povezuje volatilnost trga, ravnoteženo potrebno vrednost in prihodnje tržne dinamike. To je koristan znanstveni rezultat za trgovce, ki se uporabljajo v modelih ocenjevanja vrednosti, saj omogoča bolj natančno razumevanje in predviđanje tržnih povratkov.

NASLOV: 7465759-acquisition-side-uncertainty-in-relative-rationality-theory-emotional-adjusted-e AVTORJI: Massimiliano Di Toro, Ph.D. POVZETEK: Članek razvija koncept neodločitve na strani kupca v teoriji relativne razumnosti (RRT) z uporabo emocijsko dostosovane očekovane stote (EAEC). Standardna teorija potrebnosti, teorija stroškov iskanja in psihosociološka raziskava kupcev ponuja pomembne razprave o necijenovnih lastnostih, iskanju, času, čakanju, izdelekotoku in kupčevem iskustvu. Ključno je, da neodločitve na strani kupca ne smeta oceniti le s nominalnim cijenom ili očekovanim finansijskim troškom. EAEC prevede ekonomske in emocijske posljedice učinka neodločitve učinkovitostni troškove učinka u ekvivalentni novac strukturi. Relativno razumna odločba ne mora biti najpoučnija opcija, ampak opcija s najnižjim očekovanim emocijsko dostosovanim troškom. METODA: Članek razvija koncept neodločitve na strani kupca v RRT s uporabo emocijsko dostosovane očekovane stote (EAEC). Standardne modelne teorije, kot so teorija potrebnosti, teorija stroškov iskanja in psihosociološka raziskava kupcev, predstavljajo necijenovne lastnosti, iskanje, čas, čakanje, izdelekotoku in kupčevo iskustvo. Ključno je, da EAEC prevede ekonomske in emocijske posljedice učinka neodločitve u ekvivalentni novac strukturi. Relativno razumna odločba ne mora biti najpoučnija opcija, ampak opcija s najnižjim očekovanim emocijsko dostosovanim troškom. KLJUČNE UGOTOVITVE: - EAEC prevede ekonomske in emocijske posljedice učinka neodločitve u ekvivalentni novac strukturi. - Relativno razumna odločba ne mora biti najpoučnija opcija, ampak opcija s najnižjim očekovanim emocijsko dostosovanim troškom. - EAEC razvija koncept neodločitve na strani kupca v RRT s uporabo emocijsko dostosovane očekovane stote. - EAEC razlikuje među jednokratnim očekivanim emocijsko dostosovanim troškom i ponovnim iskanjem emocijsko dostosovane očekovane stote. POMEN ZA TRGOVANJE: Ugotovitve o EAEC so uporabne za trgovce, ker omogočajo ocenjevanje neodločitve na strani kupca z upoštevanjem ne samo finansijskih, ampak tudi emocijskih faktorjev. To pomeni, da trgovci morajo razmotriti ne samo cijene, ampak tudi emocijsko iskustvo kupcev pri iskanju in kupnji izdelkov. To lahko pomeni, da nekatere opcije s visokim cijenom lahko imajo manjši emocijski trošek in tudi boljši razpon za trgovce.

NASLOV: Mandatory Disclosure of Investor Complaints and Firm Risk: Evidence from India AVTORJI: Ni navedeno POVZETEK: Članek raziskuje, kako se likvidni trgi odzivijo na obvezno izvajanje o podatkih o nasiljanih zastopanih in podatkih o podjetiščnem riziku. V času, ko je 1. decembra 2015 v Indiji vnesli pravila o obvezni izvajanju o nasiljanih zastopanih, je izveden raziskovalni projekt, ki je obravnaval 722 indijskih podjetij od finančnega leta 2011 do 2021. Raziskovalci nameravali so oceniti, kako se podjetniško riziko in idiosinkratično riziko spremeni, ko se podjetja zaposlijo v obvezni izvajanje o nasiljanih zastopanih. Natančno so najdeni rezultati, da podjetja z visoko pred-izvajansko nasiljeno zastopanje so zaznamovala večjega podnebnega rizika, kar se je znotraj petletnega obdobja znotraj 4,7% podjetjaščega rizika. METODA: Raziskovalci so uporabili različico metode razlike v različicah, ki je okupila podatke o 722 indijskih podjetjih od finančnega leta 2011 do 2021. Podatki so bili zbrani, saj so pravila obveznega izvajanja zaposlila vse odbrojene podjetja. Pred-trendni testi in tri pseudo-izdelki so bili neznačilni, kar je podpiralo natančnost rezultatov. KLJUČNE UGOTOVITVE: - Podjetja z visoko pred-izvajansko nasiljeno zastopanje so zaznamovala večjega podnebnega rizika. - Podnebno riziko je povečalo o 2,6% med 25. in 75. percentilom pred-izvajanske nasiljene zastopanje. - Efekt se ni zaznamal pri implementaciji, ampak je znotraj petletnega obdobja znotraj 4,7% podjetjaščega rizika. POMEN ZA TRGOVANJE: Raziskovalni prispevek je pomemben za trgovce, ker pokaže, da obvezno izvajanje o nasiljanih zastopanih lahko poveča podnebno riziko podjetij. Trgovci bi morali upoštevati te ugotovitve pri ocenjevanju rizikov in investicij v podjetja, ki so zaposlila v obvezni izvajanje o nasiljanih zastopanih.

NASLOV: 7465182-rebuilding-the-fama-french-factor-construction-inside-the-point-in-time-s-p-500 AVTORJI: Serhat Girgin, QuanterLab UG (haftungsbeschränkt), Germany POVZETEK: Raziskava opisuje, kako četiri Fama-French signali (vrednost, dividendni zasah, znesek zavajnosti in nizka neto izdaja) se oblikujejo v pet konstrukcij v S&P 500 in kako to učinek imajo na povratke in smere. V raziskavi so testirani tri različne konstrukcije: faktorska sredstva, sredstva, ki so privzetka trideset najlažje imen, in sredstva, ki so privzetka trideset najlažje imen z enakomerno težo. Raziskava pokazuje, da je odločitev signala pred naraščajočim zasahom zlasti signalu zasedena, while konstrukcija je odločila za povratke. Vrednost in znesek zavajnosti delujejo v nasprotni smeri, z vrednostjo imenovano 3.5 točke letno pred konstrukcijo, vendar je znesek zavajnosti 2.9 točke letno nazaj. Nizka neto izdaja je edin signal, kjer je signačna smer v S&P 500 obrnjen proti signačni smeri v Fama-Frenchovem arhivu. METODA: Raziskava je izvedena na podlagi 20 letnih okvar, ki se natančno registrirajo pred njihovim izvajanjem, na S&P 500, z uporabo četiri signali: vrednost, dividendni zasah, znesek zavajnosti in nizka neto izdaja. Kaže se, da so vsaka slika testirana s četiri različne konstrukcije: 30 imen z enakomerno težo, 30 imen z vrednostno težo, 100 imen z vrednostno težo, 300 imen z vrednostno težo in vrednostno vmesni razlikovni spredaj. Sprejem signala pred naraščajočim zasahom je zlasti signalu zasedena, while konstrukcija je odločila za povratke. KLJUČNE UGOTOVITVE: - Vrednost in dividendni zasah naraščajočim zasahom predhajajo, while znesek zavajnosti in nizka neto izdaja naraščajočim zasahom nazaj. - Vrednost imenovana 3.5 točke letno pred konstrukcijo, vendar je znesek zavajnosti 2.9 točke letno nazaj. - Nizka neto izdaja je edin signal, kjer je signačna smer v S&P 500 obrnjen proti signačni smeri v Fama-Frenchovem arhivu. POMEN ZA TRGOVANJE: Raziskava je pomembna za trgovce, ker je pokazalo, da konstrukcija imen, ki so privzetka trideset najlažje imen, prinaša večjih povratkov, kot je pokazano s četrtimi konstrukcijami. To pomeni, da trgovci lahko izpeljajo večjih povratkov, če uporabljajo konstrukcije, ki so privzetke trideset najlažje imen, kot je pokazano s četrtimi konstrukcijami.

NASLOV: 7459518-finrl-deepseek-revisited-a-reproduction-and-multi-seed-robustness-analysis-of-ll AVTORJI: Arnold Leumale POVZETEK: Raziskava ponovno izvede in razširi FinRL-DeepSeek [Benhenda, 2025], razvojno okolje za trgovino, ki kombinira Proximal Policy Optimization (PPO), Conditional Value-at-Risk (CVaR) omejitve in signalje z velikimi jezikovnimi modeli (LLM) iz finančnih novic. Izvedba četrtih agentov iz začetka v PyTorchu in njihova povratna testiranje na oborožju Nasdaq-100 (2019–2023) proti enakomerno vloženemu vključenemu vključku (buy-and-hold) pokazuje, da LLM vključevanje poveča vrednost in Sharpe razmerje za omejeni in CVaR-omejeni agenciji. Ta trend pa ne je stabilen pri dveh neodvisnih semih, posebej za PPO-DeepSeek, ki se obrne v drugačno rangiranje vrednosti Sharpe. Vrstni red celotne vrednosti za CPPO-DeepSeek pa je najmanj splošen med dvema semima. Raziskava predstavlja te napačke kot prenovljeno dokazljivost in podpira, da je večsega scale multi-seeda ocene potrebna v finančnem učenju s podobnimi raziskavami. METODA: Raziskava ponovno izvede FinRL-DeepSeek, implementirane četrti agenti (PPO, CPPO, PPO-DeepSeek, CPPO-DeepSeek) v PyTorchu in povratno testira na oborožju Nasdaq-100 (2019–2023) proti enakomerno vloženemu vključku. Izvedba je preizkusljiva in dokumentirana, in vključuje vključek trgovinskega okolja, implementacije PPO/CPPO in evaluacijski potok. Raziskava uporablja dve neodvisni semi, da preveri stabilnost rezultatov. KLJUČNE UGOTOVITVE: - LLM vključevanje poveča vrednost in Sharpe razmerje za omejeni in CVaR-omejeni agenciji. - Trenutni trend ni stabilen pri dveh neodvisnih semih, posebej za PPO-DeepSeek. - CPPO-DeepSeek prikazuje najmanj splošen rangiranje celotne vrednosti med dvema semima. POMEN ZA TRGOVANJE: Raziskava podpira, da je multi-seeda ocena neprimerno v finančnem učenju, kjer se pogosto izvedejo povratna testiranja na enem treniranem izpitu. To podpira potrebo po večji skalirani multi-seeda oceni, ki bi omogočila boljšo razumevanje stabilnosti in prednosti različnih agencij.

NASLOV: 7458418-structural-fragility-and-escape-dynamics-in-liquidity-networks AVTORJI: ni navedeno POVZETEK: Raziskava razvija stohastično teorijo potenciala za opis strukturne slabosti in katastrofne dinamike v finančnih omrežjih. Model opira se na kvartični dvojno-valen potencial, ki je izpeljan iz teorije katastrof. Kramersovo teorijo izhodnih hitrosti uporablja za izračunavanje povprečne prvega pristanka do sistemskega razpadka v slabnem šumovnem obdobju. Kritično slowing down je določen kot korolar. METODA: Raziskava uporablja kvantitativne metode, kot so stohastične dinamike in Kramersovo teorijo izhodnih hitrosti. Klasifikacija je opredeljena z dva merila: ščitljivost preko parametra η in strukturno omejeno preko parametra θ. Euler–Maruyama simulacije potrdijo Kramersovo predpoved. KLJUČNE UGOTOVITVE: - Strukturna slabost finančnih omrežij modelira se z kvartičnim dvojno-valen potencialom. - Kritično slowing down je določen kot korolar. - Povprečna prva čas do sistemskega razpadka je predviđena z Kramersovo teorijo v slabnem šumovnem obdobju. POMEN ZA TRGOVANJE: Ugotovitve te raziskave so uporabne za razumevanje in prepoznavanje rizikov v finančnih omrežjih. Kritično slowing down omogoča prepoznavanje občutljivosti sistemov pred katastrofami, kar je ključno za razvoj macroprudencialnih politik. Predpovedi povprečne prvega pristanka do razpadka omogočajo boljše planiranje in odziv na rizike v finančnih trgovinah.

NASLOV: The Common Source of Option Return Anomalies AVTORJI: Daniil Gerchik POVZETEK: Raziskava razkrije, da večja večina anomašči, ki predviduje vrhunčne vrstne vrstne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunčne vrhunč

NASLOV: 7458140-ranks-into-returns-neural-learning-to-rank-for-equity-selection-under-real-world AVTORJI: Lucas Jaccard POVZETEK: Raziskava obravnava, kako različne re-encodiranje metode učenja in rangiranja vplivajo na vrstni red in vrhunek vloženosti v podjetja. Izpeljana je, da je prehod od rangiranja s centrirano percentilsko rangiravo do inverzne normalne rangirave poveča vrednost vrhunek vloženosti za 0,370 (4,46 t-stat). Tega vpliva deli v dve: 0,224 z pomenom (0,020) se nanaša na skalarni prehod, medtem ko 0,146 (0,065) se nanaša na oblikovni prehod. Raziskava je izvedena na podlagi 144 kakovostnih mer na 1000 največjih ameriških podjetij, z 272 mesečnimi obravnavami od aprila 2003 do novembra 2025. METODA: Raziskava je izvedena na podlagi 144 kakovostnih mer na 1000 največjih ameriških podjetij, z 272 mesečnimi obravnavi. Različne re-encodiranje metode učenja in rangiranja so preizkusljiva na različnih rangiravah. Raziskava je uporabila gradient-boosted ranker kot kontrolno metodo, ki je vrnila natančno nulto razliko med različnimi rangiravami. KLJUČNE UGOTOVITVE: - Prehod od centrirane percentilne rangirave do inverzne normalne rangirave poveča vrednost vrhunek vloženosti za 0,370 (4,46 t-stat). - Skalarni prehod poveča vrednost vrhunek vloženosti za 0,224 (0,020). - Oblikovni prehod poveča vrednost vrhunek vloženosti za 1,95% (0,015). - Vrednost vrhunek vloženosti poveča od 18,51% na 22,14%, medtem ko volatilnost zmanjša od 16,81% na 15,06%. - Vpliv izvajanja je večji, saj vsako od 12 specifičnih postavk izgublja med 0,45 in 0,56 vrednosti vrhunek vloženosti. POMEN ZA TRGOVANJE: Raziskava je uporabna za trgovce, saj ukazuje na pomembnost pravilnega rangiranja in re-encodiranja podatkov pri izbiri vključenih podjetij v poročilo. Skalarni in oblikovni prehodi lahko povečajo vrednost vrhunek vloženosti, kar je pomembno za učinkovito trgovino.

NASLOV: 7453580-gate-design-and-stage-dependent-incentives-in-retail-proprietary-trading-evaluat AVTORJI: Nicholas Hall POVZETEK: Raziskava opisuje, kako geometrija kontraktov različnih etap v evaluacijah proizvajalni trgovine v več etapah vključno z omejitvami, ki ustvarljavo odlične in preproste inčive, ki so različne po etapih. Raziskava pokazuje, da je prehod v evaluaciji slab signal za moč, ker omejitve vključno z omejitvami trailing drawdown in minimalnimi oborožilišči trgovine vključno z pravilami za udrževanje, ustvarljavo inčive, ki so različne po etapih. To pomeni, da je prehod v evaluaciji slab signal za moč, saj omejitve vključno z omejitvami trailing drawdown in minimalnimi oborožilišči trgovine vključno z pravilami za udrževanje ustvarljavo inčive, ki so različne po etapih. Raziskava tudi pokazuje, da je omejeno pravilo za udrževanje zelo omejeno v moči, saj je možnost prehoda v evaluaciji približno 0.40, proti merjenemu kohezijskemu razmerju 0.168. Raziskava tudi pokazuje, da je omejeno pravilo za udrževanje zelo omejeno v moči, saj je možnost prehoda v evaluaciji približno 0.40, proti merjenemu kohezijskemu razmerju 0.168. METODA: Raziskava uporablja merjenja in simulacije, da opisuje inčive, ki so ustvarljave v kontraktih različnih etap v evaluacijah proizvajalni trgovine. Raziskava meri omejitve, ki ustvarljave inčive, ki so različne po etapih, in opisuje, kako te omejitve vključno z omejitvami trailing drawdown in minimalnimi oborožilišči trgovine vključno z pravilami za udrževanje ustvarljavo inčive, ki so različne po etapih. Raziskava tudi meri možnost prehoda v evaluaciji in omejeno pravilo za udrževanje, in opisuje, kako te omejitve vključno z omejitvami trailing drawdown in minimalnimi oborožilišči trgovine vključno z pravilami za udrževanje ustvarljavo inčive, ki so različne po etapih. KLJUČNE UGOTOVITVE: - Geometrija kontraktov ustvarja inčive, ki so različne po etapih, in prehod v evaluaciji ni dovolj dobro signal za moč. - Omejitve vključno z omejitvami trailing drawdown in minimalnimi oborožilišči trgovine vključno z pravilami za udrževanje ustvarljavo inčive, ki so različne po etapih. - Možnost prehoda v evaluaciji je približno 0.40, proti merjenemu kohezijskemu razmerju 0.168. - Omejeno pravilo za udrževanje je zelo omejeno v moči. POMEN ZA TRGOVANJE: Raziskava pokazuje, da je prehod v evaluaciji slab signal za moč, saj omejitve vključno z omejitvami trailing drawdown in minimalnimi oborožilišči trgovine vključno z pravilami za udrževanje ustvarljavo inčive, ki so različne po etapih. To pomeni, da je prehod v evaluaciji slab signal za moč, saj omejitve vključno z omejitvami trailing drawdown in minimalnimi oborožilišči trgovine vključno z pravilami za udrževanje ustvarljavo inčive, ki so različne po etapih. Trgovci bi morali upoštevati, da prehod v evaluaciji ni dovolj dobro signal za moč, saj omejitve vključno z omejitvami trailing drawdown in minimalnimi oborožilišči trgovine vključno z pravilami za udrževanje ustvarljavo inčive, ki so različne po etapih.

NASLOV: Deleveraging When Investors Trade Again AVTORJI: Zihan Chen, New York University POVZETEK: Članek raziskuje, ali znanje o prihodnjih trgovinah dovoli za izbiro alokacije, ki minimalizira očekovane nedovoljene sredstva računov. V treh-oddevniciški ekonomi s dvema hederji in eno preostalo rizikno nosilce, institucija deloži odrejen ukaz o razkrščju med hederji. Cene, trgovine in položaji se ujemajo na skupnem regularnem ekvilibrijsnem obsegu, vendar imajo različne minimalne alokacije. Cene sredstev spremenijo terminalne stanja, ki potrebujejo ponovno zaopatitev, brez spremembe neogranicene trgovinske potrebe. Ključne ugotovitve vključujejo, da je informacija o kontrahentovih nedovoljenih sredstvih ključna za izbiro alokacije, če je poznata razdelitev odzivov. METODA: Članek razvija teorijo o treh-oddevniciških ekonomi, kjer hederji in kontrahenti izbirajo svoje položaje pod omejitvami z dolgoročnimi sredstvi, položaji in količine kontrahentov. Cene, trgovine in položaji se ujemajo na skupnem regularnem ekvilibrijsnem obsegu, vendar imajo različne minimalne alokacije. Ključno je, da informacija o kontrahentovih nedovoljenih sredstvih je ključna za izbiro alokacije, če je poznana razdelitev odzivov. KLJUČNE UGOTOVITVE: - V treh-oddevniciški ekonomi s dvema hederji in eno preostalo rizikno nosilce, skupne cene, trgovine in položaji ujemajo na skupnem regularnem ekvilibrijsnem obsegu, vendar imajo različne minimalne alokacije. - Cene sredstev spremenijo terminalne stanja, ki potrebujejo ponovno zaopatitev, brez spremembe neogranicene trgovinske potrebe. - Informacija o kontrahentovih nedovoljenih sredstvih je ključna za izbiro alokacije, če je poznana razdelitev odzivov. POMEN ZA TRGOVANJE: Ugotovitve poudarjajo, da je informacija o kontrahentovih nedovoljenih sredstvih ključna za izbiro alokacije, ki minimalizira očekovane nedovoljene sredstva računov. To je relevantno za praktiko, saj pomeni, da institucije morajo upoštevati te informacije pri odločanju o alokaciji, če želijo minimizirati finančne težave računov.

NASLOV: 7452838-geopolitical-supply-chain-vulnerability-and-relative-sectoral-equity-returns-evi AVTORJI: Dr Arsene Oka POVZETEK: Raziskava obravnava, ali finančne trge ocenjejo spremembe v geopoliticalni naraščajoči nevarnosti zavisanosti od Kitajske v zvezi z visoke in nizke industrije v ZDA in Kitaji. Izpeljana je, da ena standardna devijacija povečanje zaznamane nevarnosti v visokih industrijskih sektorjih poveča z 1,10 točke enotne stopnje stopnjo visoke visoke stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje stopnje

NASLOV: 7445920-gated-physics-informed-neural-networks-for-inverse-local-volatility-calibration-. AVTORJI: Manoov Rajapandy, Vrinda Bajaj, Veydant Katyal POVZETEK: Raziskava obravnava problem lokalne volatilnosti calibracije iz diskretnih cijen opcij, ki je v realnih trgovinskih okoliščinah zelo težki. Standardni Dupire-ov formula ni učinkovit pod zelo šumskimi ali raskrščajnimi podatki. Naučeni metodi z uporabo Fizike-informiranih neuronnih mrež (PINN) ponujajo alternativno rešitev, vendar standardna skupna optimizacija cijene in volatilnosti mrež povzroča problem s četrtišnjo gradientom, ki preprečuje konvergenco. Raziskovalci predlagajo decoupled PINN arhitekturo, kjer je cijena učenja pod Black-Scholes omejitvami, medtem ko je volatilnost učenje neodvisno. Stop-gradient operacija odstrani problem s četrtišnjo gradientom in omogoča strukturno invarianco. Volatilnost je predstavljena kot gatovano množico dveh ekspertnih mrež, ki omogoča naravno učenje regime strukture. Metoda je potrdljiva na sintetičnih dvojregimnih površinah in stvarnih opcij, prikazuje značajne izboljške v stabilnosti prepoznavanja pod raskrščajnimi podatki. METODA: Raziskovalci razvijajo decoupled PINN arhitekturo, kjer je cijena učenja pod Black-Scholes omejitvami, medtem ko je volatilnost učenje neodvisno. Stop-gradient operacija odstrani problem s četrtišnjo gradientom in omogoča strukturno invarianco. Volatilnost je predstavljena kot gatovano množico dveh ekspertnih mrež, ki omogoča naravno učenje regime strukture. Metoda je potrdljiva na sintetičnih dvojregimnih površinah in stvarnih opcij. KLJUČNE UGOTOVITVE: - Decoupled PINN arhitektura omogoča strukturno invarianco volatilnosti. - Volatilnost je predstavljena kot gatovano množico dveh ekspertnih mrež. - Metoda je potrdljiva na sintetičnih dvojregimnih površinah in stvarnih opcij. - Volatilnost R² na 10% podatkovna denščina je 0,97, pri čemer je Dupire-ov baseline 0,342. POMEN ZA TRGOVANJE: Raziskovalci predstavljajo nov metode, ki omogočajo učinkovitejšo calibracijo lokalne volatilnosti iz diskretnih cijen opcij, sestavljenih iz gatovane množice dveh ekspertnih mrež. To je posebno koristno za trgovce, ki delujejo pod raskrščajnimi podatki ali šumskimi podatki, saj omogoča stabilnejši in učinkovitejši učenje volatilnosti.

NASLOV: 7436520-when-information-breaks-the-historical-pattern-preliminary-evidence-from-a-deployed-bitcoin-forecaster AVTORJI: Kibaek Kim, Kiok Kim, Danielle Ahn POVZETEK: Raziskave je uradili trebači, ki so auditirali predviščilnik za Bitcoina in ProShares Bitcoin Strategy ETF (BITO). Natančno je preverjeno, da je predviščilnik natančen pri normalnih dneh, vendar se zavoljeno pri dneh z večjo novino in večjo zmoto. Natančnost pri zmotnih dneh je padla do 36.7% za Bitcoina in 37.5% za BITO. Vse nominalne 80%-ne razponke so prihodnje zmotne dne napačne. Spikes v številkah novic so povezani z večjim standardiziranim nesporazumom, vendar se natančnost smeri zavoljeno le za Bitcoina. METODA: Raziskovalci so uporabili 1,973 Bitcoina in 1,125 BITO predviščilnikov, ki so bili aktivni od leta 2021 do 2026. Predviščilnik je bil opisan kot sestavljen iz sedmi zgodovinskih analoga. Predviščilnik je bil testiran na dneh s večjo novino in dneh z večjo zmoto. Standardizirana nesporazumnost in natančnost smeri so bile ključne merila. KLJUČNE UGOTOVITVE: - Natančnost smeri je padla do 36.7% za Bitcoina in 37.5% za BITO na dneh z večjo zmoto. - Vse nominalne 80%-ne razponke so prihodnje zmotne dne napačne. - Spikes v številkah novic so povezani z večjim standardiziranim nesporazumom. - Natančnost smeri se zavoljeno le za Bitcoina, vendar ne za BITO. POMEN ZA TRGOVANJE: Raziskave ukazujo, da predviščilniki lahko ne morejo predviščiti zmotnih dneh, ki so pogosto povezani z večjo novino. Trgovci bi morali biti ogledni, da predviščilniki lahko ne morejo pravilno odgovarjati na predpovedne spremembe, ki so pogosto povezane z večjo novino. To pomeni, da je natančnost predviščilnikov zavoljena pri dneh z večjo novino in večjo zmoto.

NASLOV: 7433780-shield-a-sulistyardi-hedging-indicator-for-equity-loss-defense-a-regime-conditio AVTORJI: Hanifah Bagus Sulistyardi POVZETEK: Članek razvija SHIELD, ki je kompozitni regimski odkritiški oscilator, ki je izgraden iz trdnih predstavnikov - Infobank15 indeksa (domestične bankovne tečajnosti), Telkom Indonesia (TLKM) (tečajnost prebivalstveno vplivanega tečajnega indeksa) in Indo Tambangraya Megah (ITMG) (tečajnega cikla za komoditete). SHIELD je namenjen filtriranju rizika v različnih regimih na Indonezijskem akcionarnem trgu. Raziskava dokazuje, da inicialna specifikacija, ki optimizira faktorske težavnosti, normalizacijsko okno in regimske meje, je slabša izven vzorca, zato se odloča za specifikacijo, ki nastavijo faktorske težavnosti na strukturno osnovano vrednost in prilagaja samo regimske meje na natančnem planu. METODA: 1. SHIELD je izgraden iz treh strukturno osnovanih predstavnikov: Infobank15, TLKM in ITMG. 2. Raziskava uporablja regimski odkritiški oscilator, ki je natančno kalibriran na natančnem planu. 3. Sistem SHIELD je testiran na ciklu 2022-2026, pri čemer se izvede full-sample, walk-forward in parameter-ablation primerjava. 4. Kalibriran SHIELD je uporabljen za analizo opadanja Indonezijevega kompozitnega indeksa (IHSG) v zvezi s 2026-tem MSCI statusom. KLJUČNE UGOTOVITVE: - SHIELD je kompozitni regimski odkritiški oscilator, ki je izgraden iz strukturno osnovanih predstavnikov. - Inicialna specifikacija, ki optimizira faktorske težavnosti, normalizacijsko okno in regimske meje, je slabša izven vzorca. - Kalibriran SHIELD z natančno prilagajajočo regimsko mejo je bolj natančen. POMEN ZA TRGOVANJE: Raziskava je uporabna za trgovce, ker SHIELD omogoča filtriranje rizika v različnih regimih na Indonezijskem akcionarnem trgu. Kalibriran SHIELD omogoča bolj natančno odkritje regimov in pomoč pri upravljanju z rizikom.

NASLOV: 6836498-0dte-options-and-the-price-of-tail-protection AVTORJI: James O’Donovan POVZETEK: V članku je raziskan vpliv dnevnih 0DTE (zero-day-to-expiry) opcij na sklenjeno volatilnost (skew) S&P 500 opcij. V Mayu 2022 je CBOE dodal tedenične 0DTE opcije za Toreček in Četrtek, češči dnevnih opcij na vsak teden. Sklenjeno volatilnost se zmanjšala, največ na 30-dnevni volatilnosti. Raziskava uporabila je različne tenorje in podatke, da je dokazala, da je ta sprememba prinesla značilno zmanjšanje sklenjene volatilnosti. Sklenjeno volatilnost se zmanjšala za 0.72 punkta na 30-dnevni volatilnosti, pri čemer je zmanjšanje bilo manjše na dolgoterorjnih volatilnostih. Tako je zmanjšanje sklenjene volatilnosti vložilo značilno prehod v cene določenega dolgoročnega risika. METODA: Raziskava je uporabila različne tenorje in različne datume, da je identificirala spremembo sklenjene volatilnosti. Določila je, da je sklenjeno volatilnost zmanjšala 0.72 punkta na 30-dnevni volatilnosti, pri čemer je zmanjšanje bilo manjše na dolgoterorjnih volatilnostih. Raziskava je tudi preverila, da je ta sprememba značilna in ne odraža običajnih prehodov. KLJUČNE UGOTOVITVE: - Sklenjeno volatilnost S&P 500 opcij se zmanjšala, največ na 30-dnevni volatilnosti. - Raziskava je identificirala, da je ta sprememba prinesla značilno zmanjšanje sklenjene volatilnosti. - Zmanjšanje sklenjene volatilnosti je bilo manjše na dolgoterorjnih volatilnostih. POMEN ZA TRGOVANJE: Raziskava je podala pomembne informacije o vplivu dnevnih 0DTE opcij na cene sklenjene volatilnosti. Trgovci lahko uporabijo te ugotovitve za boljšo oceno cijen dolgoročnega risika in za izboljšanje strategij za dolgoročno risiko. Tako lahko trgovci prepoznajo, da je cena dolgoročnega risika spremenila, če je vložili v dnevnih 0DTE opcij.

NASLOV: Are Family Firm Stocks More Prone to Crash? AVTORJI: Heng An, Xu Niu POVZETEK: Raziskava se strinje, da so akcijske akcie družb, v katerih velja porodično vladavstvo (family firms), več naraščajočih v riziki, da se razpadneta. To je zato, ker porodično vladavstvo lahko zmanjša tradicionalen agencijni problem med menadžerji in akcionarji, kar pomeni manjši rizik, da bi menadžeri izkoriščali družbo. Vendar pa lahko porodično vladavstvo tudi poveča agencijni konflikt med porodico in drugimi akcionarji, kar pomeni večji rizik. Raziskava najdemo, da so akcijske akcie porodičnih družb več naraščajočih v riziki, kot so akcijske akcie družb, v katerih velja neporodično vladavstvo. Tudi nepristranska informacija ima večjim utemeljenostim v porodičnih družbah. Učinkovito je, da se porodične družbe zdržajo od razpadneta, če je vodilni odbor neodvisnejši. METODA: Raziskava je izvedena na podlagi podatkov o 35,9% državljanstev S&P 1500 indeksa od leta 1992 do 2019. S pomočjo dveh alternativnih meril akcijskega razpadnega rizika je najdena večja naraščajoča v riziki akcijskih akcij porodičnih družb. Raziskava je tudi pokazala, da je nepristranska informacija večjim utemeljenim v porodičnih družbah. Učinkovito je, da se porodične družbe zdržajo od razpadneta, če je vodilni odbor neodvisnejši. KLJUČNE UGOTOVITVE: - Porodične družbe so več naraščajočih v riziki, da se razpadneta, kot neporodične družbe. - Nepristranska informacija ima večjim utemeljenostim v porodičnih družbah. - Neodvisnejši vodilni odbor je učinkovito v zdržanju porodičnih družb od razpadneta. POMEN ZA TRGOVANJE: Raziskava je pomembna za trgovce, ker podpira, da je porodično vladavstvo lahko izkušenje, ki poveča riziki, da se akcijske akcie razpadneta. Trgovci bi morali upoštevati to, ko izbirajo akcijske akcije za svoje investicije. Tudi regulativni organi in politiki bi morali upoštevati te ugotovitve pri pripravljanju zakonodaje in politik, ki se nanašajo na porodične družbe.

NASLOV: SOCIAL INTERACTIONS AND LOTTERY STOCK MANIA AVTORJI: Turan G. Bali, David Hirshleifer, Lin Peng, Yi Tang, Qiguang Wang POVZETEK: Raziskava je proučila, kako socialne interakcije vplivajo na vložnično čustvo in cene akcij, ki so podobne loteriji. Izrazi socialne omiljene aktivnosti o akciji na platformah, kot so StockTwits in Facebook, so povezane z večjim številom ekstremnih cinkov in večjo vrednostjo, ki jo vloženci pripišeta te akciji. Akcije, ki so v vejeh z večjo socialno povezanostjo, imajo nizko spodnjo vrednost in večja razbiralnost med vloženci. Te ugotovitve podpirajo teorije, ki pravijo, da socialne interakcije vplivajo na vložnično čustvo in cene akcij. METODA: Raziskovalci je uporabili podatke iz dveh družbenih omiljenih platform: StockTwits in Facebook. Podatki o socialni aktivnosti o akciji na StockTwits so povezani z ekstremnimi cinkovimi dogodki, kot so večja cinkovita povezavnost in večja vrednost, ki jo vloženci pripišeta akciji. Podatki iz Facebooka so podrobno preverili, kako socialna povezanost v regijah, v katerih so akcije izdane, vpliva na cene akcij. KLJUČNE UGOTOVITVE: - Večja socialna aktivnost o akciji na StockTwits povezana je z večjim številom ekstremnih cinkov in večjo vrednostjo, ki jo vloženci pripišeta te akciji. - Akcije, ki so v regijah z večjo socialno povezanostjo, imajo nizko spodnjo vrednost in večja razbiralnost med vloženci. - Te ugotovitve podpirajo teorije, ki pravijo, da socialne interakcije vplivajo na vložnično čustvo in cene akcij. POMEN ZA TRGOVANJE: Raziskava podpira teorije, da socialne interakcije lahko vplivajo na vložnično čustvo in cene akcij, ki so podobne loteriji. To je posebno relevantno za vložnike, ki uporabljajo družbeni omiljeni platforme za informacije o akcijah. Trgovci bi morali biti v poročilu o socialnih interakcijah in njihovem vplivu na cene akcij, da lahko bolje razumejo trendi in prilagajajo svoje strategije.

NASLOV: External Equity Financing Shocks, Financial Flows, and Asset Prices AVTORJI: Frederico Belo, Xiaoji Lin, Fan Yang POVZETEK: Raziskava opisuje vpliv oglašenih ekvitynih finančnih šokov na cene lastnikov, realne količine in finančne tokove v skupnem podatkovnem vzoru ameriških javnih podjetij. Pravilno izveden model z učinke finančnih oglašenih šokov omogoča razumevanje sistematične rizike in povezave med različnimi porazdelitvami povratnih stopinj. Ključno je, da je časovna varijacija tekočih oglašenih šokov ekvitynega finančnega vloge pomembna za model, da bi lahko kvantitativno opisal splošne dinamike lastnikov, realnih količin in finančnih tokov podjetij. METODA: Raziskovalci uporabljajo podatke o tekočih oglašenih šokih ekvitynega finančnega vloge, ki so izvedeni z vektorsko avtomatsko regresijo, in jih interpretirajo kot finančne šoke. Model, ki ga napišu, vključuje slučajne oglašenih šoke ekvitynega finančnega vloge in standardno ogrožanje kreditnega vloge. KLJUČNE UGOTOVITVE: - Oglašeni šoki ekvitynega finančnega vloge so pomembni za model, da bi kvantitativno opisal splošne dinamike lastnikov, realnih količin in finančnih tokov podjetij. - Tekoči oglašeni šoki ekvitynega finančnega vloge so povezani z sistematičnimi riziki in vplivajo na povratne stopnje različnih portfeljev. - Vključitev tekočih oglašenih šokov ekvitynega finančnega vloge v standardni model kapitalnega vloge (CAPM) poveča njegovo učinkovitost pri opredeljevanju povratnih stopinj različnih portfeljev. POMEN ZA TRGOVANJE: Raziskava je uporabna za trgovce, saj pokaže, da je časovna varijacija tekočih oglašenih šokov ekvitynega finančnega vloge pomembna za razumevanje sistematičnih rizikov in povratnih stopinj. To pomeni, da trgovci bi morali upoštevati tekoči oglašeni šoki, ko pridružujejo rizike in povratne stopnje različnih portfeljev.

NASLOV: Regression Discontinuity and the Price Effects of Stock Market Indexing AVTORJI: Yen-cheng Chang, Harrison Hong, Inessa Liskovich POVZETEK: Članek obravnava učinke trgovine z dodajanjem in odstranjanjem akcij v Standard and Poor’s (S&P) 500 indeks. Tradicionalna metoda, ki je bila uporabljena v mnogih raziskavah, prepostavlja, da je pozitivna cijena učinka dodajanja akcij v S&P 500 zato, ker so akcije kupljene zaradi obveznega sledenja od strani pasivnih indeksnih fonde in aktivnih institucionalnih investitorjev. Avtorji razvijajo novu metodu, ki je založena na regresijsko nesmiselno (RD) analizo z uporabo Russell 2000 indeksa. Na podlagi vrednosti tržnega kapitalizma na konec maja so akcije razvrščene v Russell 1000 ali Russell 2000 indeks. To pomeni, da so akcije, ki so le pod 1000, v Russell 2000, zato imajo večjo vrednost v indeksu, kot akcije, ki so le zgoraj 1000. Tega uporabljajo avtorji za merjenje učinkov dodajanja in odstranjanja akcij, ki so vključene v Russell 2000. Raziskava naredi, da so cijene akcij, ki so dodane ali odstranjene, učinkovite, če se učinke nesmiselno razvrščajo okoli 1000. METODA: Avtorji uporabljajo regresijsko nesmiselno (RD) analizo z uporabo Russell 2000 indeksa. Akcije so razvrščene na podlagi vrednosti tržnega kapitalizma na konec maja. Prve 1000 akcij so vključene v Russell 1000, zato imajo večjo vrednost v indeksu, kot akcije, ki so le zgoraj 1000. Tega uporabljajo za merjenje učinkov dodajanja in odstranjanja akcij, ki so vključene v Russell 2000. KLJUČNE UGOTOVITVE: - Akcije, ki so dodane ali odstranjene iz Russell 2000, imajo učinke na cene. - Učinki so učinkoviti, če se učinke nesmiselno razvrščajo okoli 1000. - Tradicionalna metoda, ki je bila uporabljena v mnogih raziskavah, prepostavlja, da je pozitivna cijena učinka dodajanja akcij v S&P 500 zato, ker so akcije kupljene zaradi obveznega sledenja od strani pasivnih indeksnih fonde in aktivnih institucionalnih investitorjev. POMEN ZA TRGOVANJE: Avtorji razvijajo nov metode, ki so založene na regresijsko nesmiselno (RD) analizo, za merjenje učinkov dodajanja in odstranjanja akcij v indeksih. To je pomembno, ker omogoča bolj natančno identifikacijo učinkov obveznega sledenja od strani pasivnih indeksnih fonde. Ta metoda je koristan za razumevanje in predviditev učinkov dodajanja in odstranjanja akcij v indeksih, kar je pomembno za strategije trgovine z indeksnimi fonde.

NASLOV: 7471398-stock-bond-correlation-a-case-against-de-risking AVTORJI: Paisan Limratanamongkol POVZETEK: Raziskava obravnava vpliv pozitivne korelacije med akcijskimi in brezplačnimi vložkami na prihodnje vrednosti in volatilnost brezplačnih vložk. Izpeljane so ključne ugotovitve, da pozitivna korelacija ne je predpovedljiva za slaba prihodnja v 60/40 portfeliu. V prihodnosti je pozitivna korelacija povezana z nizko volatilnostjo brezplačnih vložk pred 2009, medtem ko je po 2009 povezana z visoko volatilnostjo. Tega predstavlja čisto mehanizem, ki je odvisen od okolja monetarne politike. METODA: Raziskava uporablja dnevne vrstne vrednosti MSCI USA Indeksa in Bloomberg US Treasury Total Return Indeksa od januarja 1995 do junija 2026. Metodologija vključuje 60/40 portfeli s letnimi razvrstevanjem in uporablja dvojno mnenje za ustrezen uvoz podatkov. Za ugotavljanje učinka pozitivne korelacije na prihodnje volatilnost brezplačnih vložk uporablja prilagojeni bootstrap inverzni postopek. KLJUČNE UGOTOVITVE: - Pozitivna korelacija med akcijskimi in brezplačnimi vložkami ni predpovedljiva za slaba prihodnja v 60/40 portfeliu. - Pred 2009 pozitivna korelacija povezana je z nizko volatilnostjo brezplačnih vložk, medtem ko je po 2009 povezana z visoko volatilnostjo. - Pozitivna korelacija je najbolj razumljiva kot indektor okolja, ki je odvisen od okolja monetarne politike, ne kot trikotnični trik za de-risking. POMEN ZA TRGOVANJE: Raziskava ukazuje, da de-risking, ki je opredeljen po pozitivni korelacji, ni zelo učinkovit. V prihodnosti je ta signal povezan z volatilnostjo brezplačnih vložk, ne prihodnji prihodki. To pomeni, da je učinkovitejše upoštevati volatilnost brezplačnih vložk pri odločitvah o razvrstevanju, kot da se zavleči za de-risking, ki je opredeljen po pozitivni korelacji.

NASLOV: Does Congress Polarization Extend to Stock Tradings? AVTORJI: Ye Aung (Brandon) Moe, Chen Zhang POVZETEK: Članek raziskuje, ali politična polarizacija v Kongresu vpliva na finančne odločitve poslancev. Izpelja, da polarizacija ima pozitiven povezavo z podobnostjo skupnih akcijskih poročil Demokratov in Republikanov. To je zaznamano tudi pri aktivnih tradiranjih poslancev, ki so bolj informacijsko uspešni. Raziskava pokaže, da polarizacija v Kongresu ne vpliva na podobnost finančnih odločitv v tradiranju akcij. METODA: Raziskovalci uporabljajo DW-NOMINATE-jevo prvi dimenzijo skorajnosti med centralnimi demokratičnimi in republikanskih poslancem, da merijo polarizacijo. Podatki o tradiranju akcij poslancev od leta 2012 do 2024 so analizirani, da je podobnost skupnih akcijskih poročil raziskovanih politik povezana z polarizacijo. KLJUČNE UGOTOVITVE: - Polarizacija je povezana z podobnostjo skupnih akcijskih poročil Demokratov in Republikanov. - Podobnost skupnih akcijskih poročil je pogostejša med aktivnimi tradiranjema. - Polarizacija ne vpliva na podobnost finančnih odločitv v tradiranju akcij poslancev. POMEN ZA TRGOVANJE: Raziskava pokaže, da politična polarizacija v Kongresu ne vpliva na finančne odločitve poslancev pri tradiranju akcij. To je pomembno znanstveno ugotovitev, ker pripomaga k razumevanju, da bi politične razlike morali biti več odločitev o finančnih trgovinah. Trgovci lahko uporabljajo to znanje za bolj učinkovito analizo političnih vplivov na finančne trge.

NASLOV: When AUC Survives but Portfolios Do Not: Leakage Accounting Errors and Selection Instability in a Retail-Scale Machine-Learning Equity Study AVTORJI: Parker Fawcett POVZETEK: Raziskava ocenjuje, kako prediktivna razlika (AUC) in portfeljna performanca v zvezi s strojnimi učenji v finančnih trgovinah. Narejeno je repreproducibilno iskustvo z umetno inteligenco, ki ocenjuje verjetnost, da se akcijska varnost konča v najboljši kategoriji v 20-trading-dnevnem obdobju. Rezultati izkažejo, da se AUC lahko ustreznoma poveča, dokler se ne spremeni poročevalna metrika, kar povzroča neustaljeno izbiro portfeljev. Prispevki so, da poročevalne neskončnosti lahko vplivajo na finančne rezultate, če se ne odpravi, in da implementacijska preglednost je ključna za malo kvantitativno raziskovanje. METODA: Raziskava uporablja drevesni razvrstevalec, ki ocenjuje verjetnost, da se akcijska varnost konča v najboljši kategoriji. Poročevalna neskončnost je izkazana, ko se ne odpravi poročevalni koda. Kontrola ponovitvenega obnovega protokola spremeni le pravilo poročevanja, zato da se izvede preglednost. Testni AUC se spremeni minimalno, vendar se spremeni poročevanje portfeljev. KLJUČNE UGOTOVITVE: - AUC lahko poveča, dokler se ne spremeni poročevalna metrika, kar povzroča neustaljeno izbiro portfeljev. - Poročevalne neskončnosti lahko vplivajo na finančne rezultate, če se ne odpravi. - Implementacijska preglednost je ključna za malo kvantitativno raziskovanje. POMEN ZA TRGOVANJE: Raziskava podpira, da je implementacijska preglednost ključna za uspešno strojno učenje v finančnih trgovinah. Če se poročevalne neskončnosti ne odpravi, lahko povzročijo neustaljeno izbiro portfeljev, kar vpliva na finančne rezultate. Torej je pomembno, da se implementacijska preglednost uveljavlja pri raziskovanju in razvoju strojnega učenja v finančnih trgovinah.

NASLOV: Do critical-minerals policy acts move US mining stocks? AVTORJI: Marco Belardi POVZETEK: Tega raziskava se spopada z vprašanjem, ali politične akcije o kritičnih mineralih v ZDA poteza trge v prizadevanjih za vlaganje v delovne krase minijstva, in ali to se dogaja pred javno objavo informacij. Raziskava uporablja dve zasebno izgradeni korpora z dogodkih dogodkih, ki vključujejo informacije o pravičnih vložkih, tarifi, bilateralske pogajanja in zveze s Češko. Rezultati raziskave kažejo, da trgi reagirajo na dogodke na dan, ko se informacije javno objavijo, z značajno značilnostjo, ki je značilna za administrativne akcije, kot so tarifi. Predobjava trga pa ni sistematična, če se kontrolirova s kroženjem dogodkov in sektoromom značilnostjo. Raziskava tudi najde, da je predobjava trga na dogodkih z visoko prepoznavnostjo ne obstajala v prvi mandatnem obdobju, ko je predsednik zelo ograničen pri povezovanju z kritičnimi minerali. METODA: Raziskava uporablja dve zasebno izgradeni korpora z dogodkih dogodkih, ki vključujejo informacije o pravičnih vložkih, tarifi, bilateralske pogajanja in zveze s Češko. Rezultati se analyzirajo z uporabo tržnih modelov dogodkov, ki se uporabljajo za dve kozeljki (rare earths in uranium, battery minerals in koper) in tri odklicnike (S&P 500, Russell 2000, sektor ETF). KLJUČNE UGOTOVITVE: - Trgi reagirajo na dogodke na dan, ko se informacije javno objavijo, z značajno značilnostjo, ki je značilna za administrativne akcije, kot so tarifi. - Predobjava trga ni sistematična, če se kontrolirova s kroženjem dogodkov in sektoromom značilnostjo. - Predobjava trga na dogodkih z visoko prepoznavostjo ne obstajala v prvi mandatnem obdobju, ko je predsednik zelo ograničen pri povezovanju z kritičnimi minerali. POMEN ZA TRGOVANJE: Raziskava pokazuje, da trgi reagirajo na dogodke o kritičnih mineralih na dan, ko se informacije javno objavijo, kar pomeni, da vlagatelji lahko uporabljajo informacije o prihodnjih dogodkih za trženje. Ta znanstveni rezultat je koristan za vlagatelje, ki se poskušajo prepoznavati in uporabljati informacije o prihodnjih dogodkih za trženje.

NASLOV: 7468418-why-emerging-venture-capital-managers-matter-rethinking-institutional-portfolio- AVTORJI: ni navedeno POVZETEK: Raziskava opisuje, kako se večina institucionalnih večeršnjih kapitalnih sredstev (VC) sestavlja z osredotočenostjo na velikih, obstoječih menadžerje, ki ne vključujejo novih menadžerjev, ki so izključeni iz tradicionalnih preiskovalnih postopkov. Novi menadžerji, ki so izključeni, preizkusno preobstvarajo obstoječih menadžerjev z 7,2 točke v srednji vrednosti vrhunske vrednosti (IRR) in 0,34x v vrednosti, ki je ustvarjena. To voditev rezultira izostanku od 72 milijonov dolara na 100 milijonov dolara večeršnjih kapitalnih sredstev. Raziskava podpira, da so te menadžerje izključeni, saj se osredotočajo na kriterije, ki ne predstavljajo vrednosti, kot je veliki kapital, dolg zanesljivost in znanje o imenih. METODA: Raziskava analizira 2.471 ameriških VC sredstev, ki so bila sestavljena med letoma 2000 in 2024. Izračunana je, da so izključeni menadžerji preobstvarajojo vse ključne merila performans, zmanjševanje nesporazumov in preostankov. KLJUČNE UGOTOVITVE: - Izključeni menadžerji preobstvarajo obstoječih menadžerjev z 7,2 točke v srednji vrednosti vrhunske vrednosti (IRR) in 0,34x v vrednosti, ki je ustvarjena. - Menadžerji s ženskimi glavnimi partnerji (GP) preobstvarajo ali izpolnjujejo zahtevane vrednosti. - Izključeni menadžerji so izključeni zaradi kriterijev, ki ne predstavljajo vrednosti, kot je veliki kapital, dolg zanesljivost in znanje o imenih. POMEN ZA TRGOVANJE: Raziskava podpira, da so institucionalni večeršniki izključeni iz menadžerjev, ki bi lahko ustvarili več vrednosti. To voditev rezultira izostanku od 72 milijonov dolara na 100 milijonov dolara večeršnjih kapitalnih sredstev. Menadžerji, ki se pridružijo izključenim menadžerjem, preobstvarajo prednostne vire pri odpravi nesporazumov in preostankov.

NASLOV: 7468398-admissible-portfolio-optimization-information-constraints-conditional-efficient-frontiers-and-the-price-of-causal-identification AVTORJI: Alejandro Rodr´ıguez Dom´ınguez POVZETEK: Članek obravnavava optimizacijo portfelja z upoštevanjem informacij, ki jih je mogoče izbirati kot odločitev. Raziskava predstavlja admissible informacije kot odločitev, ki jih je mogoče izbirati z upoštevanjem omejev, kot so dostopnost, ohranjanje brez-arbitražne virene, odstranitev skupne odvisnosti med vložkami in, pri intervencijskih tvrdenjih, invariantnost pred oznakami. Ključna je admissible informacijska klasa, ki jo izbere na osnovi leksikografske admissible urejenosti, brez upoštevanja rizika in povratnega kapitala. Klasična portfeljna problematika se reši samo v tej kategoriji informacij. Ugotovitve vključujejo obstoj rešitev, ohranjanje podatkov pod recodiranjem, teorema o vrednosti admissible informacije, ki prikazuje, da se uporaba admissible informacije dovoljuje pogled v prihodnosti, ko je to vključeno v kandidatno množico. Podatki o trgu z 127 kandidatnimi vložkami pokazujejo, da admissible informacijska klasa je v moči na individualnih vložkah, kjer se reducira maksimalna residualna korelacija med vložkami, vendar je v prediversificiranih portfeljih neustanova, saj je residualna odvisnost skupna vložka. METODA: Članek predstavlja dve koraki problem, kjer je informacija odločitevna spremenljivka. Prvi korak je določanje admissible informacijske klase, ki je urejena leksikografsko, brez upoštevanja rizika in povratnega kapitala. Drugi korak je reševanje klasičnega portfeljne problema v tej klasi informacij. Ugotovitve so podlagane na teoriji informacij, ohranjanju brez-arbitražne virene, odstranitvi skupne odvisnosti med vložkami in invariantnosti pred oznakami pri intervencijskih tvrdenjih. KLJUČNE UGOTOVITVE: - Admissible informacijska klasa je izbrana z leksikografsko admissible urejenostjo, brez upoštevanja rizika in povratnega kapitala. - Klasična portfeljna problematika se reši samo v tej admissible informacijski kategoriji. - Ugotovitve prikazujejo, da se admissible informacijska klasa lahko določi na individualnih vložkah, kjer se reducira maksimalna residualna korelacija med vložkami. - V prediversificiranih portfeljih je admissible informacijska klasa neustanova, saj je residualna odvisnost skupna vložka. POMEN ZA TRGOVANJE: Raziskava je uporabna za trgovce, ker omogoča izbiro admissible informacij kot odločitev, ki jih je mogoče upoštevati pri optimizaciji portfelja. To lahko poveča vrednost informacije, saj se uporaba admissible informacije dovoljuje pogled v prihodnosti. V primeru individualnih vložk lahko admissible informacijska klasa poveča efektivnost portfelja z redukcijo residualne korelacije med vložkami. V prediversificiranih portfeljih pa je admissible informacijska klasa neustanova, saj je residualna odvisnost skupna vložka, ki je že izolirana s pomočjo diversifikacije.

NASLOV: 7467438-inflation-is-economic-reflection-of-portfolio-return-part-i-elementary-index-adj AVTORJI: Victor Olkhov POVZETEK: Avtor je raziskoval povezave med inflacijo in povratki portfelja, ki so običajno smiselno različne. Prikazuje, kako se lahko inflacijo modelira z uporabo teorije portfelja. Raziskava vključuje izračunaj elementarnega inflacijskega indeksa, ki je prilagajan samoumno inflacijo, varijanco tržne inflacije in Sharpejev razmerje, ki je uporabljen kot merilnik nesporazumnosti. METODA: Avtor je primerjaval inflacijo z povratki portfelja, ki so opisani v teoriji portfelja. Prva delitvena metoda je uporabljena za izračunaj elementarnega inflacijskega indeksa, ki je prilagajan samoumno inflacijo, varijanco tržne inflacije in Sharpejev razmerje, ki je uporabljen kot merilnik nesporazumnosti. KLJUČNE UGOTOVITVE: - Elementarni inflacijski indeks bi moral biti izračunajen kot razmerje cijen, prilagajanih isti času s samoumno inflacijo, pred izvajanjem cijenovnega srednjičevanja. - Varijanca inflacije je merila nesporazumnosti inflacije, ki je utrujena nakupom. - Sharpejev razmerje meri nesporazumnosti inflacije v trenutku in pri predviđanem ocenju inflacije. POMEN ZA TRGOVANJE: Avtorjev model omogoča predviđanje inflacije z uporabo teorije portfelja, kar je koristan za finančne trgovce. Predviđanje inflacije je ključno za uspešno trgovino v inflacionih razmerjih.

NASLOV: Price Informativeness in a Retail-Dominated After-Hours Market: Evidence from Korea’s Alternative Trading System AVTORJI: Munbak Choe, Seoul National University POVZETEK: Tega raziskava obravnava, kako informativne so cene, ki so formirane predvsem po kmetijskimi trgovci v po-urah trgovini na Nextrade (NXT) v Koreji. NXT ponuja kontinuirano trgovino po-urah v sestavljene akcije, ki se nato trgujejo na KRX. Po-urne cene, ki so formirane predvsem po kmetijskimi trgovci, so več informativne kot pred-urne cene, ki so formirane po-urah. Cene po-urah so predvsem informativne, če so formirane predvsem po kmetijskimi trgovci, čeprav so težko oceniti, ali se pri času pričnejo. METODA: Raziskava uporablja tri komplementarne testne metode: povratnico regresijo, direktno poročanje cijen in dolgo-terminne regresije. Cene po-urah so izračunate kot volume-weighted average price (VWAP) po-urah. Raziskava je podrobno preverila, kako informativne so cene po-urah, če so formirane predvsem po kmetijskimi trgovci, in je pokazala, da so te cene predvsem informativne, čeprav so težko oceniti, ali se pri času pričnejo. KLJUČNE UGOTOVITVE: - Cene po-urah, ki so formirane predvsem po kmetijskimi trgovci, so predvsem informativne. - Cene po-urah so predvsem informativne, čeprav so težko oceniti, ali se pri času pričnejo. - Cene po-urah so predvsem informativne, če so formirane predvsem po kmetijskimi trgovci, čeprav so težko oceniti, ali se pri času pričnejo. POMEN ZA TRGOVANJE: Raziskava pokazuje, da so po-urne cene, ki so formirane predvsem po kmetijskimi trgovci, predvsem informativne. To je pomembno za trgovce, ker pomoči v ocenjevanju prihodnjih cijen in trgovskih okolij. Trgovci lahko uporabljajo te cene za boljšo predvidljivost trgovskih odločitev.

NASLOV: The multiplicative index of riskiness in the cross-section of U.S. equity returns: Theory and evidence for 1928-2025 AVTORJI: Tom Miller POVZETEK: Članek obravnava teorijo in empirično potvrdo množičnega indeksa rizika v razdelu ameriških akcij. Raziskava je narejena za obdobje od 1928 do 2025. Teorija indeksa je opredeljena z zvezdami podlogne normalne različice, kjer je indeks zvezd v enostavni zatvoreni obliki. Ključno je, da se množični indeks nanaša le na levobok, medtem ko določen indeks rizika s količino absolutnega rizika (CARA) nanaša le na desnobok. Praktično je najdeno, da se množični indeks zvezd ne razlikuje od določenega indeksa rizika v večjem delu razdelka, vendar se obrnejo med decilom vrednosti. Ključno je, da kritična koeficient rizika (CRRA) je ne monotono po meri razmerja knjižnice na trgu, ki se zveda od rastnih do srednjih razmerja knjižnice in se opusti v profunkcijske razmerja knjižnice. METODA: Teorija indeksa je razvijena z zvezdami podlogne normalne različice, kjer je indeks zvezd v enostavni zatvoreni obliki. Praktično je pokazano, da množični indeks zvezd ne razlikuje od določenega indeksa rizika v večjem delu razdelka, vendar se obrnejo med decilom vrednosti. Kritična koeficient rizika (CRRA) je pokazana za različne razmerja knjižnice na trgu. KLJUČNE UGOTOVITVE: - Množični indeks rizika je teoretično naravna za akcijske vratilne. - Množični indeks zvezd je ne monotono po meri razmerja knjižnice na trgu. - Kritična koeficient rizika (CRRA) zvezd je ne monotono po meri razmerja knjižnice na trgu. POMEN ZA TRGOVANJE: Raziskava pokazuje, da množični indeks rizika je boljši instrument za merjenje rizika in filtriranje poročil, kot za cene v razdelu akcij. Ključno je, da kritična koeficient rizika (CRRA) je ne monotono po meri razmerja knjižnice na trgu, kar pomeni, da profunkcijske akcije so manj atraktivne za srednje rizik-odpornega inverzora, čeprav imajo najvišji povprečni log-vrtenje.

NASLOV: 7465421-nominal-price-preferences-investor-welfare-and-market-quality AVTORJI: Allaudeen Hameed, Zhenghui Ni, Yuanyuan Pan POVZETEK: Raziskava raziskuje vpliv nizkih nominalnih cijena akcija na trgovino in tržno kakovost. Naloga je preveriti, ali je preferenca trgovcev za nizke cijene akcija, ki jo imajo, koristna za tržno kakovost. Iz podatkov o obratih trgovcev iz Singapura je zaznamano, da se trgovci zmanjšajo obrade s nizko-nominalnih akcija za 77% in prenašajo na druge nizke cijene akcije. To realociranje vodilo je do pomanjkanja likvidnosti, pomaljajočih ocen akcija za 5% in pomaljajočih performans obrad trgovcev s nizko-nominalnimi akcijami. Pravila za ohranjanje nizkih cijen akcija, ki jih narečijo, prenose privatne stroške trgovcev na javne koristečne vrednosti tržne kakovosti. METODA: Raziskava uporablja kvazi-ekperimentalni pristop, ki je založen na pravilu Singapurskej bursne namizije (SGX) za ohranjanje najmanjšega tržnega cijena (MTP). Pravilo zahteva, da se podatkovni akcije državil na glavnem biskupstvu državljansko ohranjajo na vsaj S$0,20 ali se izključijo iz trga. To pravilo je vodilo do obratov s obratnim deljenjem, ki so pridobili dovolj dovoljenje, da so ekonomske temelje in nameni menadžerjev verjetno ekzogeni. Raziskovalci sledijo obradam trgovcev iz Singapura, ki so zapisani v največji trgovski broker, ter podatkom o akcijah na bursi. KLJUČNE UGOTOVITVE: - Trgovci zmanjšajo obrade s nizko-nominalnih akcija za 77%. - Trgovci prenašajo obrade na druge nizke cijene akcije. - Pomanjkanje likvidnosti povzroča pomaljajoče ocene akcija za 5%. - Obrade s nizko-nominalnimi akcijami imajo manjša performanse, očitno ne glede na informacije. POMEN ZA TRGOVANJE: Raziskava pokaže, da pravilo za ohranjanje nizkih cijen akcija prenese privatne stroške trgovcev na javne koristečne vrednosti tržne kakovosti. Trgovci, ki se odločijo za nizke cijene akcija, izgubljajo finančno vrednost, vendar je to koristno za celotno tržno kakovost. Ta rezultat je pomemben za razumevanje vpliva tržnih regulacij na trgovce in tržno kakovost.

NASLOV: 7461901-quoting-the-touch-does-not-pay-its-adverse-selection-a-true-aggressor-signed-ent AVTORJI: Daniel Gatto POVZETEK: Raziskava obravnava, kako se odzivajo trgovci pri zagonu z uporabo najboljšega nabira in najboljšega ponudnika (best bid/offer) v trgovini kriptovalut. Natančno merila, ki jih uporablja avtor, razdelijo vsak preplon na polpribližek, ki ga je trgovec zaznamal pri trgovini, in potezo srednje cene v času merila. Rezultati kažejo, da je vse tri kriptovalne trgovine (Bybit, Binance USD-M in Hyperliquid) negativni preplon pri zagonu z najboljšim nabirom in najboljšim ponudnikom, pri čemer je negativnost preplonov na vseh trgovinah preden je dodana naročniplačila. To pomeni, da je trgovska selekcija (adverse selection) večja od polpribližka, ki ga je trgovec zaznamal pri trgovini. METODA: Avtor uporablja merila, ki razdelijo vsak preplon na polpribližek, ki ga je trgovec zaznamal pri trgovini, in potezo srednje cene v času merila. To merilo je uporabljal tudi na trgovinah CME futures, kjer je merilo negativnost preplonov pri zagonu z najboljšim nabirom in najboljšim ponudnikom. Merila so izvedena na podlagi 9,983 kriptovalnih dnevnikov in 125,828,922 simuliranih preplonov. Avtor tudi preverja, kako so različne merila uporabljena na različnih trgovinah in kako se njihove rezultate ujemajo s merili, ki jih uporablja trgovina. KLJUČNE UGOTOVITVE: - Vse tri kriptovalne trgovine (Bybit, Binance USD-M in Hyperliquid) imajo negativen preplon pri zagonu z najboljšim nabirom in najboljšim ponudnikom. - Negativnost preplonov je preden je dodana naročniplačila. - Trgovska selekcija (adverse selection) je večja od polpribližka, ki ga je trgovec zaznamal pri trgovini. POMEN ZA TRGOVANJE: Raziskava je pomembna za trgovce, ker ukazuje na to, da je trgovska selekcija večja od polpribližka, ki ga je trgovec zaznamal pri trgovini. To pomeni, da je trgovec pri zagonu z najboljšim nabirom in najboljšim ponudnikom v več kot polpribližek zmanjšen. Trgovci bi morali upoštevati to, ko izvirajo informacije o ceni, da bi lahko bolje upravljali svoje riske in prihodki.

NASLOV: 7461560-autoencoders-for-international-equity-markets AVTORJI: Bo Yuan, University of Cambridge POVZETEK: Raziskava obravnava latentne faktorske modele, kot so Principal Component Analysis (PCA), Instrumented PCA (IPCA) in autoencodere (AE), za objašnjanje vmesnih vrat državne akcij. Uporabljajo se 13 firmnih faktorski tem in 52 makroekonomske indikatorje, omejeni na obdobje od 1995 do 2024. Leto. Modeli, posebej linearna AE, podajajo učinkovito objašnjanje, vendar so njihova prediktivna moč izven skupa nekonzistentna. Linearna AE dosegla največjo prediktivno moč, s upoštevanjem teško merljivega 0.45% R². IPCA se pokaže metodološko neuspešen v malih skupinah podatkov, kjer je njegova linearna predpostavka zahtevana. Modeli skupaj identificirajo vrednost, trenutno gibanje in rast GDE kot ključne faktorje za risiko. METODA: Raziskava uporablja različne latentne faktorske modele: PCA, IPCA in različne AE. Podatki se sestavljajo iz 13 firmnih faktorskih tem in 52 makroekonomskeh indikatorjev, omejenih na obdobje od 1995 do 2024. Leto. Modeli ocenjujejo vmesne vrate državne akcij. Linearna AE je najučinkovitejši, s 0.45% R², vendar je njihova prediktivna moč izven skupa nekonzistentna. IPCA se ukvarja z časovno spreminjajočimi se faktorskimi lastnostmi. KLJUČNE UGOTOVITVE: - Linearna AE je najučinkovitejši model, s 0.45% R² izven skupa. - IPCA se ukvarja z časovno spreminjajočimi se faktorskimi lastnostmi. - Vse modele identificirajo vrednost, trenutno gibanje in rast GDE kot ključne faktorje za risiko. POMEN ZA TRGOVANJE: Raziskava podaja učinkovite informacije za globalno akcijalno allokacijo in risiko upravljanje. Linearna AE je najučinkovitejši model, s 0.45% R² izven skupa, vendar je njihova prediktivna moč izven skupa nekonzistentna. Modeli skupaj identificirajo vrednost, trenutno gibanje in rast GDE kot ključne faktorje za risiko.

NASLOV: Manufacturing Speculation: Contract Design and Trader Performance in Prediction Markets AVTORJI: Yinru Lei, Alberto G. Rossi POVZETEK: Raziskava se posvešča, kako izbira kontraktov prediktivne trgovine utrušči trgovske dejavnosti in trgovske zmage. Izpolnjevši trgovske dejavnosti z polnimi podatki o Polymarketu od februarja 2024 do marca 2026, dokumentira raziskava premik od enočnih in mesečnih kontraktov na petiminutne zaznamke o povečanju cene. Trgovska aktivnost se hitro nagnje, zmanjšavajc trgovske zmage. Delovne sredstva profitabilnih računov padajo s 60% v enočnih kontraktih na 25% v petiminutnih. S povečano trgovsko aktivnostjo in nizko zmagovalno stopnjo, $100 kapitala bi se izročil v 404 dneh v enočnih, vpetiminutnih pa v 1,6 dneh. Novi računi upoštevajo volumen, ko se obstoječi trgovci odločijo. Raziskava razvija model, ki razloži te trendi: platforma, ki želi povečati trgovske volumene, ponuja kraščije kontraktne oblike, ker vsako novo kontranko ponovno obnovi možnosti za zaznamke o sporu. Ponovno zaznamkovanje pa zmanjšuje zmage večine trgovcev, tudi brez naložb. Natančne ugotovitve raziskave pokažejo konflikt med platformo in trgovci: brez regulativnih intervencij, kontraktni nalogi, ki vlagajo v več trgovske dejavnosti, tudi hitreje povečajo zmagovalne stopnje. METODA: Raziskava uporablja kompletni trgovski zaznamki Polymarketa za finančne kontrakte od februarja 2024 do marca 2026. Raziskovalci razvijajo model, ki razloži, kako platforma, ki želi povečati volumen, izbere kontraktne oblike, ki ponavljajo zaznamke o sporu. Trgovska aktivnost se nagnje na kraščije kontrakte, ki ponavljajo zaznamke o sporu, kar zmanjšuje zmage večine trgovcev. KLJUČNE UGOTOVITVE: - Platforma, ki želi povečati volumen, ponuja kraščije kontraktne oblike, ki ponavljajo zaznamke o sporu. - Trgovska aktivnost se nagnje na kraščije kontrakte, kar zmanjšuje zmage večine trgovcev. - Delovne sredstva profitabilnih računov padajo s 60% v enočnih kontraktih na 25% v petiminutnih. - S povečano trgovsko aktivnostjo in nizko zmagovalno stopnjo, $100 kapitala bi se izročil v 404 dneh v enočnih, vpetiminutnih pa v 1,6 dneh. POMEN ZA TRGOVANJE: Raziskave ugotovitve pokažejo, kako platforma, ki želi povečati volumen, ponuja kraščije kontraktne oblike, ki zmanjšuje zmage večine trgovcev. Trgovci morajo razumeti, da se njihove zmage lahko povečajo, če platforma ponuja dolgočasne kontrakte, ki ponavljajo zaznamke o sporu. Ta raziskava podpira strategije, ki upoštevajo trgovske zmage in platformo, ki želi povečati volumen.

NASLOV: 7461299-political-bias-in-decentralized-prediction-markets-evidence-from-trump-related-c AVTORJI: Simone Skovgaard, Noah Wenneberg Junge, Bulat Ibragimov POVZETEK: Tega raziskava opazuje politično pristojnost v decentraliziranih predviščnih trgovinah, uporabljajuči Polymarket, blockchain-osebno predviščno trgovinsko platformo. Raziskovalci simulirajo strategijo, ki sistematično postavljajo zasebne predpise proti Trumpovim rezultatom v 218 Trump-odvisnih trgovinah, in primerjajo to z neutralnimi trgovinami. Anti-Trump strategija prinaša večjo povprečno vrednost ($1,006) kot pro-Trump strategija ($1,028). Analiza ukazuje na sistematično prekaznjenost Trumpovih rezultatov, ki je verjetno vplival politično pristojna skupina trgovcev. METODA: Raziskovalci uporabljajo Polymarket za simulacijo strategije, ki postavlja zasebne predpise proti Trumpovim rezultatom. Strategija je benchmarkirana z 10.000 Monte Carlo simulacijami neutralnih trgovin. Analiza pokazuje, da je povprečna vrednost strategije večja kot vrednost benchmarka, z R2 = 0.662 in p < 0.001. KLJUČNE UGOTOVITVE: - Anti-Trump strategija prinaša večjo povprečno vrednost ($1,006) kot pro-Trump strategija ($1,028). - Analiza ukazuje na sistematično prekaznjenost Trumpovih rezultatov. - Efekt ne je objašnjen s favourite–longshot pristojnostjo. - Efekt je razporeden po različnih temah, ne odvisen od enega određenega temata. POMEN ZA TRGOVANJE: Raziskava podpira idejo, da so decentralizirane predviščne trgovine predpise za politično pristojnost. To je vplivalo na trgovce, ki so politično pristojni, in lahko vplivalo na cene. Trgovci, ki so podpore Trumpa, so lahko vplivali na prekaznjenost cijen, kar je lahko vplivalo na trgovino vseh. Trgovci, ki so politično pristojni, bi morali biti previdni pri analizi cijen v takšnih trgovinah.

NASLOV: 7461079-two-sided-volatility-jumps-in-short-maturity-vix-options AVTORJI: Gurdip Bakshi, Xiaohui Gao, Zhaowei Zhang POVZETEK: Članek raziskuje model, ki omogoča dvostranske volatilnostsko skokove v kratkoterminnih VIX opcijah. Raziskovalci razvijajo mehanizem, ki je opredeljen v stanju in generira dvostranske skokove volatilnosti, ki so povezane z volatilnostskimi skokoma v akcijskem cijenu. Negativni skoki akcijske cijene povečujejo volatilnost, dokler pozitivni skoki akcijske cijene jo smanjujejo. Volatilnostsko skokovsko vrednost je multiplikativna in povezana z predskokovno vrednostjo. Podobno analizirajo vrednosti opcij s različnimi stopnji volatilnosti in pokazujejo, da dvostranski model omogoča pozitivne vrednosti za oba tipa opcij, če je volatilnostsko skokovsko vrednost pozitivna. METODA: Raziskovalci razvijajo risk-neutralen stanje-zavisni zaznamani Hawkesov tip, ki je opredeljen v stanju in generira dvostranske skokove volatilnosti. Volatilnostsko skokovsko vrednost je povezana z predskokovno vrednostjo akcijske cijene. Negativni skoki akcijske cijene povečujejo volatilnost, pozitivni pa jo smanjujejo. Volatilnostsko skokovsko vrednost je multiplikativna in povezana z predskokovno vrednostjo. Stopnja volatilnostskih skokov je omejena in zagotavlja pozitivno stopnjo skokov akcijske cijene. KLJUČNE UGOTOVITVE: - Negativni skoki akcijske cijene povečujejo volatilnost, pozitivni pa jo smanjujejo. - Volatilnostsko skokovsko vrednost je multiplikativna in povezana z predskokovno vrednostjo. - Dvostranski model omogoča pozitivne vrednosti za oba tipa opcij, če je volatilnostsko skokovsko vrednost pozitivna. - Volatilnostsko skokovsko vrednost je omejena in zagotavlja pozitivno stopnjo skokov akcijske cijene. POMEN ZA TRGOVANJE: Raziskave pokажeta, da dvostranski model volatilnosti skokov v kratkoterminnih VIX opcijah omogoča pravilnejši ocenjevanje vrednosti opcij. To je posebno pomembno za trgovce, ki uporabljajo VIX opcije za ocenjevanje rizika volatilnosti. Pomembno je, da model omogoča pravilno ocenjevanje vrednosti oba tipa opcij, če je volatilnostsko skokovsko vrednost pozitivna.

NASLOV: Physical climate risks and equity markets: a survey of empirical evidence AVTORJI: Matteo Bagnara, Vincent Bouchet POVZETEK: Točno 47 raziskovalnih članek je pregledano, ki raziskuje učinek fizikalnih okoljskih težav na trge sodobnih akcij. Raziskava ukazuje, da so okoljske dogodki pomembni za vratke, volatilnost, korelacije in, če je manjši učinek, za likvidnost. Negativni učineki prevladajo, posebej za ekstremno teploto, tropske torklone, šečerine in opalbe. Učinek je raznolik v različnih področjih in se enačno prikazuje v podatkih o Združenih državah in akutnih dogodkih. METODA: Raziskava je zgrajena na 47 raziskavah, ki se strinjajo, da so okoljske dogodki pomembni za trge sodobnih akcij. Metodologija vključuje analizo dogodkov in presek analiza, ki uporabljajo različne vrste okoljskih težav. KLJUČNE UGOTOVITVE: - Okoljske dogodke imajo značilne učinek na vratke, volatilnost in korelacije sodobnih akcij. - Negativni učineki prevladajo, posebej za ekstremno teploto, tropske torklone, šečerine in opalbe. - Učinek je raznolik v različnih področjih in se enačno prikazuje v podatkih o Združenih državah in akutnih dogodkih. POMEN ZA TRGOVANJE: Raziskava je pomembna za razumevanje učinkov fizikalnih okoljskih težav na trge sodobnih akcij. To vprašanje je ključno za razumevanje in upravljanje težav, ki jih prinašajo okoljske dogodki. To vprašanje je posebno pomembno za upravljane in regulirane entitete, ki morajo razumevati in upravljati težave, ki jih prinašajo okoljske dogodki.

NASLOV: Clustering Investor Relative Performance and Crypto-Asset Return Dynamics AVTORJI: Guillaume Tchoffo Talla POVZETEK: Tega raziskava je posvetovana vprašanju, kako investorji izkušnjejo relativno vrednost svojih kriptovalut v različnih tržnih okoljih. Autorji razširijo obstoječo teorijo o smestilnih zavarjah (regret) in vnesli novega koncepta, kateri se imenuje Investor Relative Performance (IRP), ki omogoča oceno relativne vrednosti kriptovalut v pet različnih kategorijah: kriptovalute, akcijske vloge, komoditete, valute in sestavljene vloge. Uporabljajo Gaussovo mlečno modeliranje (GMM) za razvrstitev 122 kriptovalut v tri ekonomske različne skupine: stabilne kriptovalute, mainstream kriptovalute in vrhunske speculativne kriptovalute. Natančno je zaznamano, da je vrednost kriptovalut, ki so v vrhunske skupini, negativno odvisna od relativne vrednosti, medtem ko so kriptovalute v manjši skupini pozitivno odvisne. Te znavke so pravilne tudi po drugih indeksih in po kontingenčni razdelitvi vrednosti. METODA: Raziskovalci uporabljajo GMM za razvrstitev kriptovalut v tri ekonomske skupine. Nato konstruirajo Relative Performance Intensity Index (RII) na skupinskev ravni in ga povezujeta s vrednostimi kriptovalut. KLJUČNE UGOTOVITVE: - Definiranje IRP omogoča oceno relativne vrednosti kriptovalut v pet različnih kategorijah. - Razvrstitev kriptovalut v tri ekonomske skupine: stabilne, mainstreamne in speculativne. - Posledice RII so odvisne od članovanja v skupine: manjše skupine imajo pozitivno odvisnost, medtem ko so veže skupine negativno odvisne. POMEN ZA TRGOVANJE: Raziskovalni rezultati pokazujejo, da je relativna vrednost kriptovalut, ki se nahajajo v vrhunski skupini, negativno odvisna od RII, medtem ko so kriptovalute v manjši skupini pozitivno odvisne. Te znavke so težko skladne s standardno teorijo o odškodnici za smestilne zavarjah. Tako je ta raziskava uporabna za trgovce, ki potrebujejo razumevanje relativne vrednosti kriptovalut in njihovega vpliva na vrednost.

NASLOV: Does wash trading distort asset prices? Evidence from NFT markets AVTORJI: Khanh Quoc Nguyen, Vinay Patel, Tālis J. Putniņš POVZETEK: Raziskava uporablja NFT tržnike kot naravno laboratorij, da analizira 42,9 milijuna transakcij na Ethereum blokchainu. Natančno raziskava potrjuje, da wash trading poveča volumes, vendar pa tudi poveča vrednost tržnih aktivnosti. Wash trading generira 0,32% povečanje vrednosti, kar je statistično enak 0,34% povečanju, ki ga generira pravilno tržno akcijo. Wash trading se skupina na platforme, ki ponujajo nagrade za volume, in se premakne na tržnike z nižjimi transakcijskimi naporci, ko se nagrade spreminjajo. METODA: Raziskovalci analizirajo 42,9 milijuna transakcij NFT na Ethereum blokchainu. Wash trading predstavlja le 2% transakcij, vendar je odgovoren za 40% vrednosti transakcij, ki je celotno 89 milijardi dolara. Wash trading poveča vrednost tržnih aktivnosti z 0,32% pri 10 ETH shocku, kar je statistično enako 0,34% povečanju, ki ga generira pravilno tržno akcijo. Wash trading se skupina na platforme, ki ponujajo nagrade za volume, in se premakne na tržnike z nižjimi transakcijskimi naporci, ko se nagrade spreminjajo. KLJUČNE UGOTOVITVE: - Wash trading poveča volumes, vendar pa tudi vrednost tržnih aktivnosti. - Wash trading generira povečanje vrednosti, ki je statistično enako povečanju, ki ga generira pravilno tržno akcijo. - Wash trading se skupina na platforme, ki ponujajo nagrade za volume, in se premakne na tržnike z nižjimi transakcijskimi naporci, ko se nagrade spreminjajo. POMEN ZA TRGOVANJE: Raziskava potrjuje, da wash trading lahko poveča vrednost tržnih aktivnosti, čeprav je statistično enak pravilno tržni akciji. To pomeni, da je težko različiti wash trading od pravilne tržne akcije, kar poveča možnost manipulacije tržnih cijen. To je posebno zelo pomembno za tradicionalne tržnike, kjer so tržne aktivnosti nevidne in je težko različiti manipulacije. Tržni regulatorji morajo upoštevati, da je problem manipulacije v tradicionalnih tržniki verjetno večji, saj je težko shraniti in prepoznati tržne aktivnosti.

NASLOV: 7459859-retail-trading-and-the-varying-predictive-power-of-aggregate-short-interest AVTORJI: Shiran Froymovich, Shera Gong, Charles Wasley, Jason J. Xiao POVZETEK: Raziskava je identificirala, da je skupna brezplačna zanimanje (short interest) ena od najinformativnejših prediktorjev prihodnjih skupnih povratkov trga. No, to informacijno vrednost skupnega brezplačnega zanimanja odvisna je od sestave tržnih delavcev, ki ga omenjajo, ki se je signifikantno spremenila v zadnjih desetletjih. Raziskovalci nameravajo, da je predikativen moč skupnega brezplačnega zanimanja padel v času in da ta padec je negativno povezan z narastom delovanja brezplačnih tržnikov. Dodatne analize pokažejo, da delovanje brezplačnih tržnikov učinkovito vpliva skozi dva mehanizma: narastajočo delo brezplačnih tržnikov pri brezplačnem zanimanju in večja rizika zatiska brezplačnega zanimanja. Naši rezultati podčrtajo pomembnost sestave tržnikov pri ustreznosti skupnega brezplačnega zanimanja kot prediktorja skupnih povratkov trga. METODA: Raziskovalci uporabljajo mesečne podatke od leta 2012 do 2023. Definirajo predikativen moč skupnega brezplačnega zanimanja (βSII) kot koeficient v regresiji, kjer je skupno brezplačno zanimanje (SII) prediktor skupnih mesečnih povratkov S&P 500 za naslednjih 12 mesecev. Raziskovalci preučujejo, kako se predikativen moč skupnega brezplačnega zanimanja vpliva delovanje brezplačnih tržnikov, ki je definirano kot delo mesečnega tržnega obdobja, ki mu je pripadajoče brezplačno zanimanje. Podatki kontrolirajo mrežno likvidnost, makroekonomske nesigurnosti, volatilnost trga in skupno ekonomsko dejavnost. KLJUČNE UGOTOVITVE: - Predikativen moč skupnega brezplačnega zanimanja je padel v času. - Padek predikativen moči je negativno povezan z narastom delovanja brezplačnih tržnikov. - Delovanje brezplačnih tržnikov vpliva skozi dva mehanizma: narastajočo delo brezplačnih tržnikov pri brezplačnem zanimanju in večja rizika zatiska brezplačnega zanimanja. POMEN ZA TRGOVANJE: Raziskava podpira pomembnost sestave tržnikov pri ustreznosti skupnega brezplačnega zanimanja kot prediktorja skupnih povratkov trga. Trgovci lahko uporabljajo te rezultate za boljšo razumevanje in predviđanje tržnih potez, sicer pa je pomembno razumeti, da se predikativen moč skupnega brezplačnega zanimanja lahko spremeni glede na spremembe sestave tržnikov.

NASLOV: 7459731-target-volatility-strategies-as-mean-preserving-contractions AVTORJI: Kais Dachraoui POVZETEK: Raziskava poskuša ugotoviti, kako se vrne izpiše pri ciljno-težavnostnih strategijah (TVS). Pod pogojem, da je ciljna težavnost enaka povprečni težavnosti, je TVS vrna enaka pogojenemu pripadku težavnosti podlagne vrne. To pomeni, da je podlagna vrna ena vrednostnoprščljivega razširjila TVS vrne, kar pomeni, da je TVS vrednostnoprščljiv kontrakcija podlagne vrne. Ta rezultat je vendar v večji konkavni vrsti vrščenja, kar pomeni, da je TVS priljubljen vseh konkavnih vrednostnih funkcij. METODA: Raziskava uporablja multiplicative skalijsko predstavitev vrne in pogoji pogojenega pripadka skali. Pogoj, da je ciljna težavnost enaka povprečni težavnosti, je ključ. Raziskava dokazuje, da je TVS vrna enaka pogojenemu pripadku težavnosti podlagne vrne. KLJUČNE UGOTOVITVE: - Pri ciljno-težavnostnih strategijah (TVS) vrna podlagne vrne je enaka pogojenemu pripadku težavnosti podlagne vrne. - TVS je vrednostnoprščljiv kontrakcija podlagne vrne, kar pomeni, da je priljubljen vseh konkavnih vrednostnih funkcij. - TVS vrna je v večji konkavni vrsti vrščenja, kar pomeni, da je priljubljen vseh konkavnih vrednostnih funkcij. POMEN ZA TRGOVANJE: Raziskava pokaže, da ciljno-težavnostne strategije (TVS) so vrednostnoprščljive, saj kontrakcija podlagne vrne pomeni, da so priljubljen vseh konkavnih vrednostnih funkcij. To pomeni, da so TVS koristne za trgovce, ki se biskupijo na vrednostnoprščljivosti. Ta rezultat je pomemben za razumevanje, kako TVS vplivajo na vrednostnoprščljivost vrne, kar je ključno za strategije risk-managementa.

NASLOV: Price-Driven Churn in Islamic Equity Screening: Evidence from a Point-in-Time Compliance Panel AVTORJI: Sandeep Singh Rai, Columbia University POVZETEK: Raziskava obravnava divergenco v standardih islamskih finančnih omejev, posebej v oznaku leverage. AAOIFI uporablja spot market capitalisation kot imenovalec, zatistek pa uporabljajo trajnega obdobja. Raziskava konstruirala točno časovno panel za 426 ameriški družb od leta 2010 do 2026, ki razdelila vsako prehod v oznakovanost na del, ki je odgovoran za cenovno potezo, in del, ki je odgovoran za spremembe v izvajenih dlužbe. Podobno AAOIFI standardu, 56.9% prehodov v oznakovanost je odgovorilo samo cenovni potezi, zatistek pa je odgovoril za 23.3%. Zamenjavo s trajnega obdobja zmanjšala celotno časovno potezo v oznakovanosti s 5.25% na 3.22% in potezo, ki je odgovorila za cenovne poteze, s 67%. METODA: Raziskava je osnovana na podatkih iz SEC XBRL odlagala, ki so konstruirale točno časovno panel za 426 ameriški družb od leta 2010 do 2026, s skupno število 23,248 družb-kvartalov. Raziskava je razdelila vsako prehod v oznakovanost na del, ki je odgovoran za cenovno potezo, in del, ki je odgovoran za spremembe v izvajenih dlužbe. KLJUČNE UGOTOVITVE: - 56.9% prehodov v oznakovanost je odgovorilo samo cenovni potezi pod AAOIFI standardom. - Zamenjavo s trajnega obdobja zmanjšala celotno časovno potezo v oznakovanosti s 5.25% na 3.22%. - Poteza, ki je odgovorila za cenovne poteze, je zmanjšala s 67%. POMEN ZA TRGOVANJE: Raziskava pokazala, da je del oznakovanosti, ki je odgovoril za cenovne poteze, zelo značilen. To pomeni, da je del oznakovanosti, ki je odgovoril za cenovne poteze, zelo značilen. Trgovci, ki uporabljajo islamski finančni omejevi, morajo razumeti, da je del oznakovanosti, ki je odgovoril za cenovne poteze, zelo značilen. To lahko vpliva na strategije za obnovljeno oznakovanost in portfeljno obrtljivost.

NASLOV: The Primacy of the Replication Portfolio Rate in Modern Option Pricing Theory AVTORJI: Dr. Gary Moore POVZETEK: Članek predstavlja nov pristop do teorije cenovanja opcij, ki se osredotoča na replicacijski portfeljno stopnjo zamišljanja. Tradicionalni modeli cenovanja opcij, kot so Black-Scholes in Mooreov model, uporabljajo Fokker-Planckov reševalni enači, ki modelirajo opcije z stopnjo zamišljanja, ki je odvisna od replicacijskega portfelja. Ključno je, da se stopnja zamišljanja v realnem času stabilizira v eno vrednost, kar pomeni, da je Mooreova R (R) stopnja, ki je potrebna za zadržavo enakosti portfelja. METODA: Avtor je sintetiziral 25 let empiričnih raziskav, ki so pokazale, da stopnja zamišljanja, ki je odvisna od replicacijskega portfelja, je ključna za stabilizacijo opcij. Pristop je bil validiran s pomočjo Fokker-Planckove enačbe in konvergenčnih testov. KLJUČNE UGOTOVITVE: - Stopnja zamišljanja, ki je odvisna od replicacijskega portfelja, je ključna za stabilizacijo opcij. - Mooreova R (R) je stopnja, ki je potrebna za zadržavo enakosti portfelja. - Tradicionalni modeli cenovanja opcij, kot je Black-Scholes, uporabljajo stopnjo zamišljanja, ki je podan kot konstanta, kar vodite do napačnega modeliranja. - Stopnja zamišljanja v realnem času stabilizira v eno vrednost, kar pomeni, da je Mooreova R stopnja, ki je potrebna za zadržavo enakosti portfelja. POMEN ZA TRGOVANJE: Avtorjev pristop je pomemben za trgovce, saj pomeni, da tradicionalni modeli cenovanja opcij, kot je Black-Scholes, niso nujno natančni. Avtorji navodijo, da stopnja zamišljanja, ki je odvisna od replicacijskega portfelja, je ključna za stabilizacijo opcij. To pomeni, da je potrebno upoštevati stopnjo zamišljanja, ko cenimo opcije, kar lahko vodi do bolj natančnih ocen vrednosti opcij.

NASLOV: The Propagation of Arbitrage Constraints: Evidence from Settlement Mismatch AVTORJI: Yuet Ning Chau POVZETEK: Članek raziskuje, ali prizadevanja za arbskat trgovino prenosejo v drugo trgovino, ki je drugače odvečna. Izpeljano je, da se arbska omejitev prenese na ETFs, ki se ne naraščajo na lokalno omejitvijo, vendar je ta prenosenje omejeno. ETFs, ki so direktno izpostavljene na T+2 uvrstitev, prenosejo arbsko omejitev, ki se je ustvarila zaradi prehodka v T+1 uvrstitev v ZDA. ETFs, ki se zavzema več na T+1 uvrstitev, prenosejo manj arbske omejitve. Domestični ETFs, ki se zavzemajo več na arbskih operacijah s prihodnjimi uvrstitevmi, prenosejo več omejitve. METODA: Članek uporablja razlikovanje razlik (DID) za primerjavo ETFs, ki so narejene v ZDA in ETFs, ki nasledijo isto inšpektor, vendar so narejene v drugih državah. Izpeljano je, da arbska omejitev, ustvarjena zaradi prehodka v T+1 uvrstitev v ZDA, zmanjšuje arbsko aktivnost domestičnih ETFs, ki se zavzema več na prihodnjih uvrstitevih. KLJUČNE UGOTOVITVE: - Arbska omejitev, ustvarjena zaradi prehodka v T+1 uvrstitev v ZDA, zmanjšuje arbsko aktivnost domestičnih ETFs, ki se zavzema več na prihodnjih uvrstitevih. - ETFs, ki so direktno izpostavljene na T+2 uvrstitev, prenosejo arbsko omejitev. - ETFs, ki se zavzema več na T+1 uvrstitev, prenosejo manj arbske omejitve. - ETFs, ki se zavzema več na arbskih operacijah s prihodnjimi uvrstitevami, prenosejo več omejitve, če je neting slabe in je globala izpostavitev visoka. POMEN ZA TRGOVANJE: Ugotovitve iz članka poudarjajo, da je arbska omejitev, ustvarjena zaradi različnih uvrstitev, lahko pomenna za ETFs, ki se naraščajo na prihodnjih uvrstitevih. To značilnost lahko vpliva na arbsko aktivnost in lančno prenosenje omejitve med ETFs. Trgovci bi morali upoštevati te omejitve pri planiranju in izvajanje trgovine.

NASLOV: Transparency or Opacity? Global Evidence on Financial Statement Comparability and Stock Price Crash Risk AVTORJI: Balasingham Balachandran, Arifur Khan, Darniya Prabu, Han Zhou, Yun Zhou POVZETEK: V tem raziskavi se študira povezava med višjo finančno odprto prikazovanje finančnih izkazov (FSC) in manjšo riziko za klapenje cene akcij v prihodnosti. Raziskovalci analizirajo 183,177 podatkov o finančnih izkazih in cih akcij 21,179 podjetij v 45 državah od leta 1998 do 2024. Natančno je najdeno, da je višja FSC povezana z manjšo riziko za klapenje cene akcij. Ta povezava je robustna za različne metode analize in obstaja tudi za dolgočasno riziko. Višja FSC je povezana z manjšo opasko za nepričakovano investicijo, čeprav ta povezava ni tak natančna izven ZDA. Natančno je najdeno, da je FSC posebno koristna za podjetja z nizko vlasniškim vnosom in nizko sledenjem analista, saj je podjetja z večjo javno informacijo koristnejša prihodnji podjetjem. METODA: Raziskovalci uporabljajo 183,177 podatkov o finančnih izkazih in cih akcij 21,179 podjetij v 45 državah od leta 1998 do 2024. Analiza je robustna za različne metode, vključno z alternativnimi opasnostimi klapenja cene akcij, entropijsko bilanciranje in instrumentne metode. Natančno je najdeno, da je višja FSC povezana z manjšo riziko za klapenje cene akcij, čeprav ta povezava ni tak natančna izven ZDA. KLJUČNE UGOTOVITVE: - Višja FSC je povezana z manjšo riziko za klapenje cene akcij. - Višja FSC je povezana z manjšo nepričakovano investicijo. - Višja FSC je posebno koristna za podjetja z nizko vlasniškim vnosom in nizko sledenjem analista. - Višja FSC ni sistematično povezana z opasnostmi, ki so odvisne od učinkovitosti vlade ali javne zaveze. POMEN ZA TRGOVANJE: Natančno ugotovitve o povezavi med FSC in manjšo riziko za klapenje cene akcij poskušajo razumljiti, kako finančna odprto prikazovanje lahko poveča informacijsko okolje podjetij in učinkovitost zunanje nadzora. To je pomembno za trgovce, saj pomoči v razumevanju in upravljanju rizikom, ki je povezan z finančnimi izkazami in cimi akcij.

NASLOV: Nuclear Energy, Uranium, and the Future of Data Centers AVTORJI: Yosef Bonaparte, Professor of Finance, University of Colorado Denver Business School POVZETEK: Raziskava se osredotoča na ekonomske možnosti uporabe nuklearen energije, srednje zelo značilne električne energije, za podatkovne centra. Nuklearna energija je posebno koristna za podatkovne centra, ki potrebujejo visok uporabniški obseg, nizko-karbone električno energijo v vsakem urah, omejen prostor in predvisne dolžnosti. Ta raziskava razvija model za oceno nuklearen energije, ki odlično razlikuje ceno električne energije od vrednosti nenehnosti, karbone lastnosti, časa do energije in integracije v sistem. Raziskava se koncentriра na pet različnih pristopov do nuklearen energije: obstoječi elektrane, upečenje in povprečje, nove velike elektrane, mali modularni ali mikroelektrani in nuklearna-izmenjalniksko zbirno mrežno kombinacijo. Praktična vrednost raziskave je, da podatkovne centra bi morala plačati dodatno nenehno in pravilno razdeljeno čisto energijsko kapaciteto, ko je to najpoučnejše način, kako zadovoljiti celoten paket električnih storitev, ki jih potrebujejo. METODA: Raziskava je narejena kot del delovne znanstvene urabe, ki je pripravljena za distribucijo na SSRN. Raziskava je narejena brez eksternih finančnih sredstev in ne more biti preučena kot imenovana. Raziskava je narejena na podlagi kontraktov in poročil, ki jih je podal avtor. KLJUČNE UGOTOVITVE: - Nuklearna energija je posebno koristna za podatkovne centra, ki potrebujejo visok uporabniški obseg, nizko-karbone električno energijo v vsakem urah, omejen prostor in predvisne dolžnosti. - Raziskava razvija model za oceno nuklearen energije, ki odlično razlikuje ceno električne energije od vrednosti nenehnosti, karbone lastnosti, časa do energije in integracije v sistem. - Raziskava se koncentriра na pet različnih pristopov do nuklearen energije: obstoječi elektrane, upečenje in povprečje, nove velike elektrane, mali modularni ali mikroelektrani in nuklearna-izmenjalniksko zbirno mrežno kombinacijo. - Praktična vrednost raziskave je, da podatkovne centra bi morala plačati dodatno nenehno in pravilno razdeljeno čisto energijsko kapaciteto, ko je to najpoučnejše način, kako zadovoljiti celoten paket električnih storitev, ki jih potrebujejo. POMEN ZA TRGOVANJE: Raziskava je pomembna za podatkovne centra, ki potrebujejo nenehno in čisto električno energijo. Raziskava razvija model za oceno nuklearen energije, ki odlično razlikuje ceno električne energije od vrednosti nenehnosti, karbone lastnosti, časa do energije in integracije v sistem. To je pomembno za odločitev, ali je nuklearna energija najpoučnejša način, kako zadovoljiti celoten paket električnih storitev, ki jih potrebujejo podatkovni centri.

NASLOV: 7448940-term-structure-with-investor-tax-heterogeneity-application-to-the-muni-puzzle AVTORJI: Kevin Zheyuan Cui POVZETEK: Članek raziskuje muni puzo, ki se sklicuje na razliko med kratkotrajnimi in dolgotrajnimi muni zastopami. Standardni model uporablja top-bracket household-jev poštev, vendar je U.S. podatek različen za različne vloge. Cui razvija model, ki razume različne podatke in različne vloge, ki jih imajo različni vložniki. Model razkrije, da so vložniki, ki imajo višje podatke, najbolj vrednujejo iznimke, kar povezuje kratkotrajne zastopke. Nizko podatek vložniki, kot so institucije, se udeležijo dolgotrajnih zastopk, saj je vrednost dolgotrajnih vložkov v zvezi z odgovarjanjem na odvajanje dolgov večja od podatek-izogib. Model razloži, da so dolgotrajne zastopke začasno večje, saj vložniki, ki imajo manjši podatke, vrednujejo iznimke manj. Model načrta 86% od 110-basis-point-kega razlike za 30 let. METODA: Cui razvija teoretični model, ki razumemo različne vloge vložnikov in njihove vplave na krivuljo zastopov. Model uporablja podatke o različnih vložnikih, kot so visoko podatek vložniki in nizko podatek institucije, in razume, kako jih vložniki udeležijo različnih dolgotrajnosti. Model razkrije, da so krivulje zastopov vplivne na dolgotrajnost, saj vložniki, ki imajo manjši podatke, vrednujejo iznimke manj. KLJUČNE UGOTOVITVE: - Vložniki, ki imajo višje podatke, najbolj vrednujejo iznimke in zato udeležijo kratkotrajne zastopke. - Nizko podatek vložniki, kot so institucije, udeležijo dolgotrajne zastopke, saj je vrednost dolgotrajnih vložkov v zvezi z odgovarjanjem na odvajanje dolgov večja. - Model razloži, da so dolgotrajne zastopke začasno večje, saj vložniki, ki imajo manjši podatke, vrednujejo iznimke manj. - Model načrta 86% od 110-basis-point-kega razlike za 30 let. POMEN ZA TRGOVANJE: Cuije model omogoča boljšo razumevanje muni puz, ki se sklicuje na razliko med kratkotrajnimi in dolgotrajnimi muni zastopami. Model omogoča predvidenje, kateri vložniki udeležijo različne dolgotrajnosti in kako to vpliva na cene zastopov. To je uporabno za finančne trgovce, saj omogoča boljšo predvidnost in razumevanje tržnih potez.

NASLOV: 7448198-quantifying-the-contributions-of-clustering-to-statistical-arbitrage AVTORJI: Lunji Zhu, Yixuan He, Mihai Cucuringu POVZETEK: Članek obravnava vlogo klasterizacije v statistični arbitraziji. Raziskovalci razdelijo klasterizatorje in model za trgovino, in uporabljajo 12 klasterizatorjev in dve kontrolne opremljave, da ocenijo njihovo vlogo v 27 letih velikih ameriških akcij. Klasterizacija povečuje alfa, zmanjšuje volatilnost knjižnice za 28% do 42%, zato klasterizatorji se strinjajo v klasičnih skupinah, vendar se njihove knjižnice zelo korelirajo. Klasterizatorji se strinjajo v klasičnih skupinah, vendar se njihove knjižnice zelo korelirajo. Večina klasterizatorjev ohranja alfa, ki ga faktorjev in regresij po akcijah ne morejo objašniti. METODA: Raziskovalci uporabljajo 12 klasterizatorjev iz šestih rodov (geometrijskih, hierarhičnih, eigenstrukturalnih, učenih, trgovskih vedenih in industrijske taxonomije) in dve kontrolne opremljave (vsi tržiški skupin in slučajna razdelitev) na 27 letih velikih ameriških akcij. Klasterizacija povečuje alfa in zmanjšuje volatilnost knjižnice za 28% do 42%, zato klasterizatorji se strinjajo v klasičnih skupinah, vendar se njihove knjižnice zelo korelirajo. Klasterizatorji se strinjajo v klasičnih skupinah, vendar se njihove knjižnice zelo korelirajo. Večina klasterizatorjev ohranja alfa, ki ga faktorjev in regresij po akcijah ne morejo objašniti. KLJUČNE UGOTOVITVE: - Klasterizacija povečuje alfa in zmanjšuje volatilnost knjižnice za 28% do 42%. - Klasterizatorji se strinjajo v klasičnih skupinah, vendar se njihove knjižnice zelo korelirajo. - Večina klasterizatorjev ohranja alfa, ki ga faktorjev in regresij po akcijah ne morejo objašniti. POMEN ZA TRGOVANJE: Klasterizacija je pomembna v statistični arbitraziji, saj povečuje alfa in zmanjšuje volatilnost knjižnice. Klasterizatorji se strinjajo v klasičnih skupinah, vendar se njihove knjižnice zelo korelirajo. Večina klasterizatorjev ohranja alfa, ki ga faktorjev in regresij po akcijah ne morejo objašniti. Klasterizacija je pomembna v trgovini, saj povečuje alfa in zmanjšuje volatilnost knjižnice.

NASLOV: When Does Reinforcement Learning Beat Naive Diversification? Learning Architecture, Regime Information, and Trading Frictions AVTORJI: Andre Sealy, Jingrui Li POVZETEK: Članek raziskuje, kjer se vrnemo, če algoritmi za učenje z podporo (RL) lahko preklopijo enostavno raznovrstno razdelitev v izbiri poročil. Raziskava se osredotoča na tri ključne elemente: učilno arhitekturo, informacije o regijah in občutljivosti za transakcije. Natančno je najdeno, da tradicionalne online algoritmi za učilno arhitekturo ponuja le mali izboljški nad 1/N referenčno strategijo, while the Decision Transformer delivers significantly better risk-adjusted performance. Vrednost informacije o regijah ni monotonna: izbrana makroekonomska signali zmanjšajo izhodne razmere, če se kombinirajo z več regijom-odvisnimi spremenljivkami, to zgodilo se je skoraj v skladu z informacijsko-kompleksnostnega zveza. Te rezultate so odvisne tudi od različnih predpogojkov za transakcije, in poročila o vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih vključenih v

NASLOV: BRICS 2026 India — Research Development Manuscript | Synthetic pilot data only AVTORJI: Dr. Garv Nijhara POVZETEK: Raziskava se osredotoči na učinek finančne infrastrukturi na vnitropolitično trgovino med članiki BRICS iz indijskega perspektivnega stansa. Raziskava razvija empirično model, ki povezuje možnost lokalne valuti za zaustavljanje in interoperabilnost digitalnih plačilnih sistemov s vlaganjem v vnitropolitično trgovino, preko zmanjšanja rizika valutnega rizika, učinkovitosti transakcijskih stroškov in sposobnosti za oblačenje trgovine. Model integrira logiko meje valuti, teorijo transakcijskih stroškov, teorijo institucij in perspektivo digitalnih platform. Ključne ugotovitve so, da je lokalno valutno zaustavljanje in interoperabilnost digitalnih plačilov pomembne za učinkovito trgovino, vendar je regulativno in institucionalno kompatibilnost pomembna za to, da te tehnološke možnosti postanejo komercialno uporabne. METODA: Raziskava uporablja PLS-SEM strategijo z SmartPLS 4, ki vključuje merilno model, HTMT, bootstrapped path coefficients, medijacijo, interakcijske učinki, prediktivno oceno in merilo vpliva. Model je razvijen na osnovi sintetične podatkovne množice, ki je namenjena testiranju predstavljenega SmartPLS/PLS-SEM modela, pri čemer je potrebno prenematanje sintetične sloj in uvrstitev pravilne podatkovne množice pri prijavi v časopis. KLJUČNE UGOTOVITVE: - Lokalno valutno zaustavljanje in interoperabilnost digitalnih plačilov lahko povečajo učinkovitost trgovine. - Medijacijo rizika valutnega rizika in učinkovitost transakcijskih stroškov lahko povežejo s učinkovitostjo trgovine. - Regulativno in institucionalno kompatibilnost je ključen faktor, ki omogoča, da te tehnološke možnosti postanejo komercialno uporabne. POMEN ZA TRGOVANJE: Raziskava prinaša testirano model, ki omogoča razumevanje mehanizmov, katerimi finančna infrastruktura vpliva na trgovino. To je pomembno za razumevanje, kako lahko lokalna valuta in interoperabilnost digitalnih plačilov pomagajo učinkovitejši vlagati v vnitropolitično trgovino, zlasti v kontekstu BRICS. Model je uporaben za razumevanje, kako regulativna in institucionalna kompatibilnost lahko vpliva na učinkovitost trgovine.

NASLOV: 7445438-a-regime-aware-free-boundary-framework-for-statistical-arbitrage-kalman-filtered AVTORJI: Berk Bektas* POVZETEK: Raziskava predstavi nov pristop k statistični arbitraži za parovno trgovino in mali sintetizirani koši. Nastavljeno je z ustrezno zamenjavo tradicionalnega pravila z scorejem z omejeno okno z naključno modelirano stohastično modelirano pravilo. Sintetizirani koš je sestavljen iz dveh največjih vzhodnih in izhodnih tečajnih vrednosti, ki so stabilne in značilne. Koš je obdržan z Kalmanovim filtranjem, vendar ne z omejeno okno OLS. Volatilnost razlik je modelirana z asimetricnim EGARCH(1,1) procesom. Trgovina je omejena na periodi, ko je razlika statistično vracajna. Vnosi in izhodi so rešeni kot slobodne granice Ornstein-Uhlenbeckove optimalne začetne vrednosti, rešene s Hamilton-Jacobi-Bellman variacno neenakomerno formulacijo z glatko prilagojitvijo. METODA: Raziskava uporablja Johansenovo VECM za ocenjevanje košov, Kalmanovo filtranje za obdržavo koša, asimetricni EGARCH(1,1) za modeliranje volatilnosti, dve-štežni Markovov model za odkrivanje regimov in optimalno začetno vrednost z Hamilton-Jacobi-Bellman variacno neenakomerno formulacijo. Vse te metode so implementirane od začetka. KLJUČNE UGOTOVITVE: - Sintetizirani koši so sestavljeni iz dveh tečajnih vrednosti, ki so največje vzhodne in najmanjše izhodne, in so stabilne. - Koš je obdržan z Kalmanovim filtranjem, ki posluje z enostavno ekonomskega modela. - Volatilnost razlik je modelirana z asimetricnim EGARCH(1,1) procesom. - Trgovina je omejena na periodi, ko je razlika statistično vracajna. - Vnosi in izhodi so rešeni kot slobodne granice Ornstein-Uhlenbeckove optimalne začetne vrednost, rešene s Hamilton-Jacobi-Bellman variacno neenakomerno formulacijo. POMEN ZA TRGOVANJE: Ugotovitve iz raziskave so uporabne za praktiko, saj pristop omogoča bolj natančno odkrivanje regimov in optimiziranje vnosov in izhodov. Kalmanovo filtranje in asimetricni EGARCH proces omogočajo bolj natančno modeliranje volatilnosti in pravila za odkrivanje regimov. Omejitev trgovine na periodi, ko je razlika statistično vracajna, poveča učinkovitost trgovine.

NASLOV: High-Dimensional Portfolio Laws via Rank-Space Contraction AVTORJI: Daniel Grosshans, Patrick Lucescu POVZETEK: Raziskava se osredotoča na razvoj arhitekturi, ki omogoča odvisno oceno različnih kompozitum v visokodimenzionalnih portfolijskih sestavnikih z polinomialno stroškovno in odrejenostno natančnostjo. Metoda razdeljuje čestokrat spremembne marge, ki se posodablja v času, in manj spremembno zavisnost, ki se pripravlja predhodno in ponovno uporablja. Vrankov prostor in prvi drevesni Bernstein vine omogočajo izračun brez simulacije in skalabilnost v višji dimenziji. Ugotovitve vključujejo natančnost, ki je odvisna od izbranih vstavkov, in omogočajo arbitražno besedilo v rizikovem upravljanju pod rizikovimi vstavki, ter distribucionalne rizikovne merila pod fizičnimi vstavki. METODA: Raziskave je namenjeno razvoj arhitektur, ki omogoča odvisno oceno visokodimenzionalnih portfolijskih sestavnikov z polinomialno stroškovno natančnostjo. Metoda razdeljuje čestokrat spremembne marge, ki se posodablja v času, in manj spremembno zavisnost, ki se pripravlja predhodno in ponovno uporablja. Vrankov prostor in prvi drevesni Bernstein vine omogočajo izračun brez simulacije in skalabilnost v višji dimenziji. KLJUČNE UGOTOVITVE: - Vrankov prostor in prvi drevesni Bernstein vine omogočajo izračun brez simulacije in skalabilnost v višji dimenziji. - Natančnost je odvisna od izbranih vstavkov in omogoča arbitražno besedilo v rizikovem upravljanju pod rizikovimi vstavki. - Distribucionalne rizikovne merila pod fizičnimi vstavki omogočajo eficientno rizikovno upravljanje. POMEN ZA TRGOVANJE: Raziskovalni pristop omogoča odvisno oceno visokodimenzionalnih portfolijskih sestavnikov z polinomialno stroškovno natančnostjo. To je uporabno za rizikovno upravljanje in oceno rizikov v velikih portfolijskih sestavnikih, saj omogoča natančno oceno rizikov v tih sestavnikih brez odvisnosti od simulacij.

NASLOV: 7444799-heterogeneous-liquidity-fragility-search-frictions-and-electronic-execution-in-the AVTORJI: Pengjie Zhou, Yucheng Wu POVZETEK: Raziskava obravnava heterogeno slabost likvidnosti, pretraževalne omejitve in elektronsko izvrševanje v trgu lokalnih zaveznih bonov. Izpeljana je, da slabost likvidnosti ne prikazuje se enako v različnih stresnih okoliščinah. V razdoblju finančnega kriza 2007-2009 se slabost likvidnosti izraža premorenimi troški, medtem ko v razdoblju pandemije COVID se slabost prikazuje v manjši tržni aktivnosti in parnem obnemoru. Raziskava tudi povezava med poškodbo likvidnosti in elektronskim izvrševanjem skozi alternativne tržne sisteme (ATS), zaznamov je, da elektronsko izvrševanje ni vedno povezano z manjšimi pretraževalnimi omejitvami in troški. METODA: Raziskava uporablja podatke o 215.773.158 transakcijah lokalnih zaveznih bonov v obdobju od 2005 do 2025, ki pokrivajo 71.17 trilijonov dolara v parnem obnemoru. Prva delovnica raziskave obravnava slabost likvidnosti v različnih stresnih okoliščinah, kot so finančni krizni obdobje in pandemija COVID. Druga delovnica povezuje dolgo obdobje slabosti likvidnosti z razvojem elektronskega izvrševanja skozi alternativne tržne sisteme (ATS). KLJUČNE UGOTOVITVE: - Slabost likvidnosti v lokalnem zaveznem trgu ne prikazuje se enako v različnih stresnih okoliščinah. - V razdoblju finančnega kriza 2007-2009 se slabost likvidnosti izraža premorenimi troški, medtem ko v razdoblju pandemije COVID se slabost prikazuje v manjši tržni aktivnosti in parnem obnemoru. - Elektronsko izvrševanje skozi ATS ni vedno povezano z manjšimi pretraževalnimi omejitvami in troški. POMEN ZA TRGOVANJE: Raziskava je uporabna za trgovce, saj pokaže, da elektronsko izvrševanje ni vedno povezano z manjšimi troški in pretraževalnimi omejitvami. Trgovci morajo razumeti, da slabost likvidnosti v lokalnem zaveznem trgu se prikazuje različno v različnih stresnih okoliščinah, kar pomeni, da morajo sprejemati različne strategije za manjšanje pretraževalnih omejitv in troškov.

NASLOV: Which Learned Forecaster Wins, and When? AVTORJI: Yuvraj Bharadia POVZETEK: Članek raziskuje, kateri mehanizmi strojnega učenja delujejo najbolje za prognoze tržnih cijen akcij, in najdemo, da je najboljši model odvisen od tržnega stanja in predhodnega razdoblja. Raziskava uporablja štiri različne arhitekture strojnega učenja: ponavljajoči se mreže (LSTM), pozornostno mrežo (Transformer), generativno mrežo (TimeGAN) in gradient-boostirane drevesa. Vse modelje so implementirane v enem okvirju s podobnimi vstupnimi podatki in metrikami. Natančno je pokazano, da najboljši model različno je v različnih razdobljih in predhodnih razdaljah. Tudi kombinacija modelov skozi stanje spremembne množice osebnikov (RS-MoE) je predstavljena in pokazana je bolj stabilna in natančnejša kot pojedine modelji. Ključna je, da tradicionalne modelje (ARIMA in sestavni putnik s preteklo toploto) ostajajo nesprejeti pri skupno napakam. METODA: Raziskava uporablja štiri različne arhitekture strojnega učenja: attention-pooled LSTM, Transformer encoder, TimeGAN in gradient-boosted trees. Vse modelje so implementirane v enem okvirju s podobnimi vstupnimi podatki in metrikami. Okvir uporablja 69-features vstup, skupno okno in eksandrirno okno za prihodnje testiranje. Modelji so ocenjeni na 7,303 radni dnevi podatkov S&P 500 (1990-2019). Raziskava uporablja trih stanja Gaussian HMM za razdelitev razdoblja v bull, bear in sideways. Predhodni rezultati so uporabljeni za učenje, a trenutni podatki ne vtrti v učenje. Raziskava uporablja Diebold–Mariano test za ocenjevanje natančnosti. KLJUČNE UGOTOVITVE: - Najboljši model različno je v različnih razdobljih in predhodnih razdaljah. - RS-MoE kombinira modelje skozi hierarhično prilagojeno vrata, ki je bolj stabilen in natančnejši kot pojedine modelji. - Tradicionalni modelji (ARIMA in sestavni putnik s preteklo toploto) ostajajo nesprejeti pri skupno napakam. POMEN ZA TRGOVANJE: Raziskava pokazuje, da je najboljši model za prognoze tržnih cijen akcij odvisen od tržnega stanja in predhodnega razdoblja. Trgovci morajo biti pripravljeni za promene najboljšega modela v različnih razdobljih in predhodnih razdaljah. RS-MoE ponuja možnost stabilnejšega in natančnejšega predviđanja, kar je koristno za praktično uporabo.

NASLOV: 7444359-tax-rate-risk-pricing-and-hedging-legislative-uncertainty-in-after-tax-portfolio AVTORJI: Anirban Majumdar POVZETEK: Raziskava se osredotoča na ekonomske učinke, ki so povezani z neodvisnostjo od podatek o prihodnji stoplji zasebnega dohodka. Raziskava prikazuje, da je neodvisnost od prihodnje stopljevanje ekonomska kratica za neizvedeno zasebno dohodkovno zasebnost, in določa njeno vrednost. Ključne ugotovitve so, da deferral vrednost je linearno zveza med prihodnjo stopljo in vključeno zasebno dohodkovno zasebnostjo, in da je prihodnja stoplja neodvisna od prihodnje stopljevanja. METODA: Raziskava uporablja dve datumni model, ki določa deferral vrednost in njeno vrednost v neodvisnosti od prihodnje stopljevanja. Nato modelira stopljo kot neskončno časovno Markovovo nizko, ki se spreminja po zakonodajnih regimih, in reši optimalno ukvarjanje problem. KLJUČNE UGOTOVITVE: - Deferral vrednost je ekonomska kratica za neizvedeno zasebno dohodkovno zasebnost, ki je linearno zveza med prihodnjo stopljo in vključeno zasebno dohodkovno zasebnostjo. - Prihodnja stoplja neodvisna od prihodnje stopljevanja. - Prihodnja stoplja vpliva na vrednost deferral opcije, ki je kvadratno zveza s vključeno zasebno dohodkovno zasebnostjo. POMEN ZA TRGOVANJE: Raziskava je uporabna za trgovce, ker določa, da je deferral vrednost zveza med prihodnjo stopljo in vključeno zasebno dohodkovno zasebnostjo. To pomeni, da trgovci lahko ocenijo vrednost deferral opcije in odločijo, ko je deferral zaslužen.

NASLOV: 7444240-cylindrical-yield-curve-dynamics-and-the-arbitrage-free-class AVTORJI: Zeyu Cao, Shaosai Huang POVZETEK: Raziskava obravnava dinamiko z vezjami in arbitražno slobodno klasifikacijo v okviru končno-razsežnega modela. Model, ki je predstavljen v članku, določa neodvisne šok za vsak vrednost časa do zavrsne maturitete, kar povezava do arbitražne cene odkriva kot težavo. Raziskava reši težavo in pravi model arbitražno sloboden, znotraj končne razširitve. Ključno je, da se deformacija, ki je določena s damped sine mode, prilagodi realni vrednosti, vendar je povezava do risk-neutralne cene dejansko odkriti. METODA: Raziskave je posvečeno odpravljanje težav z končno-razsežnimi modeli z vezjami. Model, ki je predstavljen, določa neodvisne šok za vsak vrednost časa do zavrsne maturitete. Tako je povezava do arbitražne cene dejansko odkriti. Raziskave se nanašajo na končne razširitve in pravi model arbitražno sloboden, znotraj končne razširitve. KLJUČNE UGOTOVITVE: - Deformacija, ki je določena s damped sine mode, prilagodi realni vrednosti, vendar je povezava do risk-neutralne cene dejansko odkriti. - Končna razširitev modela, ki je predstavljen, ne vključuje ekvivalentne lokalne martingalne merjeve. - Odpravljanje težav z končno-razsežnimi modeli z vezjami zahteva, da se model prilagodi realni vrednosti, znotraj končne razširitve. POMEN ZA TRGOVANJE: Raziskave so pomembne za razumitev dinamike z vezjami in razvijanje arbitražno slobodnih modelov. Model, ki je predstavljen, pravi model arbitražno sloboden, znotraj končne razširitve, kar je koristno za finančno trgovino. Prilagajanje modela realnim vrednostem in odpravljanje težav z končno-razsežnimi modeli je ključno za uspešno uporabo v praktiki.

NASLOV: 7441959-when-does-llm-extracted-central-bank-sentiment-add-value-a-fragility-analysis-of AVTORJI: Seyed Parsa Ehtesham Hosseini POVZETEK: Članek obravnava, ali je LLM-izvlekena sentimenat centralnega banka v dodatni vrednost pri optimizaciji riske v finančnih poročilih. Izvirnik uporablja 45 FOMC izjave, Googlevo Gemini 2.5 Flash model, cene IG Marketsa in stavek centralnega banka za nosilnost. Konstruirane so strategije s podanih šest USD valutnih parov, ki jih obravnavajo različne stabilnostne testne metode. Ključno je, da so ugotovili, da je LLM-izvlekena sentimenat češči na stabilnost, kot je pokaženo s pomenom p = 0.699, in da je to rezultat češči na stabilnost, kot je pokaženo s pomenom p = 0.699. Vse stabilnostne testne metode so pokazale, da je rezultat češči na stabilnost, kot je pokaženo s pomenom p = 0.699. METODA: Izvirnik je uporabil 45 FOMC izjave, Googlevo Gemini 2.5 Flash model, cene IG Marketsa in stavek centralnega banka za nosilnost. Konstruirane so strategije s podanih šest USD valutnih parov, ki jih obravnavajo različne stabilnostne testne metode, vključno s formalnim testom enakosti Sharpe-razmerja, 500-permutacijskim placebo testom, event-level clustered infeerenco z blokno in stanovniško bootstrap konfidenčno intervala, nesmerjenim postopkom za izbiro stopnje naklonitve brez pogleda v prihodnje, minimumnega varijansnega benchmarka, primerjanjem s Loughran-McDonald slovarjem, parospecifičnimi transakcijskimi stroški in okvirnimi stress testi, in dve stabilnostni probe – ponovno pitanje LLM in zamika trditvenih modelov. KLJUČNE UGOTOVITVE: - LLM-izvlekena sentimenat ni zelo stabilna in je češči na stabilnost, kot je pokaženo s pomenom p = 0.699. - Vse stabilnostne testne metode so pokazale, da je rezultat češči na stabilnost, kot je pokaženo s pomenom p = 0.699. - LLM-izvlekena sentimenat je češči na stabilnost, kot je pokaženo s pomenom p = 0.699, vendar je to rezultat češči na stabilnost, kot je pokaženo s pomenom p = 0.699. POMEN ZA TRGOVANJE: Ugotovitve iz članka kažejo, da je LLM-izvlekena sentimenat češči na stabilnost, kot je pokaženo s pomenom p = 0.699, vendar je to rezultat češči na stabilnost, kot je pokaženo s pomenom p = 0.699. To pomeni, da je potrebnost za dodatno raziskovanje in stabilnostni testi, ki jih je izvirnik predlagal, zelo važna za pravilno oceno vrednosti LLM-izvlekene sentimenate pri optimizaciji riske v finančnih poročilih.

NASLOV: Monetary Policy Shocks vs. Bubbles in the Stock and Housing Markets AVTORJI: Dan Gabriel Anghel, Petre Caraiani POVZETEK: Tega raziskava raziskuje vpliv monetarnih političnih šokov na bubi v akcijskem in zahodilnem trgu. Izpeljani so, da bubi odgovarjajo okoli 25 bps monetarnih političnih šokov z negativnim odzivom od -2% za akcijski tržni bub in pozitivnim odzivom od 1% za zahodilni tržni bub. Monetarna politika je lahko uporabljena za preprečevanje bubi, vendar je vpliv nekajen za akcijski tržni bub in nejasen za zahodilni tržni bub. Raziskava poudarja, da je potrebno nadzirati bubi, saj lahko povzročijo finančne krize. METODA: Raziskava uporabljajo kombiniran model s različno frekvenco (Mixed-Frequency VAR) za povezavo mesečnih podatkov o bubih s kvartilskimi ekonomskimi podatki. KLJUČNE UGOTOVITVE: - Bubi v akcijskem in zahodilnem trgu odgovarjajo okoli 25 bps monetarnih političnih šokov. - Negativni odziv akcijskega buba je -2%, medtem ko je pozitiven odziv zahodilnega buba 1%. - Vpliv monetarne politike na bubi je nekajen za akcijski tržni bub, vendar nejasen za zahodilni tržni bub. POMEN ZA TRGOVANJE: Raziskava podpira uporabo monetarne politike za preprečevanje bubi, saj lahko vplivajo na ekonomske indikatorje. Ta znanstveni prispevek je koristan za trgovce, ki upoštevajo vpliv monetarne politike na trge. Vendar je potrebno paziti, ker je vpliv nekajen za akcijski tržni bub, trgovci morajo biti varnejši pri upravljanju z bubi v zahodilnem trgu.

NASLOV: 7440220-stocks-as-time-varying-investment-states-cross-ticker-similarity-dynamic-allocat AVTORJI: Shenggang Li POVZETEK: Članek raziskuje, ali podobnost po različnih akcionarnih tickerjih podpira predikcijo in adaptivno razdelitev. Analiza uporablja 4.597 obsežnih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih obseženih

NASLOV: Evaluating Total Portfolio Performance: Pitfalls and Solutions AVTORJI: Laurent Barras, Alexander D. Beath, Sebastien Betermier Povzetek: Raziskava obravnava, kako se izvaja ocena celotne portfeljne performansne, z uporabo referenčnega portfelja kot odkaznega modela. Raziskovalci proučijo, kako izboljšanje portfelja v zvezi s širšo diverzifikacijo, boljšo strategično allokacijo kapitala in aktivno menovanje vpliva na merjenje vrednostnega dodatka. Natančno ugotovljeno je, da za učinkovito razlikovanje med vrednostnega dodatka in šumom potrebno je več kot 20 let performansnega podatka, kar je znatno več, kot je običajno uporabljeno v praksi. Raziskovalci ponudijo metode za merjenje in interpretacijo nesigurnosti, razdelitev vrednostnega dodatka in izboljšanje izbora odkaznega modela. Metoda: Raziskovalci uporabljajo standardno merilo srednje varijancne optimizacije, da izdelajo optimalan portfelj, ki se odliča od referenčnega portfelja z treh različnih kanalov: širše diverzifikacije, boljše strategične allokacije kapitala in aktivne menovanje. Natančno ugotovljeno je, da 20 let performansnega podatka potrebujeta za učinkovito razlikovanje med vrednostnega dodatka in šuma. KLJUČNE UGOTOVITVE: - Referenčni portfelj test je zelo šumljiv, če je portfelj odličen od referenčnega. - Več kot 20 let performansnega podatka potrebujeta za učinkovito razlikovanje med vrednostnega dodatka in šuma. - Nekaj 36-37% šans je, da se portfelj ne uspešno zgodovi nad referenčnim portfeljem v eno leto, in 13-15% v deset let. POMEN ZA TRGOVANJE: Raziskava podpira uporabo večjega časa za oceno performans, saj je 20 let potrebno za učinkovito razlikovanje med vrednostnega dodatka in šuma. To pomeni, da se morajo menjava in osebne sredstva izmenjati z večjim odborom in določenjem, da je 20 let dovolj za učinkovito oceno.

NASLOV: 7439639-do-capital-goods-incentive-announcements-move-Italian-industrial-stocks-an-event AVTORJI: Marco Belardi POVZETEK: Članek raziskuje, ali se tržiščne cene italijanskih industrijskih akcionarnih del ješčenj vplivajo na oglas o politiki za podnebnih delov, ki se izvaja od leta 2019 do 2026. Raziskava uporablja oglejene zakonske in administrativne akte in jih kodira s pomočjo velikih jezikovnih modelov (LLM). Njegova ključna vprašanja so, ali se tržiščne cene vplivajo na oglas o teh politikah in ali je LLM sposoben izdolžiti signal, ki je vključen v tržiščne cene s napajanjem. Raziskava najde, da so tržiščne cene neodvisne od oglasov o te politikah, saj ne morejo izdolžiti signala, ki bi bil vključen v tržiščne cene s napajanjem. METODA: Raziskava uporablja 37 zakonskih in administrativnih akтов之间是否存在因果关系。本文通过手动和独立的大型语言模型（LLM）对这些法案进行编码，验证了市场在公告后的异常回报情况。研究发现，在任何编码下，事件后的异常回报均不显著。此外，研究还进行了稳健性检验，表明结果在不同的估计窗口、篮子组成和幸存者控制下依然稳健。 KLJUČNE UGOTOVITVE: - Tržiščne cene italijanskih industrijskih akcionarnih delov ne odvisijo od oglasov o podnebnih delovih. - Veliki jezikovni modeli (LLM) ne morejo izdolžiti signala, ki je vključen v tržiščne cene s napajanjem. POMEN ZA TRGOVANJE: Raziskava podpira idejo, da je tržiščno cene neodvisna od oglasov o podnebnih delovih, kar pomeni, da investitorji ne morejo uporabiti takih oglasov za predviđanje zmagovalnih akcionarnih delov. To je pomembno znanstveno dokazovanje, da je tržiščno cene neodvisna od takšnih informacij, kar lahko vpliva na strategije za izbiro akcionarnih delov. Kritično: Raziskava podpira idejo, da tržiščne cene ne odvisijo od oglasov o podnebnih delovih, kar pomeni, da investitorji ne morejo uporabiti takšnih oglasov za predviđanje zmagovalnih akcionarnih delov. To je pomembno znanstveno dokazovanje, da je tržiščno cene neodvisna od takšnih informacij, kar lahko vpliva na strategije za izbiro akcionarnih delov.

NASLOV: The Role of Stock Markets in Economic Growth: Empirical Evidence from Panel Data Analysis AVTORJI: Ishak Demir† POVZETEK: Raziskava opisuje povezavo med razvojem trgovinskih trgovin in ekonomskim rastom, uporabljajuč panelno podatkovno analizo. Izpeljana je, da je povezava pozitivna in značilna v visoko dohodnih državah, vendar slabija in neznačilna v nizko in srednje dohodnih državah. Kratek panel VECM ukazuje, da se prilagodilni proces razlikuje glede na različne ravni finančnega razvoja. Izpeljane so, da finančno manj razvijene države lahko premočno prednostno prejemajo podporo od oboljelih tržnih institucij, boljše likvidnosti in izboljšane regulativne kakovosti. METODA: Raziskava uporablja panelno cointegracijsko testiranje, fully modified OLS in panelni VECM. Podatki se razdelijo v 36 državah od leta 2003 do 2022. KLJUČNE UGOTOVITVE: - Povezava med trgovinsko kapitalizacijo in ekonomskim rastom je pozitivna in značilna, vendar je večja v visoko dohodnih državah. - Kratek prilagodilni proces je bidirekcijski v visoko dohodnih državah, vendar se lahko prilagadi le od razvoja trgovin do rasti v nizko in srednje dohodnih državah. - Prilagodilni proces je neenakoten glede na ravni finančnega razvoja, s tem da države z nizko razvojno stopnjo se prilagajajo hitreje in pogostejše, vendar države z višjo razvojno stopnjo se prilagajajo pomembno pomembnejše in pogostejše. POMEN ZA TRGOVANJE: Izpeljana je, da finančno manj razvijene države lahko premočno prednostno prejemajo podporo od oboljelih tržnih institucij, boljše likvidnosti in izboljšane regulativne kakovosti. To pomeni, da je pomembno, da takšne države prilagajajo svoje tržne institucije, ki jih običajno oblikujejo razvoj finančnega sistema, za boljšo prilagodljivost in podporo ekonomske rasti.

NASLOV: The Anatomy of Repatriation: Investor Heterogeneity in Japan's Overseas Portfolio Adjustment AVTORJI: Fuli Yang POVZETEK: Članek raziskuje, ali je zmanjšanje narijančnih tokov kapitala iz Japonske dovolj za zaključek, da se kapital vrnje domov. Iz analyze mesečnih transakcij finančnih varstvenih varstnikov, vključno z bankami, varnostnimi računi, življenjskimi ubezpičniki in investičnimi varnostnimi varstniki, in dnevnih zmagovalcev Japonskih državnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezplačnih brezpla

NASLOV: 7436699-tax-driven-illiquidity-portfolio-constraints-and-municipal-borrowing-costs AVTORJI: Viet-Dung Doan, Chotibhak Jotikasthira, Qifei Zhu POVZETEK: Članek raziskuje, kako U.S. podatek o danah utruje ponašanje vložnikov in stroške vprašanja v municipalnih zalogah. Ko je zaloga kupljena pod specifičnim de minimis omejitevom razmerjem, je njeno oskrbovanje do parja danesnega vrednosti podatkovno danesnega vrednosti danega dohodka, kar jo razkriva kot neatragibilno za podatkovne zaloge. To pomeni, da so zaloge s podobnimi vlagatelji, ki so bližje omejitevam, manj gotovi za novi ponudbe, če je njihova poševna vlagateljska sestava bližje omejitevam. To povečuje ponudne stopnje in smanjuje finančno moč. METODA: Raziskovalci uporabljajo podatke o zalogah in vlagateljskih sestavah, najpomembnejše zalog, ki jih vlagajo v zaloge, in njihove vlagateljske sestave. Prikazujejo, kako povečanje vlagateljske sestave, ki je bližje omejitevam, povečuje ponudne stopnje in smanjuje finančno moč. KLJUČNE UGOTOVITVE: - Ko je vlagateljska sestava bližje omejitevam, so vlagatelji manj gotovi za novi ponudbe. - Ponudne stopnje povečajo se z omejevanjem vlagateljske sestave. - Vlagateljska sestava, ki je bližje omejitevam, povečuje stroške finančnega vprašanja za vlagatelje, ki uporabljajo te zaloge. POMEN ZA TRGOVANJE: Raziskave pokažejo, da je podatek o danah vpliva na ponašanje vlagateljev in stroške vprašanja v municipalnih zalogah. Vlagatelji, ki so bližje omejitevam, so manj gotovi za novi ponudbe, kar povečuje stroške finančnega vprašanja. To je posebno pomembno za vlagatelje, ki uporabljajo zaloge, ki so bližje omejitevam, v svojih poševnih sestavah.

NASLOV: 7436409-medium-horizon-return-predictability-in-the-kospi-200-futures-basis-timing-signa AVTORJI: Jaehyun Jo POVZETEK: Ta raziskava se posvetuje predvidljivosti vrednosti v futures trgu KOSPI 200 za dolgo obdobje, okoli dveh do trih mesecev. Raziskovalnik uporablja dnevne podatke za predviđanje, ki se razlikujejo od kratkoročnih obdobjev, in konstruirane je eno dnevnega ravnoteženega futures cene. Natančno vemo, da so višja futures cene povezane s nižjimi prihodnimi KOSPI 200 vrednostmi, največjih prednosti prihaja v obdobjih od dveh do trih mesecev. Tudi drugi merilni pristopi, kot so median, srednji vrednosti in dolgih pozitivnih nizov, ustrezno predstavljajo negativne vrednosti prihodnjih vrednosti. Raziskava tudi pokazuje, da je eno dnevnega prosenevanje futures cene pozitivno pri predviđanju v realnem času. METODA: Raziskovalnik uporablja dnevne podatke za predviđanje od januarja 2004 do julija 2026. Konstruirane je eno dnevnega ravnoteženega futures cene z uporabo koreanske CD 91-dnevnega rate, prihodnosti KOSPI 200 za leto 1 in stvari do konca kontraktov. Prediktivne regresije se končajo v aprilu 2026, ker morajo biti prihodnje vrednosti opazljive. Raziskovalnik se zanaša na eno dnevnega prosenevanje futures cene, ki je vključno z medijanom, srednjo vrednostjo, delitvijo po dnevnih pozitivnih nizih in eno dnevnega prosenevanja. KLJUČNE UGOTOVITVE: - Višja futures cene so povezane s nižjimi prihodnimi KOSPI 200 vrednostmi, največjih prednosti prihaja v obdobjih od dveh do trih mesecev. - Median, srednja vrednost, delitve po dnevnih pozitivnih nizih in eno dnevnega prosenevanje futures cene ustrezno predstavljajo negativne vrednosti prihodnjih vrednosti. - Eno dnevnega prosenevanje futures cene je pozitivno pri predviđanju v realnem času. POMEN ZA TRGOVANJE: Raziskava pokazuje, da je futures cene KOSPI 200 v dolgo obdobje predvidljivih za trgovce, kar lahko pomaga pri izbiri strategije za dolgo obdobje. Preproste merilne pristopi, kot so medijana in srednja vrednost, so lahko boljše merila za predvidljivost prihodnjih vrednosti, kot je dejanska eno dnevnega prosenevanje. Ta informacija lahko pomaga trgovcem pri izbiri pravilne prediktivne strategije za dolgo obdobje.

NASLOV: Market Integration, Ambiguity, and Expected Returns: Evidence from the Stock Connect in China AVTORJI: Jianhua Mei1 and Nikolai Sheung-Chi Chow2 POVZETEK: Tovorniki raziskave poredijo, ali povezovanje trgovine z vnešnimi investitorji smanjuje ambiguiteto in kako to vpliva na cene prihodnosti. Uporabljajo program Stock Connect v Kine za izkušnje, ki so založeni v prekinjenem, pravilnem razširjevanju programov. Izračunajo ambiguiteto na podlagi visokotežnih, uravnoteženih povratnih vrstil. Natančno je ukazano, da ambiguiteta smanjuje, vendar je to sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sproženo sprož

NASLOV: 7432878-one-theme-four-portfolios-from-monopoly-themed-etfs-to-the-stocks-behind-their-r AVTORJI: Hesham Mashhour, Monopoly MOAT (mplymoat.com) POVZETEK: Raziskava se ukvarja z četrtimi ETF-ji, ki so v temelju monopolne moči in ekonomske moči, in jih primerjava v zvezi z njihovimi strategijami in povratki. ETF-ji MPLY, MOAT, TOLL in razstopirla CZAR so različni v izbiro akcij, težavnostih in pravila za obnovljavo. Podatki znamenajo, da so povratki ETF-jev zelo odvisni od osebnih vnosov in strategij, niso pa zelo različni v zvezi z monopolnimi močmi. METODA: Raziskava primerja ETF-je na dveh ravneh. Prvič, po njihovih dokumentiranih strategijah in povratkih, ki jih so dosegli. Drži, se konstruirajo simulacije z osebnimi vnosoma, ki jih so imela ETF-ji, in jih primerjajo pod dvema različnima obnovljavanim praviloma. Znotraj ETF-jev, MPLY in TOLL so največjih akcij, ki imajo največ vpliv na povratke. KLJUČNE UGOTOVITVE: - MPLY in TOLL so največjih akcij, ki imajo največ vpliv na povratke. - Monthly rebalancing spodbuja različne akcije, ki so vključene v ETF-je. - Povratki ETF-jev so zelo odvisni od osebnih vnosov in strategij, niso pa zelo različni v zvezi z monopolnimi močmi. POMEN ZA TRGOVANJE: Raziskava poudarja, da so ETF-ji z monopolnimi močmi lahko različni v izbiro akcij in pravilom obnovljave, čeprav delijo eno temo. To pomeni, da je pomembno razumeti, kako se ETF-ji izvajajo, da se izognete nepredvidenim povratkom. Trgovci bi morali preučiti konkretno strategijo ETF-ja, ne le njegov temo.

NASLOV: 7431660-uncapped-unrepresentative-a-structural-critique-of-the-nifty-50-index-constructi AVTORJI: Sam S, ne navedeno POVZETEK: Članek razkrije nekaj ključnih problemov v konstrukciji Nifty 50 indeks, ki je osnovan na neomejenem skupinah akcij in sektorov, divergenčnem odstotku sektorov, ki se razlikuje od indijanskega GVA, in pravila za prenovljavo, ki je opremljena z razmerje zračunljivosti. Nastopajoči problemi pomenijo, da indeks več odraža ekonomskega predstavnika Indije, ampak bolj likvidnostno teženo porazdelitev portfela največjih formalskih sektorovnih družb Indije. METODA: Članek je založen na NSE-jevih razpisih, regulativnih podatkih in akademskih publikacijah o učinkih indeksov. Raziskava se sicer osredotoča na četrto konstrukcijsko omejeno indeksa: neomejen skupina akcij, divergenčna porazdelitev sektorov, pravila za prenovljavo, ki je opremljena z razmerje zračunljivosti, in zahteva za derivativne operacije. KLJUČNE UGOTOVITVE: - Nifty 50 indeks je več likvidnostno teženo porazdelitev portfela največjih formalskih sektorovnih družb Indije, kot predstavnika ekonomskega predstavnika Indije. - Divergenčna porazdelitev sektorov indeksa je odstotna od indijanskega GVA. - Pravila za prenovljavo, ki so opremljena z razmerje zračunljivosti, so lahko prenarežena. - Zahteva za derivativne operacije lahko omejuje konkurenčnost indeksa. POMEN ZA TRGOVANJE: Ugotovitve v članku pomenijo, da je Nifty 50 indeks več likvidnostno teženo porazdelitev portfela največjih formalskih sektorovnih družb Indije, kot predstavnika ekonomskega predstavnika Indije. To pomeni, da se vložniki morajo osredotočiti na drugačne indeksa za bolj predstavnike predstavljene ekonomske situacije Indije.

NASLOV: 7431219-digital-innovation-government-support-credibility-and-stock-returns-evidence-from-china AVTORJI: Yuxiang Jiang, Ruijie Liu, Cristian Ioan Tiu, Shenglong Xiang POVZETEK: Članek raziskuje, kako se digitalna inovacija, podpora vlade in verjetnost, da bo ta podpora učinkovita, vpliva na vrednost akcij v Kitaju. Nastopajoči podatki pokazujo, da je digitalna patentna intenzivnost (DPI) povezana z večjimi prihodnji vrednostmi akcij. Ta povezava je močnejša, če provincijske vlade poudarjajo razvoj digitalnih tehnologij in imajo manjšo finančno napetost, in slabša, če se politična nesporazumnost poveča. V okviru prve javno oznake LGFV-ovega defaulta v letu 2017 so akcije s visokim DPI preizkusne in nato izkušajo večjo rast prihodkov zaveznih. DPI tudi predviđa digitalne subsidije in vladske zakupnike, povezujec inovacije z vladsko podporo finančnih tokov. Premija je nesporazumnost vodilna, ne se skuplja okoli prihodkov oznake, in ne se obrne. METODA: Raziskava uporabljajo podatke o digitalnih patentih, subsidijah in vladskih zakupnikih v Kitaju. Vlada se razlikuje v svoji sposobnosti in voličnosti podpirati digitalne inovacije, ki je uporabljanje digitalnih tehnologij (DPI) merilo za to. Vladska podpora se meri s podatki o finančni napetosti provincijskih vlad. Raziskava uporablja podatke o akcijah, ki jih so razdelili glede na DPI, in event study okoli javne oznake LGFV-ovega defaulta v letu 2017. KLJUČNE UGOTOVITVE: - Digitalna patentna intenzivnost (DPI) povezana je z večjimi prihodnji vrednostmi akcij. - Premija je močnejša, če provincijske vlade poudarjajo razvoj digitalnih tehnologij in imajo manjšo finančno napetost. - Premija je slabša, če se politična nesporazumnost poveča. - V okviru prve javno oznake LGFV-ovega defaulta v letu 2017 so akcije s visokim DPI preizkusne in nato izkušajo večjo rast prihodkov zaveznih. - DPI povezana je z digitalnimi subsidijami in vladskimi zakupniki. POMEN ZA TRGOVANJE: Raziskava je uporabna za trgovce, ker pokaže, da je verjetnost, da bo vlada učinkovita, ključna za oceno vrednosti akcij. Trgovci bi morali upoštevati, da premija za digitalne inovacije se lahko obrne, če se politična nesporazumnost poveča. Tudi analisti bi morali upoštevati, da premija ne povezuje z napakami prihodkovih očitkov in ne se skuplja okoli prihodkov oznake.

NASLOV: The Reallocation of Information by Relative Value Arbitrage AVTORJI: Marius Savatier POVZETEK: Raziskava opisuje, kako relativno vrednostni arbitraziranci vplivajo na informativnost cijena. Dva rizikna vložilca s povezanih odnosov odplačevanja se trgujejo na razdeljenih trguh, while relativno vrednostni arbitraziranci npravijo obnovo informacij med cijenama. Ugotovljeno je, da relativno vrednostni arbitraziranci lahko povečajo informativnost cijena z informacijo, ki je vključena v povezane cene, vendar lahko tudi prenesejo šum iz ene cene v drugo. Raziskava karakterizira ravno stopnjo rizika, ki jo lahko prenese arbitraziranci, za maksimalizacijo informativnosti. Kalibracija z empirskimi ocenami informativnosti cijena suggestira, da čakajši nivo arbitraziranja lahko preteče informacijski kanal, čakajši nivo informativnosti cijena spadne. METODA: Raziskava je izvedena v eno-odnosnem konkurenčnem ekonomskem okolju z eno besporednim in dva rizikna vložilca. Vloženci so specializirani po trgu: vloženci v vložilcu A trgujejo samo vložilcem A in besporednim vložilcem, vloženci v vložilcu B trgujejo samo vložilcem B in besporednim vložilcem. Konečna odplačanja so povezana, a cene so šumuječe. Povezana odplačanja vključujejo dve kanala: informacijski kanal, ki poveča informativnost, in šumski kanal, ki jo zmanjša. KLJUČNE UGOTOVITVE: - Relativno vrednostni arbitraziranci lahko povečajo, ampak tudi zmanjšajo informativnost cijena. - Informacijski kanal poveča informativnost, zatiranjem šuma pa jo zmanjša. - Stopnja informativnosti zavisi od stopnje rizika, ki jo lahko prenese arbitraziranci. - Čakajši nivo rizika, ki ga lahko prenese arbitraziranci, za maksimalizacijo informativnosti je odvisen od stopnje šuma in nehedgeabilne komponente odplačevanja. POMEN ZA TRGOVANJE: Raziskava je pomembna za razumevanje vpliva relativno vrednostnega arbitraziranja na informativnost cijena. Vključno z kalibracijo z empirskimi ocenami informativnosti cijena, suggirira, da čakajši nivo arbitraziranja lahko preteče informacijski kanal, če je stopnja šuma velika. Tako lahko arbitraziranci zmanjšajo informativnost cijena, če je rizik prenese.

NASLOV: 7429198-the-profit-bias-identity-in-sports-betting-bookmaker-profit-as-the-public-s-prediction-error AVTORJI: Jacek P. Dmochowski POVZETEK: Raziskava obravnava, kako se zasliženka trgovca v sportnem stazanju razdeli na tri kanale: zasliženka, katera je rezultat odstotka zasebnega prepoznavanja, težavnost prepoznavanja in kovarianco med delom staze in rezultatom. Natančno je pokazano, da je zasliženka vendar nekaj, če je prepoznavanje nekaj drugo. Raziskava testira predpisi na podatkih 1.139 MLB igre in najde, da je videti odvisnost med delom staze in rezultatom enotni primer Simpsonove paradoxne. METODA: Raziskava je založena na modelu, ki razdeli zasliženke na tri kanale: zasliženke, težavnosti prepoznavanja in kovarianco med delom staze in rezultatom. Metodologija je opisanja zasliženke, prepoznavanja in kovarianco. Podatki so testirani na 1.139 MLB igrah. KLJUČNE UGOTOVITVE: - Zasliženka je affine in narašča z pričakovanim delom staze na strani porazitelja. - Zasliženka je ena trih kanalov, ki jih razdelijo zasliženka, težavnost prepoznavanja in kovarianco med delom staze in rezultatom. - Zasliženka je nekaj, če je prepoznavanje nekaj drugo. - V skupini igre je videti odvisnost med delom staze in rezultatom, vendar je ta odvisnost nevidna, ko so igre razdeljene glede na stran, ki jo je trgovec preferiral. POMEN ZA TRGOVANJE: Raziskava pokaže, da je zasliženka vendar nekaj, če je prepoznavanje nekaj drugo. Trgovci morajo razumeti, da je zasliženka odvisna od prepoznavanja in težavnosti prepoznavanja, kar lahko pomaga pri izboljšanju strategij za trgovljanje.

NASLOV: Beta-Neutral Alpha of Game-Theoretically Classified Multi-LLM Disagreement: A Decomposition Theorem and Its Implications for Agentic AI in Quantitative Investing AVTORJI: Joey Qiyu Zhang POVZETEK: Članek je raziskuje, ali predvišči, ki ga določijo različne družbeno-teoretične razenpravnosti (game-theoretic disagreement) v različnih razenpravnostnih tipih (Coordination, Prisoner's Dilemma), je dokazan na podlagi informacijske vrednosti ali pa je to le posledica prebivalne razenpravnosti trga. Izračunano je, da je predvišči, ki ga določijo razenpravnostne tipove, večja za razenpravnosti tip Coordination, vendar je ta razlika več posledica neurejenega trga (beta) kot dokaz informacijske vrednosti razenpravnostnih tipov. METODA: 1. Izračunali so, da vsaka razenpravnostna kategorija (Coordination, Prisoner's Dilemma) vsebuje beta term, ki je enak za vse signalke, in alfa term, ki je kategorijospecifičen. 2. Za vsako napredno povratno vrednost vsake razenpravnosti je bil izračunana beta in alfa, kjer je beta term enak za vse signalke, a alfa term različen glede na razenpravnost. 3. Razlika med beta in alfa je preverjena na več naprednih povratnih razdobljih (N = 5, 20, 60), in je za razenpravnosti tip Prisoner's Dilemma alfa bila statistično enaka nič. KLJUČNE UGOTOVITVE: - Predvišči, ki ga določijo razenpravnostne tipove, je večja za razenpravnosti tip Coordination, vendar je ta razlika več posledica prebivalne razenpravnosti trga (beta) kot dokaz informacijske vrednosti razenpravnostnih tipov. - Predvišči, ki ga določijo razenpravnosti tip Prisoner's Dilemma, je statistično enaka nič na več naprednih povratnih razdobljih. - Predvišči, ki ga določijo razenpravnosti tip Coordination, je enak 46.2% za večje napredna povratna razdoblja, kar se prilega predvišči, ki ga določijo nizko-sogласen razenpravnosti tip. POMEN ZA TRGOVANJE: Raziskava ukажe, da razenpravnostne tipove, ki so razenpravnosti tip Coordination, ne nujno vsebujejo dodatne informacijske vrednosti, ki bi lahko uporabljene za generiranje alfa predvišči. To pomeni, da razenpravnosti tip Prisoner's Dilemma, ki so nizko-sogласen, ne morejo biti uporabljene za direktno generiranje alfa predvišči. Torej, agentic AI bi bilo bolje uporabljeno za razdeljevanje rizika in upravljanje prebivalne razenpravnosti, kot za direktno generiranje alfa predvišči.

NASLOV: 7427340-voting-rights-and-the-volatility-content-of-federal-reserve-communication AVTORJI: Fuli Yang, Tingting Qiu, Jing Huang POVZETEK: Članek raziskuje, kako formalna glasbeni prava vplivajo na varnostno vsebino v javnih izjavi Federalnega odbora za tržnike (FOMC). Raziskovalci se osredotočijo na mehanizem letne rotacije glasbenih pravic med predsednikom Federalnih rezervnih banke in razkazujejo, da je vsebina, ki jo glasbeni predsedniki izračunajo, v večjem stopnju pripovedana v dolgotrajno komponento varnosti ameriških akcionarnih vložil. Izračunano različico dolgočasne varnosti (delta) je -0.461, z verjetnostjo 0.0007. Ključna je napačnost, da glasbeni prava ne spremenijo vsebine javnih izjave, če je vrednost enaka. METODA: Raziskovalci uporabljajo podatke iz 819 javnih izjav, izravanih od 2015 do 2023, in 71 programiranih zasedanj. Podatki so pridobljeni iz arhiviranega Yahoo Finance S&P 500 cikla in uporabljene so za ohranjeni zavestni model. Metoda vključuje analizo vpliva nesporazumov in modalnih komunikacij na statistično glasbeno izposojo. KLJUČNE UGOTOVITVE: - Formalna glasbeni prava spodbuja manjšo pripovedavo varnosti v dolgotrajnem delu varnosti akcionarnih vložil. - Vsebina, ki jo glasbeni predsedniki izračunajo, ima manjši vpliv na dolgotrajno varnost. - Glasbeni prava ne spremenijo vsebine javnih izjave, če je vrednost enaka. - Rotacija glasbenih pravic ne razloži razlik v varnostnih vrednostih med glasbenimi in neglasbenimi predsedniki. POMEN ZA TRGOVANJE: Raziskava pokaže, da formalna glasbeni prava vplivajo na varnostno vsebino v javnih izjave FOMC. Trgovci lahko uporabljajo te ugotovitve za boljše razumevanje vpliva glasbenih predsednikov na varnost akcionarnih vložil. To lahko pomaga v boljšem prepoznavanju trendov in predviđanj v trgu.

NASLOV: 7425478-return-volatility-correlations-and-option-returns AVTORJI: Xinfeng Ruan† in Jingda Yan‡ POVZETEK: Raziskava raziskuje vezi med povratkoma v opcijah in korrelationom med povratki in volatilnostjo (RVC). Natančno razdeljeni opcije glede na RVC, raziskovalci natančno prepoznajo, da so delta-hedirane povratke zmanjšane od nizko do visok RVC poročil. Tradicionalne objašnje z uporabo leverage ne morejo razložiti te vezi, zato se raziskovalci obrnejo na kombinirano potrebno in ponudilsko kanal, ki je povezan z volatilnostjo. Trgovci lahko uporabijo te ugotovitve za predvidevanje povratkov v opcijah. METODA: Raziskovalci uporabljajo podatke o individualnih ameriških opcijah iz OptionMetrics Ivy DB od januarja 1996 do avgusta 2023, združeni z podatki o povratkih in balansnih razmerah CRSP-ja in predviđanjih analista iz I/B/E/S. Raziskovalci filtrirajo opcije, da so le liquidne, in izračunajo delta-hedirane povratke. Ključna varijabilna RVC je korlacja med dnevno spremembo povratkov in dnevno spremembo implikirane volatilnosti v preteklosti leta. Raziskovalci testirajo ugotovitve s različnimi definicijami povratkov in RVC, ki so vse konzistentne z prvi ugotovitvami. KLJUČNE UGOTOVITVE: - Delta-hedirane povratke opcij zmanjšajo od nizko do visok RVC poročil. - Razlika v povratkih med nizko in visok RVC poročili je statistično in ekonomsko značilna. - Ugotovitve niso zavisanke od poznanih faktorjev rizika, vključno z Fama-French-ovimi petimi faktori. - Negativna veza med RVC in povratki ostaja značilna pod realnimi transakcijskimi stroški. POMEN ZA TRGOVANJE: Raziskovalci natančno prepoznajo, da je korlacija med povratki in volatilnostjo značilna in ne more biti objašnjena s tradicionalnimi leveragejstvenimi razlogi. Trgovci lahko uporabijo te ugotovitve za predvidevanje povratkov v opcijah in za razumevanje volatilnosti v trgu.

NASLOV: Decision-Equivalent Volatility Forecasts: Statistical Loss, Portfolio Value, and Allocation Commonality AVTORJI: Fuli Yang, GARCH Institute POVZETEK: Tega raziskava formalizira pojmovi o decizionalno enakovrednih volatilnostnih predviđanj. Volatilnostna predviđanja so decizionalno enakovredna, če podano pravilo za portfeljizacijo generirajo enake relativne težave. Raziskava uporabljajoča zastopan in statistična metode pokaze, da so enakovredna volatilnostna predviđanja lahko zelo različna od pogleda statistične izgube, vendar podobna pri izračunu portfeljizacije. Tega seveda razlikujejo statistična izguba, vrednost portfelja in enakovrednost pri delitvi. METODA: Raziskava uporablja zastopano istorično ponovitev, ki spremembuje šest predenkrščanskih napak v volatilnostnem vektorju peti ETF-jev, pričakovane korelacije pa ohranja. To razdeli volatilnost v skali, razsežnost, akcijske in zasebne vloge, ameriške in mehurške akcijske vloge, razvijene in razvijene zasebne vloge in finančne vloge. KLJUČNE UGOTOVITVE: - Volatilnostna predviđanja, ki so statistično različna, lahko podobne predviđanja generirajo pri portfeljizaciji podano pravilo. - Volatilnostna predviđanja lahko vplivajo na statistično izgubo, vendar ne na vrednost portfelja ali enakovrednost pri delitvi. - Največja predstavljena napaka v akcijskih vlogeh v nasprotni smeri lahko poveča letnjo enakovrednost vrednostno vrednostjo za 4,19 točk. - Volatilnostna predviđanja lahko vplivajo na enakovrednost pri delitvi, vendar ne na vrednost portfelja. POMEN ZA TRGOVANJE: Raziskava je pomembna za trgovce, ker razjasni, da so statistična izguba, vrednost portfelja in enakovrednost pri delitvi različne ocene volatilnostnih predviđanj. Trgovci morajo razumeti, da so volatilnostna predviđanja, ki so statistično različna, lahko podobna pri portfeljizaciji, kar pomeni, da morajo upoštevati vse tri oznake pri izbiri volatilnostnih predviđanj.

Formuliranje portfela akcij na osnovi dolginog rizika in priljubljenosti pričakivanja Abstrakt Investoři često uporabijo historsko risanje na podlagi dostopnih podatkov za navedeni izbire akcij, vend so se očeni, da omejeno analiziranje dolginog rizika le po spodnji volatilnosti lahko v srednjem in daljšem období identificira akcje, ki bo prinesla večšje prihode. Ta študija raziskava, ali Sortino razdel, kot mera dolginog rizika, lahko podprema delovno izbiro in konstrukcijo portfela, da se osredotoči na akcje s visokim Sortino razdelom. Hipoteza je, da portfeli s pito akcijami, ki ima najvisje Sortino razdele v posameznem sektoru, bo v delovnem období od 2015 do 2025 let prevznesli vsebine prihoda od portfeli s pito akcijami z nizjimi Sortino razdelami in od celotnega trga. Analizirani so podati za 179 velikih, javno trgovalnih ameriških spleti, razdeljeni na 141 akcij, ki splňujo kritija trgovalnosti in historske podatke. Sortino razdele so obliceni za periodo od 2011 do 2014, a naslednje so ustvarjeni dva portfeli - en s najboljšimi 5 akcijami in drug s najmanjšimi 5 akcijami po Sortino razdelu v každem sektoru. Vpravna in metodologija Studija je izvedena na podlagi počini in analizi historskih podatkov o 141 akcijah iz 6 sektorov: zdravje, tehnologia, poslovno diskretno, industrije, energetika in financije. Sortino razdele so obliceni s pomoćjo mesečnih vrstnih vrednosti od 2011 do 2014. Rezultate pokazujo, da portfeli s visokimi Sortino razdelami prevznesli vsebine prihoda v delovnem období, s osrednjim razlikom 78,65% mednje s portfeli s nizjimi Sortino razdelami. Ob oba portfelih se nad gradom prevznesli tudi S&P 500 in naslovni trg, ki smo uporabili kot točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno točno

Robust Arbitrage Certificates on Prediction Sets: Attainment and Discretization Vstavka V tej raziskavi se razgledamo tematiko *executable robust arbitrage certificates* na *prediction sets*, s osredotočevanjem na martingale klase in arbitrajne strategije. Na koncu predstavimo ključne navetke in uporabimo njih za praktične aplikacije. Metodologia 1. Predstavitev predmetnega prostora: Definiramo *finite-calendar prediction set* s *finite traded menu* in *bid–ask quoted* náklada. 2. Rozdelično razumevanje nekonzistente in arbitraja: Oprostite odročitev med martingale klasi in preferirani modeli, in osredotočite se na prevedenie nekonzistente v explicitni trade. 3. Uporabni metode: Vključamo *Farkas alternative* za finite predstavitve in *martingale cubature* za analizo nekončne predstavitve. Ključne navetke - Uporabno razumevanje radijsa: Ključna matematska navetka, ki osredotočuje *uniform relative-interior ball* v každem aktivnem increment hull, omogoča attainment in diskretizacijo. - Attained margin in optimal slopes: Navedena marginalna vrednost in optimalni krovni spodi so omejeni, ki omogoča praktično uporabno certifikat. - Martingale cubature in lokalni radiji: Martingale cubature omogoča rekonstrukci martingale klase in vsebe v nekončnem predstavitvi, z njegovim lastnimi lokalni radiji, ki se smanjujeta. Praktične aplikacije 1. Farksa alternativa in certifikat: Na finite predstavitvi, Farkas alternativa priniša jedino močno martingale pravilo ali executable certifikat. 2. Diskretena implementacija: Augmentirani nodovi v martingale cubature restavljata uniformen radij in omogoča certifikat za nekončne predstavitve. 3. Praktična uporabnost: Navedeni metodi in navetke so praktično uporabni za generiranje executablnih arbitrajnih certifikatov v različnih finansijskih scenarijah. Zavernica Raziskava predstavlja kompleksen pogodbni sistem, ki vključuje matematsko analizo, diskretizacijo in praktične aplikacije. Navedeni ključni navetki in navodje omogočata uporabnikom prepoznavo in generiranje executablnih robustnih arbitrajnih certifikatov, ki so vredni za različne finansijske strategije in aplikacije.

The Scale–Shape Divide in Bitcoin and Ether Perpetuals: Roughness, Quarticity, and Bipower Residuals Vstavka Članek se zabava v raziskavi "sklopa meja in tvarja" v Bitcoin in Ether perpetuals, z osredotočevanje na kratkoročne volnje kvartičnosti in težavne strane. Analiza raziskava, ali rol trgovne volnje (roughness) v kratkoročni sklađenosti variacije je informativna o tvaru oglednega taška, ali pa ne o trgovnih priložnostih ali stabilni sklađenosti. Metodologia Raziskava uporablja Binance Visiono archiv USD-marginih perpetual-futurov Bitcoin (BTCUSDT) in Ether (ETHUSDT) za periodu od 1. januarja 2020 do 31. julija 2026. V skladu s prejšnimi raziskavami se raziskava kratkoročni increment varianci (Ĥᴵ) in težavni p-varianci (Ĥᴾᴬ) s katerimi se povezuje volatilnost. Kroki metodologije: 1. Měřenje: V ključnih momentih se uporabljajo realizirana kvartičnost (RQ), normalizirana kvartičnost (NRQ) in bipower variacija (BV) kot mierni za kratkoročno volnjo kvartičnosti. 2. Zamik in kontrol: Kontrolira se absolutno financirano osredotočenje, absolutno osredotočenje na osnovi, trenutni logaritmski realizirani varianci in osredotočenje na prejnosni ohvat. 3. Inferencja: Blokova kljucna inferencja se izveda s pomočjo 14-dnevnih blokov in 112-lag Newey–West HAC korelacijskih intervalov. Ključne nalaženja - Kratkoročni kvartični sklop: V Bitcoin in Ether se pri osredotočenju na osem-určno časovno okno pokazuje, da logaritmska kvartičnost (RQ) ima pozitivne koeficienti pri spojenju s Ĥᴵ, ki potvrzuje, da kratkoročna volnja kvartičnosti se povezuje s increment varianci. - Normalizacija in relativna koncentracija: Normalizacija kvartičnosti razdelja kvartično sklopeno na trenutni realizirani varianci, razlaga pa relativno nagrano skupino kvartičnosti po sklopu. - Bipower variacija kot osredotočenje: Bipower variacija je navedena kot osredotočenje za kontinuirne komponenti, ki pomaga razlikovati trgovno volnjo od številnih drugeh faktorov. Praktične uporabe Raziskava pokaza, da trgovna volnja (roughness) v kratkoročni skladu ne vseeno informacije o tvaru oglednega taška. Kvantitativno, kvartičnost in bipower residuali lahko pomagajo v rekogniziranju trgovnih številk in variacij, vendar ne ponudita stabilnega tla sklađenosti ali trgovnih priložnosti. Podvarniki in omejitev - Omejena validnost: Raziskava se osredotoča na Binance Visiono archiv in može imeti omejene generalizacijne moči za druge trgove ali platforme. - Kvaliteta datal: Preprosta validacija časovnih intervalov in izključevanje mesecnih začetkov ne obletita vse potencialne številke pomembnih odstotkov v datalih. Vnioski Članek pripobuda, da trgovna volnja v kratkoročni skladu ne obsega samo sklopenost, vendar tudi relativno nagrano skupino kvartičnosti. To ima implikacije za trgovne strategije in modeliranje volatilnosti, ponuditev bolj številnih perspektiv za razumevanje trgovnih dinamik.

"7412898-a-missing-market: Analiza kosťa termovho úvěru na trhu s akcijami" Vstav Članek se zabava v tematu "7412898-a-missing-market", ki se osredotoča na analizu kosťa dodavka fixnega termovho úvěra na trhu s akcijami. Avtor, Spencer Andrews, analizira neuporabnožnost termovho trga za uporabnikov short sell v trgu s akcijami, ki je v praksi postrakovano absencijo trga za termovhovo financiranje. Osnovne naloge in metodologija 1. Identifikacija neuporabnega trga: Andrews uvajata, da trg s termovmi úvěrami v trgu s akcijami postrakuje absencijo, ki je v silni sporo s postatno uveljeno trgem repo (odnošenim trgu za odloženih posredničnih usluga). 2. Merenje kosťa dodavka: Uporablja dane za U.S. uporabni akcijski úvěr od leta 2007 do 2024, da izmeri trejno kosť dodavka fixnega termovho úvěra. 3. Analiza trejnih komponent: Kosť se sskladava iz troje glavnih komponent: očakivano prevozdne (expected carry), riziko (replication risk) in izvorno neprediktivnega rizika (sourcing leg). Klíčne ustavki - Očakivano prevozdne (expected carry): Posegne blizu dnevnega úvěra, v srednjem delu predvidi razmerne prevozdnega oplatka. - Riziko (replication risk): Odraža različnost v realizem úskupnem kosťu v zavisnosti od očakivanj. Vzrosta s trajenim termom in je osredotočena na akcije z neprediktivnimi úskupnimi kosťmi. - Izvorno neprediktivno riziko (sourcing leg): Prikuplja kosť, ki se pojavi, ko akcije ne mogu biti uporabne za úvěr, in je osredotočeno na akcije, kjer je najtežje dobiti zamennik. Praktični upor Andrews naličuje, da za diversifikirane uporabnike je kosť dodavka termovhogo úvěra o značno manjšo, kot je očakivano na osnovi individuálních rizik. Napak, za leveraged short seller, ki se srenja z napakou na zaključitev pozicije, bi kosť bila blizu plnega oplatka. Pomeni za trg - Smanjena neuporabnost: Vlastni oblikovanje rizik in smanjena neuporabnost kosťa lahko odstraňata trg z neuporabnega stanja. - Možnosti za popravo: Navedena so četne možnosti za popravo trga, vključno z cash-settljem in poolingom, ki smanjuje kosť za najmanj predstavne akcije. Vnosi v literaturo Članek přispiva trih primernim literaturam: - Kroki za merjenje kosťa dodavka: Predstavlja prvi term strukturu kosťev akcijskega úvěra, ki je postavljen na realizem uporabnih danih. - Povezava med fee in rizikom: Identificira in cenja drugi constraint, ki operi na strani neuporabnega rizika, ki se srenja s oskrbi za zamikavanje dnevnega úvěra. - Neuporabni trgi za rizikovo transfer: Doda v svojo literaturo o neuporabnih trghi za rizikovo transfer, kjer so obe, ceno in priljubljeno, mierno mierne měnitelne.

Trgova likvidnost, visoka frekvencija trgovanja (HFT) in cene bondov: Analiza slasti trgovanja in cene v ameriškem korporacijskim bondovnem trgu Avtorji: Muhammad Umair in Ling Yang Skica Članek se udeva v analizo slasti trgovanja in cene v ameriškem korporacijskim bondovnem trgu med 2003 in 2023. Uporabja kompleksno setje podatkov iz FINRA Enhanced TRACE, CRSP Mutual Fund Database, Mergent FISD in TAQ intraday rekordov, da konstrui fond-inak in bond-inak ukrepje likvidnosti, intenzivnosti HFT, in vpliv na ceno. Ključne nalažbe so: zmanjšena bid-ask rozstra (spred) in Amihudova likvidnost med normalni trgovne uspeše, ampifikirana intradayna cenova volatilnost in odziv na stresne dogodke v fondov, in visoka korelacija med redkimi bondovnimi portfelimi in odzivom na negativne performanse. Metoda - Podatki in prepracovanje: Uporabljeno je FINRA Enhanced TRACE za bond-inak transakcije, CRSP Mutual Fund Database za fondovne vrednosti in vplive, Mergent FISD za bondovne atributi, in TAQ za HFT intenzivnost. - Konstrukcia ukrepa: Na podlagi portfelov v CRSP databazi so konstruirani portfelni ukrepi likvidnosti na bond-inak in na fondovni ravni, uporabno pravilnik z delno srednimi vrednostmi. - HFT intenzivnost: Intenzivnost HFT je merdena z pomočjo TAQ in alternativnega metoda na podlagi distribuce TRACE intraday časovih razmerov. Ključne nalažbe: - Normalni trgovne uspeše: Visoka HFT intenzivnost se svetla na zmanjšeni bid-ask rozstra in Amihudova likvidnost, kar pomeni boljšo likvidnost in stabilneje cene. - Stresne dogodki: Med stresnimi trgovnimi dogodki, kot so Globalna finansna kriza, Taper Tantrum in COVID-19 Dash-for-Cash, HFT intenzivnost pada, rozstra se vsečine in cenova nestabilnost se nasilja. - Fondovni odziv: Mutni fondi z manj likvidnimi portfelimi odzivata na negativne performanse bolj številno in ima visokošte konkavne odzive na odliv fondov. - Cena in volatičnost: Korelacija med HFT intenzivnost in fondovni slast pomeni, da tempni poskusi na ceno so spremembne v zavisnosti od trgovnega okola in HFT medij. Pomen za trgovanje - Likvidnost in HFT: HFT odigra ključno rolj v oblikovanju likvidnosti v korporacijskih bondovnih trgho, ampifikirani svojo ušivost med normalni trgovni uspeše, ali se smanjša v stresnih dogodkih. - Fondovni odziv in strategija: Institucionalni investirji, posebej otpridni fondi, so vplivni na cenova dynamika in volatilnost v zavisnosti od slasti njihovih portfelov in HFT okola. - Cena formiranje in mikrostruktura: Interakcja med elektronskim trgovanjem, algoritmičnimi strategijami in bondovni mikrostrukturo utvara fragilnost in cenoformirajše moči ameriškega korporacijskega bondovnega trga.

Akuratenost in razmanitost v adaptivnem prognoziranju volatilnosti Vstavka Članek se osredotoča na temo akuratenosti in razmanitosti v adaptivnem prognoziranju volatilnosti, raziskavajči, kako različne uporabne sisteme prognoziranja zachovavajo historsko informacijo. Avtor Fuli Yang raziskava, kako se kolektivna razmanitost modelov spremenja, ko adaptivne prognozirajše sistemo uporabita različne množi historskih podatkov. Metodologia Studija vključuje četne trgove (S&P 500, FTSE 100, Nikkei 225 in Hang Seng) od leta 2000 do 2026. Prognoze se evaluirata vno številnega letnjega perioda od janusarja 2015. Uporablja se številna modelov, vključno z Ridge, Elastic Net, k-najboljših sosedov in omejenim hlubokostnimi regresijskimi državami. Adaptivne modele se vsebo vsebuje v dva kategorije: te, ki so zamrajeni pre 2015, in te, ki uporabljajo različne okroje historskih podatkov (3, 5, 10 let ali vse historsko podatke). Ključne naložbe - Akuratenost in razmanitost: Adaptivne sistemy prognoziranja, izjemno te, katerih individulnich prognoz je nizja, uporabljajo različne množi historskih informacij. To vvede vprašanje: kako se kolektivna razmanitost modelov spremenja, ko se uporabita različne množi historskih podatkov? - Akuratenost–razmanitost frontier: V 16 različnih trg–prognozirajških celic, adaptivne sisteme s vsema historskimi podatkih ima nižje QLIKE (pogostni izrazenje prognoziranej greše) in nižje efektivne razmanitosti kot sme vsega finite-memorijne specifikacije. - Stres oneti in pozicijska korelacija: Stres na trgu, definiran na podlagi historske distribucije volatilnosti, lahko vpliwa v razmanitost prognoz. Adaptivne modele, mapirane na volitno-tehnološno pravilo, pokazata korelacijo poskocov pozicij, sugerirajočo možno smerjenje v postopku synchronizacije. Pomen za trgovo delo Ovdje prikazana metodolija in naložbe ima več upor: - Sistemni design: Raziskava preučeva, kako se različne prognozirajše sistemi, ki uporabljajo različne množi historskih podatkov, spremenja v njihovih akuratenosti in razmanitosti. - Predlog za evaluacijo: Predlaga se novi kritičen standard za evaluacijo adaptivnih sistemov prognoziranja, ki uključa ne le individuarno prognozirano grešo, velja tudi kolektivno razmanitost modelov. - Praktične implikacije: Znanje, da adaptivne sistemi lahko oslabita prognozirano razmanitost posle stresov trga, ima pomen za strategije rizika in stabilnost na finančni trgoviskih.

7408778: Trgovni standard za ocenjevanje portfelov - novi pristop do posjužjanja vrednosti Prevod: Članek se osredotoča na koncept "trgovnega standarda za ocenjevanje portfelov", ki predstavlja nov pristop do posjužjanja vrednosti in ocene institucionah portfelov. Avtor, Mario Bajo Traver, je v hlavi oddelka za strategije investicij v Banco de España. Glavne nalože: - Dinamični standard: Trgovni standard, ki se v ključnih momentih osredotoča na "preskoceno vredno" portfela v padeju trga in na "benčmark" v njegovem rastu, predstavlja preoblikovanje tradicionalne ocene portfelov. - Reordanje portfelov: Ocena portfelov zato, da se osredotoča na ta dinamični standard, lahko vedne do reordanja njih v porovnanju z Sharpe razmerom. Portfeli s silnoj Sharpe razmerom moho biti pogosto razmisljeni kot slabji v okviru tega novega standarda. - Defenzivni nalag: Portfeli z defenzivni nalag, ki se osredotočata na spremembo trga, lahko pridobita nišo v oceni, vendar ima tudi svoji upor. Metodologia: - Dato: Članek uporablja srednje tjedne vrate inkludirajuce 15 ameriških seznamov ETF-ov iz perioda od 2007 do 2026. - Ocena portfelov: Osredotoča se na osem standardnih portfelov s različnimi mandatimi: akcij, fixnih dobi in multi-aktivni. - Ključna metoda: V ključnih naložeh se uporablja "yardstick", ki izmerja kratkočasno padejo portfelov pod referenčno področje (preskoceno vredno ali benčmark). Pomen za trgovino: - Reordanje portfelov: Ta dinamični standard lahko vedne do reordanja portfelov v porovnanju z Sharpe razmerom, osredotočiv se na padeje trga. - Defenzivni portfeli: Portfeli z defenzivni nalag, ki se osredotočata na spremembo trga, moho biti bolj oceni, vendar tudi postopno smanjujeta realizirani maksimum dolgost. - Uporne vysvětljenje: Ta pristop dodaja izbolj številno dimenzij za razumevanje, zakaj se institucionali managerji hujata s trgem in kar uporabljajo za oblikovanje portfelov.

Dekompozicija distributionalne volatilnosti: Skala, tvar, položenie in hitrost Vstavka Volatilnost je persistentna, ale objekt, ki trva, je zmanjšen, ko se razgledamo pretrvalnost v sklavih modelih, ki so običajno navedeni na skalarne podmrežje variacije ali funkcije preminjave vsebinne variacije. Ta članek raziskava dekompozicijo distributionalne volatilnosti na četvero premetov: skala, standardiziran tvar, položenie in hitrost. Na osnovi denarnih vnosov S&P 500, FTSE 100, Nikkei 225 in Hang Seng se prikazuje, da je skala silno spojena s 22-dnevno realizirano volatilnostjo, zaposbelno standardiziran tvar je priblizu se nuli s aktualno volatilnostjo, položenie je negativno spojeno z realizirano volatilnostjo, a hitrost se umestita med njihovmi intervali, jer merdenje jedinostnjo premik njegovih vsebin. Metodologia - Skala: Oznana kot logaritm robustne IQR-opravljenega rozptja za trenutno okno. - Tvar: Prvi Wasserstein odstup med standardiziranih rolningih distribucij. - Položenie: Med oblikom zsignirano razlika med trenutno okno medijanom in zamerno distribucijsko medijano iz pre-2007 okvirka. - Hitrost: Odstup med poslednjo in prejšno rolningo distribucijami. Ključne navodile - Skala je silno spojena s aktualno realizirano volatilnostjo. - Standardiziran tvar je priblizu se nuli, ne vplivano od aktualne volatilnosti. - Položenie je negativno spojeno z realizirano volatilnostjo. - Hitrost pokazuje premik v distribucijih med rolningi okviri. Praktično uporabnost Uporabni navodili za praktiko vključujeta: - Robustnost: Robustno merenje rozptja omeja vpliv izoliranih pozorovanj. - Sensitivnost: Blokovana bootstrap testir in korecije za mnočnik so podprla, da se osredotiti na mreženje koordinat. - Varnost: Dekompozicija ne predstavlja prognoze, ampak posluži kot testirni vnos za nastopne analize in prognoze. Kritično razmisljanje Dekompozicija distributionalne volatilnosti na skala, tvar, položenie in hitrost preučeva različne aspekte volatilnosti, kar omogoča bolj detailno razumevanje in razdelitev objekta, ki trva. Ta pristop recenim pomeni za mreženje volatilnosti kot multidimenzionalne finančne rizikovne stvori, pri čemer se osredotava na testirnost, a ne na prognoze.

Volatilnostna trvalost nisi distribucijni spomnenje: Razdelj varanje in distribucijnega spomnenja v finansnih trgih Cilj članka: Članek se osredotoča na razliko med varanje trvalostjo in distribucijni memori v finansnih trgih. Tradično se interpretira, da visoka varanje trvalostja zna, da trg ostajá v eni volati instanci dlje. Članek obravnavo, ali je to pravilno in predstavlja li varanje trvalostja samo eno sredstvo za razumevanje tržnih volatilnostei. Ključne ustne: * Varanje trvalost: Opisuje, kako se srednja meri tržne volatilnosti spremenja v času. * Distribucijni memori: Oznava spremembe v distribuciji vrednosti nališenih vrajen, ki se odrazata v tržnih datih. * GARCH model: Popisuje statistično modelirani način za razmatiranje varanje trvalostje. * Wasserstein distanc: Metoda za merenje razliki med dvomi distribucijami. * Bootstrap inferenc: Metoda za osvojeno stvaranje virovnega intervala za paramete modelov. Metodologija: Članek uporablja denesne dane za četne trgi (S&P 500, FTSE 100, Nikkei 225, Hang Seng) od leta 2000 do junja 2026. Uporablja se zmanjšena GARCH-rodina modelov (konstantno srednje Student-t GARCH, Gaussian GARCH, Student-t GJR-GARCH) in bootstrap inferenc za osvojeno stvaranje virovnega intervala. Pomen za trgovanje: Ustali rezultat ima naslednje praktične nasledije za trgove: * Razumevanje varanje trvalostje: Članek predstavlja, da varanje trvalostje ne obsega samo distribucijni memori, siromno pomeni tržne volatilnosti. * Modeli za prepoznavanje tržnih volatilnosti: Preučevanje razlik med različnimi GARCH modelima in distribucijnimi metodami omogoča bolj razumevanje volatilnosti v kontekstu distribucijnih spomenj. * Ustvarjanje strategij: Znanje o distribucijnih memori in njihovem vplivu na tržne volatilnosti lahko pomaga v ustvarjanju bolj efektivnih tržnih strategij. Kritično razmisle: Članek predstavlja novi pogled na varanje trvalostje in distribucijne spomnenje v finansnih trgih. Uporabljanje Wasserstein distanč in različnih modelov za varanje trvalostje omogoča bolj pogosto razumevanje tržnih volatilnosti. Rezultati pokazujo, da varanje trvalostje ne vsebuje samo distribucijni memori, siromno pomeni tržne volatilnosti. To oteva novi razdel v tržni analizi in modeliranih strategijih.