|
on Financial Markets |
| By: | Wanling Rudkin |
| Abstract: | Competing ESG rating providers reward different portfolio attributes. This paper models funds that choose portfolios and fees for investors with heterogeneous ESG priorities. Portfolio changes can improve both providers' scores or favour one methodology over the other, and investor demand determines which methodology each fund targets. Greater disagreement makes provider-specific positioning more productive but common improvement less productive. Funds therefore specialise more, yet both provider scores, investor participation, and equilibrium fees fall in the benchmark equilibrium. Investor heterogeneity creates matching gains from specialisation, while common improvement supports holdings-based ESG exposure. Methodology convergence improves participation and common exposure but weakens matching across investor clienteles. Convergence raises welfare when the social value of common exposure is sufficiently high. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.29583 |
| By: | Zhongtian Chen; Nikolai Roussanov; Xiaoliang Wang; Dongchen Zou |
| Abstract: | We identify a strong common risk factor structure that is pervasive across corporate securities: stocks, corporate bonds, and options. The common factors are closely linked to observable factors and key economic indicators. These factors explain much of the variation in individual asset returns, although pricing errors persist. A joint mean-variance efficient portfolio across asset classes achieves a high Sharpe ratio, resulting in part from cross-market hedging of the common sources of risk. We develop a measure of market segmentation based on differences in the common factor risk premia between markets and document a significant degree of segmentation. |
| JEL: | G1 G12 G13 G17 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35579 |
| By: | Langenbucher, Katja |
| Abstract: | How should legislators and regulators cope with technological innovation in the field of financial services? Move quickly, top-down, to provide legal certainty - or let things develop bottom-up, with decentralised legislators and agency initiatives preparing the ground? Over the last years, stablecoins, i.e., crypto assets that are framed as payment instruments and promise stability through a peg to underlying reserves, 2 have been a paradigm example for regulatory strategies and cultural differences between the U.S. and the EU. The U.S. has been inclined to take a bottom-up engagement, coupled with a distrust of government intervention, while the EU was more disposed towards quickly moving forward with comprehensive regulation, aimed at insulating financial consumers from anticipated harm. |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:safewh:343097 |
| By: | Olivier Wang |
| Abstract: | I decompose stock returns into a duration-matched Treasury component, identified from monetary policy surprises, and a payoff component. Risk and returns rise much less with duration for stocks than for their matched Treasuries. Stock volatility is dampened by rate insurance: rates fall in bad times, so the bond inside a stock provides insurance against the stock’s payoff risk. Expected stock returns are dampened because the insurance works in reverse: rates rise in good times, so stocks’ payoff gains hedge the losses borne by investors holding net duration, notably government bonds when Ricardian equivalence fails. This framework helps reconcile positive bond premia with negative stock-bond covariance, sheds light on equity anomalies and the collapse of the value premium, implies that fiscal and monetary policy shape bond and equity premia, and motivates a two-factor model that jointly prices stocks and bonds. Rate insurance can even turn the price of long-run risk negative, explaining why long bonds beat long stocks. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35636 |
| By: | Valentin Burban; Pavel Diev; Gilles Dufrénot; Nelson Mongeaud |
| Abstract: | This paper proposes a new empirical taxonomy of safe assets based on their safe-haven behavior during periods of global risk aversion. Our multi-criteria framework captures the persistent performance of bond securities, currencies, and alternative assets during episodes of acute risk-off sentiment, allowing us to construct a cross-asset ranking of safe-haven behavior by asset characteristics: global safe assets, credit-sensitive assets, and emerging assets. We find that sovereign bonds issued by G10 economies, including U.S. Treasuries, consistently exhibit the strongest safe-haven behavior. A limited set of corporate bond markets displays partial safe-asset characteristics, while gold is the only alternative asset that consistently scores highly across our safe-haven criteria, particularly during periods of geopolitical risk. We further show that U.S. Treasuries have exhibited weaker safe-haven properties since the pandemic, although this reflects a broader reconfiguration of global safe-asset hedging properties rather than a uniquely U.S. decline. We find that weaker safe-haven properties are associated with higher inflation, debt levels and scarcity of available assets. |
| Keywords: | Safe Assets, Safe-Haven, U.S. Treasuries, Asset Pricing, Risk Aversion. |
| JEL: | F31 E44 G01 G12 G15 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:bfr:banfra:1049 |
| By: | Maciej Wysocki |
| Abstract: | This paper develops the first end-to-end application of cross-sectional learning-to-rank to the S&P 500 weekly options (SPXW) zero-day-to-expiration surface, integrated with margin-aware position sizing, an abstention rule driven by model uncertainty, and a strict out-of-time integrity check. A LightGBM LambdaRank ranker scores a daily nine-strategy cross-section composed of eight delta-targeted short-put positions and a \textit{SKIP} candidate, trained against a path-aware Sortino-on-bars label computed at one-minute resolution. The framework is evaluated under index-option margin requirements, a tiered fee schedule, and bid-to-mid execution assumptions across a four-window walk-forward over 2021-2024 and a strictly held-out 2025 out-of-time slice. Seven sizing methods produce out-of-time annualized Sharpe ratios between 4.31 and 5.76, with the headline method reaching a Probabilistic Sharpe Ratio of 0.964 and a sample-period maximum drawdown of -2.28%, on a single hold-out year against a walk-forward range of 1.90 to 3.11. Out of time, every method exceeds three passive benchmarks (CBOE PUT, CBOE WPUT, SPX buy-and-hold) by at least 3.84 in Sharpe ratio and five internal selection baselines by at least 3.69. A two-by-two ablation of the confidence gate against the tail-risk features places 5.05 of the 5.59 out-of-time Sharpe gap over the CBOE PUT with the ranker and the selection layer, the two risk controls adding 0.54 between them. On walk-forward, where the gate binds, neither control comes close to the headline alone and their interaction supplies most of the result. A fifteen-group feature ablation shows that removing the multiplicative regime interactions collapses walk-forward statistical confidence. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.24786 |
| By: | Lee, Woongki (Yonsei University) |
| Abstract: | This study focuses on the stock picking dimension of tactical asset allocation. To reflect the fact that each manager’s opportunity set is constrained by the fund’s style, we construct a style-matching portfolio and use it to define manager alpha as a time-varying measure of stock picking ability. We then distinguish between this full time-varying measure of manager alpha and the portion left unexplained by factor exposures. The former captures ordinary stock picking ability, whereas the latter captures superior stock picking ability. This distinction forms the basis of our empirical analysis of stock picking ability in the Korean fund industry. |
| Date: | 2026–08–10 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:bd2pj_v1 |
| By: | Mathilde Dufouleur |
| Abstract: | This paper examines the effects of cryptocurrency regulation on price deviations in the Bitcoin market, focusing on regulatory implementations rather than announcements. I construct a unique database of regulations across 28 countries since 2009, categorized into seven types, and analyse Bitcoin price data since September 2013. Our findings indicate that the Law of One Price does not hold in the Bitcoin market. Contrary to initial conjectures, more regulated markets exhibit higher price convergence with the USD benchmark. According to the type of regulation, this result is mixed. Regulations enhancing reliability and transparency, such as the expansion of securities laws, banking and payment regulations, and the implementation of regulatory sandboxes foster price convergence. In contrast, partial bans—primarily targeting banks—exacerbate price divergence, underscoring the significant role of financial institutions in the Bitcoin market. Additionally, anti-money laundering/countering the financing of terrorism (AML/CFT) laws reduce local prices regardless of USD price level, suggesting the cryptoasset's use in illicit activities.. |
| Keywords: | Cryptocurrency, Cryptocurrency Regulation, Price Convergence, Law of One Price, Financial Institutions, Anti-Money Laundering, Regulatory Impact |
| JEL: | G15 G18 E42 K22 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:bfr:banfra:1052 |
| By: | Mohammad Ghaderi; Sébastien Plante; Nikolai Roussanov; Sang Byung Seo |
| Abstract: | Do corporate bond investors earn compensation for bearing credit risk? We construct a new historical corporate bond database spanning 128 years to estimate a corporate bond counterpart to the equity risk premium. Combining hand-collected archival data with modern sources, we assemble a panel of over 100, 000 bonds and 7 million observations. While recent samples suggest corporate bond excess returns largely reflect the term premium, our long sample reveals a sizable and statistically significant credit risk premium. Credit spreads predict future corporate bond returns and macroeconomic aggregates, though their ability to forecast business cycle fluctuations weakens when prewar data are included. |
| JEL: | G1 G12 N21 N22 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35578 |
| By: | Maria Laura Santoni; Vincent Jouanne; Matthew L. Scullin |
| Abstract: | Backtests of trading strategies are often selected after many parameter trials. A strong historical result can therefore reflect search luck rather than a persistent signal. Standard summaries such as return, Sharpe ratio, and drawdown do not record how many candidates were tried, whether the selected rule survives out-of-sample validation, or whether the available history is long enough to support the result. This paper describes the MinervaScore, a post-selection robustness grade for trading strategies. The score combines four established validation quantities: Deflated Sharpe Ratio, Probability of Backtest Overfitting, Superior Predictive Ability, and Minimum Track Record Length, with a regime-stability diagnostic. These components are converted into signed margins from their admissibility thresholds, aggregated into a raw score, and then mapped to a 0-100 display. The display is tied to a binary Robustness Seal: scores of 80 or higher are shown only when all five gates pass. The calibration uses 359, 062 production backtest records. The score is intended to rank statistical support, not to estimate the probability of future profit. In synthetic markets with known ground truth, the MinervaScore separates true signal from lucky backtest outcomes, with an AUROC of 0.989 at the headline difficulty. Its improvement over the GT-Score proxy and the gates-passed baseline is modest, and it remains close to the corrected DSR-alone baseline. In a pre-registered test on unseen real-market data, the score showed no significant forward relationship in a population with limited surviving edge (Spearman rho_s = 0.013, one-sided permutation p = 0.40). We therefore present the MinervaScore as an auditable validation and reporting layer, rather than as evidence of demonstrated real-market predictability. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.23808 |
| By: | Shinji Kakinaka; Ken Umeno |
| Abstract: | Cross-correlations between financial signals are neither scale-free nor amplitude-independent: they vary with the time scale over which they are measured and with the magnitude of the fluctuations that dominate the average. We exploit this structure to construct a portfolio allocation model in which the risk functional is the signed fluctuation function of multifractal cross-correlation analysis (MFCCA), indexed by a scale $s$ and a fluctuation order $q$. Unlike MFDCCA-type criteria, which rectify local detrended covariances before aggregation, MFCCA retains their sign, so that co-moving and counter-moving components contribute to risk with opposite signs; for $q=2$ the resulting quadratic form coincides with the detrended fluctuation function of the portfolio series itself, recovering the mean--variance criterion as a scale-dependent limit. Using two-component ARFIMA and Markov-switching multifractal processes, we show that prescribed multiscale and multifractal dependence is transmitted into the optimal weights, and that sign preservation contributes more to the reduction of tail risk than aggregation over fluctuation orders. Applied to financial multi-assets, the criterion lowers drawdown, Value-at-Risk, and expected shortfall relative to the mean--variance benchmark at every required return, in and out of sample, without any loss in realized portfolio return. The construction maps signed multiscale interaction structures onto resource-allocation decisions, and applies to any complex system whose components interact across heterogeneous scales with amplitude-dependent coupling. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.04987 |