This paper provides a comprehensive analysis of excess returns specific to corporate bonds. We construct a measure of excess returns that uses synthetic Treasury securities with identical cash flows as benchmarks, thereby fully removing interest rate effects and isolating the component of returns specific to corporate bonds. Using a monthly sample from 2002 to 2024, we find that, in addition to being lower on average, the corporate-bond-specific excess return differs significantly in the cross section from both the standard excess return based on T-bills and the duration-adjusted return. We further examine the effects of a broad set of bond-level characteristics and systematic risk factors on bond excess returns. Together, these findings provide a foundational benchmark for future research on corporate bond returns.

We document a striking anomaly in international capital flows that we term "sanction arbitrage": U.S. investors exploited the 2014 sanctions on Russia by significantly increasing holdings in Russian equities while Rest-of-World (ROW) investors fled. We rationalize this behavior through a simple game-theoretic model where the sanctioning government faces a trade-off between geopolitical objectives and domestic welfare, effectively creating a protective shield for domestic investors and driving out ROW investors. Empirically, we confirm that pre-sanction U.S flows negatively predicted subsequent sanction designations. Consequently, U.S. investors internalized this protection to act as opportunistic buyers, absorbing fire-sale assets from exiting foreign investors and capturing significant excess returns from Russian stock holdings. These findings reveal that "smart" sanctions designed to preserve market access can inadvertently generate wealth transfers from foreign to domestic agents.

We study asset demand and price formation in a two-asset rational expectations equilibrium with narrow thinking, where traders imperfectly coordinate decisions across assets under non-nested price information. When the price of one asset increases, cross-asset inference from prices reduces expected demand for the other asset, which feeds back into the demand response for the original asset. Narrow thinking weakens internal coordination and amplifies reliance on price-based inference. As a result, more severe narrow thinking leads to higher own-price elasticities. The model delivers sharp implications for market liquidity and price informativeness in the presence of bounded rationality.

Serving on the official unsecured creditors' committee (UCC) of a bankrupt firm provides hedge funds with access to material nonpublic information (MNPI), which can facilitate their informed trading across firms and asset markets. We find that hedge funds increase equity turnover and execute more large trades in the quarters following UCC membership. In contrast, hedge funds do not exhibit such trading behavior after accessing public information about bankrupt firms or holding the bankrupt firm's debt without committee involvement. Importantly, these large trades often target firms with close economic ties to the bankrupt entity. Returns from these MNPI-driven trades are substantial.

Can fully agentic AI nowcast stock returns? We deploy a state-of-the-art Large Language Model to evaluate the attractiveness of each Russell 1000 stock each trading day, starting in April 2025 when AI web interfaces enabled real-time search. Our data contribution is unique along three dimensions. First, the nowcasting framework is completely out-of-sample and free of look-ahead bias by construction: predictions are collected at the current edge of time, ensuring the AI has no knowledge of future outcomes. Second, this temporal design is irreproducible once the information environment passes. Third, our framework is fully agentic: we do not feed the model curated news or disclosures; it autonomously searches the web, filters sources, and synthesises information into quantitative predictions. We find that AI possesses genuine stock-selection ability, but that its predictive power is concentrated in identifying future winners. A daily value-weighted portfolio of the 20 highestranked stocks earns a Fama-French five-factor plus momentum alpha of 19.4 basis points and an annualised Sharpe ratio of 2.68 over April 2025–March 2026. The same portfolio accumulates roughly 49.0% cumulative return, versus 21.2% for the Russell 1000 benchmark. The strategy is economically implementable: the average bid-ask spread of the daily Top-20 portfolio is 1.79 basis points, less than 10% of gross daily alpha. However, the signal remains asymmetric. Bottom-ranked portfolios generally exhibit alphas close to zero, while the strongest predictive content sits in the extreme top ranks. Delayed-entry tests further show that predictability does not vanish after a single day; rather, the signal remains positive over a broad window of subsequent entry dates, consistent with slow information diffusion rather than a fleeting overnight anomaly.

This paper studies how mutual fund managers update their beliefs as macroeconomic conditions change. Using regulator-mandated reports from Chinese mutual funds, we measure the intensity of belief updating from year-over-year changes in stated outlooks and decompose those updates into macro and micro themes. We show that belief updating is state-contingent: funds with more intensive belief updating shift their narratives toward macro (micro) topics during recessions (expansions) and concurrently reduce (increase) procyclical stock exposures and on-site company visits. This state-contingent belief updating predicts superior performance when matched to prevailing economic conditions, with macro-oriented updates paying off mainly for high-updating funds in recessions and micro-oriented updates paying off more broadly in expansions. Investors recognize this signal of skill, allocating greater flows to these funds, especially when past returns are less informative. Finally, belief updating is stronger for younger managers and for funds from newer, smaller families, consistent with signaling under career and competitive pressures.

This paper introduces reinforcement learning to examine the effect of trading on noise in a dynamic limit order market equilibrium. It shows that intensive noise liquidity provision (consumption) increases speculators' liquidity consumption (provision), improving (reducing) market liquidity. Channeled by uninformed chasing and informed aggressive liquidity provision, the increasing noise liquidity provision and consumption, respectively, improve price efficiency, generating a U-shaped price efficiency to the noise trading uncertainty on liquidity provision and consumption. Associated with a hump-shaped (U-shaped) profitability for the informed (uninformed) at a U-shaped noise trading cost in the noise trading uncertainty, this implies that, at increasing noise trading cost, intensive noise liquidity provision improves market liquidity, price efficiency, order profitability of informed traders, and reduces the loss, even makes profit, for uninformed traders.

We document a novel "news extrapolation" behavior among investors, which distorts the market reaction to corporate news. Specifically, investors tend to extrapolate the value of past news in the immediate reaction to the newly arrived news. News extrapolation generates a biased price reaction to news, which is completely reversed afterwards. Furthermore, the tendency of news extrapolation is related to the recency, consistency, and value uncertainty of news. Investors extrapolate not only from news of the same category but also from news of different categories. By analyzing the trading behavior and sentiment of different investor groups, we find that retail investors tend to be news extrapolators, while institutional investors trade against the news extrapolators.

The Hurst exponent is a key parameter for characterizing the long memory of high-frequency time series. However, traditional estimators often exhibit systematic biases due to the influence of high-frequency endogenous noise and low-frequency trend shifts. Theoretical derivations show that endogenous noise contemporaneously correlated with the latent signal possesses a spectral density in the first-differenced series that is asymptotically equivalent to a squared sine functional form. Accordingly, the proposed estimator incorporates a corresponding spectral density component to fit the high-frequency error. Simultaneously, the model introduces a SCAD penalty term to control the low-frequency spectral divergence caused by structural breaks, thereby mitigating spurious long memory in parameter estimation. Monte Carlo simulations demonstrate that the Penalized Mixture Whittle estimator yields smaller finite-sample biases and root mean square errors in scenarios involving both trend disturbances and endogenous noise. Empirical analysis shows that the estimates obtained using this method are robust to changes in sampling frequency. In further volatility forecasting experiments on commodity futures, the linear forecasting model constructed based on the parameter set achieves higher prediction accuracy than benchmark models such as HAR, as confirmed by the Diebold-Mariano test. This paper provides an effective econometric tool for high-frequency data inference in the presence of composite statistical disturbances.

耐心资本作为一种强调长期价值创造的资本形态，对于企业可持续发展及金融市场稳定运行具有重要意义，但其难以自发形成，需要外部激励与内部治理的共同培育。基于权力—收益矩阵，本文引入了耐心资本的主要来源——股东，构建了股东权力—收益矩阵分析框架，旨在从股东权力和股东收益视角进一步探讨耐心资本的形成机制。选取2014—2023年沪深A股上市公司为样本，采用固定效应回归检验主效应，结果发现，股东权力与股东收益均显著正向促进耐心资本形成，在通过滞后一期、替换被解释变量、调整样本期、增加固定效应以及工具变量2SLS缓解潜在内生性后，结论依然具有稳健性；异质性分析显示，在制造业、获得标准审计意见、上一年未亏损、南方地区以及非重污染企业中，上述效应更为显著；中介效应检验表明，股东权力主要通过缓解融资约束促进耐心资本形成，股东收益主要通过降低融资成本促进耐心资本形成；调节效应检验证明，内部控制质量均能够正向强化股东权力与股东收益对耐心资本的促进作用。研究结论回答了耐心资本何以形成的问题，为通过提升公司股东治理质量、壮大耐心资本供给提供了理论依据与实践启示。