We are among the first to investigate how Generative AI (GenAI) shapes investors' trading activities. Using an AI‐sentiment measure extracted from earnings‐call transcripts to proxy for textual signals, we find notable shifts in trading behaviors around earnings calls. Before the wide deployment of ChatGPT, short selling was aligned with AI‐sentiment, whereas retail trading was not. However, following ChatGPT's deployment, the alignment of retail traders with AI‐sentiment significantly increases, while the alignment of short sellers weakens, albeit insignificantly. Stocks with higher information processing costs exhibit a more pronounced increase in retail trading alignment, scenarios where retail investors are likely to benefit more from AI. Using retail‐AI alignment as a proxy for the extent to which retail investors trade based on AI signals, we show that information asymmetry declines and retail investors' trading profitability improves, whereas short sale profitability declines in high retail‐AI alignment stocks. Exogenous outages reduce the alignment between retail trading and AI‐sentiment, allowing us to draw causal inferences. Collectively, this study suggests that AI is a promising technology for narrowing the information gap in the trading of complex textual financial disclosures between investor classes with clear disparities in the ability to process public disclosures.

We document that persistent aggregate capital flows to hedge and mutual funds predict monthly factor returns with an out-of-sample R2 reaching 6.6%. Transient flows display no such power despite being more predictable. We show—both empirically and theoretically—that persistent flows’ predictive power stems from active fund managers’ capital constraints. As a result, managers invest persistent, but not transient, capital flows into factor trading strategies, leading to factor-return predictability and factor momentum, yet greater price efficiency. Our key insight is that capital-constrained managers account for both current and anticipated future flows in the arbitrage sector, thereby incorporating the dynamics of capital into their strategies.

We analyze a model in which an anomaly is unknown to arbitrageurs until its discovery, and test the model implications on both asset prices and arbitrageurs’ trading activities. Using data on 99 anomalies documented in the existing literature, we find that the discovery of an anomaly reduces the correlation between the returns of its decile-1 and decile-10 portfolios. This discovery effect is stronger if the aggregate wealth of hedge funds is more volatile. Finally, hedge funds increase (reverse) their positions in exploiting anomalies when their aggregate wealth increases (decreases), further suggesting that these discovery effects operate through arbitrage trading.

This paper investigates the relation between media coverage and offering yield spreads using a comprehensive dataset of 5,338 industrial bonds issued from 1990 to 2011. We find that media coverage is negatively associated with firms’ cost of debt. This association is robust to controlling for standard yield determinants, different model specifications, and endogeneity. We identify 4 economic channels through which media coverage influences the cost of debt: Information asymmetry, governance, liquidity, and default risk. Importantly, media coverage has an independent influence beyond the effects of these economic mechanisms and is not a proxy for other firm attributes.

We provide the first systematic evidence on the link between long‐short anomaly portfolio returns—a cornerstone of the cross‐sectional literature—and the time‐series predictability of the aggregate market excess return. Using 100 representative anomalies from the literature, we employ a variety of shrinkage techniques (including machine learning, forecast combination, and dimension reduction) to efficiently extract predictive signals in a high‐dimensional setting. We find that long‐short anomaly portfolio returns evince statistically and economically significant out‐of‐sample predictive ability for the market excess return. The predictive ability of anomaly portfolio returns appears to stem from asymmetric limits of arbitrage and overpricing correction persistence.