We find a robust negative relation between skewness/lottery-like features, proxied by maximum return (MAX) over the last month, and future returns for stocks preferred by individual investors. This negative relation is nonexistent for the rest of stocks. We identify stocks preferred by individual investors through bundling ten stock characteristics associated with their stock preferences. The negative relation between MAX and future return is produced by the stocks preferred by individuals that account for less than 5% of the overall market capitalization. Our results are robust to alternative definitions of MAX and lottery-like features such as total, idiosyncratic, and expected skewness.

Using the Troubled Asset Relief Program (TARP) as a laboratory, this paper examines the impacts of bank bailouts on bank-dependent clients. We find that large TARP recipient banks reduce credit supply to dependent borrowers in the post-TARP period. A large fraction of credit supply reduction is due to regulatory uncertainty on account of an increased likelihood of fines. Liquidity hoarding by TARP banks also drives part of the reduction in credit supply. Relationship borrowers experience a valuation loss around the announcements of their main banks’ TARP approvals consistent with a credit supply reduction.

Using a unique proprietary account-level trading dataset in China, we investigate how active information acquisition alleviates price-based return comovement, a typical anomaly in stock splits. We find that: 1) individual trading drives the comovement and the trading correlation between split stocks and the low-price portfolio increases significantly after splits; 2) individuals can learn the firm fundamentals through information acquisition, which effectively alleviates their categorized bias; and 3) the role of information acquisition is more significant in environments characterized by greater uncertainty. Our results are robust to different specifications and alternative measures. Taken together, this paper emphasizes the important role of information acquisition in alleviating behavioral bias and improving decision-making.

Using a large sample of block acquisitions, this paper examines the governance role of customers that acquire block ownership in supplier shares. We find that compared to targets acquired by noncustomers, those acquired by customers experience higher abnormal announcement returns, larger increases in post-acquisition long-term operating performance, and higher non-routine turnover of poorly performing CEOs. These results are evident when target managerial agency problems are highly detrimental to the supplier–customer relationship. The results support the view that customers’ nonfinancial claims in targets provide customers strong incentives to monitor target managers above and beyond previously documented monitoring by large shareholders.

This paper examines the effect of financial literacy on mortgage stress. Using data from the Panel Study of Income Dynamics (PSID), we find that borrowers with high levels of financial literacy are 60.3 percent less likely to suffer from mortgage stress than borrowers with low levels of financial literacy after controlling for observables. Our estimated results are robust to potential sample selection bias and functional mis-specification. In addition, we also find that the effect of financial literacy varies across borrowers of different ages. Further analysis reveals strong cross effects of financial literacy and quantitative reasoning on mortgage stress.

We investigate housing market distortions with the collusion of agents. The agency problem where agents sell clients' houses with price discounts while selling their own homes with price premiums is quite straightforward. However, the issue that agents collude with each other to further maximize their own interests is elusive. When agents collude, the resulting market distortions may even be worse than previous studies suggested. Indeed, this paper finds that the agency problem and market distortions are much more severe with agent collusion, as both the discounts associated with clients' houses and the premiums with agents' own homes become much larger when the two agents collude.

Using a sample of bank loan announcements in Japan, we examine whether or not banks have incentives to engage in suboptimal lending that results in wealth transfer from the banks to the borrowing firms. We find that abnormal returns for borrowing firms are significantly positive, but those for lending banks are sometimes significantly negative. Furthermore, the announcement returns for borrowing firms are negatively related to those for lending banks, especially when poorly performing firms borrow from financially healthy (low-risk) banks. Our results suggest that the positive valuation effect of bank loan announcements for borrowing firms is mainly due to a wealth transfer from lending banks.

ABSTRACT This paper investigates whether the business relations between mutual funds and brokerage firms influence sell‐side analyst recommendations. Using a unique data set that discloses brokerage firms’ commission income derived from each mutual fund client as well as the share holdings of these mutual funds, we find that an analyst's recommendation on a stock relative to consensus is significantly higher if the stock is held by the mutual fund clients of the analyst's brokerage firm. The optimism in analyst recommendations increases with the weight of the stock in a mutual fund client's portfolio and the commission revenue generated from the mutual fund client. However, this favorable recommendation bias toward a client's existing portfolio stocks is mitigated if the stock in question is highly visible to other mutual fund investors. Abnormal stock returns are significantly greater both for the announcement period and, in the long run, for favorable stock recommendations from analysts not subject to client pressure than for equally favorable recommendations from business‐related analysts. In addition, we find that, subsequent to announcements of bad news from the covered firms, analysts are significantly less likely to downgrade a stock held by client mutual funds. Mutual funds increase their holdings in a stock that receives a favorable recommendation but this impact is significantly reduced if the recommendation comes from analysts subject to client pressure.

This paper explores how the emergence of generative artificial intelligence is reshaping the information environment in capital markets. Leveraging an unexpected ban on ChatGPT in Italy, we examine its impact on the information processing capabilities of market participants. We employ metrics for AI-generated text detection to show that the ban coincides with decreased AI usage by domestic financial analysts and fewer earnings forecasts issued relative to foreign analysts covering the same firm. The negative effects are more pronounced among analysts whose pre-ban reports are more consistent with AI use or analysts with a technical background. The ban also diminishes forecast accuracy, increases reliance on industry-specific information, and reduces information efficiency. Furthermore, investor reactions to earnings announcements become more pronounced, and bid–ask spreads widen, reflecting lower market efficiency.