Securities
Fiduciary Duty and the Market for Financial Advice
Vivek Bhattacharya, Gastón Illanes & Manisha Padi
Econometrica, July 2025, Pages 1449-1480
Abstract:
Fiduciary duty aims to solve principal-agent problems, and the United States is in the middle of a protracted debate surrounding the merits of extending it to all financial advisers. Leveraging a transaction-level data set of deferred annuities and state-level variation in common law fiduciary duty, we find that it raises risk-adjusted returns by 25 bp and leads to a 16% decline in the entry of affected firms. Through the lens of a model of entry and advice provision, we show that this effect can be due to both an increase in fixed costs and an increase in the cost of providing low-quality advice. We show how to disentangle these channels and find that both are empirically relevant. Counterfactual simulations show that further increases in the stringency of fiduciary duty monotonically improve advice quality.
Does Generative AI Facilitate Investor Trading? Early Evidence from ChatGPT Outages
Qiang Cheng, Pengkai Lin & Yue Zhao
Journal of Accounting and Economics, forthcoming
Abstract:
In this paper, we use ChatGPT outages to provide early evidence on whether investors rely on generative artificial intelligence (GenAI) to perform professional tasks and the associated impact on stock price informativeness. We document a significant decline in stock trading volume during ChatGPT outages. The effect is stronger for firms with corporate news released immediately before or during the outages and for firms with higher ownership held by transient institutional investors. We then document declines in short-run price impact and return variance during the outage periods, consistent with reduced informed trading. Lastly, we document a positive effect of GenAI-assisted trading on long-run stock price informativeness. Overall, our findings indicate that a significant number of investors use ChatGPT in ways that influence their trading decisions and market outcomes. Future research can investigate the mechanisms underlying these GenAI effects and the potential risks of using GenAI for trading.
Retail Investors’ Contrarian Behavior Around News, Attention, and the Momentum Effect
Patrick Luo et al.
NBER Working Paper, August 2025
Abstract:
Using a large and representative panel of U.S. brokerage accounts, we show that retail investors trade as contrarians after large earnings surprises, especially for loser stocks, and that such contrarian trading contributes to price momentum and post earnings announcement drift (PEAD). We show that extreme return streaks and surprises are not enough for stocks to exhibit PEAD and momentum and that the intensity of contrarian retail trading plays a key role: the PEAD of loser stocks with bad earnings surprises becomes increasingly more negative as retail buying pressure increases, and the PEAD of the stocks with the highest past returns and largest earnings surprises is the most positive for the stocks with the biggest net retail outflow. Finer sorts confirm the results, as do sorts by firm size and institutional ownership level. Younger and more attentive individuals are more likely to be contrarian, and a firm’s dividend yield, leverage, size, book to market, and analyst coverage are associated with the fraction of contrarian trades they face around earnings announcements. The disposition effect and stale limit orders, while present in our sample, do not explain our results. Our findings are consistent with investors’ conservatism, sticky beliefs, and cognitive uncertainty, as well as an incorrect belief in the Law of Small Numbers.
Do Professional Rankings Affect Analyst Behavior? Evidence From a Regression Discontinuity Design
Michael Jung et al.
Management Science, forthcoming
Abstract:
This study examines how winning a significant industry award affects the behavior of finance professionals. Focusing on sell-side equity analysts and utilizing a novel data set from Institutional Investor on analyst rankings, we employ a regression discontinuity design that compares the postaward research outputs and behavior of third-place, all-star analysts with those of first runner-up analysts who barely miss the distinction. Our results show that third-place all-star winners are more optimistic in their forecasts and recommendations compared with first runner-up analysts after winning the award, and market reactions to their forecast revisions are stronger. The third-place winners also receive higher priority during earnings conference calls and experience better career outcomes. Our evidence is consistent with award-winning analysts leveraging their increased reputation and market influence to generate more trading commissions and career benefits. The broader inference of our findings is that finance professionals who win a significant award are likely to become more, rather than less, strategic.
Financial Advisors and Investors' Bias
Marianne Andries, Maxime Bonelli & David Sraer
NBER Working Paper, August 2025
Abstract:
We study an intervention by a brokerage firm providing advisory services to high-net-worth investors. In 2018, the firm changed the information displayed on its internal platform, so that financial advisors could no longer observe which clients’ holdings were in paper gain or loss. Using data on portfolio stock transactions between 2016 and 2021, we show that, while all investors exhibit a significant disposition effect before 2018, i.e., a greater propensity to realize gains than losses, highly-advised investors see their bias significantly reduced afterward. Our paper shows that by appropriately manipulating advisors’ information, financial firms can successfully reduce their clients’ biases.
Cross-Extrapolative Beliefs: Evidence from Equity Analysts
Rex Wang Renjie & Patrick Verwijmeren
Management Science, forthcoming
Abstract:
We study whether individuals form extrapolative beliefs across different tasks. In line with cross-extrapolation, equity analysts who experience bad news from some of their coverage firms become overly pessimistic about other firms in their coverage. This leads to disagreement among analysts and has implications for the stock market, as we observe effects on trading volume, return volatility, and pricing. Our findings highlight the relevance of non-domain-specific belief models.
The up side of being down: Depression and crowdsourced forecasts
Sima Jannati, Sarah Khalaf & Du Nguyen
Journal of Banking & Finance, October 2025
Abstract:
This study examines the role of non-severe depression as a psychological anchor against overoptimism. Using earnings forecasts from Estimize, we find that an increase in the proportion of the U.S. population with depression is associated with improved forecast accuracy among users. This effect is concentrated among forecasts that are optimistic and analysts who take longer time to issue forecasts, highlighting reduced optimism and slow information processing as economic mechanisms that explain our results. We also show that this effect is distinct from the influence of temporary seasonal depression or other sentiment measures on decision-making. Overall, our research establishes a link between depression and crowdsourced financial evaluations.
Birth order and fund manager’s trading behavior: Role of sibling rivalry
Vikas Agarwal, Alexander Cochardt & Vitaly Orlov
Journal of Corporate Finance, November 2025
Abstract:
Using rich data on familial background of US mutual fund managers, this paper sheds light on the formation of risk preferences by investigating birth order effects. Consistent with sensation-seeking behavior, we find that managers who are born later in the sibling hierarchy take more risks but perform worse relative to lower-birth-order managers. Later-born managers take more extreme style bets, hold more lottery stocks, churn their portfolios more, and engage in more civil and regulatory violations. These birth-order effects are more pronounced when parental resources are limited and age spacing is lower between siblings, suggesting sibling rivalry as a potential mechanism.