We investigate the effects of financial reporting on current employee job search, that is, whether firms' public financial reports cause their employees to reevaluate their jobs and consider leaving. We develop theory for why current employees use earnings announcements (EAs) to inform job search decisions, and empirically investigate job search based on employees' activity on a popular job market website. We find that job search by current employees increases significantly during EA weeks, especially when employees are more mobile and when their information frictions are greater. We also find that employees use EAs to update their expectations about their employers' economic prospects, consistent with learning, and some evidence that positive announcements elicit less search. Our paper contributes to the burgeoning labor and accounting literature by providing among the first evidence closely linking financial reports to employee learning and job search.

Fixed effects (FE) have emerged as a ubiquitous and powerful tool for eliminating unwanted variation in observational accounting studies. Unwanted variation is plentiful in accounting research because we often use rich data to test precise hypotheses derived from abstract theories. By eliminating unwanted variation, FE reduce concerns that omitted variables bias our estimates or weaken test power. FE are not costless, though, so their use should be carefully justified by theoretical and institutional considerations. FE also transform samples and variables in ways that are not immediately apparent, and in doing so affect how we should interpret regression results. This primer explains the mechanics of FE and provides practical guidance for the informed use, transparent reporting, and careful interpretation of FE models.

We analyze over 27,000 social media livestreams by Chinese mutual funds to investigate whether they achieve regulators’ goal of improving retail investment decisions. Our findings indicate that livestreams generate significant inflows, often within minutes of their start times. Yet rather than educating investors, livestreams amplify return-chasing behavior and predict sharp declines in fund performance. Investors who buy in response to livestreams would earn higher returns by holding index funds or even cash. Further analyses using deep learning algorithms reveal that livestreams are more persuasive when speakers are more physically attractive, use more positive language, and sound more excited. We conclude that livestreams primarily function as persuasive advertising and that regulators should be wary of educational efforts led by sellers of consumer financial products. We also conclude that prior evidence on the benefits of firms’ social media use in equity markets does not extend to financial product markets in this setting.

We investigate the frictions that impede individual investors’ use of accounting information and, in particular, their costs of monitoring and acquiring accounting disclosures. We do so using an archival setting in which individuals are presented with automated media articles that report both current earnings news and past stock returns. Although these investors have earnings information readily available, we find no evidence that their trades incorporate it. Instead we find that their trading responds to the trailing stock returns presented in the articles. Our study raises questions about the efficacy of regulations that aim to aid less sophisticated investors by increasing their awareness of and access to accounting information.

Generative artificial intelligence (GAI) will likely alter many aspects of the financial reporting process and spawn a deep stream of academic research. We take an early step by examining the extent to which firms have begun using GAI in one important part of the reporting process: writing disclosures. We begin by evaluating a commercial tool's ability to detect GAI writing in disclosures, and we find that it reliably identifies even very small amounts of GAI usage in realistic samples. We then examine firms’ actual earnings press releases, conference call prepared remarks, risk factors, MD&As, and IPO filings through 2024 and find statistically significant GAI usage in all five disclosure types, with up to 4.5% of new text written by GAI in 2024. Usage is predictably higher in the cross‐section, and filings with higher GAI have systematically different linguistic properties. Our study provides early insights into the use and effects of GAI in financial reporting, and it motivates future research in this evolving area.

Mutual funds hold 32% of the U.S. equity market and comprise 58% of retirement savings, yet retail investors consistently make poor choices when selecting funds. Theory suggests poor choices are partially due to fund managers creating unnecessarily complex disclosures and fee structures to keep investors uninformed and obfuscate poor performance. An empirical challenge in investigating this “strategic obfuscation” theory is isolating manipulated complexity from complexity arising from inherent differences across funds. We examine obfuscation among S&P 500 index funds, which have largely the same regulations, risks, and gross returns but charge widely different fees. Using bespoke measures of complexity designed for mutual funds, we find evidence consistent with funds attempting to obfuscate high fees. This study improves our understanding of why investors make poor mutual fund choices and how price dispersion persists among homogeneous index funds. We also discuss insights for mutual fund regulation and academic literature on corporate disclosures.

Local-thinking bias, wherein agents overweight information that comes readily to mind, is a prominent finding in cognitive psychology. In this study, we investigate local-thinking bias in the context of sell-side analysts and measure each analyst’s “local” information as news stemming from their individual coverage portfolio. Tests examining multiple analysts forecasting on the same focal firm at the same time find that individual analysts overweight idiosyncratic local news and underweight news from economically linked firms that are not in their coverage portfolios. Market prices track the analyst bias from local news, leading to predictable and economically significant return reversal patterns in the future. A trading strategy that adjusts for analysts’ biases earns meaningful abnormal returns. We discuss the implications of these findings for three literatures: (1) cognitive psychology, (2) analyst behavior, and (3) behavioral asset pricing.