This paper examines how market participants trade on private information about firm fundamentals using the largest known case of informed trade of earnings announcements. From 2011 to 2015, a cartel of sophisticated traders illegally obtained early access to and traded on over 1,000 firm earnings announcements. Using this setting, I identify the information in earnings announcements that these market participants found most price relevant. The informed traders preferred announcements with larger earnings and sales surprises relative to forecasts, quantitative managerial guidance, and more extreme news sentiment. Despite their perfect foresight, the traders performed, perhaps surprisingly, poorly relative to hypothetical trading strategies based on comparable foresight. Frictions that limited their performance include price impact, risk aversion, and information processing costs. The trading performance of these informed traders implies that information about firm fundamentals explains little of the cross‐sectional variation in earnings announcement returns, even for sophisticated market participants.

This paper provides early evidence on the integration and impact of generative artificial intelligence (GenAI) in accounting at the accountant and task levels. Using survey data from 277 professional accountants, we document substantial heterogeneity in adoption patterns, perceived benefits, and concerns about GenAI. Using proprietary field data from an AI‐enabled accounting platform serving 79 small‐ and medium‐sized enterprises, we analyze over 200,000 transaction‐level records. We document that GenAI adoption is associated with significant productivity gains and systematic reallocation of effort away from routine data entry toward business communication and quality assurance tasks. GenAI use is also associated with improvements to financial reporting quality, evidenced by more granular ledgers and faster month‐end closing. Examining human–AI interaction, we find that accountants selectively intervene when AI confidence scores are low, consistent with complementarity between professional expertise and AI. A framed field experiment further shows that while AI assistance improves classification accuracy on average, reliance on non‐consensus AI recommendations can increase the risk of error. Overall, our findings highlight both the promise and the risks of GenAI in accounting and suggest that, in practice, AI is most effective as a tool that augments—rather than replaces—professional judgment.

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.