Firms with more positive employee expectations tend to earn higher future returns, delivering annualized abnormal returns ranging from 8% to 11%. Employees’ forward-looking expectations are a stronger return predictor than employee satisfaction, which is backward-looking. Employee expectations can predict returns because they reflect information about firms’ fundamentals that has not yet been reflected in traditional data sources, such as earnings reports. Hedge funds actively trade on this information, consistent with a decay in forecasting power over longer holding horizons. Overall, this paper highlights the importance of labor in asset pricing, specifically from the perspective of employee expectations.

We examine the informativeness of fund risk disclosure by combining fund returns and textual data. We develop a novel measure, INF, to capture the explanatory power of disclosed risks for fund returns. Average INF is 55% after controlling for market risk and remains 26–29% after excluding risks related to fund name or strategy. We explain variations in INF through disclosure costs and benefits: (1) Funds with less informative disclosures face SEC comment letters and reduced flow, (2) informative disclosures attract institutional flows, and (3) performance concerns may disincentivize disclosure. Overall, funds face trade-offs between transparency and maintaining their competitive advantage.

Using a novel measure of investment companies’ reliance on generative artificial intelligence (GenAI), we document a sharp increase in GenAI usage by hedge funds after ChatGPT’s 2022 launch. A difference-in-differences test shows that hedge funds adopting GenAI earn 2-4% higher annualized abnormal returns than nonadopters, while non-hedge funds do not benefit. The outperformance originates from funds’ AI talent and ChatGPT’s strength in analyzing firm-specific information. We conduct a new survey of fund managers’ GenAI usage to provide direct validation of our measure and offer additional new insights on how managers adopt GenAI tools in their practice.