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.

In contrast with theoretical predictions, high-fee active equity funds generate worse net-of-expenses performance. We show that this fee-performance puzzle is driven by the preference of high-fee funds for stocks with low operating profitability and high investment rates, characteristics associated with low expected returns. After controlling for exposures to profitability and investment factors, we find high-fee funds significantly outperform low-fee funds before expenses and achieve similarly poor net-of-fees performance. In resolving the fee-performance puzzle, our findings provide support to the theoretical prediction that net alphas are unrelated to fees and challenge the common advice to prefer low-fee funds over high-fee counterparts.

We study how the arrival of macro-news affects the stock market's ability to incorporate the information in firm-level earnings announcements. Existing theories suggest that macro and firm-level earnings news are attention substitutes; macro-news announcements crowd out firm-level attention, causing less efficient processing of firm-level earnings announcements. We find the opposite: the sensitivity of announcement returns to earnings news is 17% stronger, and post-earnings announcement drift 71% weaker, on macro-news days. This suggests a complementary relationship between macro and micro news that is consistent with either investor attention or information transmission channels.

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.

We construct macroeconomic attention indexes (MAI), which are new measures of attention to different macroeconomic risks, including unemployment and monetary policy. Individual MAI tend to increase around related announcements and following changes in related fundamentals. Further, bad news raises attention more than good news. For unemployment and FOMC, attention predicts announcement risk premiums and implied volatility changes with large economic magnitudes. Our findings support theories of endogenous attention and announcement risk premiums, while demonstrating future research directions, including that announcements can raise new concerns. Macroeconomic announcements are important not only for contents and timing but also for attention.

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.