We study the out‐of‐sample and post‐publication return predictability of 97 variables shown to predict cross‐sectional stock returns. Portfolio returns are 26% lower out‐of‐sample and 58% lower post‐publication. The out‐of‐sample decline is an upper bound estimate of data mining effects. We estimate a 32% (58%–26%) lower return from publication‐informed trading. Post‐publication declines are greater for predictors with higher in‐sample returns, and returns are higher for portfolios concentrated in stocks with high idiosyncratic risk and low liquidity. Predictor portfolios exhibit post‐publication increases in correlations with other published‐predictor portfolios. Our findings suggest that investors learn about mispricing from academic publications.

Some U.S. firms have women directors and executives, while many do not. We seek to explain this heterogeneity. Using U.S. Census data from 1900, we find that U.S. counties with populations originating from countries with stronger gender-egalitarian beliefs have more women in the labor market and in STEM occupations, and lower gender-pay gaps. Firms headquartered in such counties have more women executives and directors. When firms move to more gender-egalitarian counties, the representation of women on board increases. Our findings are consistent with the idea that inherited beliefs about gender roles impact the labor market and corporate leadership.

Firm-level variables that predict cross-sectional stock returns, such as price-to-earnings and short interest, are often averaged and used to predict market returns. Using various samples of cross-sectional predictors and accounting for the number of predictors and their interdependence, we find only weak evidence that cross-sectional predictors make good time-series predictors, especially out-of-sample. The results suggest that cross-sectional predictors do not generally contain systematic information.