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Idiosyncratic Risk, Long-Term Reversal, and Momentum

Journal of Financial and Quantitative Analysis 2010 45(4), 883-906
This paper tests whether the persistence of the momentum and reversal effects is the result of idiosyncratic risk limiting arbitrage. Idiosyncratic risk deters arbitrage, regardless of the arbitrageur’s diversification. Reversal is prevalent only in high idiosyncratic risk stocks, suggesting that idiosyncratic risk limits arbitrage in reversal mispricing. This finding is robust to controls for transaction costs, informed trading, and systematic relations between idiosyncratic risk and subsequent returns. Momentum is not related to idiosyncratic risk. Momentum generates a smaller aggregate return than reversal, so the findings along with those in related studies suggest that transaction costs are sufficient to prevent arbitrageurs from eliminating momentum mispricing.

Corporate Leadership and Inherited Beliefs About Gender Roles

Journal of Financial and Quantitative Analysis 2023 58(8), 3274-3304 open access
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.

Do Cross-Sectional Predictors Contain Systematic Information?

Journal of Financial and Quantitative Analysis 2023 58(3), 1172-1201 open access
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.