Skewness from High-Frequency Data Predicts the Cross-Section of

Theoretical and empirical research documents a negative relation between the cross-section of stock returns and individual skewness. Individual skewness has been de\u85ned with coskewness, industry groups, predictive models, and even with options skewness. However, measures of skewness computed only from stock returns, such as historical skewness, do not con\u85rm this negative relation. In this paper, we propose a model-free measure of individual stock skewness directly obtained from high-frequency intraday prices, which we call realized skewness. We hypothesize that realized skewness predicts future stock returns. To test this hypothesis, we sort stocks every week according to realized skewness, form \u85ve portfolios and analyze subsequent weekly returns. We \u85nd a negative relation between realized skewness and stock returns in the cross section. A trading strategy that buys stocks in the lowest realized skewness quintile and sells stocks in the highest realized skewness quintile generates an average raw return of 38 basis points per week with a t-statistic of 9.15. This result is robust to di¤erent market periods, portfolio weightings, \u85 rm characteristics and is not explained by linear factor models. Comments are welcome. We both want to thank IFM2 for \u85nancial support. Any remaining inadequacies ar