Abstract
We study the predictability of cross-sectional uncertainty (CSU) proposed by Dew-Becker and Giglio (2021) for stock returns. We find that CSU exhibits significant power for predicting monthly stock returns with annual out-of-sample R^2 of 6.34%, greater than popular predictors. CSU generates significant economic gains for a mean-variance investor with annualized utility gain of 7.92% and a Sharpe ratio of 1.19. The predictability of CSU is better in good than bad times, and a bivariate combination forecast using CSU with one of Goyal and Welch (2008) variables produces annual out-of-sample R^2 ranging from 16.22% to 42.88%. A vector autoregression decomposition shows that the source of predictability mainly comes from a cash flow channel.
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