Cross-sectional uncertainty and expected stock returns

Deshui Yu and Difang Huang

Journal of Empirical Finance, 2023, vol. 72, issue C, 321-340

Abstract: We study the predictability of cross-sectional uncertainty (CSU) for stock returns. We find that CSU exhibits significant power for predicting monthly stock returns both in and out of sample with annual R2 of 11.89% and 6.34%, respectively, greater than popular predictors. A bivariate combination forecast using CSU with one of the alternative predictors yields annual out-of-sample R2 up to 18.08%. CSU generates significant economic gains for a mean–variance investor with a utility gain of over 400 basis points per annum. A vector autoregression decomposition shows that the source of predictability mainly comes from a cash flow channel.

Keywords: Cross-sectional uncertainty; Stock return predictability; Out-of-sample forecast; Cash flow channel (search for similar items in EconPapers)
JEL-codes: C58 G12 G13 G19 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:72:y:2023:i:c:p:321-340

DOI: 10.1016/j.jempfin.2023.04.001

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