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Cross-sectional uncertainty and expected stock returns

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  • Yu, Deshui
  • Huang, Difang

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.

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  • Yu, Deshui & Huang, Difang, 2023. "Cross-sectional uncertainty and expected stock returns," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 321-340.
    • Handle: RePEc:eee:empfin:v:72:y:2023:i:c:p:321-340
    DOI: 10.1016/j.jempfin.2023.04.001
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    5. Jiang, Fuwei & Liu, Hongkui & Yu, Jiasheng & Zhang, Huajing, 2023. "International stock return predictability: The role of U.S. uncertainty spillover," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).

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    More about this item

    Keywords

    Cross-sectional uncertainty; Stock return predictability; Out-of-sample forecast; Cash flow channel;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G19 - Financial Economics - - General Financial Markets - - - Other

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