Economic evaluation of asset pricing models under predictability

Erwin Hansen

Journal of Empirical Finance, 2022, vol. 68, issue C, 50-66

Abstract: This paper performs an out-of-sample comparison of linear factor asset pricing models from an economic perspective under predictability. I assess the economic value added of several factor models when a Bayesian investor is faced with a portfolio allocation problem whereby each model imposes cross-sectional restrictions on the parameters of a predictive stock return regression. The empirical framework explicitly accounts for investor skepticism about the model, i.e., mispricing uncertainty. Despite vast statistical work on in-sample model comparison for the new-generation asset-pricing models, their out-of-sample performance cannot beat a simple benchmark on a wide range of tests from an economic perspective. This is consistent with thus extends the conclusion for the first-generation of factor models. An exceptional case of factor models yielding significant economic gains is observed when evaluating industry portfolios at the shortest horizon (1-month). In this case, I find that the q5 model of Hou et al. (2021) and the behavioral factor model of Stambaugh and Yuan (2017) outperform the historical benchmark in several cases.

Keywords: Factor models; Stock return predictability; Bayesian investors; Portfolio performance (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:68:y:2022:i:c:p:50-66

DOI: 10.1016/j.jempfin.2022.06.001

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