Shrinking the cross-section

Serhiy Kozak, Stefan Nagel and Shrihari Santosh

Journal of Financial Economics, 2020, vol. 135, issue 2, 271-292

Abstract: We construct a robust stochastic discount factor (SDF) summarizing the joint explanatory power of a large number of cross-sectional stock return predictors. Our method achieves robust out-of-sample performance in this high-dimensional setting by imposing an economically motivated prior on SDF coefficients that shrinks contributions of low-variance principal components of the candidate characteristics-based factors. We find that characteristics-sparse SDFs formed from a few such factors—e.g., the four- or five-factor models in the recent literature—cannot adequately summarize the cross-section of expected stock returns. However, an SDF formed from a small number of principal components performs well.

Keywords: Factor models; SDF; Cross section; Shrinkage; Machine learning, (search for similar items in EconPapers)
JEL-codes: C11 G11 G12 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (110)

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Related works:
Working Paper: Shrinking the Cross Section (2017)
Working Paper: Shrinking the Cross Section (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jfinec:v:135:y:2020:i:2:p:271-292

DOI: 10.1016/j.jfineco.2019.06.008

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