Shrinking the Cross Section

Stefan Nagel, Shrihari Santosh and Serhiy Kozak

No 12463, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: We construct a robust stochastic discount factor (SDF) that summarizes 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 the contributions of low-variance principal components of the candidate factors. While empirical asset pricing research has focused on SDFs with a small number of characteristics-based factors—e.g., the four- or five-factor models discussed in the recent literature—we find that such a characteristics-sparse SDF cannot adequately summarize the cross-section of expected stock returns. However, a relatively small number of principal components of the universe of potential characteristics-based factors can approximate the SDF quite well.

Date: 2017-11
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Related works:
Journal Article: Shrinking the cross-section (2020)
Working Paper: Shrinking the Cross Section (2017)
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