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Journal of Financial Economics Vol. 135 No. 2 2020

Shrinking the cross-section

Serhiy Kozak1; Stefan Nagel2,3; Shrihari Santosh4

1 University of Maryland, College Park · 2 University of Chicago · 3 Center for Economic and Policy Research · 4 University of Colorado Boulder

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.

DOI
10.1016/j.jfineco.2019.06.008
Volume
135
Issue
2
Pages
271-292
Language
en
Sources
crossref bibtex:phds-export.bib openalex

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