Shrinking the Cross Section

60 Pages Posted: 1 Dec 2017

See all articles by Serhiy Kozak

Serhiy Kozak

University of Maryland - Robert H. Smith School of Business

Stefan Nagel

University of Chicago - Booth School of Business; National Bureau of Economic Research (NBER); Centre for Economic Policy Research; CESifo (Center for Economic Studies and Ifo Institute)

Shrihari Santosh

University of Colorado at Boulder - Department of Finance

Multiple version iconThere are 4 versions of this paper

Date Written: November 2017

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.

Suggested Citation

Kozak, Serhiy and Nagel, Stefan and Santosh, Shrihari, Shrinking the Cross Section (November 2017). CEPR Discussion Paper No. DP12463. Available at SSRN: https://ssrn.com/abstract=3079431

Serhiy Kozak (Contact Author)

University of Maryland - Robert H. Smith School of Business ( email )

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Stefan Nagel

University of Chicago - Booth School of Business ( email )

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National Bureau of Economic Research (NBER)

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Centre for Economic Policy Research ( email )

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CESifo (Center for Economic Studies and Ifo Institute) ( email )

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Shrihari Santosh

University of Colorado at Boulder - Department of Finance ( email )

Campus Box 419
Boulder, CO 80309
United States

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