Shrinking the Cross Section

Serhiy Kozak, Stefan Nagel and Shrihari Santosh

No 24070, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C38 G12 (search for similar items in EconPapers)
Date: 2017-11
New Economics Papers: this item is included in nep-ore
Note: AP
References: Add references at CitEc
Citations: View citations in EconPapers (48)

Published as Serhiy Kozak & Stefan Nagel & Shrihari Santosh, 2019. "Shrinking the Cross-Section," Journal of Financial Economics, .

Downloads: (external link)
http://www.nber.org/papers/w24070.pdf (application/pdf)

Related works:
Journal Article: Shrinking the cross-section (2020)
Working Paper: Shrinking the Cross Section (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberwo:24070

Ordering information: This working paper can be ordered from
http://www.nber.org/papers/w24070

Access Statistics for this paper

More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().