Stefan Nagel, Chicago Booth
Shrinking the Cross Section
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.
Event Sponsor:
Advanced Financial Technologies Laboratory
Related Topics
Explore More Events
-
AFTLab Seminars
Dominik Rothenhaeusler (Stanford): Out-of-distribution generalization under random, dense distributional shifts
-Huang 305
475 Via Ortega
Stanford, CA 94305
United States -
Conferences
AI in Fintech Forum: 2024
326 Galvez Street
Frances C. Arrillaga Alumni Center
Stanford, CA 94305
United States