Journal
REVIEW OF FINANCIAL STUDIES
Volume 33, Issue 5, Pages 2326-2377Publisher
OXFORD UNIV PRESS INC
DOI: 10.1093/rfs/hhz123
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Funding
- Fama-Miller Center at the University of Chicago's Booth School of Business
- Fama Research Fund at the University of Chicago's Booth School of Business
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We propose a nonparametric method to study which characteristics provide incremental information for the cross-section of expected returns. We use the adaptive group LASSO to select characteristics and to estimate how selected characteristics affect expected returns nonparametrically. Our method can handle a large number of characteristics and allows for a flexible functional form. Our implementation is insensitive to outliers. Many of the previously identified return predictors don't provide incremental information for expected returns, and nonlinearities are important. We study our method's properties in simulations and find large improvements in both model selection and prediction compared to alternative selection methods.
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