Dissecting Characteristics Nonparametrically
105 Pages Posted: 11 Aug 2016 Last revised: 5 Aug 2018
Date Written: July 26, 2018
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 they affect expected returns nonparametrically. Our method can handle a large number of characteristics, allows for a flexible functional form, and 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 the properties of our method in simulations and find large improvements both in model selection and prediction compared to alternative selection methods.
Keywords: Cross Section of Returns, Anomalies, Expected Returns, Model Selection
JEL Classification: C14, C52, C58, G12
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