Dissecting Characteristics Nonparametrically
105 Pages Posted: 20 Nov 2017 Last revised: 27 Jul 2018
Date Written: July 1, 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 is insensitive to outliers. Many of the previously identified return predictors do not provide incremental information for expected returns, and nonlinearities are important. Our proposed method has higher out-of-sample explanatory power compared to linear panel regressions.
Keywords: Cross Section of Returns, Anomalies, Expected Returns, Model Selection
JEL Classification: C14, C52, C58, G12
Suggested Citation: Suggested Citation