Dissecting Characteristics Nonparametrically
79 Pages Posted: 11 Aug 2016 Last revised: 20 Nov 2017
Date Written: November 18, 2017
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