Dissecting Characteristics Nonparametrically

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

Freyberger, Joachim and Freyberger, Joachim and Neuhierl, Andreas and Weber, Michael, Dissecting Characteristics Nonparametrically (July 1, 2018). Fama-Miller Working Paper, Chicago Booth Research Paper No. 17-32, Available at SSRN: https://ssrn.com/abstract=3073170 or http://dx.doi.org/10.2139/ssrn.3073170