66 Pages Posted: 11 Aug 2016 Last revised: 7 Mar 2017
Date Written: February 6, 2017
We propose a nonparametric method to test which characteristics provide independent 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, and increases Sharpe ratios by 50%.
Keywords: Cross Section of Returns, Anomalies, Expected Returns, Model Selection
JEL Classification: C14, C52, C58, G12
Suggested Citation: Suggested Citation
Freyberger, Joachim and Neuhierl, Andreas and Weber, Michael, Dissecting Characteristics Nonparametrically (February 6, 2017). Available at SSRN: https://ssrn.com/abstract=2820700 or http://dx.doi.org/10.2139/ssrn.2820700
By Andrew Ang