Dissecting Characteristics Nonparametrically

107 Pages Posted: 16 Oct 2018

See all articles by Joachim Freyberger

Joachim Freyberger

University of Wisconsin - Madison

Andreas Neuhierl

University of Notre Dame - Department of Finance

Michael Weber

University of Chicago - Finance

Multiple version iconThere are 6 versions of this paper

Date Written: August 09, 2018

Abstract

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 do not provide incremental information for expected returns, and nonlinearities are important. We study the properties of our method in an extensive simulation study and out-of-sample prediction exercise and find large improvements both in model selection and prediction compared to alternative selection methods. Our proposed method has higher out-of-sample Sharpe ratios and explanatory power compared to linear panel regressions.

Keywords: cross section of returns, anomalies, expected returns, model selection

JEL Classification: C140, C520, C580, G120

Suggested Citation

Freyberger, Joachim and Neuhierl, Andreas and Weber, Michael, Dissecting Characteristics Nonparametrically (August 09, 2018). CESifo Working Paper Series No. 7187. Available at SSRN: https://ssrn.com/abstract=3254246

Joachim Freyberger

University of Wisconsin - Madison ( email )

716 Langdon Street
Madison, WI 53706-1481
United States

Andreas Neuhierl

University of Notre Dame - Department of Finance ( email )

P.O. Box 399
Notre Dame, IN 46556-0399
United States

Michael Weber (Contact Author)

University of Chicago - Finance ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

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