Dissecting Characteristics Nonparametrically

68 Pages Posted: 19 Mar 2017 Last revised: 20 Jun 2021

See all articles by Joachim Freyberger

Joachim Freyberger

University of Bonn; University of Wisconsin - Madison

Andreas Neuhierl

Washington University in St. Louis - John M. Olin Business School

Michael Weber

University of Chicago - Finance; National Bureau of Economic Research (NBER)

Multiple version iconThere are 6 versions of this paper

Date Written: March 2017

Abstract

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%.

Suggested Citation

Freyberger, Joachim and Freyberger, Joachim and Neuhierl, Andreas and Weber, Michael, Dissecting Characteristics Nonparametrically (March 2017). NBER Working Paper No. w23227, Available at SSRN: https://ssrn.com/abstract=2935432

Joachim Freyberger (Contact Author)

University of Bonn ( email )

Regina-Pacis-Weg 3
Postfach 2220
Bonn, D-53012
Germany

University of Wisconsin - Madison ( email )

716 Langdon Street
Madison, WI 53706-1481
United States

Andreas Neuhierl

Washington University in St. Louis - John M. Olin Business School ( email )

St. Louis, MO
United States

Michael Weber

University of Chicago - Finance ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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