Dissecting Characteristics Nonparametrically. (17th April 2020)

• Record Type: Journal Article
• Authors: Freyberger, Joachim; Neuhierl, Andreas; Weber, Michael
• Editors: Karolyi, Andrew
• Is Part Of: Review of financial studies. Volume 33:Number 5(2020)
• Page Start: 2326
• Page End: 2377
• Publication Date: 2020-04-17
• Subjects: C14 -- C52 -- C58 -- G12; Finance -- United States -- Periodicals; Finance -- Periodicals; 332
• DOI: 10.1093/rfs/hhz123
• Languages: English
• ISSNs: 0893-9454

Abstract

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 selected characteristics affect expected returns nonparametrically. Our method can handle a large number of characteristics and allows for a flexible functional form. Our implementation is insensitive to outliers. Many of the previously identified return predictors don't provide incremental information for expected returns, and nonlinearities are important. We study our method's properties in simulations and find large improvements in both model selection and prediction compared to alternative selection methods. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.