Dissecting Characteristics Nonparametrically

• 成果类型: Article
• 署名作者: Freyberger, Joachim; Neuhierl, Andreas; Weber, Michael
• 署名单位: University of Wisconsin System; University of Wisconsin Madison; Washington University (WUSTL); University of Chicago
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhz123
• 发表日期: 2020
• 页码: 2326
• 关键词: CAPITAL-ASSET PRICES; cross-section; ADDITIVE REGRESSION; variable selection; COMMON-STOCKS; RISK; INVESTMENT; earnings; returns; equilibrium

摘要

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.