.png){kind=icon}
.png){kind=icon}
Return
Dissecting Characteristics Nonparametrically
.png){kind=icon}
OA
AI
DOI：10.1093/rfs/hhz123.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
CAPITAL-ASSET PRICES
CROSS-SECTION
ADDITIVE REGRESSION
VARIABLE SELECTION
COMMON-STOCKS
RISK
INVESTMENT
EARNINGS
RETURNS
EQUILIBRIUM
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.4
Papers:
2.8K
Citations:
3.0W
Organization
View more organizations