Working Paper: NBER ID: w23227
Authors: Joachim Freyberger; Andreas Neuhierl; Michael Weber
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%.
Keywords: Nonparametric methods; Expected returns; Characteristics; Adaptive group lasso
JEL Codes: C14; C52; C58; G12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| characteristics (L15) | expected returns (G17) |
| 15 out of 36 characteristics (C52) | independent explanatory power (C29) |
| nonparametric model (C52) | higher average Sharpe ratio (G40) |
| linear model (C51) | lower average Sharpe ratio (G40) |
| predictive power of characteristics (C52) | varies over time (J29) |
| nonlinearities (C32) | overfitting of linear model (C52) |