Working Paper: CEPR ID: DP14266
Authors: Stefano W. Giglio; Gavin Feng; Dacheng Xiu
Abstract: We propose a model selection method to systematically evaluate the contribution to asset pricing of any new factor, above and beyond what a high-dimensional set of existing factors explains. Our methodology accounts for model selection mistakes that produce a bias due to omitted variables, unlike standard approaches that assume perfect variable selection. We apply our procedure to a set of factors recently discovered in the literature. While most of these new factors are shown to be redundant relative to the existing factors, a few have statistically significant explanatory power beyond the hundreds of factors proposed in the past.
Keywords: factors; stochastic discount factor; postselection inference; regularized two-pass estimation; variable selection; machine learning; lasso; elastic net; PCA
JEL Codes: No JEL codes provided
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| new factors (gt) (F29) | expected returns (G17) |
| existing factors (ht) (I12) | expected returns (G17) |
| new factors (gt) (F29) | omitted variable bias (C20) |
| double selection process (C34) | omitted variable bias (C20) |
| new factors (gt) (F29) | redundant factors (C39) |
| new factors (gt) (F29) | significant explanatory power (C20) |
| methodology (B41) | reliable inference (C20) |