Working Paper: NBER ID: w25481
Authors: Guanhao Feng; Stefano Giglio; 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 explicitly accounts for potential model-selection mistakes, unlike the standard approaches that assume perfect variable selection, which rarely occurs in practice and produces a bias due to the omitted variables. We apply our procedure to a set of factors recently discovered in the literature. While most of these new factors are found to be redundant relative to the existing factors, a few — such as profitability — have statistically significant explanatory power beyond the hundreds of factors proposed in the past. In addition, we show that our estimates and their significance are stable, whereas the model selected by simple LASSO is not.
Keywords: No keywords provided
JEL Codes: C01; C12; C23; C52; C55; C58; G00; G1; G10; G12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| existing factors (ht) (I12) | new factors (gt) (F29) |
| new factors (gt) (F29) | asset pricing (G19) |
| existing factors (ht) (I12) | omitted variable bias (C20) |
| new factors (gt) (F29) | expected returns (G17) |
| existing factors (ht) and new factors (gt) (C22) | asset pricing (G19) |