Taming the Factor Zoo: A Test of New Factors

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 that produce a bias due to the omitted variables, unlike the standard approaches that assume perfect variable selection, which rarely occurs in practice. 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. Finally, we provide additional applications of our procedure that illustrate how it could help control the proliferation of factors in the zoo.

Keywords: Factors, Stochastic Discount Factor, Post-Selection Inference, Regularized Two-Pass Estimation, Variable Selection, Machine Learning, LASSO, Elastic Net, PCA