Bayesian Solutions for the Factor Zoo: We Just Ran Two Quadrillion Models

Abstract

We propose a novel framework for analyzing linear asset pricing models: simple, robust, and applicable to high dimensional problems. For a (potentially misspecified) standalone model, it provides reliable price of risk estimates for both tradable and non-tradable factors, and detects those weakly identified. For competing factors and (possibly non-nested) models, the method automatically selects the best specification -- if a dominant one exists -- or provides a Bayesian model averaging (BMA-SDF), if there is no clear winner. We analyze 2.25 quadrillion models generated by a large set of factors, and find that the BMA-SDF outperforms existing models in- and out-of-sample.

Keywords: Cross-Sectional Asset Pricing, Factor Models, Model Evaluation, Multiple Testing, Data Mining, P-Hacking, Bayesian Methods, shrinkage, SDF.

JEL Classification: G12, C11, C12, C52, C58