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 risk premia estimates of 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 model averaging, if there is no clear winner given the data. We analyze 2.25 quadrillion models generated by a large set of existing factors, and gain novel insights on the empirical drivers of asset returns.