Bayesian selection of asset pricing factors using individual stocks

Abstract

We apply Bayesian variable selection to investigate linear factor asset pricing models for a large set of candidate factors identified in the literature. We extract model and factor posterior probabilities from thousands of individual stocks via Markov Chain Monte Carlo estimation together with the exact distribution of pricing statistics. Our results show that only a small number of factors are relevant and, except for the market and size factors, these are not the factors in widely used linear factor models such as Fama & French (2015) or Hou et al. (2015). Moreover, many different linear factor models achieve similar empirical performance, suggesting that the search for a single linear factor model is unlikely to yield a definitive answer.

Keywords: Multi-factor model, Factor zoo, Factor selection, Bayesian variable selection, SeeminglyUnrelated Regressions

JEL Classification: G12, C52