The Co-Pricing Factor Zoo

TL;DR

This paper demonstrates that a Bayesian Model Averaging approach using numerous observable factors effectively explains corporate bond and stock return risk premia, outperforming simpler models.

Contribution

It introduces a dense stochastic discount factor model that aggregates many factors, showing its robustness and predictive power for asset returns.

Findings

A Bayesian Model Averaging SDF explains risk premia with an out-of-sample Sharpe ratio of 1.5 to 1.8.

Equity and nontradable factors alone suffice to explain bond risk premia.

The model's SDF tracks the business cycle and predicts future asset returns.

Abstract

We analyze 18 quadrillion models for the joint pricing of corporate bond and stock returns. Strikingly, we find that equity and nontradable factors alone suffice to explain corporate bond risk premia once their Treasury term structure risk is accounted for, rendering the extensive bond factor literature largely redundant for this purpose. While only a handful of factors, behavioral and nontradable, are likely robust sources of priced risk, the true latent stochastic discount factor is dense in the space of observable factors. Consequently, a Bayesian Model Averaging Stochastic Discount Factor explains risk premia better than all low-dimensional models, in- and out-of-sample, by optimally aggregating dozens of factors that serve as noisy proxies for common underlying risks, yielding an out-of-sample Sharpe ratio of 1.5 to 1.8. This SDF, as well as its conditional mean and volatility, are persistent, track the business cycle and times of heightened economic uncertainty, and predict future asset returns.