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Journal of Finance Vol. 78 No. 1 2023

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

Svetlana Bryzgalova1; JIANTAO HUANG2,3,4,5,6,7,8; Christian Julliard2,3,4,5,6,7,8

1 e-mail: [email protected] · 2 Goethe University Frankfurt · 3 University of London · 4 London School of Business and Finance · 5 University College London · 6 London Business School · 7 University of Hong Kong · 8 London School of Economics and Political Science

open access

Abstract

We propose a novel framework for analyzing linear asset pricing models: simple, robust, and applicable to high‐dimensional problems. For a (potentially misspecified) stand‐alone model, it provides reliable price of risk estimates for both tradable and nontradable factors, and detects those weakly identified. For competing factors and (possibly nonnested) models, the method automatically selects the best specification— if a dominant one exists—or provides a Bayesian model averaging–stochastic discount factor (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.

DOI
10.1111/jofi.13197
Volume
78
Issue
1
Pages
487-557
Language
en
Sources
bibtex:phds-export.bib openalex crossref

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