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

• 成果类型: Article
• 署名作者: Bryzgalova, Svetlana; Huang, Jiantao; Julliard, Christian
• 署名单位: University of London; London Business School; Centre for Economic Policy Research - UK; University of Hong Kong; University of London; London Business School
• 刊物名称: JOURNAL OF FINANCE
• ISSN/ISSBN: 0022-1082
• DOI: 10.1111/jofi.13197
• 发表日期: 2023
• 页码: 487-557
• 关键词: stock return predictability; ASSET-PRICING-MODELS; cross-section; PORTFOLIO SELECTION; Investor sentiment; variable selection; COMMON-STOCKS; RISK; prices; performance

摘要

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.