Characteristics are covariances: A unified model of risk and return

• 成果类型: Article
• 署名作者: Kelly, Bryan T.; Pruitt, Seth; Su, Yinan
• 署名单位: Yale University
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/j.jfineco.2019.05.001
• 发表日期: 2019
• 页码: 501-524
• 关键词: Cross section of returns; latent factors; Anomaly; factor model; Conditional betas; PCA; BARRA

摘要

We propose a new modeling approach for the cross section of returns. Our method, Instrumented Principal Component Analysis (IPCA), allows for latent factors and time-varying loadings by introducing observable characteristics that instrument for the unobservable dynamic loadings. If the characteristics/expected return relationship is driven by compensation for exposure to latent risk factors, IPCA will identify the corresponding latent factors. If no such factors exist, IPCA infers that the characteristic effect is compensation without risk and allocates it to an anomaly intercept. Studying returns and characteristics at the stock-level, we find that five IPCA factors explain the cross section of average returns significantly more accurately than existing factor models and produce characteristic-associated anomaly intercepts that are small and statistically insignificant. Furthermore, among a large collection of characteristics explored in the literature, only ten are statistically significant at the 1% level in the IPCA specification and are responsible for nearly 100% of the models accuracy. (C) 2019 Elsevier B.V. All rights reserved.