Sequential Learning, Predictability, and Optimal Portfolio Returns

• 成果类型: Article
• 署名作者: Johannes, Michael; Korteweg, Arthur; Polson, Nicholas
• 署名单位: Columbia University; Stanford University; University of Chicago
• 刊物名称: JOURNAL OF FINANCE
• ISSN/ISSBN: 0022-1082
• DOI: 10.1111/jofi.12121
• 发表日期: 2014
• 页码: 611-644
• 关键词: STOCK RETURNS; PREDICTIVE REGRESSIONS; stochastic volatility; ECONOMIC VALUE; inference; prices; CHOICE

摘要

This paper finds statistically and economically significant out-of-sample portfolio benefits for an investor who uses models of return predictability when forming optimal portfolios. Investors must account for estimation risk, and incorporate an ensemble of important features, including time-varying volatility, and time-varying expected returns driven by payout yield measures that include share repurchase and issuance. Prior research documents a lack of benefits to return predictability, and our results suggest that this is largely due to omitting time-varying volatility and estimation risk. We also document the sequential process of investors learning about parameters, state variables, and models as new data arrive.