Portfolio Choices with Many Big Models

• 成果类型: Article
• 署名作者: Anderson, Evan; Cheng, Ai-ru (Meg)
• 署名单位: Northern Illinois University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2020.3876
• 发表日期: 2022
• 页码: 690-715
• 关键词: finance; portfolio; INVESTMENT; ECONOMICS; econometrics; model uncertainty

摘要

This paper proposes a Bayesian-averaging heterogeneous vector autoregressive portfolio choice strategy with many big models that outperforms existing methods out-of sample on numerous daily, weekly, and monthly datasets. The strategy assumes that excess returns are approximately determined by a time-varying regression with a large number of explanatory variables that are the sample means of past returns. Investors consider the possibility that every period there is a regime change by keeping track of many models, but doubt that any specification is able to perfectly predict the distribution of future returns, and compute portfolio choices that are robust to model misspecification.