Portfolio Choices with Many Big Models

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...

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Bibliographic Details
Published in:Management science 2022-01, Vol.68 (1), p.690-715
Main Authors: Anderson, Evan, Cheng, Ai-ru (Meg)
Format: Article
Language:English
Subjects:
Bayesian analysis
econometrics
economics
finance
investment
Investors
model uncertainty
portfolio
Portfolio management
Rates of return
Specification
Tax returns
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Summary: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. This paper was accepted by Tyler Shumway, finance.
ISSN:0025-1909
1526-5501
DOI:10.1287/mnsc.2020.3876