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Portfolio Choices with Many Big Models

Author

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  • Evan Anderson

    (Department of Economics, Northern Illinois University, DeKalb, Illinois 60115)

  • Ai-ru (Meg) Cheng

    (Department of Economics, Northern Illinois University, DeKalb, Illinois 60115)

Abstract

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.

Suggested Citation

  • Evan Anderson & Ai-ru (Meg) Cheng, 2022. "Portfolio Choices with Many Big Models," Management Science, INFORMS, vol. 68(1), pages 690-715, January.
    • Handle: RePEc:inm:ormnsc:v:68:y:2022:i:1:p:690-715
    DOI: 10.1287/mnsc.2020.3876
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