Title

Portfolio Choices with Many Big Models

Author ORCID Identifier

Evan Anderson: https://orcid.org/0000-0002-8541-9120

Publication Title

Management Science

ISSN

00251909

E-ISSN

15265501

Document Type

Article

Abstract

This paper proposes a Bayesian-averaging heterogeneous vector autoregressive portfolio choice strategy with many big models that outperforms existing methods out-ofsample 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.

First Page

690

Last Page

715

Publication Date

1-1-2022

DOI

10.1287/mnsc.2020.3876

Keywords

Econometrics, Economics, Finance, Investment, Model uncertainty, Portfolio

Original Citation

Evan Anderson, Ai-ru (Meg) Cheng (2021) Portfolio Choices with Many Big Models. Management Science 68(1):690-715. https://doi.org/10.1287/mnsc.2020.3876

Department

Department of Economics

Link to Full Text

Share

COinS