Model Uncertainty in the Cross Section

46 Pages Posted: 14 Sep 2021 Last revised: 29 Oct 2021

See all articles by Jiantao Huang

Jiantao Huang

London School of Economics & Political Science (LSE) - Department of Finance

Ran Shi

London School of Economics & Political Science (LSE) - Department of Finance

Date Written: September 12, 2021

Abstract

We develop a transparent Bayesian approach to quantify uncertainty in linear stochastic discount factor (SDF) models. We show that, for a Bayesian decision maker, posterior model probabilities increase with maximum in-sample Sharpe ratios and decrease with model dimensions. Entropy of posterior probabilities represents model uncertainty. We apply our approach to quantify the time series of model uncertainty in North American, European, and Asian Pacific equity markets. Model uncertainty is countercyclical in these markets. It predicts investors’ asset allocation decisions across equity and fixed-income funds. In survey data, investors tend to be more pessimistic about equity performance during periods of higher model uncertainty.

Keywords: Model Uncertainty, Linear Stochastic Discount Factor, Bayesian Inference

JEL Classification: C11, G11, G12.

Suggested Citation

Huang, Jiantao and Shi, Ran, Model Uncertainty in the Cross Section (September 12, 2021). Available at SSRN: https://ssrn.com/abstract=3922077 or http://dx.doi.org/10.2139/ssrn.3922077

Jiantao Huang (Contact Author)

London School of Economics & Political Science (LSE) - Department of Finance ( email )

Houghton St, Holborn
London, WC2A 2AE
Great Britain

Ran Shi

London School of Economics & Political Science (LSE) - Department of Finance ( email )

United Kingdom

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