Optimizing Return Forecasts: A Bayesian Intermediary Asset Pricing Approach

45 Pages Posted: 20 Aug 2023 Last revised: 7 Dec 2024

See all articles by Ming Gao

Ming Gao

University of Chicago - Booth School of Business

Cong Zhang

University of Chicago - Booth School of Business

Date Written: September 12, 2024

Abstract

This study presents a novel Bayesian approach incorporating financial frictions into a panel structural break model, utilizing economically informed priors from intermediary asset pricing theories. Our data-driven prior selection method, adept at handling unbalanced panels, enhances the identification of regime shifts and the selection of return predictors, thereby improving equity return forecasts. Validated through simulations and empirical analysis, our approach boosts out-of-sample cumulative returns and Sharpe ratios. Leveraging asset holdings data and intermediary-induced priors, the framework facilitates precise real-time regime change detection and provides Bayesian insights into the inconsistencies of risk prices associated with intermediary risks.

Keywords: Cross-sectional asset pricing; Bayesian analysis; Regime change; Model instability and model uncertainty; Intermediary asset pricing; Holdings data

Suggested Citation

Gao, Ming and Zhang, Cong, Optimizing Return Forecasts: A Bayesian Intermediary Asset Pricing Approach (September 12, 2024). Available at SSRN: https://ssrn.com/abstract=4545015 or http://dx.doi.org/10.2139/ssrn.4545015

Ming Gao

University of Chicago - Booth School of Business ( email )

5807 S Woodlawn Ave
Chicago, IL 60637
United States

Cong Zhang (Contact Author)

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
690
Abstract Views
2,525
Rank
76,164
PlumX Metrics