Optimizing Return Forecasts: A Bayesian Intermediary Asset Pricing Approach

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