Mislearning of Factor Risk Premia under Structural Breaks: A Misspecified Bayesian Learning Framework

TL;DR

This paper introduces a Bayesian learning framework to analyze how investors' misspecified models under structural breaks affect asset pricing, revealing complex long-term and cross-sectional implications.

Contribution

It develops a tractable measure of mislearning and demonstrates its impact on asset returns, volatility, and market stability under model misspecification.

Findings

Mislearning correlates with stronger long-horizon returns and Sharpe ratios.

Mislearning is linked to future drawdowns and downside volatility.

The relation between instability and mislearning varies across market environments.

Abstract

While asset-pricing models increasingly recognize that factor risk premia are subject to structural change, existing literature typically assumes that investors correctly account for such instability. This paper studies how investors instead learn under a misspecified model that underestimates structural breaks. We propose a minimal Bayesian framework in which this misspecification generates persistent prediction errors and pricing distortions, and we introduce an empirically tractable measure of mislearning intensity $(\Delta_t)$ based on predictive likelihood ratios. The empirical results yield three main findings. First, in benchmark factor systems, elevated mislearning does not forecast a deterministic short-run collapse in performance; instead, it is associated with stronger long-horizon returns and Sharpe ratios, consistent with an equilibrium premium for acute model uncertainty. Second, in a broader anomaly universe, this pricing relation does not generalize uniformly: mislearning is more strongly associated with future drawdowns, downside semivolatility, and other measures of instability, with substantial heterogeneity across anomaly families. Third, the cross-sectional relation between instability and mislearning is inherently conditional: while a monotonic link between break-proneness and average mislearning does not hold in the full cross-section, it re-emerges in low-friction (low-IVOL) environments where break-state severity is more comparable across assets.