Bayesian Mislearning of Factor Premia under Structural Breaks

Summary

This paper examines how investors’ beliefs and asset prices may be distorted when their learning model understates structural breaks in factor risk premia. It proposes a minimal Bayesian framework in which this misspecification creates persistent forecast errors, and an empirically tractable measure of mislearning intensity based on predictive likelihood ratios. The analysis connects estimated mislearning to subsequent returns and measures of instability.

Findings differ across settings. In benchmark factor systems, higher mislearning is linked to stronger long-horizon returns and Sharpe ratios, but does not signal a predictable short-run collapse. Across a broader set of anomalies, the relationship is less uniform: mislearning is more closely associated with future drawdowns, downside semivolatility, and other instability measures, with variation across anomaly families. A relationship between break-proneness and average mislearning appears in low-IVOL environments, where break severity is more comparable. These are conditional empirical associations; the summary does not establish a universal forecasting rule or provide details on sample construction and estimation.

Key ideas

• A Bayesian learning model can generate persistent forecast errors when it understates structural breaks.
• Mislearning intensity is measured using predictive likelihood ratios.
• In benchmark factor systems, elevated mislearning is associated with stronger long-horizon performance, not a deterministic near-term collapse.
• Across anomalies, mislearning is linked to instability measures, with substantial variation between families.
• The relationship between break-proneness and mislearning depends on conditions such as low idiosyncratic volatility.

Tags

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


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









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 $(Δ_t)$ 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.

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.