Mislearning of Factor Risk Premia under Structural Breaks A Misspecified Bayesian Learning Framework
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摘要
The paper develops a minimal misspecified Bayesian-learning model where investors assume a stable latent process while the true factor premia undergo occasional structural breaks; it proposes a tractable mislearning intensity Δ_t defined as the log predictive-likelihood ratio between a break-aware benchmark and the investor’s stable model, and shows empirically that (i) in benchmark factor systems elevated Δ_t predicts higher 12‑month cumulative returns and Sharpe ratios consistent with a long-horizon uncertainty premium, (ii) in a broad anomaly universe Δ_t more often predicts future instability (drawdowns, downside semivolatility) with strong family heterogeneity, and (iii) passive capital does not uniformly absorb mislearning but shifts whether mislearning is expressed as compensation or as realized risk [page::0][page::5][page::21]
