Paper: arXiv 2603.21672

Authors: Yimeng Qiu

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 $(Δ_t)$ 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.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper introduces an innovative misspecified Bayesian learning framework to capture model uncertainty around structural breaks in factor risk premia. It combines rigorous formal modeling with extensive cross-sectional empirical tests across both benchmark factor systems and a broad universe of market anomalies.

Research Flowchart

  flowchart TD
    A["Research Question: How do investors' mislearning of structural breaks impact factor risk premia?"] --> B["Framework & Metric: Minimal Bayesian Learning Model to quantify Mislearning Intensity (Δt)"]
    
    C["Data Inputs: Benchmark Factor Systems & Broader Cross-Section of Stock Anomalies"] --> B
    
    B --> D["Empirical Analysis: Measure Δt against Long-Horizon Returns, Risk Measures, & Asset Frictions"]
    
    D --> E1["Finding 1: Benchmark Factors<br/>High Δt predicts higher long-term returns & Sharpe ratios (Uncertainty Premium)"]
    D --> E2["Finding 2: Broader Anomalies<br/>High Δt predicts severe drawdowns & downside risk (Heterogeneous Instability)"]
    D --> E3["Finding 3: Friction Conditional<br/>Break-proneness links to mislearning primarily in low-IVOL environments"]