Sentiment Feedback in Equity Markets: Asymmetries, Retail Heterogeneity, and Structural Calibration

ArXiv ID: 2509.11970 “View on arXiv”

Authors: Lucas Marques Sneller

Abstract

We study how sentiment shocks propagate through equity returns and investor clientele using four independent proxies with sign-aligned kappa-rho parameters. A structural calibration links a one standard deviation innovation in sentiment to a pricing impact of 1.06 basis points with persistence parameter rho = 0.940, yielding a half-life of 11.2 months. The impulse response peaks around the 12-month horizon, indicating slow-moving amplification. Cross-sectionally, a simple D10-D1 portfolio earns 4.0 basis points per month with Sharpe ratios of 0.18-0.85, consistent with tradable exposure to the sentiment factor. Three regularities emerge: (i) positive sentiment innovations transmit more strongly than negative shocks (amplification asymmetry); (ii) effects are concentrated in retail-tilted and non-optionable stocks (clientele heterogeneity); and (iii) responses are state-dependent across volatility regimes - larger on impact in high-VIX months but more persistent in low-VIX months. Baseline time-series fits are parsimonious (R2 ~ 0.001; 420 monthly observations), yet the calibrated dynamics reconcile modest impact estimates with sizable long-short payoffs. Consistent with Miller (1977), a one standard deviation sentiment shock has 1.72-8.69 basis points larger effects in low-breadth stocks across horizons of 1-12 months, is robust to institutional flows, and exhibits volatility state dependence - larger on impact but less persistent in high-VIX months, smaller on impact but more persistent in low-VIX months.

Keywords: sentiment shocks, structural calibration, impulse response, sign-aligned kappa-rho, cross-sectional returns, Equities

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 7.2/10
• Quadrant: Holy Grail
• Why: The paper employs advanced econometric techniques like structural calibration and impulse response functions with sign-aligned parameters (κ–ρ), but the empirical analysis is heavily backed by real-world data, including portfolio sorts, Sharpe ratios, and extensive cross-sectional robustness tests across multiple volatility regimes.

  flowchart TD
    A["Research Goal<br>Quantify sentiment shock propagation<br>in equity markets and investor clientele"] --> B["Data & Methodology<br>4 sentiment proxies, sign-aligned κ-ρ parameters<br>Structural calibration & Impulse Response"]
    B --> C["Computational Processes<br>Calibrate ρ = 0.940 (half-life 11.2 mos)<br>Cross-section D10-D1 sorting<br>Impulse response estimation"]
    C --> D["Key Findings & Outcomes<br>1. Pricing Impact: 1.06 bps per SD shock<br>2. Asymmetry: Positive shocks > Negative<br>3. Clientele: Retail/Non-optionable concentration<br>4. Cross-section: D10-D1 earns 4.0 bps/mo<br>5. State-Dependence: High-VIX impact vs Low-VIX persistence<br>6. Miller (1977) consistency: 1.72-8.69 bps in low-breadth"]