Investor Sentiment in Asset Pricing Models: A Review of Empirical Evidence

ArXiv ID: 2411.13180 “View on arXiv”

Authors: Unknown

Abstract

This study conducted a comprehensive review of 71 papers published between 2000 and 2021 that employed various measures of investor sentiment to model returns. The analysis indicates that higher complexity of sentiment measures and models improves the coefficient of determination. However, there was insufficient evidence to support that models incorporating more complex sentiment measures have better predictive power than those employing simpler proxies. Additionally, the significance of sentiment varies based on the asset and time period being analyzed, suggesting that the consensus relying on the BW index as a sentiment measure may be subject to change.

Keywords: investor sentiment, predictive modeling, sentiment measures, return modeling, BW index, Equities

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 2.0/10
• Quadrant: Philosophers
• Why: The paper is a literature review that discusses complex asset pricing models and behavioral theories but does not present new mathematical derivations or implementations, resulting in low math complexity and low empirical rigor.

  flowchart TD
    A["Research Goal: Review 71 papers (2000-2021)<br>Assess Investor Sentiment in Asset Pricing"] --> B["Data: BW Index & Other Proxies<br>Applied to Equities/Assets"]
    B --> C["Methodology: Comparative Analysis<br>Simple vs. Complex Sentiment Models"]
    C --> D{"Computational Processes<br>Model Estimation & Validation"}
    D --> E["Key Finding 1: Complexity ↑ R²<br>(Model Fit Improves)"]
    D --> F["Key Finding 2: Complexity ↛<br>Better Predictive Power"]
    D --> G["Key Finding 3: Significance varies<br>by Asset & Time Period"]
    E --> H["Outcome: BW Index Consensus<br>May Shift; Context-Dependent Validity"]
    F --> H
    G --> H