Navigating Uncertainty in ESG Investing

ArXiv ID: 2310.02163 “View on arXiv”

Authors: Unknown

Abstract

The widespread confusion among investors regarding Environmental, Social, and Governance (ESG) rankings assigned by rating agencies has underscored a critical issue in sustainable investing. To address this uncertainty, our research has devised methods that not only recognize this ambiguity but also offer tailored investment strategies for different investor profiles. By developing ESG ensemble strategies and integrating ESG scores into a Reinforcement Learning (RL) model, we aim to optimize portfolios that cater to both financial returns and ESG-focused outcomes. Additionally, by proposing the Double-Mean-Variance model, we classify three types of investors based on their risk preferences. We also introduce ESG-adjusted Capital Asset Pricing Models (CAPMs) to assess the performance of these optimized portfolios. Ultimately, our comprehensive approach provides investors with tools to navigate the inherent ambiguities of ESG ratings, facilitating more informed investment decisions.

Keywords: ESG Investing, Reinforcement Learning, Portfolio Optimization, Capital Asset Pricing Model (CAPM), Double-Mean-Variance, Equities

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical frameworks including Reinforcement Learning, Double-Mean-Variance models, and ESG-adjusted CAPM, indicating high mathematical complexity. Empirical rigor is solid, evidenced by the use of real-world ESG data from multiple agencies, standardization procedures, and specific calibration exercises.

  flowchart TD
    A["Research Goal<br>Address ESG Rating Uncertainty for Portfolio Optimization"] --> B{"Key Methodologies"}
    B --> C["ESG Ensemble Strategies"]
    B --> D["Reinforcement Learning RL Model"]
    B --> E["Double-Mean-Variance Model<br>Classifies Investor Risk Profiles"]
    
    C --> F["Computational Process<br>Optimize Portfolio: Returns + ESG Scores"]
    D --> F
    E --> G["Outcomes & Tools<br>ESG-Adjusted CAPM for Performance Assessment"]
    
    F --> G
    G --> H["Key Findings<br>Customized Strategies for Navigating ESG Ambiguity"]