🤖 AI Summary
ESG rating discrepancies impede investor decision-making and hinder sustainable investment practices. To address this, we propose a personalized sustainable investment framework tailored to heterogeneous risk preferences. First, we develop a Double-Mean-Variance investor classification model to capture nuanced risk attitudes with high granularity. Second, we design an ESG-enhanced Capital Asset Pricing Model (CAPM) that systematically integrates multi-source ESG scores into asset pricing. Third, we formulate an ESG-integrated portfolio optimization algorithm based on reinforcement learning (RL), jointly optimizing financial return and ESG performance within an extended mean–variance framework. The framework significantly enhances strategy robustness, adaptability, and interpretability under ESG rating uncertainty. It constitutes the first unified methodology that jointly incorporates investor segmentation, ESG-informed pricing correction, and dynamic portfolio optimization—thereby offering a principled solution to ESG rating inconsistency.
📝 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.
