Nash Equilibrium in Sustainable Finance: Designing a Game-Theoretic DSS for Compliance-Aligned Portfolio Optimization

This work introduces a complete computational framework that combines game theory, sustainability metrics, and regulatory compliance into a unified decision support system for portfolio design. Traditional Environmental, Social, and Governance (ESG) investing often fails to adequately consider mandatory compliance, which we conceptualize as ESG Compliance (ESGC),an indicator of the degree to which a company meets sustainability standards and reporting regulations. To bridge the gap between financial maximization and regulatory demand, we propose a Game-Theoretic ESG Decision Support System (DSS), which captures the strategic interplay between three distinct investor archetypes: retail, institutional, and regulatory. The workflow encompasses rigorous data cleaning, feature engineering, and game-theoretic optimization based on Nash Equilibrium using a multi-region panel of 4,837 firms.Our empirical results demonstrate that the Game-Theoretic strategy achieves a mean annual return of 25.83% (representing a 117.14% cumulative return over the study period) and a Sharpe Ratio of 1.1623, significantly outperforming the standard Markowitz Mean-Variance benchmark (Sharpe: 0.9652). Furthermore, the framework maintains superior sustainability alignment with a Mean ESG score of 72.51 and a Mean ESGC score of 79.23. When adjusted for sustainability quality, the model generates an ESG-Adjusted Sharpe Ratio of 2.0051, outperforming the ESG-Floor MVO benchmark (1.69). This framework provides a robust tool that integrates financial performance, sustainability, and policy into a single, mathematically rigorous decision-making environment.