Modelling ESG dynamics and financial performance using time-series, surface regressions, and graph-theoretic networks in U.S. corporations

The evolving integration of Environmental, Social, and Governance (ESG) practices among firms, driven by stakeholder pressures and regulation, finds expression in time-varying patterns of ESG scores, and there is significant modelling needed to uncover their financial implications. This study employs a multi-model approach combining time-series logistic growth functions, ESG-weighted surface regression, structural multivariate modelling, and graph-theoretic analysis to investigate the ESG-finance nexus in ten listed U.S. companies for ten years drawn from the Global ESG Research Database (GESRD). Results show high interannual variance in the ESG scores, with firms such as Netflix and Tesla reporting extreme volatility, while Microsoft and IBM report consistent ESG integration. Nonlinear surface regressions reveal that ESG factors are entangled with revenues and market capitalization in a sophisticated way and may reduce short-run profitability through excessive ESG spending without strategic alignment. Correlation analyses confirm weak linear associations between ESG dimensions and financial metrics but detect clustered interactions between environmental, social, and governance measures and financial performance measures. We represent the firms as nodes and ESG co-movements as edges to find out ESG behavioural clusters and systemic interdependencies among firms. Weighted and unweighted adjacency matrices based on ESG trends reveal potential contagion channels and community structures that influence ESG investment targeting and policy design. This graph-theoretic ESG-finance methodological analysis makes new methodological frontiers by taking up relational dynamics left out of traditional models, providing actionable insights for aligning ESG strategy with financial performance and navigating sectoral transformation towards sustainability.