This study examines the impact of Artificial Intelligence (AI)-enhanced Environmental, Social, and Governance (ESG) reporting on firm valuation in emerging markets. It aims to explore how AI integration enhances the interpretability and predictive accuracy of ESG metrics in shaping market perceptions and investor decisions, particularly in non-financial sectors where ESG performance is experiencing significant growth. This study employs a panel dataset from 2018 to 2024, and it analyses publicly listed non-financial firms across five major sectors: manufacturing, energy, telecommunica-tions, consumer goods, and industrials. This study employs AI-powered multimodal analysis to examine the relationships between ESG and firm valuation in emerging markets. The research combines Fixed-Effects Regression and Machine Learning (ML) algorithms such as Extreme Gradient Boosting (XGBoost) and Random Forest to identify both linear and non-linear relationships between ESG scores and firm valuation. The results show empirical evidence that integrating ML enhances the explanatory power of ESG data. Findings indicate that ESG performance is positively correlated with higher market valuations, particularly in Environmental and Social dimensions. Governance metrics show more inconsistent effects. Firms identified in ESG controversies tend to face valuation penalties, which stresses market sensitivity to reputational risks. ML algo-rithms outperform conventional techniques in predictive accuracy, revealing complex, non-linear interactions within ESG data. The findings contribute to both academic lit-erature and practitioner understanding of the implications of how AI-driven ESG re-porting can improve robust firm valuation models and complex interdependencies in the ESG dataset.
Aruwaji et al. (Thu,) studied this question.