journal-of-statistics-and-management-systems Journal of Statistics and Management Systems 2169-0014 0972-0510 Taru Publications 10.47974/JSMS-1754 AI-augmented ESG investing : Measuring and predicting sustainability impact in capital markets Ali Mohd Shahid School of Management, IILM University, Gurugram, Haryana, 122003, India Waghray Ritika Bajaj Department of Finance, Vignana Jyothi Institute of Management, Hyderabad, Telangana, 500118, India Rahman Afzalur School of Business, Woxsen University, Hyderabad, Telangana, 502345, India Mohideen M. A. Honest Department of Commerce, Motilal Nehru College, University of Delhi, Benito Juarez Marg, South Campus, New Delhi, 110021, India Wiquar Refia Sharda School of Business Studies, Sharda University, Greater Noida, Uttar Pradesh, 201310, India Hayat Mohsina School of Commerce, Jain (Deemed to be University), Bengaluru, Karnataka, 560069, India 29 10 1151 1160 31 10 2026

This paper examines whether artificial intelligence (AI) improves the information that environmental, social and governance (ESG) ratings provide to investors. Sustainalytics ESG risk ratings, market indicators and natural language processing (NLP) features from business descriptions are combined for 429 S&P 500 firms (81,843 firm-months, 2010–2026). Random forest, XGBoost and LSTM models tested out of sample (January 2024 – September 2026) identify firms entering high-volatility months far more accurately than ESG ratings alone (AUC 0.82–0.84 versus 0.50; p < 0.001). However, no model predicts returns better than the ratings-only benchmark, and AI portfolio Sharpe ratios do not differ significantly from that of a best-in-class ESG portfolio (0.81 versus 0.84). AI therefore adds most value as a forward-looking risk-monitoring tool rather than as a source of excess returns.

 Artificial intelligence ESG investing Machine learning Portfolio performance Sustainability impact Capital markets access open retracted no