Venkatachalam, V (2025) Leveraging Machine Learning to Decode ESG Impact in Capital Markets: Evidence from the Indian Stock Market. Leveraging Machine Learning to Decode ESG Impact in Capital Markets: Evidence from the Indian Stock Market: 64833.

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Abstract

The investment paradigm is increasingly shaped by
Environmental, Social, and Governance (ESG) factors, fostering
responsible and sustainable investing among both institutional and
retail investors. This study investigates the implementation of
advanced machine learning techniques to examine the impact of
ESG performance scores on stock returns within the Indian capital
market. We utilized a dataset from 2015 to 2024 that includes ESG
ratings, firm-level financial indicators. Powerful machine learning
models such as Random Forest, XGBoost, and Long Short-Term
Memory (LSTM) were employed. The objective is to assess the
predictive power, robustness, and interpretability of these models
in capturing the relationship between ESG performance and stock
returns. Our study findings indicate a positive impact of ESG scores
on stock performance, with the effect being particularly
pronounced among large-cap firms. Among the three ESG
dimensions—environmental, social, and governance—only
environmental scores showed a strong overall impact, while the
social dimension demonstrated sector-specific relevance. Among
the machine learning models used, XGBoost exhibited the highest
predictive accuracy, whereas LSTM proved effective in capturing
time-based patterns and dependencies within the data. This
research contributes to the growing literature on sustainable
finance in emerging markets and offers actionable insights for
institutional investors, asset managers, corporate executives, and
policymakers aiming to integrate ESG considerations into long
term investment strategies.

Item Type: Article
Uncontrolled Keywords: —ESG, Machine Learning, Indian Stock Market, Sustainable Finance, Random Forest, LSTM, XGBoost, Capital Markets, Stock Returns
Divisions: PSG College of Arts and Science > Department of Commerce
Depositing User: Dr. B Sivakumar
Date Deposited: 10 Feb 2026 05:20
Last Modified: 10 Feb 2026 05:20
URI: https://ir.psgcas.ac.in/id/eprint/2597

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