International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 6 (November-December 2026) Submit your research before last 3 days of December to publish your research paper in the issue of November-December.

Machine Learning Based ESG Disclosure Sentiment and Financial Performance: Corporate Sustainability and Behavioural Finance Implications from Nifty 50 Firms

Author(s) Ms. A Binita Singha, Ms. Moonmoon Kalita, Prof. Dr. Biju Mani Das
Country India
Abstract This study explores the sentiment inherent in Environmental, Social, and Governance (ESG) disclosures from fifteen Nifty 50 firms and its relationship with financial performance from perspectives of corporate sustainability and behavioral finance. The study addresses the gap existing between sentiment of India's post-Business Responsibility and Sustainability Reporting (BRSR) and ESG disclosure literature, wherein the prior research has relied on third-party ratings to measure ESG disclosures. The findings reveal that ESG disclosures have negative tones in most cases (56.3%) indicative of a corporate culture that promotes risk disclosure, although significant differences exist between firms. Overall, negative sentiments are highest in social disclosures (61.1%), followed by governance (57.8%), and environmental (50.0%). However, governance dominates negative sentiment at firm level. Correlation and Ordinary Least Squares (OLS) regression analyses on 44 firm-year observations confirm that sentiment scores are strong positive predictors of Return on Assets (ROA) and Return on Equity (ROE), with greater significance in ROE conform to behavioral finance theories of investor reaction to tone.
Keywords ESG Sentiment Analysis, Corporate Disclosure, Financial Performance, Nifty 50, Machine Learning, Natural Language Processing
Field Business Administration
Published In Volume 8, Issue 6, November-December 2026
Published On 2026-11-03
DOI https://doi.org/10.36948/ijfmr.2026.v08i06.91246

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