This paper is published in Volume-12, Issue-3, 2026
Area
Financial Economics
Author
Sheena Syed
Org/Univ
Symbiosis University, India, India
Keywords
ESG Rating Divergence, Greenwashing, NLP, FinBERT, TF-IDF, Panel Regression, Fama MacBeth, Asset Management, Sustainable Finance, Factor Models, Python, Bloomberg ESG Ratings.
Citations
IEEE
Sheena Syed. Do ESG Rating Divergences Predict Stock Underperformance?, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Sheena Syed (2026). Do ESG Rating Divergences Predict Stock Underperformance?. International Journal of Advance Research, Ideas and Innovations in Technology, 12(3) www.IJARIIT.com.
MLA
Sheena Syed. "Do ESG Rating Divergences Predict Stock Underperformance?." International Journal of Advance Research, Ideas and Innovations in Technology 12.3 (2026). www.IJARIIT.com.
Sheena Syed. Do ESG Rating Divergences Predict Stock Underperformance?, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Sheena Syed (2026). Do ESG Rating Divergences Predict Stock Underperformance?. International Journal of Advance Research, Ideas and Innovations in Technology, 12(3) www.IJARIIT.com.
MLA
Sheena Syed. "Do ESG Rating Divergences Predict Stock Underperformance?." International Journal of Advance Research, Ideas and Innovations in Technology 12.3 (2026). www.IJARIIT.com.
Abstract
While the ESG investing trend has shifted to the forefront, a rather worrying paradox is also becoming ever more evident, that of the dramatically divergent ESG evaluations rendered for the identical companies, consistently and by rating agencies across the industry. This paper investigates whether that difference of opinion, particularly when combined with unambiguous and highly optimistic environmental rhetoric in corporate filings, might be used as a quantifiable indicator of greenwashing, and if companies with that pattern of behaviour tend subsequently to underperform in equity markets. I employ a sample of 135 international firms from the S&P 500 and MSCI world indices, 2015-2023, providing a dataset of 1,080 firm-year observations. I calculated an aggregated ESG divergence index using pairwise disagreements from MSCI, Sustainalytics, and Bloomberg ESG ratings, and combined it with two text-based indicators from annual reports: a FinBERT sustainability sentiment index and a TF-IDF-based ESG keyword intensity measure for inclusion in my analysis. Ordinary Least Squares (OLS) regressions, two-way fixed effects panel models, and Fama-MacBeth cross-section estimation are used to carry out the empirical investigation. I find, throughout all specifications, that ESG rating divergence is negatively and significantly associated with risk-adjusted returns; each unit of added divergence relates to an annual excess return that is approximately 0.39–0.42% lower (p<0.01). Positive sustainability sentiment in disclosures correlates with better performance, whereas high keyword density unaccompanied by external rating agreement points in the opposite direction, consistent with rhetorical inflation rather than genuine ESG progress. A long-short portfolio sorted on divergence quintiles accumulates approximately 8.7 percentage points of excess return over the nine-year window. The results speak directly to the concerns of asset managers, index providers, and regulators engaged in the ongoing effort to bring rigour to sustainable finance.
