Predicting Environmental Social and Governance Scores: Applying Machine Learning Models to French Companies

Sina Belkhiria, Azhaar Lajmi () and Siwar Sayed
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Sina Belkhiria: Higher Institute of Management, GEF2A-Lab, University of Tunis, Tunis 2000, Tunisia
Azhaar Lajmi: Higher Institute of Management, GEF2A-Lab, University of Tunis, Tunis 2000, Tunisia
Siwar Sayed: Higher Institute of Management, University of Tunis, Tunis 2000, Tunisia

JRFM, 2025, vol. 18, issue 8, 1-21

Abstract: The main objective of this study is to analyse the relevance of financial performance as an accurate predictor of ESG scores for French companies from 2010 to 2022. To this end, Machine Learning techniques such as linear regression, polynomial regression, Random Forest, and Support Vector Regression (SVR) were employed to provide more accurate and reliable assessments, thus informing the ESG rating attribution process. The results obtained highlight the excellent performance of the Random Forest method in predicting ESG scores from company financial variables. In addition, the approach identified specific financial variables (operating income, market capitalisation, enterprise value, etc.) that act as powerful predictors of companies’ ESG scores. This modelling approach offers a robust tool for predicting companies’ ESG scores from financial data, which can be valuable for investors and decision-makers wishing to assess and understand the impact of financial variables on corporate sustainability. Also, this allows sustainability investors to diversify their portfolios by including companies that are not currently rated by ESG rating agencies, that do not produce sustainability reports, as well as newly listed companies. It also gives companies the opportunity to identify areas where improvements are needed to enhance their ESG performance. Finally, it facilitates access to ESG ratings for interested external stakeholders. Our study focuses on using advances in artificial intelligence, exploring machine learning techniques to develop a reliable predictive model of ESG scores, which is proving to be an original and promising area of research.

Keywords: sustainability; ESG score; financial performance; machine learning; random forest (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
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