Download pdf
Predicting Corporate ESG Scores Using Machine Learning: A Comparative Study
- 1 Business school, University of St Andrews, St Andrews, UK
* Author to whom correspondence should be addressed.
Abstract
This paper investigates the application of machine learning to predict corporate Environmental, Social, and Governance (ESG) scores, focusing on identifying the most influential factors within companies' reports. Three models—linear regression, random forests, and gradient boosting—were utilized to estimate ESG risk scores. Hyperparameter tuning through Grid Search with cross-validation ensured that the models were optimized for robust performance, and their accuracy was assessed using Root Mean Square Error (RMSE) and R-squared (R²) metrics. The findings from the experiments indicate that the gradient boosting model surpasses other methods in accuracy. According to Shapley Additive Explanations (SHAP) analysis, industry classification emerges as the primary influencer of ESG scores, followed by financial indicators such as Price/Sales, Price/Book, and Market Capitalization. This predictive model can provide valuable insights for both investors and companies, aiding in investment decisions and strategic improvements in ESG performance. While the study effectively demonstrates the potential of machine learning for ESG forecasting, future research could further refine the models by exploring more advanced ensemble methods like Extreme Gradient Boosting (XGBoost) or Categorical Boosting (CatBoost) and incorporating qualitative data to enhance predictive power.
Keywords
ESG, Gradient Boosting, Financial Indicators, Machine Learning
[1]. Aue, T., Jatowt, A., & Färber, M. (2022) Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis. arXiv preprint:2212.11765.
[2]. D’Amato, V., D’Ecclesia, R., & Levantesi, S. (2021) Fundamental ratios as predictors of ESG scores: A machine learning approach. Decisions in Economics and Finance, 44(2), 1087-1110.
[3]. Del-Vitto, A., Marazzina, D., & Stocco, D. (2023) ESG ratings explainability through machine learning techniques. Annals of Operations Research, 1-30.
[4]. Fisher, P. (2021) S&P 500 companies with financial information. Retrieved on 2024, Retrieved from: https://www.kaggle.com/datasets/paytonfisher/sp-500-companies-with-financial-information
[5]. Sariyer, G., Mangla, S.K., Chowdhury, S., Sozen, M.E., & Kazancoglu, Y. (2024) Predictive and prescriptive analytics for ESG performance evaluation: A case of Fortune 500 companies. Journal of Business Research, 181, 114742.
[6]. Zhang, A.Y., Zhang, J.H. (2023) Renovation in environmental, social and governance (ESG) research: the application of machine learning. Asian Review of Accounting, 12, 15-18.
[7]. Kumar, P. (2024) S&P 500 ESG risk ratings. Retrieved on 2024, Retrieved from: https://www.kaggle.com/datasets/pritish509/s-and-p-500-esg-risk-ratings.
[8]. Lai, C., Chen, D.N., Tsuo, Y., & Liu, J.H. (2022) Predicting firm performance using ESG scores, annual reports, and patents, 2, 11-13.
[9]. Rajesh, R., & Rajendran, C. (2020) Relating environmental, social, and governance scores and sustainability performances of firms: An empirical analysis. Business Strategy and the Environment, 29(3), 1247-1267.
[10]. Song, Y., Li, R., Zhang, Z., & Sahut, J.M. (2024) ESG performance and financial distress prediction of energy enterprises. Finance Research Letters, 65, 105546.
Cite this article
Jiang,X. (2024). Predicting Corporate ESG Scores Using Machine Learning: A Comparative Study. Advances in Economics, Management and Political Sciences,118,141-147.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 3rd International Conference on Financial Technology and Business Analysis
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).