Applying artificial intelligence techniques for predicting the environment, social, and governance (ESG) pillar score based on balance sheet and income statement data: A case of non-financial companies of USA, UK, and Germany - Raza, Hassan; Khan, Muhammad Anees; Mazliham, M. S.; Alam, Muhammad Mansoor; Aman, Nida; Abbas, Kumail (Frontiers in Environmental Science, 2022)
Due to globalization Environment, Social and Governance (ESG) issues has gained importance over the last few decades. ESG is a worldwide issue, which clarify that organization throughout the world are lacking in contribution to the environment, society, and corporate governance characteristics for sustainable development. The problem of ESG spread over all stakeholders that needs to be address. In this regard rating agencies also have a close eye on ESG issues, who have developed the methodology of score that aims to provide disclosure on ESG metrics which, in return help investors and asset managers in better differentiating among responsible and irresponsible companies. ESG score has become important tool among asset managers but highly questioned in terms of reliability. The study aims to develop Machine Learning algorithms to assess how balance sheet and income statement data impact Thomson Reuters ESG score for non-financial public companies of USA, UK, and Germany from 2008 to 2020. The study also aims to assess which ML algorithm better predicts ESG score using structural data i.e., Return on Assets (ROA), Return on assets (ROE), Earning per Share (EPS), Earnings before interest and taxes (EBIT), dividend yield, and net sales. The results concluded that balance sheet and income statement data is critical in explaining ESG score, and ANN algorithm outperforms with minimum RMSE and MAE values.