Citation

Raza, Hassan and Khan, Muhammad Anees and Mohd Su'ud, Mazliham and Alam, Muhammad Mansoor and Aman, Nida and Abbas, Kumail (2022) 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. Frontiers in Environmental Science, 10. ISSN 2296-665X

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Abstract

Due to globalization, environment, social, and governance (ESG) issues have gained importance over the last few decades. ESG is a worldwide issue, which clarifies that organizations 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 needs to be addressed. In this regard, rating agencies also have a close eye on ESG issues and have developed the methodology of score that aims to provide disclosure on ESG metrics which, in return, help investors and asset managers better differentiate between responsible and irresponsible companies. The ESG score has become an important tool among asset managers but is highly questioned in terms of reliability. The study objective was to develop machine learning algorithms to assess how balance sheet and income statement data impact the Thomson Reuters ESG score for non-financial public companies of USA, UK, and Germany from 2008 to 2020. In addition, the study also has an objective to assess which machine learning (ML) algorithm better predicts the ESG score using structural data, that is, return on assets (ROA), return on equity (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 are critical in explaining the ESG score, and the ANN algorithm outperforms with minimum RMSE and MAE values. All in all, the results of the study, based on the concept of artificial intelligence, bring suggestion for improvement to regulatory bodies, researchers, academia, practitioners, publicly listed companies around the globe, and last but not the least to the US, UK, and Germany markets. Moreover, it also provides suggestions for up-to-date compliance of ESG-relevant activities for boosting the firm performance.

Item Type:	Article
Uncontrolled Keywords:	ESG score, machine learning, prediction, sustainability, balance sheet
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	31 Oct 2022 07:29
Last Modified:	31 Oct 2022 07:29
URII:	http://shdl.mmu.edu.my/id/eprint/10576

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