Prediction of Controversies and Estimation of ESG Performance: An Experimental Investigation Using Machine Learning

Jan Svanberg (), Tohid Ardeshiri (), Isak Samsten (), Peter Öhman () and Presha Neidermeyer ()
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Jan Svanberg: University of Gävle and Centre for research on Economic Relations
Tohid Ardeshiri: University of Gävle and Centre for research on Economic Relations
Isak Samsten: Stockholm University
Peter Öhman: Mid Sweden University
Presha Neidermeyer: West Virginia University

Chapter Chapter 4 in Handbook of Big Data and Analytics in Accounting and Auditing, 2023, pp 65-87 from Springer

Abstract: Abstract We develop a new methodology for computing environmental, social, and governance (ESG) ratings using a mode of artificial intelligence (AI) called machine learning (ML) to make ESG more transparent. The ML algorithms anchor our rating methodology in controversies related to non-compliance with corporate social responsibility (CSR). This methodology is consistent with the information needs of institutional investors and is the first ESG methodology with predictive validity. Our best model predicts what companies are likely to experience controversies. It has a precision of 70–84 per cent and high predictive performance on several measures. It also provides evidence of what indicators contribute the most to the predicted likelihood of experiencing an ESG controversy. Furthermore, while the common approach of rating companies is to aggregate indicators using the arithmetic average, which is a simple explanatory model designed to describe an average company, the proposed rating methodology uses state-of-the-art AI technology to aggregate ESG indicators into holistic ratings for the predictive modelling of individual company performance. Predictive modelling using ML enables our models to aggregate the information contained in ESG indicators with far less information loss than with the predominant aggregation method.

Keywords: Artificial Intelligence; Controversies; Corporate Social Performance; ESG; Machine Learning; Socially Responsible Investment (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-19-4460-4_4

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DOI: 10.1007/978-981-19-4460-4_4

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