A Machine Learning approach for Predicting Corporate ESG Ratings and analysing the impact of Country ESG data on Prediction

Bhuie, Gurpreet Kaur (2023) A Machine Learning approach for Predicting Corporate ESG Ratings and analysing the impact of Country ESG data on Prediction. Masters thesis, Dublin, National College of Ireland.

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Abstract

This research tries to explain Environmental, Social and Governance (ESG) need and importance in corporate finance. How ESG ratings from different rating agencies differ for the same organization. An examination is made about how having a consistent rating is important for organizations and failing to do this will result in loss or misallocation of millions of dollars. By investigating previous work done in this field the reason for inconsistency is discussed and many other approaches to overcome this inconsistency is also discussed. To address this problem the search, propose a generic model for ESG prediction which can be easily used by companies to predict their ESG rating and get an idea about if their current ESG roadmap is good enough to achieve their targets or not. This research also discusses the impact of country ESG ratings on any company’s ESG rating and how it can be included in ESG prediction. For implementing the solution, first both datasets are gathered from sources and cleaned then, both the company and country ESG datasets are combined based by joining them on relevant column. Random forest algorithm is applied to entire dataset and on only ESG dataset to compare the impact of country ESG data. This paper concludes the research thoroughly discussing the results and impact of country ESG datapoints on corporate ESG ratings predictions.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Staikopoulos, Athanasios UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HD Industries. Land use. Labor > Large Industry. Corporations. > Corporate Governance Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	07 May 2025 11:40
Last Modified:	07 May 2025 11:40
URI:	https://norma.ncirl.ie/id/eprint/7501

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