Proposing an Integrated Approach to Analyzing ESG Data via Machine Learning and Deep Learning Algorithms

Ook Lee, Hanseon Joo, Hayoung Choi and Minjong Cheon
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Ook Lee: Department of Information Systems, Hanyang University, Seoul 04764, Korea
Hanseon Joo: Department of Information Systems, Hanyang University, Seoul 04764, Korea
Hayoung Choi: Department of Information Systems, Hanyang University, Seoul 04764, Korea
Minjong Cheon: Department of Information Systems, Hanyang University, Seoul 04764, Korea

Sustainability, 2022, vol. 14, issue 14, 1-14

Abstract: In the COVID-19 era, people face situations that they have never experienced before, which alerted the importance of the ESG. Investors also consider ESG indexes as an essential factor for their investments, and some research yielded that the return on sustainable funds is more significant than on non-sustainable ones. Nevertheless, a deficiency in research exists about analyzing ESG through artificial intelligence algorithms due to adversity in collecting ESG-related datasets. Therefore, this paper suggests integrated AI approaches to the ESG datasets with the five different experiments. We also focus on analyzing the governance and social datasets through NLP algorithms and propose a straightforward method for predicting a specific firm’s ESG rankings. Results were evaluated through accuracy score, RMSE, and MAE, and every experiment conducted relevant scores that achieved our aim. From the results, it could be concluded that this paper successfully analyzes ESG data with various algorithms. Unlike previous related research, this paper also emphasizes the importance of the adversarial attacks on the ESG datasets and suggests methods to detect them effectively. Furthermore, this paper proposes a simple way to predict ESG rankings, which would be helpful for small businesses. Even though it is our limitation that we only use restricted datasets, our research proposes the possibility of applying the AI algorithms to the ESG datasets in an integrated approach.

Keywords: ESG; machine learning; deep learning; COVID-19; data analysis; data science (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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