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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ook Lee & Hanseon Joo & Hayoung Choi & Minjong Cheon, 2022. "Proposing an Integrated Approach to Analyzing ESG Data via Machine Learning and Deep Learning Algorithms," Sustainability, MDPI, vol. 14(14), pages 1-14, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8745-:d:864955
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    References listed on IDEAS

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    1. Jérémi Assael & Laurent Carlier & Damien Challet, 2023. "Dissecting the Explanatory Power of ESG Features on Equity Returns by Sector, Capitalization, and Year with Interpretable Machine Learning," JRFM, MDPI, vol. 16(3), pages 1-22, March.
    2. Xuesong Tian & Yuping Zou & Xin Wang & Minglang Tseng & Hua Li & Huijuan Zhang, 2022. "Improving the Efficiency and Sustainability of Intelligent Electricity Inspection: IMFO-ELM Algorithm for Load Forecasting," Sustainability, MDPI, vol. 14(21), pages 1-19, October.
    3. Sariyer, Gorkem & Kumar Mangla, Sachin & Chowdhury, Soumyadeb & Erkan Sozen, Mert & Kazancoglu, Yigit, 2024. "Predictive and prescriptive analytics for ESG performance evaluation: A case of Fortune 500 companies," Journal of Business Research, Elsevier, vol. 181(C).
    4. Archana Saxena & Rajesh Singh & Anita Gehlot & Shaik Vaseem Akram & Bhekisipho Twala & Aman Singh & Elisabeth Caro Montero & Neeraj Priyadarshi, 2022. "Technologies Empowered Environmental, Social, and Governance (ESG): An Industry 4.0 Landscape," Sustainability, MDPI, vol. 15(1), pages 1-17, December.

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