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Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis

Author

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  • Tanja Aue
  • Adam Jatowt
  • Michael Farber

Abstract

Environmental, social and governance (ESG) engagement of companies moved into the focus of public attention over recent years. With the requirements of compulsory reporting being implemented and investors incorporating sustainability in their investment decisions, the demand for transparent and reliable ESG ratings is increasing. However, automatic approaches for forecasting ESG ratings have been quite scarce despite the increasing importance of the topic. In this paper, we build a model to predict ESG ratings from news articles using the combination of multivariate timeseries construction and deep learning techniques. A news dataset for about 3,000 US companies together with their ratings is also created and released for training. Through the experimental evaluation we find out that our approach provides accurate results outperforming the state-of-the-art, and can be used in practice to support a manual determination or analysis of ESG ratings.

Suggested Citation

  • Tanja Aue & Adam Jatowt & Michael Farber, 2022. "Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis," Papers 2212.11765, arXiv.org.
    • Handle: RePEc:arx:papers:2212.11765
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    References listed on IDEAS

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    1. Gunnar Friede & Timo Busch & Alexander Bassen, 2015. "ESG and financial performance: aggregated evidence from more than 2000 empirical studies," Journal of Sustainable Finance & Investment, Taylor & Francis Journals, vol. 5(4), pages 210-233, October.
    2. Valeria D’Amato & Rita D’Ecclesia & Susanna Levantesi, 2022. "ESG score prediction through random forest algorithm," Computational Management Science, Springer, vol. 19(2), pages 347-373, June.
    3. Gillan, Stuart L. & Koch, Andrew & Starks, Laura T., 2021. "Firms and social responsibility: A review of ESG and CSR research in corporate finance," Journal of Corporate Finance, Elsevier, vol. 66(C).
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    Cited by:

    1. Gregor Dorfleitner & Rongxin Zhang, 2024. "ESG News Sentiment and Stock Price Reactions: A Comprehensive Investigation via BERT," Schmalenbach Journal of Business Research, Springer, vol. 76(2), pages 197-244, June.
    2. Yupeng Cao & Zhi Chen & Prashant Kumar & Qingyun Pei & Yangyang Yu & Haohang Li & Fabrizio Dimino & Lorenzo Ausiello & K. P. Subbalakshmi & Papa Momar Ndiaye, 2024. "RiskLabs: Predicting Financial Risk Using Large Language Model based on Multimodal and Multi-Sources Data," Papers 2404.07452, arXiv.org, revised May 2025.
    3. Zhu, Hongtao & Rahman, Md Jahidur, 2025. "Reprint of: Ex-ante expected changes in ESG and future stock returns based on machine learning," The British Accounting Review, Elsevier, vol. 57(1).
    4. yunwoo, Kim & Hwang, Junhyuk, 2025. "Benchmarking Machine Learning Models for ESG Prediction in South Korea Using News-Derived Time Series," SocArXiv v2738_v1, Center for Open Science.
    5. C'esar Pedrosa Soares, 2024. "Leveraging Natural Language and Item Response Theory Models for ESG Scoring," Papers 2407.20377, arXiv.org.

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