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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. Yupeng Cao & Zhi Chen & Qingyun Pei & Fabrizio Dimino & Lorenzo Ausiello & Prashant Kumar & K. P. Subbalakshmi & Papa Momar Ndiaye, 2024. "RiskLabs: Predicting Financial Risk Using Large Language Model Based on Multi-Sources Data," Papers 2404.07452, arXiv.org.

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