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

• URL: http://arxiv.org/abs/2212.11765v1
• Date: Sun, 13 Nov 2022 11:23:02 GMT
• Title: Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis
• Authors: Tanja Aue, Adam Jatowt, Michael F\"arber
• Abstract summary: 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. 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.
• Score: 17.332692582748408
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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