Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 31 references that are in the map. First posted on arXiv 13 Nov 2022.
Builds on
6 of 31 references.
References · 6
- 2017Attention Is All You Needcited by 60,361
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
- 2019On the Variance of the Adaptive Learning Rate and Beyondcited by 772
- 2018Prediction of ESG Compliance using a Heterogeneous Information Networkcited by 6
- 2021Heterogeneous Ensemble for ESG Ratings Predictioncited by 2
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Citing papers per year: 2024: 1.
1 paper.