Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis
This addresses the demand for transparent and reliable ESG ratings for investors and regulators, though it is incremental as it applies existing deep learning techniques to a new domain.
The paper tackles the problem of forecasting companies' ESG ratings by developing a model that uses multivariate time series analysis of news articles, achieving results that outperform the state-of-the-art.
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.