  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.
