  Incorporating environmental, social, and governance (ESG) considerations into
systematic investments has drawn numerous attention recently. In this paper, we
focus on the ESG events in financial news flow and exploring the predictive
power of ESG related financial news on stock volatility. In particular, we
develop a pipeline of ESG news extraction, news representations, and Bayesian
inference of deep learning models. Experimental evaluation on real data and
different markets demonstrates the superior predicting performance as well as
the relation of high volatility prediction to stocks with potential high risk
and low return. It also shows the prospect of the proposed pipeline as a
flexible predicting framework for various textual data and target variables.
