ESG2Risk: A Deep Learning Framework from ESG News to Stock Volatility Prediction
This work addresses the problem of improving systematic investment strategies for financial analysts by incorporating ESG news, though it appears incremental as it builds on existing deep learning and text analysis methods.
The paper tackled predicting stock volatility using ESG-related financial news by developing a deep learning pipeline for news extraction and Bayesian inference, demonstrating superior performance on real data across markets and linking high volatility predictions to high-risk, low-return stocks.
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.