# ESG2Risk: A Deep Learning Framework from ESG News to Stock Volatility Prediction

arXiv: https://arxiv.org/abs/2005.02527 · HTML: https://mlsanity.com/paper/2005.02527/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2020.

## Built on by (most cited first, 5 of 5)

- [Fine-Tuning Large Language Models for Stock Return Prediction Using Newsflow](https://mlsanity.com/paper/2407.18103/) (2024)
- [ESG Accountability Made Easy: DocQA at Your Service](https://mlsanity.com/paper/2311.18481/) (2023)
- [ESG-FTSE: A corpus of news articles with ESG relevance labels and use cases](https://mlsanity.com/paper/2405.20218/) (2024)
- [A Comprehensive Review on Summarizing Financial News Using Deep Learning](https://mlsanity.com/paper/2109.10118/) (2021)
- [The Battle of Information Representations: Comparing Sentiment and Semantic Features for Forecasting Market Trends](https://mlsanity.com/paper/2303.14221/) (2023)

## Abstract (arXiv)

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.