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

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  • Tian Guo
  • Nicolas Jamet
  • Valentin Betrix
  • Louis-Alexandre Piquet
  • Emmanuel Hauptmann

Abstract

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.

Suggested Citation

  • Tian Guo & Nicolas Jamet & Valentin Betrix & Louis-Alexandre Piquet & Emmanuel Hauptmann, 2020. "ESG2Risk: A Deep Learning Framework from ESG News to Stock Volatility Prediction," Papers 2005.02527, arXiv.org.
    • Handle: RePEc:arx:papers:2005.02527
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    References listed on IDEAS

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    1. Remmer Sassen & Anne-Kathrin Hinze & Inga Hardeck, 2016. "Impact of ESG factors on firm risk in Europe," Journal of Business Economics, Springer, vol. 86(8), pages 867-904, November.
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    1. Jérémi Assael & Laurent Carlier & Damien Challet, 2023. "Dissecting the Explanatory Power of ESG Features on Equity Returns by Sector, Capitalization, and Year with Interpretable Machine Learning," JRFM, MDPI, vol. 16(3), pages 1-22, March.
    2. Jeremi Assael & Laurent Carlier & Damien Challet, 2022. "Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning," Working Papers hal-03791538, HAL.
    3. Carlos A. Piccioni & Saulo B. Bastos & Daniel O. Cajueiro, 2024. "Stock Price Reaction to Environmental, Social, and Governance News: Evidence from Brazil and Financial Materiality," Sustainability, MDPI, vol. 16(7), pages 1-25, March.
    4. Ook Lee & Hanseon Joo & Hayoung Choi & Minjong Cheon, 2022. "Proposing an Integrated Approach to Analyzing ESG Data via Machine Learning and Deep Learning Algorithms," Sustainability, MDPI, vol. 14(14), pages 1-14, July.

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