ESG2Risk:从ESG新闻到股票波动预测的深度学习框架

ERN: Stock Market Risk (Topic) Pub Date : 2020-05-05 DOI:10.2139/ssrn.3593885

Tian Guo, N. Jamet, Valentin Betrix, Louis-Alexandre Piquet, E. Hauptmann

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引用次数: 16

摘要

最近，将环境、社会和治理(ESG)因素纳入系统性投资引起了许多关注。本文以财经新闻流中的ESG事件为研究对象，探讨ESG相关财经新闻对股票波动的预测能力。特别是，我们开发了ESG新闻提取、新闻表示和深度学习模型的贝叶斯推理管道。对真实数据和不同市场的实验评价表明，该方法具有较好的预测效果，同时也证明了高波动率预测与潜在高风险低收益股票的关系。它还显示了所提出的管道作为各种文本数据和目标变量的灵活预测框架的前景。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

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.

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ERN: Stock Market Risk (Topic)

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