论文标题
使用多元时间表分析从新闻文章中预测公司的ESG评分
Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis
论文作者
论文摘要
近年来，公司的环境，社会和治理（ESG）参与成为公众关注的重点。随着实施强制性报告的要求以及将可持续性纳入其投资决策的要求，对透明和可靠的ESG评级的需求正在增加。但是，尽管该主题的重要性越来越高，但自动预测ESG评级的方法仍然很少。在本文中，我们建立了一个模型，以使用多元时间表构建和深度学习技术的组合来预测新闻文章的ESG评分。还为培训创建并发布了一个大约3,000家美国公司的新闻数据集以及其评级。通过实验评估，我们发现我们的方法提供了超出最先进的准确结果，并且可以在实践中用于支持对ESG评分的手动确定或分析。
Environmental, social and governance (ESG) engagement of companies moved into the focus of public attention over recent years. With the requirements of compulsory reporting being implemented and investors incorporating sustainability in their investment decisions, the demand for transparent and reliable ESG ratings is increasing. However, automatic approaches for forecasting ESG ratings have been quite scarce despite the increasing importance of the topic. In this paper, we build a model to predict ESG ratings from news articles using the combination of multivariate timeseries construction and deep learning techniques. A news dataset for about 3,000 US companies together with their ratings is also created and released for training. Through the experimental evaluation we find out that our approach provides accurate results outperforming the state-of-the-art, and can be used in practice to support a manual determination or analysis of ESG ratings.
