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.
翻译：近年来,公司的环境、社会和治理(ESG)参与成为公众关注的焦点。随着强制报告要求得到实施,投资者将可持续性纳入投资决策,对透明和可靠的ESG评级的需求正在增加。然而,尽管这一专题的重要性越来越重要,预测ESG评级的自动办法仍然相当稀少。在本文中,我们建立了一个模型,利用多种变式时间序列建设和深层学习技术的组合,预测ESG从新闻报道中获得的评级。还创建并公布了约3,000家美国公司的新闻数据集及其评级,用于培训。通过实验评估,我们发现我们的方法提供的准确结果超过了最新水平,并可用于实际支持对ESG评级进行手工确定或分析。
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