利用动态公司网络预测ESG评级

IF 2.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Management Information Systems Pub Date : 2023-07-08 DOI:10.1145/3607874
Gary (Ming) Ang, Zhiling Guo, E. Lim
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引用次数: 1

摘要

由于全球对可持续性的日益关注,环境、社会和治理(ESG)考虑因素发挥着越来越重要的作用。银行和投资者等实体利用专门评级机构发布的公司ESG评级来评估公司的ESG风险。然而,人类分析师分配ESG评级的过程既费力又耗时。开发预测ESG评级的方法可以缓解这些挑战,使ESG评级能够更及时地生成,覆盖更多的公司,并且更容易获得。大多数工作研究ESG评级对目标变量(如公司股价或财务基本面)的影响,但很少有工作研究如何利用不同类型的公司信息来预测ESG评级。以前的工作也主要集中在只使用个别公司的财务信息来预测ESG评级,而忽略了不同类型的公司间关系网络。这种公司间关系网络通常是动态的,即它们随着时间的推移而演变。在本文中,我们专注于利用动态公司间关系进行ESG评级预测,并考察不同财务和动态网络信息在该预测任务中的相对重要性。我们的分析表明,利用基于共同董事、共同投资者和新闻事件的知识图关系的动态公司间网络信息,可以显著提高ESG评级预测性能。未来不同时间段和不同时间步数的稳健性检查进一步验证了这些见解。
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On Predicting ESG Ratings Using Dynamic Company Networks
Environmental, social and governance (ESG) considerations play an increasingly important role due to the growing focus on sustainability globally. Entities, such as banks and investors, utilize ESG ratings of companies issued by specialized rating agencies to evaluate ESG risks of companies. The process of assigning ESG ratings by human analysts is however laborious and time intensive. Developing methods to predict ESG ratings could alleviate such challenges, allow ESG ratings to be generated in a more timely manner, cover more companies, and be more accessible. Most works study the effects of ESG ratings on target variables such as stock prices or financial fundamentals of companies, but few works study how different types of company information can be utilized to predict ESG ratings. Previous works also largely focus on using only the financial information of individual companies to predict ESG ratings, leaving out the different types of inter-company relationship networks. Such inter-company relationship networks are typically dynamic, i.e., they evolve across time. In this paper, we focus on utilizing dynamic inter-company relationships for ESG ratings prediction, and examine the relative importance of different financial and dynamic network information in this prediction task. Our analysis shows that utilizing dynamic inter-company network information, based on common director, common investor and news event-based knowledge graph relationships, can significantly improve ESG rating prediction performance. Robustness checks over different time-periods and different number of time-steps in the future further validate these insights.
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来源期刊
ACM Transactions on Management Information Systems
ACM Transactions on Management Information Systems COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
6.30
自引率
20.00%
发文量
60
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