用于ESG评级预测的异构嵌入
Heterogeneous Ensemble for ESG Ratings Prediction
摘要：
在过去几年中，从气候变化到人权等议题对投资决策越来越重要。因此，希望纳入这些问题的投资者（资产管理公司和资产所有者）开始根据公司如何处理这些问题来评估公司。对于这一评估，投资者依赖于专门的评级机构，这些机构根据环境、社会和治理（ESG）维度发布评级。此类评级使他们能够做出有利于可持续发展的投资决策。然而，评级机构的分析基于对可持续性报告的主观评估，而不是每家公司都提供。此外，由于涉及人力，评级机构目前面临着及时扩大覆盖范围的挑战。为了缓解这些挑战，并有助于支持可持续发展的总体目标，我们提出了一个异构集成模型来预测ESG评级使用的基本数据。该模型是基于前馈神经网络，CatBoost和XGBoost集成成员。鉴于基本数据的公开可用性，拟议的方法将允许具有成本效益和可扩展性的初始ESG评级（也适用于没有可持续性报告的公司）。使用我们的方法，我们能够解释54%的变化，在评级R2使用基本数据和优于以前的工作在这方面。
英文摘要：
Over the past years, topics ranging from climate change to human rights have seen increasing importance for investment decisions. Hence, investors (asset managers and asset owners) who wanted to incorporate these issues started to assess companies based on how they handle such topics. For this assessment, investors rely on specialized rating agencies that issue ratings along the environmental, social and governance (ESG) dimensions. Such ratings allow them to make investment decisions in favor of sustainability. However, rating agencies base their analysis on subjective assessment of sustainability reports, not provided by every company. Furthermore, due to human labor involved, rating agencies are currently facing the challenge to scale up the coverage in a timely manner. In order to alleviate these challenges and contribute to the overall goal of supporting sustainability, we propose a heterogeneous ensemble model to predict ESG ratings using fundamental data. This model is based on feedforward neural network, CatBoost and XGBoost ensemble members. Given the public availability of fundamental data, the proposed method would allow cost-efficient and scalable creation of initial ESG ratings (also for companies without sustainability reporting). Using our approach we are able to explain 54% of the variation in ratings R2 using fundamental data and outperform prior work in this area.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
