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.

通过使用前馈神经网络、CatBoost 和 XGBoost 集成成员的异构集成模型来预测 ESG 评级，基于基本数据既能够降低成本、提高可扩展性，也能够超越此领域的既往成果，解决可持续性相关问题。