# Heterogeneous Ensemble for ESG Ratings Prediction

arXiv: https://arxiv.org/abs/2109.10085 · HTML: https://mlsanity.com/paper/2109.10085/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 62 references that are in the map.
- First posted on arXiv 21 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Prediction of ESG Compliance using a Heterogeneous Information Network](https://mlsanity.com/paper/1811.12166/) (2018)
- [Empirical Study on Detecting Controversy in Social Media](https://mlsanity.com/paper/1909.01093/) (2019)
- [ESG investments: Filtering versus machine learning approaches](https://mlsanity.com/paper/2002.07477/) (2020)

## Built on by (most cited first, 2 of 2)

- [Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis](https://mlsanity.com/paper/2212.11765/) (2022)
- [Creating a Systematic ESG (Environmental Social Governance) Scoring System Using Social Network Analysis and Machine Learning for More Sustainable Company Practices](https://mlsanity.com/paper/2309.05607/) (2023)

## Abstract (arXiv)

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.