# Prediction of ESG Compliance using a Heterogeneous Information Network

arXiv: https://arxiv.org/abs/1811.12166 · HTML: https://mlsanity.com/paper/1811.12166/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2018.

## Built on by (most cited first, 6 of 6)

- [Heterogeneous Ensemble for ESG Ratings Prediction](https://mlsanity.com/paper/2109.10085/) (2021)
- [Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis](https://mlsanity.com/paper/2212.11765/) (2022)
- [ESG-FTSE: A corpus of news articles with ESG relevance labels and use cases](https://mlsanity.com/paper/2405.20218/) (2024)
- [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)
- [Aligning ESG Controversy Data with International Guidelines through Semi-Automatic Ontology Construction](https://mlsanity.com/paper/2509.10922/) (2025)
- [No Data Is Not No Risk: Visibility Aware Graph-Based Inference of Business Conduct Risk](https://mlsanity.com/paper/2607.26859/) (2026)

## Abstract (arXiv)

Negative screening is one method to avoid interactions with inappropriate entities. For example, financial institutions keep investment exclusion lists of inappropriate firms that have environmental, social, and government (ESG) problems. They create their investment exclusion lists by gathering information from various news sources to keep their portfolios profitable as well as green. International organizations also maintain smart sanctions lists that are used to prohibit trade with entities that are involved in illegal activities. In the present paper, we focus on the prediction of investment exclusion lists in the finance domain. We construct a vast heterogeneous information network that covers the necessary information surrounding each firm, which is assembled using seven professionally curated datasets and two open datasets, which results in approximately 50 million nodes and 400 million edges in total. Exploiting these vast datasets and motivated by how professional investigators and journalists undertake their daily investigations, we propose a model that can learn to predict firms that are more likely to be added to an investment exclusion list in the near future. Our approach is tested using the negative news investment exclusion list data of more than 35,000 firms worldwide from January 2012 to May 2018. Comparing with the state-of-the-art methods with and without using the network, we show that the predictive accuracy is substantially improved when using the vast information stored in the heterogeneous information network. This work suggests new ways to consolidate the diffuse information contained in big data to monitor dominant firms on a global scale for better risk management and more socially responsible investment.