Prediction of ESG compliance using a heterogeneous information network

• Published in: Journal of big data Vol. 7; no. 1; pp. 1 - 19
• Main Authors: Hisano, Ryohei, Sornette, Didier, Mizuno, Takayuki
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 16.03.2020; Springer Nature B.V; SpringerOpen
• Subjects: Big Data; Communications Engineering; Compliance; Computational investigation; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Database Management; Datasets; Finance; Financing; Heterogeneous information network; Information Storage and Retrieval; International organizations; Investment; Investment exclusion list; Label propagation; Lists; Mathematical Applications in Computer Science; Networks; News; News prediction; Risk management; News prediction; Heterogeneous information network; Label propagation; Investment exclusion list; Finance; Computational investigation
• ISSN: 2196-1115; 2196-1115
• DOI: 10.1186/s40537-020-00295-9

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 governance (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.