Building Machine Learning Systems for Automated ESG Scoring

Abstract

Although investing in environment, social, and governance (ESG)-driven portfolios is already a large and growing portion of global assets under management, applications of quantitative techniques to improve and standardize ESG scoring and the construction of ESG portfolios are underutilized. In this article, the authors propose an approach to automatically convert unstructured text data into ESG scores by using the latest advances in deep learning for natural language processing (NLP). They also show how a state-of-the-art NLP technique, BERT, can be incorporated to improve the accuracy of assessing relevance and content of documents in an ESG context using social media data as an example and discuss the relevance of this approach to automating ESG scoring and constructing ESG portfolios.

TOPICS: ESG investing, big data/machine learning, portfolio construction

Key Findings

• ▪ The authors demonstrate the feasibility and advantages to applying state-of-the art natural language processing (NLP) to identify environmental, social, governance (ESG) risks using social media data.

• ▪ The authors discuss how advances in modern NLP can be leveraged to continuously build up algorithmic capabilities for processing ESG-relevant documents, by leveraging the capabilities of deep learning models for learning general representations of text data, which can then be applied across many tasks in the ESG domain.

• ▪ The authors discuss results of NLP models can be used for creating aggregated ESG scores, as well design considerations for creating fully or semi-autonomous ESG scoring systems.