Mapping ESG Trends by Distant Supervision of Neural Language Models

Abstract

The integration of Environmental, Social and Governance (ESG) considerations into business decisions and investment strategies have accelerated over the past few years. It is important to quantify the extent to which ESG-related conversations are carried out by companies so that their impact on business operations can be objectively assessed. However, profiling ESG language is challenging due to its multi-faceted nature and the lack of supervised datasets. This research study aims to detect historical trends in ESG discussions by analyzing the transcripts of corporate earning calls. The proposed solution exploits recent advances in neural language modeling to understand the linguistic structure in ESG discourse. In detail, firstly we develop a classification model that categorizes the relevance of a text sentence to ESG. A pre-trained language model is fine-tuned on a small corporate sustainability reports dataset for this purpose. The semantic knowledge encoded in this classification model is then leveraged by applying it to the sentences in the conference transcripts using a novel distant-supervision approach. Extensive empirical evaluations against various pretraining techniques demonstrate the efficacy of the proposed transfer learning framework. Our analysis indicates that in the last 5 years, nearly 15% of the discussions during earnings calls pertained to ESG, implying that ESG factors are integral to business strategy.