Srishti Mehra, Robert Louka, Yixun Zhang, University of California Berkeley, USA

Environmental, Social, and Governance (ESG) are non-financial factors that are garnering attention from investors as they increasingly look to apply these as part of their analysis to identify material risks and growth opportunities. Some of this attention is also driven by clients who, now more aware than ever, are demanding for their money to be managed and invested responsibly. As the interest in ESG grows, so does the need for investors to have access to consumable ESG information. Since most of it is in text form in reports, disclosures, press releases, and 10-Q filings, we see a need for sophisticated natural language processing (NLP) techniques for classification tasks for ESG text. We hypothesize that an ESG domain specific pre-trained model will help with such and study building of the same in this paper. We explored doing this by fine-tuning BERT’s pre-trained weights using ESG specific text and then further fine-tuning the model for a classification task. We were able to achieve accuracy better than the original BERT and baseline models in environment-specific classification tasks.

ESG, NLP, BERT, Universal Sentence Encoder, Deep Averaging Network.