CORRECT? CORECT!: Classification of ESG Ratings with Earnings Call Transcript

• Haein Lee,
• Hae Sun Jung,
• Heungju Park,
• Jang Hyun Kim,

Vol. 18, No. 4, April 30, 2024
10.3837/tiis.2024.04.015, Download Paper (Free):

• BERT
• Earnings Call Transcript
• ESG
• Machine Learning
• Natural language processing (NLP)

Abstract

While the incorporating ESG indicator is recognized as crucial for sustainability and increased firm value, inconsistent disclosure of ESG data and vague assessment standards have been key challenges. To address these issues, this study proposes an ambiguous text-based automated ESG rating strategy. Earnings Call Transcript data were classified as E, S, or G using the Refinitiv-Sustainable Leadership Monitor's over 450 metrics. The study employed advanced natural language processing techniques such as BERT, RoBERTa, ALBERT, FinBERT, and ELECTRA models to precisely classify ESG documents. In addition, the authors computed the average predicted probabilities for each label, providing a means to identify the relative significance of different ESG factors. The results of experiments demonstrated the capability of the proposed methodology in enhancing ESG assessment criteria established by various rating agencies and highlighted that companies primarily focus on governance factors. In other words, companies were making efforts to strengthen their governance framework. In conclusion, this framework enables sustainable and responsible business by providing insight into the ESG information contained in Earnings Call Transcript data.

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