NLP-Based Quantification of ESG in Sustainability Reports and Firm-Specific Risk: Evidence from Borsa İstanbul
NLP-Based Quantification of ESG in Sustainability Reports and Firm-Specific Risk: Evidence from Borsa İstanbul

Summary/Abstract: This study examines the effect of the sentiment used in ESG (environmental, social, and governance) communication in corporate sustainability reports on firm-specific (idiosyncratic) risks in the Turkish stock market. The analysis focuses on 28 firms listed on the Borsa Istanbul Sustainability Index (XUSRD) between 2014 and 2023, based on a panel of 280 firm-year observations drawn from their publicly available 10-year sustainability reports. First, ESG disclosures in corporate sustainability reports were classified using natural language processing (NLP) techniques and transfer learning. Sentiment analysis was performed for each ESG dimension, and sentiment indices were created based on the analysis results. The data obtained were then analyzed using panel ARDL and panel causality test to test the effect of ESG sentiment on firm risks. The findings reveal that among the ESG dimensions, environmental and governance components play a particularly important role in reducing firm-specific idiosyncratic risk. Also, the results demonstrate the usability of AI-supported analyses in investment strategies and the economic benefit potential of ESG-focused corporate communication. In this context, ESG sentiment is critical not only from a social responsibility perspective but also in terms of risk management and investment decisions.

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