This study aims to examine the impact of environmental, social and governance (ESG) disclosure on predicting financial distress among Indian non-financial firms, using 2,478 firm-year observations from 2015 to 2022.
The study adopts a comparative modeling framework to evaluate the predictive accuracy of logistic regression (LR) and artificial neural networks (ANN). Robustness checks are conducted to ensure the validity of results, and a sensitivity analysis is performed to identify the most influential predictors.
The findings reveal that ESG disclosure significantly influences financial distress prediction, alongside other key firm-specific variables. While ANN demonstratehigher predictive accuracy, LR is preferred for its balanced performance in terms of distress detection, precision, recall, and discrimination power. Furthermore, this study contributes to the sustainable development goals (SDGs) by highlighting the value relevance of ESG disclosure transparency in promoting financial resilience.
The results emphasize the importance of integrating ESG disclosure into financial models to manage financial distress risks effectively, aligning business strategies with SDGs.
This work uniquely links ESG disclosure to achieving specific SDGs, underscoring the broader societal relevance of these disclosures. It enriches the literature on financial distress prediction while providing practical insights for policymakers and investors.
