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We perform an empirical evaluation of fourteen multinomial classifiers in the prediction of credit ratings on a large dataset consisting of macroeconomic, firm-level financial, and environmental, social, and governance (ESG) variables. Random forests and extremely randomized trees exhibit the highest predictive power for US and global firms. We show that environmental and social responsibility variables are important determinants for the credit ratings, specifically measures of environmental innovation, resource use, emissions, corporate social responsibility, and workforce determinants. The influence of ESG variables become more pronounced following the financial crisis of 2007-2009, and are important across both investment-grade and speculative-grade classes.
Michalski et al. (Wed,) studied this question.