TY - JOUR
T1 - Determinants of corporate credit ratings Does ESG matter?
AU - Michalski, Lachlan
AU - Low, Rand Kwong Yew
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/7
Y1 - 2024/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85189304155&partnerID=8YFLogxK
U2 - 10.1016/j.irfa.2024.103228
DO - 10.1016/j.irfa.2024.103228
M3 - Article
AN - SCOPUS:85189304155
SN - 1057-5219
VL - 94
SP - 1
EP - 23
JO - International Review of Financial Analysis
JF - International Review of Financial Analysis
M1 - 103228
ER -