Comparative Analysis of the Predictive Power of Machine Learning Models for Forecasting the Credit Ratings of Machine-Building Companies (S. Grishunin, A. Egorova, сетевое издание "Электронный журнал "Корпоративные финансы" Journal of Corporate Finance Research", N 1, 2022 г.)

Grishunin S., Egorova A. Comparative Analysis of the Predictive Power of Machine Learning Models for Forecasting the Credit Ratings of Machine-Building Companies

S. Grishunin - Candidate of Sciences (PhD), CFA, Senior Lecturer, Graduate School of Industrial Economics, Institute of Industrial Management, Economics and Trade, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia

A. Egorova - Manager, Deloitte and Touche, Moscow, Russia

The purpose of this study is to compare the predictive power of different machine learning models to reproduce Moody's credit ratings assigned to machine-building companies. The study closes several gaps found in the literature related to the choice of explanatory variables and the formation of a data sample for modeling. The task to be solved is highly relevant. There is a growing need for high-precision and low-cost models for reproducing the credit ratings of machine-building companies (internal credit ratings). This is due to the ongoing growth of credit risks of companies in the industry, as well as the limited number of assigned public ratings to these companies from international rating agencies due to the high cost of the rating process. The study compares the predictive power of three machine learning models: ordered logistic regression, random forest, and gradient boosting. The sample of companies includes 109 machine-building enterprises from 18 countries between 2005 and 2016. The financial indicators of companies that correspond to Moody's industry methodology and the macroeconomic indicators of the companies' home countries are used as explanatory variables. The results show that artificial intelligence models have the greatest predictive ability among the models studied. The random forest model demonstrated a prediction accuracy of 50%, the gradient boosting model - 47%. Their predictive power is almost twice as high as the accuracy of ordered logistic regression (25%). In addition, the article tested two different ways of forming a sample: the random method and one that accounts for the time factor. The result showed that the use of random sampling increases the predictive power of the models. The incorporation of macroeconomic variables into the models does not improve their predictive power. The explanation is that rating agencies follow a "through the cycle" rating approach to ensure rating stability. The results of the study may be useful for researchers who are engaged in assessing the accuracy of empirical methods for modeling credit ratings, as well as banking industry practitioners who use such models directly to assess the creditworthiness of machine-building companies.

Keywords: credit ratings, internal credit ratings, machine-building companies, machine learning models, rating agencies