Comparative Analysis of the Predictive Power of Machine Learning Models for Forecasting the Credit Ratings of Machine-Building Companies

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

Abstract

The purpose of this study is to compare the predictive power of different machine learning models to reproduce the credit ratings of Moody's 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 sample of data 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 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 enterprises of the machine-building industry from 18 countries for the period from 2005 to 2016. The financial indicators of companies that correspond to the industry methodology of Moody's and the macroeconomic indicators of the home countries of the companies are used as explanatory variables. The results show that among models studied the artificial intelligence models have the greatest predictive ability. The random forest model showed a prediction accuracy of 50%, the gradient boosting model showed accuracy of 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: randomly and taking into account the time factor. The result showed that the use of random sampling increases the predictive power of the models. The inclusion 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 the stability of ratings. 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 practitioners in banking industry who directly use such models to assess the creditworthiness of machine-building companies.

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References

Liao Y., Loures, E., Deschamps, F., Ramos, L.F. Past, Present and Future of Industry 4.0 – a Systematic Literature Review and Research Agenda Proposal. International Journal of Production Research. 2017; 55 (12) DOI: https://doi.org/10.1080/00207543.2017.1308576

Karminsky A.M., Peresetsky A.A. Rejtingi kak mera finansovyh riskov. Evolyuciya, naznachenie, primenenie. Zhurnal Novoj ekonomicheskoj associacii. 2009; 1-2

Karminsky A.M., Polozov A.A. Enciklopediya rejtingov: ekonomika, obshchestvo, sport. Forum; 2016.

Langohr H., Langohr P. The Rating Agencies and their Credit Ratings: What They Are, How They Work and Why They Are Relevant, John Wiley & Sons, Inc., Hoboken, New Jersey; 2008.

Karminsky, A.M. Peresetsky A.A. Modeli rejtingov mezhdunarodnyh agentstv. Prikladnaya ekonometrika. 2007; 1(5)

Karminsky, A. M. Kreditnye rejtingi i ih modelirovanie. Izd. dom Vysshej shkoly ekonomiki; 2015

Beaver, W. Financial Ratios as Predictors of Failure. Journal of Accounting Research. 1966; 4 DOI: https://doi.org/10.2307/2490171

Altman E.I. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance. 1968; 23 DOI: https://doi.org/10.2307/2978933

Martin, D. Early Warning of Bank Failure: A Logit Regression Approach. Journal of Banking and Finance. 1977; 1, 249-276 DOI: https://doi.org/10.1016/0378-4266(77)90022-X

Ohlson, J. A. Financial Ratios and Probabilistic Prediction of Bankruptcy. Journal of Accounting Research. 1980; 18, 109-131 DOI: https://doi.org/10.2307/2490395

Ederington, L. Classification models and bond ratings. The financial review. 1985; 20 DOI: https://doi.org/10.1111/j.1540-6288.1985.tb00306.x

Blume, M., Lim F., MacKinlay A. C. Thee declining quality of US corporate debt: Myth or reality? Journal of Finance. 1998; 53 DOI: https://doi.org/10.1111/0022-1082.00057

Amato, J., Furfine C. Are credit ratings procyclical? Journal of Banking & Finance. 2004; 28 DOI: https://doi.org/10.1016/j.jbankfin.2004.06.005

Ashbaugh-Skaife, H., Collins D., LaFond R. The Effects of Corporate Governance on Firms Credit Ratings. Journal of Accounting and Economics. 2006; 42 DOI: https://doi.org/10.1016/j.jacceco.2006.02.003

Demeshev B. B., Tihonova A. S. Dinamika prognoznoj sily modelej bankrotstva dlya srednih i malyh rossijskih kompanij optovoj i roznichnoj torgovli. Korporativnye finansy. 2014. T. 31. № 3. S. 4-22

Sermpinis G. Tsoukas S., Zhang P. Modelling market implied ratings using LASSO variable selection techniques. Journal of Empirical Finance 2018; 48 DOI: https://doi.org/10.1016/j.jempfin.2018.05.001

Kwon Y., Han I., Lee K. Ordinal pairwise partitioning (OPP) approach to neural networks training in bond rating. Intelligent Systems in Accounting, Finance & Management. 1997; 6: 23–40 DOI: https://doi.org/10.1002/(SICI)1099-1174(199703)6:1<23::AID-ISAF113>3.3.CO;2-W

Bellotti T., Crook J., Support vector machines for credit scoring and discovery of significant features. Expert Systems with Applications. 2009; 36 (2), p. 3302-3308 DOI: https://doi.org/10.1016/j.eswa.2008.01.005

Davis, R. H., Edelman, D. B., & Gammerman, A. J. Machine-learning algorithms for credit-card applications. IMA Journal of Management Mathematics. 1992; 4(1), 43–51 DOI: https://doi.org/10.1093/imaman/4.1.43

Zhou, S. R., & Zhang, D. Y. A nearly neutral model of biodiversity. Ecology. 2008; 89(1), 248–258 DOI: https://doi.org/10.1890/06-1817.1

Frydman, H., Altman, E. I., & Kao, D. L. Introducing recursive partitioning for financial classification: The case of financial distress. The Journal of Finance. 1985; 40(1), 269–291 DOI: https://doi.org/10.1111/j.1540-6261.1985.tb04949.x

Jensen, H. L. Using neural networks for credit scoring. Managerial Finance. 1992; 18(6), 15–26 DOI: https://doi.org/10.1108/eb013696

West, D. Neural network credit scoring models. Computers & Operations Research. 2000; 27(1), 1131–1152 DOI: https://doi.org/10.1016/S0305-0548(99)00149-5

West, D., Dellana, S., & Qian, J. X. Neural network ensemble strategies for financial decision applications. Computers & Operations Research. 2005; 32(10), 2543–2559 DOI: https://doi.org/10.1016/j.cor.2004.03.017

Huang Z., Chen H., Hsu C., Chen W., Wu S. Credit rating analysis with support vector machines and neural networks: a market comparative study. Decision Support Systems. 2004; 37 DOI: https://doi.org/10.1016/S0167-9236(03)00086-1

Kumar, K., Bhattacharya, S. Artificial neural network vs linear discriminant analysis in credit ratings forecast: a comparative study of prediction performances. Review of Accounting and Finance. 2006; 5, 216–227 DOI: https://doi.org/10.1108/14757700610686426

Chopra F., Bhilare P. Application of Ensemble Models in Credit Scoring Models. Business Perspectives and Research. 2018; 6 (4) DOI: https://doi.org/10.1177/2278533718765531

Wang, G., & Ma, J. Study of corporate credit risk prediction based on integrating boosting and random subspace. Expert Systems with Applications. 2011; 38(4), 13871–13878 DOI: https://doi.org/10.1016/j.eswa.2011.04.191

Balios, D., Thomadakis, S., Tsipouri, L. Credit rating model development: An ordered analysis based on accounting data. Research in International Business and Finance. 2016 DOI: https://doi.org/10.1016/j.ribaf.2016.03.011

Bhojraj, S., P. Sengupta. Effect of Corporate Governance on Bond Ratings and Yields: The Role of Institutional Investors and Outside Directors.Journal of Business. 2003; 76(3), 455 – 475 DOI: https://doi.org/10.1086/344114

Karminsky A. M. Metodicheskie voprosy postroeniya konstruktora dinamicheskih rejtingov. Vestnik mashinostroeniya. 2008

Saitoh, F. Predictive modeling of corporate credit ratings using a semi-supervised random forest regression. IEEE International Conference on Industrial Engineering and Engineering Management. 2016; 429-433 DOI: https://doi.org/10.1109/IEEM.2016.7797911

Grilli, L., Rampichini, C. Ordered Logit Model. Encyclopedia of Quality of Life and Well-Being Research. 2014; p. 4510-4513 DOI: https://doi.org/10.1007/978-94-007-0753-5_2023

Internet-resurs www.moodys.com

Biau, G. Analysis of a Random Forests Model. The Journal of Machine Learning Research. 2012; 98888, 1063–1095

Natekin A., Knoll A. Gradient Boosting Machines. Frontiers in Neurorobotics. 2013; 7 (21) DOI: https://doi.org/10.3389/fnbot.2013.00021

Senaviratna, N. A. M. R., Cooray A., T. M. J. Diagnosing Multicollinearity of Logistic Regression Model. Asian Journal of Probability and Statistics. 2019; 5(2), 1-9 DOI: https://doi.org/10.9734/ajpas/2019/v5i230132

Abdi, H. and Williams, L.J. Principal Component Analysis. Wiley Interdisciplinary Reviews: Computational Statistics. 2010; 2, 433-459 DOI: https://doi.org/10.1002/wics.101

Hossin M. A Review on Evaluation Metrics for Data Classification Evaluations. International Journal of Data Mining & Knowledge Management Process. 2015; 5(2):01-11 DOI: https://doi.org/10.5121/ijdkp.2015.5201

Published
2022-03-01
How to Cite
GrishuninS. and EgorovaA. (2022) “Comparative Analysis of the Predictive Power of Machine Learning Models for Forecasting the Credit Ratings of Machine-Building Companies”, Journal of Corporate Finance Research / Корпоративные Финансы | ISSN: 2073-0438, 16(1), pp. 99-112. doi: 10.17323/j.jcfr.2073-0438.16.1.2022.99-112.
Section
New Research