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The comparison of empirical methods for modeling credit ratings of industrial companies from BRICS countries

Author

Listed:
  • Alexander M. Karminsky

    (Higher School of Economics)

  • Sergei Grishunin

    (Peter the Great St. Petersburg Polytechnic University)

  • Natalya Dyachkova

    (Higher School of Economics)

  • Maxim Bisenov

    (Plekhanov Russian University of Economics)

Abstract

We compared the ability of various empirical methods to reproduce public credit ratings (PCRs) of industrial companies (ICs) from BRICS countries using publicly available information. This task is important for researchers and practitioners because many of BRICS’ ICs lack PCRs from reputable rating agencies such as Moody’s, Standard and Poor’s, and Fitch. This paper aimed at filling the gap in the existing research as insufficient efforts were focused on prediction of PCRs of ICs from the entire BRICS IC community. The modeled variables are credit ratings (CRs) of 208 BRICS’ ICs assigned by Moody’s at the year-end from 2006 to 2016. The sample included 1217 observations. Financial explanatory variables included companies’ revenue, operating profitability, interest coverage ratio, debt/book capitalization, and cash flow debt coverage. Non-financial explanatory variables included dummies for home region, industry, affiliation with the state, and a set of macroeconomic data of IC’s home countries. The set of statistical methods included linear discriminant analysis (LDA), ordered logit regression (OLR), support vector machine (SVM), artificial neural network (ANN), and random forest (RF). The resulting models were checked for in-sample and out-of-sample predictive fit. Our findings revealed that among considered methods of artificial intelligence models (AI), SVM, ANN, and RF outperformed LDA and OLR by predictive power. On testing sample, AI gave on average 55% of precise results and up to 99% with an error within one rating grade; RF demonstrated the best outcome (58% and 100%). Conversely, LDA and OLR on average gave only 37% of precise results and up to 70% with an error within one grade. LDA and OLR also gave higher share of Type I errors (overestimation of ratings) than that of AI. Therefore, AI should have higher practical application than DA and OLR for predicting the ratings of BRICS ICs.

Suggested Citation

  • Alexander M. Karminsky & Sergei Grishunin & Natalya Dyachkova & Maxim Bisenov, 2020. "The comparison of empirical methods for modeling credit ratings of industrial companies from BRICS countries," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 10(2), pages 333-348, June.
    • Handle: RePEc:spr:eurase:v:10:y:2020:i:2:d:10.1007_s40822-019-00130-4
    DOI: 10.1007/s40822-019-00130-4
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    References listed on IDEAS

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    1. Kuldeep Kumar & Sukanto Bhattacharya, 2006. "Artificial neural network vs linear discriminant analysis in credit ratings forecast: A comparative study of prediction performances," Review of Accounting and Finance, Emerald Group Publishing, vol. 5(3), pages 216-227, August.
    2. Karminsky, Alexandr & Peresetsky, Anatoly, 2007. "Models of Banks Ratings," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 5(1), pages 3-19.
    3. Демешев Борис Борисович & Тихонова Анна Сергеевна, 2014. "Прогнозирование Банкротства Российских Компаний: Межотраслевое Сравнение," Higher School of Economics Economic Journal Экономический журнал Высшей школы экономики, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», vol. 18(3), pages 359-386.
    4. Alexander Karminsky & Andrey Polozov, 2016. "Handbook of Ratings," Springer Books, Springer, number 978-3-319-39261-5, June.
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    6. Alexander Karminsky & Alexander Kostrov, 2014. "The probability of default in Russian banking," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 4(1), pages 81-98, June.
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