Skip to main content
Log in

Modelling banks’ credit ratings of international agencies

  • Original Paper
  • Published:
Eurasian Economic Review Aims and scope Submit manuscript

Abstract

The aim of this paper is to construct a reliable banks’ rating model for the main international agencies based on public information for the potential practical use. The Bankscope database for the period from 1996 to 2011 was used in the research. The ordered probit models show that inclusion of macroeconomic variables as well as the regional dummies improve their explanatory power. Moreover, the significance of the time dummies allowed us to conclude that rating agencies do change their grade when an economy operates on the different business cycle stages. Furthermore, the conclusions of a conservative nature of Standard & Poor’s ratings and overvalued Moody’s grades compared to the rating agency Fitch were performed. The models were checked for the in-sample and out-of-sample fit including distributional comparisons across agencies. The obtained model was classified as practically useful, as it gave 31 % of precise results and up to 70 % forecasts with an error within one rating grade. Moreover, 62 % of rating classes of banks were predicted without an error and more than 95 % of rating classes’ forecasts had an error within one rating class.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5 and 6

Similar content being viewed by others

References

  • Afonso, A. (2002). Understanding the determinants of government debt ratings: Evidence for the two leading agencies. Department of Economics at the School of Economics and Management, Technical University of Lisbon, Working Papers, 2002/02.

  • Altman, E., & Rijken, H. A. (2004). How rating agencies achieve rating stability. Journal of Banking & Finance, 28(11), 2679–2714.

    Article  Google Scholar 

  • Altman, E. I., & Saunders, A. (1998). Credit risk measurement: Developments over the last 20 years. Journal of Banking & Finance, 21(11–12), 1721–1742.

    Google Scholar 

  • Amato, J. D., & Furfine, C. H. (2004). Are credit ratings procyclical? Journal of Banking & Finance, 28(11), 2641–2677.

    Article  Google Scholar 

  • Ayvazian, S. A., Golovan, S. V., Karminsky, A. M., & Peresetsky, A. A. (2011). About the approaches to a comparison of rating scales. Prikladnaia Ekonometrika (Applied Econometrics), 3(23), 13–40. (in Russian).

    Google Scholar 

  • Banking Account and Ratio Definitions. (2011). Moody’s Investors Service, February 2011.

  • Bellotti, T., Matousek, R., & Stewart, C. (2011). Are rating agencies’ assignments opaque? Evidence from international banks. Expert Systems with Applications, 38, 4206–4214.

    Article  Google Scholar 

  • Berger, A. N., Davies, S. M., & Flannery, M. J. (2000). Comparing market and supervisory assessments of bank performance: who knows what when? Journal of Money, Credit and Bank, 32, 641–667.

    Article  Google Scholar 

  • Cao, L., Guan, L. K., & Jingqing, Z. (2006). Bond rating using support vector machine. Intelligent Data Analysis, 10, 285–296.

    Google Scholar 

  • Caporale, G. M., Matousek, R., & Stewart, C. (2012). Ratings assignments: Lessons from international banks. Journal of International Money and Finance, 31, 1593–1606.

    Article  Google Scholar 

  • Distinguin, I., Hasan, I., & Tarazi, A. (2013). Predicting rating changes for banks: how accurate are accounting and stock market indicators? Annals of Finance, 9(3), 471–500.

    Article  Google Scholar 

  • Duff, A., & Einig, S. (2009). Credit ratings quality: The perceptions of market participants and other interested parties. The British Accounting Review, 41, 141–153.

    Article  Google Scholar 

  • Gropp, R., Vesala, J., & Vulpes, G. (2006). Equity and bond market signals as leading indicators of bank fragility. Journal of Money, Credit and Banking, 38, 399–428.

    Article  Google Scholar 

  • Grunert, J., Norden, L., & Weber, M. (2005). The role of non-financial factors in internal credit ratings. Journal of Banking & Finance, 29, 509–531.

    Article  Google Scholar 

  • Hájek, P., & Olej, V. (2011). Credit rating modelling by kernel-based approaches with supervised and semi-supervised learning. Neural Computing and Applications, 20(6), 761–773.

    Article  Google Scholar 

  • Karminsky, A. M., & Kostrov, A. (2014). The probability of default in Russian banking. Eurasian Economic Review, 4(1), 81–98.

    Article  Google Scholar 

  • Karminsky, A. M., & Peresetsky, A. A. (2007). Models of ratings of international rating agencies. Prikladnaia Ekonometrika (Applied Econometrics), 1, 3–19. (in Russian).

    Google Scholar 

  • Karminsky, A. M., & Sosyurko, V. V. (2010). Features of modeling the international ratings of banks. Upravlenie finansovimi riskami (Financial risk management), 4, 292–305. (in Russian).

    Google Scholar 

  • Karminsky, A. M., Hainsworth, R., & Solodkov, V. M. (2013). Arm’s length method for comparing rating scales. Eurasian Economic Review, 3(2), 114–135.

    Article  Google Scholar 

  • Kostrov, A., Karminsky, A.M. (2014) Comparison of bank financial stability factors in CIS Counties. Procedia Computer Science, 31, International Conference on Computational Science (pp.766–772).

  • Kumar, K., & Bhattacharya, S. (2006). Artificial neural network vs linear discriminant analysis in credit ratings forecast: a comparative study of prediction performances. Review of Accounting and Finance, 5, 216–227.

    Article  Google Scholar 

  • Lazarides, T., & Drimpetas, E. (2016). Defining the factors of Fitch rankings in the European banking sector. Eurasian Economic Review, 6(2), 315–339.

    Article  Google Scholar 

  • Lee, Y. C. (2007). Application of support vector machines to corporate credit rating prediction. Expert Systems with Applications, 33, 67–74.

    Article  Google Scholar 

  • Morgan, D. P. (2002). Rating banks: Risk and uncertainty in an opaque industry. The American Economic Review, 92(4), 874–888.

    Article  Google Scholar 

  • Öğüt, H., Doğanay, M. M., Ceylan, N. B., & Aktaş, R. (2012). Prediction of bank financial strength ratings: The case of Turkey. Economic Modelling, 29, 632–640.

    Article  Google Scholar 

  • Peresetsky, A.A., Karminsky, A.M. (2008). Models for Moody’s Bank Ratings. Bank of Finland, BOFIT Discussion Papers, 17.

  • Teker, D., Pala, A., & Kent, O. (2013). Determination of sovereign rating: Factor based ordered probit models for panel data analysis modelling framework. International Journal of Economics and Financial Issues, 3(1), 122–132.

    Google Scholar 

  • Topaloglou, N. (2015). Minimizing bank liquidity risk: evidence from the Lehman crisis. Eurasian Business Review, 5, 23–44.

    Article  Google Scholar 

  • Vasilyuk, A. A., & Karminsky, A. M. (2011). Modeling credit ratings of domestic banks based on Russian accounting standards. Upravlenie finansovimi riskami (Financial risk management), 3, 194–205. (in Russian).

    Google Scholar 

  • Zan, H., Chen, H., Hsu, C.-J., Chen, W.-H., & Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: A market comparative study. Decision Support Systems, 37, 543–558.

    Article  Google Scholar 

Download references

Acknowledgment

Authors are grateful to acknowledge the help with data collection from Alexander Kostrov as well as the programming data correction from Amal Imangulov and Mikhail Rodichkin.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ella Khromova.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karminsky, A.M., Khromova, E. Modelling banks’ credit ratings of international agencies. Eurasian Econ Rev 6, 341–363 (2016). https://doi.org/10.1007/s40822-016-0058-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40822-016-0058-5

Keywords

JEL classification

Navigation