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Modelling sovereign credit ratings: The accuracy of models in a heterogeneous sample

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  • Ozturk, Huseyin
  • Namli, Ersin
  • Erdal, Halil Ibrahim

Abstract

The accuracy of sovereign credit ratings renewed interest toward sovereign credit ratings in the aftermath of the 2008 financial crisis. The controversy over the accuracies encouraged internal credit scoring systems to reduce reliance on sovereign credit ratings. By employing classification and regression trees (CART), multilayer perceptron (MLP), support vector machines (SVM), Bayes Net, and Naïve Bayes; we explore the prediction performance of several artificial intelligence (AI) techniques in predicting sovereign credit ratings in a heterogeneous sample. The results suggest that AI classifiers outperform the conventional statistical technique in terms of accurate prediction. According to within one notch and two notches accurate prediction measure, the prediction performances of the AI classifiers exceed 90% accuracy whereas the performance of the conventional statistical method is around 70%. The results further reveal that the prediction performance of the models declines around the threshold rating that is located between investment grade and speculative grade which is not necessarily the result of inadequacy of the models. Rather, this is potentially due to CRAs' cautious behaviour toward those countries around threshold rating which can be interpreted as the certification price of upgrading to investment grade.

Suggested Citation

  • Ozturk, Huseyin & Namli, Ersin & Erdal, Halil Ibrahim, 2016. "Modelling sovereign credit ratings: The accuracy of models in a heterogeneous sample," Economic Modelling, Elsevier, vol. 54(C), pages 469-478.
  • Handle: RePEc:eee:ecmode:v:54:y:2016:i:c:p:469-478
    DOI: 10.1016/j.econmod.2016.01.012
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    4. Cuadros-Solas, Pedro Jesús & Salvador Muñoz, Carlos, 2022. "Disentangling the sources of sovereign rating adjustments: An examination of changes in rating policies following the GFC," Research in International Business and Finance, Elsevier, vol. 59(C).
    5. Oliver Takawira & John W. Muteba Mwamba, 2020. "Determinants of Sovereign Credit Ratings: An Application of the Naïve Bayes Classifier," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 8(4), pages 279-299.
    6. Patrycja Klusak & Matthew Agarwala & Matt Burke & Moritz Kraemer & Kamiar Mohaddes, 2021. "Rising Temperatures, Falling Ratings: The Effect of Climate Change on Sovereign Creditworthiness," Working Papers EPRG2110, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    7. De Moor, Lieven & Luitel, Prabesh & Sercu, Piet & Vanpée, Rosanne, 2018. "Subjectivity in sovereign credit ratings," Journal of Banking & Finance, Elsevier, vol. 88(C), pages 366-392.
    8. Santiago Carbo-Valverde & Pedro Cuadros-Solas & Francisco Rodríguez-Fernández, 2020. "A machine learning approach to the digitalization of bank customers: Evidence from random and causal forests," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-39, October.
    9. Šergo Zdravko & Gržinić Jasmina, 2018. "Does the International Tourism Industry Relax Sovereign Credit Ratings: The Case of Countries Most Reliant on Tourism," South East European Journal of Economics and Business, Sciendo, vol. 13(2), pages 100-111, December.
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    11. Yalta, A. Talha & Yalta, A. Yasemin, 2018. "Are credit rating agencies regionally biased?," Economic Systems, Elsevier, vol. 42(4), pages 682-694.

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