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Reducing Overreliance on Sovereign Credit Ratings: Which Model Serves Better?

Author

Listed:
  • Huseyin Ozturk

    (Central Bank of the Republic of Turkey, Anafartalar Mah.)

  • Ersin Namli

    (İstanbul University)

  • Halil Ibrahim Erdal

    (Turkish Cooperation and Coordination Agency (TIKA))

Abstract

Sovereign credit ratings have been a controversial issue since the outbreak of the 2008 financial crisis. Among the debates the inaccuracies stay at the centre. By employing classification and regression trees, multilayer perceptron, support vector machines (SVM), Bayes net, and naïve Bayes; we compare the ability of various learning techniques with the conventional statistical method in predicting sovereign credit ratings. Experimental results suggest that all the techniques excluding SVM have over 90 % accurate prediction. According to within one and two notch accurate prediction measure, the prediction performance of SVM also increases above 90 %. These findings indicate a clear outperformance of AI methods over the conventional statistical method. The results have many implications for the practitioners in credit scoring industry. Amidst the regulatory measures that encourage individual credit scoring for international financial institutions, these findings suggest that up-to-date AI methods serve quite reliable technical tools to predict sovereign credit ratings.

Suggested Citation

  • Huseyin Ozturk & Ersin Namli & Halil Ibrahim Erdal, 2016. "Reducing Overreliance on Sovereign Credit Ratings: Which Model Serves Better?," Computational Economics, Springer;Society for Computational Economics, vol. 48(1), pages 59-81, June.
    • Handle: RePEc:kap:compec:v:48:y:2016:i:1:d:10.1007_s10614-015-9534-3
    DOI: 10.1007/s10614-015-9534-3
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    2. Guneren Genc, Elif & Deniz Basar, Ozlem, 2019. "Comparison of Country Ratings of Credit Rating Agencies with MOORA Method," Business and Economics Research Journal, Uludag University, Faculty of Economics and Administrative Sciences, vol. 10(2), pages 391-404, April.
    3. Esma Nur Cinicioglu & Gül Huyugüzel Kışla & A. Özlem Önder & Y. Gülnur Muradoğlu, 2024. "The Changing Behavior of the European Credit Default Swap Spreads During the Covid-19 Pandemic: A Bayesian Network Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 63(3), pages 1213-1254, March.
    4. Aykut Ekinci & Halil İbrahim Erdal, 2017. "Forecasting Bank Failure: Base Learners, Ensembles and Hybrid Ensembles," Computational Economics, Springer;Society for Computational Economics, vol. 49(4), pages 677-686, April.
    5. Bart H. L. Overes & Michel van der Wel, 2021. "Modelling Sovereign Credit Ratings: Evaluating the Accuracy and Driving Factors using Machine Learning Techniques," Papers 2101.12684, arXiv.org, revised Jul 2021.
    6. Duygun, Meryem & Ozturk, Huseyin & Shaban, Mohamed, 2016. "The role of sovereign credit ratings in fiscal discipline," Emerging Markets Review, Elsevier, vol. 27(C), pages 197-216.

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