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Modelling Sovereign Credit Ratings: Evaluating the Accuracy and Driving Factors using Machine Learning Techniques

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  • Bart H. L. Overes

    (Erasmus Universiteit Rotterdam)

  • Michel Wel

    (Erasmus Universiteit Rotterdam)

Abstract

Sovereign credit ratings summarize the creditworthiness of countries. These ratings have a large influence on the economy and the yields at which governments can issue new debt. This paper investigates the use of a multilayer perceptron (MLP), classification and regression trees (CART), support vector machines (SVM), Naïve Bayes (NB), and an ordered logit (OL) model for the prediction of sovereign credit ratings. We show that MLP is best suited for predicting sovereign credit ratings, with a random cross-validated accuracy of 68%, followed by CART (59%), SVM (41%), NB (38%), and OL (33%). Investigation of the determining factors shows that there is some heterogeneity in the important variables across the models. However, the two models with the highest out-of-sample predictive accuracy, MLP and CART, show a lot of similarities in the influential variables, with regulatory quality, and GDP per capita as common important variables. Consistent with economic theory, a higher regulatory quality and/or GDP per capita are associated with a higher credit rating.

Suggested Citation

  • Bart H. L. Overes & Michel Wel, 2023. "Modelling Sovereign Credit Ratings: Evaluating the Accuracy and Driving Factors using Machine Learning Techniques," Computational Economics, Springer;Society for Computational Economics, vol. 61(3), pages 1273-1303, March.
    • Handle: RePEc:kap:compec:v:61:y:2023:i:3:d:10.1007_s10614-022-10245-7
    DOI: 10.1007/s10614-022-10245-7
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    References listed on IDEAS

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    1. 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.
    2. B Baesens & T Van Gestel & S Viaene & M Stepanova & J Suykens & J Vanthienen, 2003. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 627-635, June.
    3. Alexander W. Butler & Larry Fauver, 2006. "Institutional Environment and Sovereign Credit Ratings," Financial Management, Financial Management Association International, vol. 35(3), pages 53-79, September.
    4. 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.
    5. Bissoondoyal-Bheenick, Emawtee, 2005. "An analysis of the determinants of sovereign ratings," Global Finance Journal, Elsevier, vol. 15(3), pages 251-280, February.
    6. Stefanos Dimitrakopoulos & Michalis Kolossiatis, 2016. "State Dependence and Stickiness of Sovereign Credit Ratings: Evidence from a Panel of Countries," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 1065-1082, September.
    7. Reusens, Peter & Croux, Christophe, 2017. "Sovereign credit rating determinants: A comparison before and after the European debt crisis," Journal of Banking & Finance, Elsevier, vol. 77(C), pages 108-121.
    8. Huseyin Ozturk*, 2014. "The origin of bias in sovereign credit ratings: reconciling agency views with institutional quality," Journal of Developing Areas, Tennessee State University, College of Business, vol. 48(4), pages 161-188, October-D.
    9. Vanja Bozic & Cosimo Magazzino, 2013. "Credit Rating Agencies: The Importance of Fundamentals in the Assessment of Sovereign Ratings," Economic Analysis and Policy, Elsevier, vol. 43(2), pages 157-176, September.
    10. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    11. Antonio Afonso, 2003. "Understanding the determinants of sovereign debt ratings: Evidence for the two leading agencies," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 27(1), pages 56-74, March.
    12. G. Ferri & L.-G. Liu & J. E. Stiglitz, 1999. "The Procyclical Role of Rating Agencies: Evidence from the East Asian Crisis," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 28(3), pages 335-355, November.
    13. Richard Cantor & Frank Packer, 1996. "Determinants and impact of sovereign credit ratings," Economic Policy Review, Federal Reserve Bank of New York, vol. 2(Oct), pages 37-53.
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    Cited by:

    1. Slapnik, Ursula & Lončarski, Igor, 2023. "Understanding sovereign credit ratings: Text-based evidence from the credit rating reports," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).

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