IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2011.14112.html
   My bibliography  Save this paper

Reconstruction Rating Model of Sovereign Debt by Logical Analysis of Data

Author

Listed:
  • Elnaz Gholipour

    (Eastern Mediterranean University)

  • B'ela Vizv'ari

    (Eastern Mediterranean University)

  • Zolt'an Lakner

    (St. Stephen University)

Abstract

Sovereign debt ratings provided by rating agencies measure the solvency of a country, as gauged by a lender or an investor. It is an indication of the risk involved in investment, and should be determined correctly and in a well timed manner. The present study reconstructs sovereign debt ratings through logical analysis of data, which is based on the theory of Boolean functions. It organizes groups of countries according to twenty World Bank defined variables for the period 2012 till 2015. The Fitch Rating Agency, one of the three big global rating agencies, is used as a case study. An approximate algorithm was crucial in exploring the rating method, in correcting the agencys errors, and in determining the estimated rating of otherwise non rated countries. The outcome was a decision tree for each year. Each country was assigned a rating. On average, the algorithm reached almost ninety eight percentage matched ratings in the training set, and was verified by eighty four percentage in the test set. This was a considerable achievement.

Suggested Citation

  • Elnaz Gholipour & B'ela Vizv'ari & Zolt'an Lakner, 2020. "Reconstruction Rating Model of Sovereign Debt by Logical Analysis of Data," Papers 2011.14112, arXiv.org.
    • Handle: RePEc:arx:papers:2011.14112
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2011.14112
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Luitel, Prabesh & Vanp�e, Rosanne, 2018. "How do sovereign credit ratings help to financially develop low-developed countries?," ECMI Papers 13956, Centre for European Policy Studies.
    2. International Monetary Fund, 1998. "The Relative Importance of Political and Economic Variables in Creditworthiness Ratings," IMF Working Papers 1998/046, International Monetary Fund.
    3. 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.
    4. Peter Hammer & Tibérius Bonates, 2006. "Logical analysis of data—An overview: From combinatorial optimization to medical applications," Annals of Operations Research, Springer, vol. 148(1), pages 203-225, November.
    5. Otaviano Canuto & Pablo F. Pereira Dos Santos & Paulo C. De Sá Porto, 2012. "Macroeconomics And Sovereign Risk Ratings," Journal of International Commerce, Economics and Policy (JICEP), World Scientific Publishing Co. Pte. Ltd., vol. 3(02), pages 1-25.
    6. 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.
    7. Bernal, Oscar & Girard, Alexandre & Gnabo, Jean-Yves, 2016. "The importance of conflicts of interest in attributing sovereign credit ratings," International Review of Law and Economics, Elsevier, vol. 47(C), pages 48-66.
    8. Spilioti, Stella & Vamvoukas, George, 2015. "The impact of government debt on economic growth: An empirical investigation of the Greek market," The Journal of Economic Asymmetries, Elsevier, vol. 12(1), pages 34-40.
    9. Mattarocci, Gianluca, 2013. "The Independence of Credit Rating Agencies," Elsevier Monographs, Elsevier, edition 1, number 9780124045699.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zoran Ivanovic & Sinisa Bogdan & Suzana Baresa, 2015. "Modeling and Estimating Shadow Sovereign Ratings," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 9(3), September.
    2. Teixeira, João C.A. & Silva, Francisco J.F. & Ferreira, Manuel B.S. & Vieira, José A.C., 2018. "Sovereign credit rating determinants under financial crises," Global Finance Journal, Elsevier, vol. 36(C), pages 1-13.
    3. 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.
    4. Andreas Fuchs & Kai Gehring, 2017. "The Home Bias in Sovereign Ratings," Journal of the European Economic Association, European Economic Association, vol. 15(6), pages 1386-1423.
    5. Hammer, P.L. & Kogan, A. & Lejeune, M.A., 2006. "Modeling country risk ratings using partial orders," European Journal of Operational Research, Elsevier, vol. 175(2), pages 836-859, December.
    6. Rosemarie Bröker Bone, 2005. "A Importância Dos Fundamentos Nos Ratings Soberanos Brasileiros, 1994-2002," Anais do XXXIII Encontro Nacional de Economia [Proceedings of the 33rd Brazilian Economics Meeting] 037, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    7. Zamira Oskonbaeva, 2020. "Determinants of credit ratings: evidence from panel discrete model," Economics and Business Letters, Oviedo University Press, vol. 9(3), pages 240-247.
    8. P. Hammer & A. Kogan & M. Lejeune, 2011. "Reverse-engineering country risk ratings: a combinatorial non-recursive model," Annals of Operations Research, Springer, vol. 188(1), pages 185-213, August.
    9. El-Shagi, Makram & von Schweinitz, Gregor, 2017. "Why they keep missing: An empirical investigation of rational inattention of rating agencies," IWH Discussion Papers 1/2017, Halle Institute for Economic Research (IWH), revised 2017.
    10. Choy, Swee Yew & Chit, Myint Moe & Teo, Wing Leong, 2021. "Sovereign credit ratings: Discovering unorthodox factors and variables," Global Finance Journal, Elsevier, vol. 48(C).
    11. Bruno Ferreira Frascaroli & Jailson Da Conceição Teixeira De Oliveira, 2014. "A Performance Das Agências De Ratings De Risco Soberano E Os Fundamentos Macroeconômicos Dos Países: Um Estudo Sobre As Atribuições De Risco," Anais do XLI Encontro Nacional de Economia [Proceedings of the 41st Brazilian Economics Meeting] 121, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    12. 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.
    13. Luitel, Prabesh & Vanpée, Rosanne & De Moor, Lieven, 2016. "Pernicious effects: How the credit rating agencies disadvantage emerging markets," Research in International Business and Finance, Elsevier, vol. 38(C), pages 286-298.
    14. Bernal, Oscar & Girard, Alexandre & Gnabo, Jean-Yves, 2016. "The importance of conflicts of interest in attributing sovereign credit ratings," International Review of Law and Economics, Elsevier, vol. 47(C), pages 48-66.
    15. Stefanos Ioannou, 2017. "Credit Rating Downgrades and Sudden Stops of Capital Flows in the Eurozone," Journal of International Commerce, Economics and Policy (JICEP), World Scientific Publishing Co. Pte. Ltd., vol. 8(03), pages 1-36, October.
    16. Bruno Ferreira Frascaroli & Luciano da Costa Silva & Osvaldo Cândido da Silva Filho, 2009. "Ratings of Sovereign Risk and the Macroeconomics Fundamentals of the countries: a Study Using Artificial Neural Networks," Brazilian Review of Finance, Brazilian Society of Finance, vol. 7(1), pages 73-106.
    17. 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.
    18. Tim de Vries & Jakob de Haan, 2016. "Credit ratings and bond spreads of the GIIPS," Applied Economics Letters, Taylor & Francis Journals, vol. 23(2), pages 107-111, February.
    19. Nima Mirzaei & Béla Vizvári, 2015. "A New Approach to Reconstruction of Moody’s Rating System for Countries Investment Risk Rating," Journal of Empirical Economics, Research Academy of Social Sciences, vol. 4(3), pages 167-182.
    20. Marc Flandreau & Juan H. Flores & Norbert Gaillard & Sebastián Nieto-Parra, 2010. "The End of Gatekeeping: Underwriters and the Quality of Sovereign Bond Markets, 1815–2007," NBER Chapters, in: NBER International Seminar on Macroeconomics 2009, pages 53-92, National Bureau of Economic Research, Inc.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2011.14112. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.