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Debt management evaluation through Support Vector Machines: on the example of Italy and Greece

Author

Listed:
  • Andrey Zahariev

    (D. A. Tsenov Academy of Economics, Bulgaria)

  • Mikhail Zveryаkov

    (Odessa National University of Economics, Ukraine)

  • Stoyan Prodanov

    (D. A. Tsenov Academy of Economics, Bulgaria)

  • Galina Zaharieva

    (D. A. Tsenov Academy of Economics, Bulgaria)

  • Petko Angelov

    (D. A. Tsenov Academy of Economics, Bulgaria)

  • Silvia Zarkova

    (D. A. Tsenov Academy of Economics, Bulgaria)

  • Mariana Petrova

    (St. Cyril and St. Methodius University of Veliko Turnovo, Bulgaria)

Abstract

The focus of this research paper is on sovereign debt management evaluation. During the first decade of the 21st century, the PIIGS countries in the EU28 were the main generator of risks in in the public finance sector, thus creating a threat for cross-border economic shocks. In 2018, Greece and Italy had the worst debt-to-GDP ratios and were earmarked as a benchmark for countries with sovereign debt problems. Greece is an example of a country with a non-systematic risk for the EU due to its low share of EU28’s GDP of 1.16% (as of 2018) despite its record debt ratio of 176%. However, Italy is not only among the top 4 EU28 economies with a share of its national GDP in that of the EU28 of 11.1%, but also has a record debt ratio of 131%, which is significant for one of the top economies in the EU28 group. In view of the above, the paper is structured into three main sections. Section One presents an analysis of the efficiency of sovereign debt management as a key element of public finance management in the 28 EU Member States. Section Two presents a justification of the use of the Support Vector Machines (SVM) method for econometric analysis of macroeconomic data. Section Three presents groups and empirically tested internal and external indicators that affect the debt ratio of Italy and Greece. The analysis was conducted with quarterly time series of data for the period 2000-2018 using support vector regression (SVR) for sovereign debt testing calculated using software for interactive and functional programming - Python. The test results and their vector distribution in terms of SVR are presented as histograms. The main conclusion is that both for Greece and for Italy, there is a strong correlation between the SVM support vectors obtained through the algorithm, which is also due of the strict selection of indicators whose correlation is reformatted by the model algorithm, limiting its negative significance on the final result.

Suggested Citation

  • Andrey Zahariev & Mikhail Zveryаkov & Stoyan Prodanov & Galina Zaharieva & Petko Angelov & Silvia Zarkova & Mariana Petrova, 2020. "Debt management evaluation through Support Vector Machines: on the example of Italy and Greece," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 7(3), pages 2382-2393, March.
    • Handle: RePEc:ssi:jouesi:v:7:y:2020:i:3:p:2382-2393
    DOI: 10.9770/jesi.2020.7.3(61)
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    References listed on IDEAS

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    1. Randall G. Holcombe & Jeffrey A. Mills, 1995. "Politics and Deficit Finance," Public Finance Review, , vol. 23(4), pages 448-466, October.
    2. Bioch, J.C. & Groenen, P.J.F. & Nalbantov, G.I., 2005. "Solving and interpreting binary classification problems in marketing with SVMs," Econometric Institute Research Papers EI 2005-46, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Ertan Mustafa Geldiev & Nayden Valkov Nenkov & Mariana Mateeva Petrova, 2018. "Exercise Of Machine Learning Using Some Python Tools And Techniques," CBU International Conference Proceedings, ISE Research Institute, vol. 6(0), pages 1062-1070, September.
    4. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
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    Citations

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    Cited by:

    1. Velichka Nikolova, 2021. "Effects Of The Global Economic Crisis And The Covid-19 Pandemic On Sovereign Debt Management In Heavily Indebted Countries," Economic Archive, D. A. Tsenov Academy of Economics, Svishtov, Bulgaria, issue 3 Year 20, pages 31-45.
    2. Mikhail I. Zveryakov & Andrii A. Gritsenko & Viktor N. Tarasevich & Pavel A. Pokrytan & Lyudmila L. Zhdanova & Andrei V. Grimalyuk & Sergii V. Sinyakov, 2021. "On The 100th Anniversary Of The Founder Of The Odessa Scientific School Of Economic Thought A. K. Pokrytan," Economic Archive, D. A. Tsenov Academy of Economics, Svishtov, Bulgaria, issue 1 Year 20, pages 3-14.
    3. Galina Zaharieva & Onnik Tarakchiyan & Andrey Zahariev, 2022. "Market Capitalization Factors Of The Bulgarian Pharmaceutical Sector In Pandemic," Business Management, D. A. Tsenov Academy of Economics, Svishtov, Bulgaria, issue 4 Year 20, pages 35-51.

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    More about this item

    Keywords

    Support Vector Machines (SVM); support vector regression (SVR); public debt to GDP ratio; debt management;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy; Modern Monetary Theory
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • H63 - Public Economics - - National Budget, Deficit, and Debt - - - Debt; Debt Management; Sovereign Debt

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