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Country Risk Prediction with Machine Learning Techniques

Year 2022, Volume: 6 Issue: 3, 1126 - 1151, 14.09.2022
https://doi.org/10.25295/fsecon.1098493

Abstract

Country risk assessment, in the most general sense, is a measure of the foreign aid a country can receive and the risk the investors will face. Therefore, the related risk has to be measured by making rather sensitive predictions with a procedure where economical, financial and political risks are taken into account. The prediction method must be chosen with great accurateness and definitely supported with different methods. To that end, LRA, KNN, CART and DVM methods, which produce good estimation result and frequently used, are preferred in country risk predictions. Different macroeconomic indicators of 75 countries between the years 2015 and 2019 are used to train the prediction model. According to the findings of the study, it can be said that quite successful prediction results are produced with all the chosen methods. When different assessment criteria are taken into account and each machine learning algorithm are repeated 100 times, it is seen that the KNN algorithm is the best method to produce results. The following methods can be arrayed as DVM, LRA and CART.

References

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Makine Öğrenmesi Teknikleri ile Ülke Riski Tahmini

Year 2022, Volume: 6 Issue: 3, 1126 - 1151, 14.09.2022
https://doi.org/10.25295/fsecon.1098493

Abstract

Ülke riski değerlendirmesi en genel anlamıyla bir ülkenin alabileceği dış yardımların ve yatırımcıların karşı karşıya kalacağı riskin bir ölçüsüdür. Bu sebeple ülke riskinin, ekonomik, finansal ve politik risk unsurlarının birlikte ele alındığı bir prosedürle oldukça hassas tahminler yapılarak ölçülmesi gerekmektedir. Tahmin yöntemi büyük bir titizlikle tercih edilmeli ve mutlaka farklı yöntemler ile desteklenmelidir. Bu amaçla çalışmada, iyi tahmin sonuçları üreten ve sıklıkla kullanılan LRA, KNN, CART ve DVM yöntemleri tercih edilmiştir. Tahmin modelini eğitmek için 2015-2019 yılları arasında 75 ülkenin farklı makroekonomik göstergeleri kullanılmıştır. Çalışmanın bulgularına göre tercih edilen tüm yöntemler ile oldukça başarılı tahmin sonuçlarının üretildiği söylenebilir. Farklı değerlendirme kriterlerinin ele alındığı ve her bir makine öğrenmesi algoritmasının 100 kez tekrar edildiği durumda, en iyi sonucu veren yöntem KNN algoritması olduğu görülmektedir. Takip eden yöntemler ise sırası ile, DVM, LRA ve CART algoritması olarak sıralanabilir.

References

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  • Arezki, R., Candelon, B., & Sy, A. N. R. (2011). Sovereign Rating News and Financial Markets Spillovers: Evidence from The European Debt Crisis. IMF Working Papers, 68.
  • Asiri, B. K., & Hubail, R. A. (2014). An Empirical Analysis of Country Risk Ratings. Journal of Business Studies Quarterly, 5(4), 52.
  • Balkan, E. M. (1992). Political İnstability, Country Risk and Probability of Default. Applied Economics, 24(9), 999-1008.
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  • Bellotti, T., Matousek, R. And Stewart, C., 2011. A Note Comparing Support Vector Machines and Ordered Choice Models' Predictions Of İnternational Banks' Ratings. Decision Support System. 51(3), Pp. 682–687. https://Doi.Org/10.1016/J.Dss.2011.03.008
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  • Berg, J., Clerc, L., Garnier, O., Nielsen, E. F., & Valla, N. (2015). From The İnvestment Plan to The Capital Markets Union: European Financial Structure and Cross Border Risk-Sharing. CEPII, Centr’ D'etudes Prospectives E’ D'informations İnternationales.
  • Brauers, W. K., & Lepkova, N. (2019). Is Credit Rating Reserved Territory for Credit Rating Agencies? A MULTIMOORA Approach for European Firms and Countries. Technological And Economic Development of Economy, 25(6), 1259-1281.
  • Breiman, L., Friedman, J., Olshen, R. And Stone, C. (1984). Classification and Regression Trees. 1984. Wadsworth & Brooks.
  • Brewer, T. L., & Rivoli, P. (1990). Politics And Perceived Country Creditworthiness İn İnternational Banking. Journal Of Money, Credit and Banking, 22(3), 357-369.
  • Broner, F., Martin, A., & Ventura, J. (2010). Sovereign Risk and Secondary Markets. American Economic Review, 100(4), 1523-55.
  • Burges, C. J. C. (1998). A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery: Kluwer Academic Publishers, Boston.
  • Busse, M., & Hefeker, C. (2007). Political Risk, İnstitutions And Foreign Direct İnvestment. European Journal of Political Economy, 23(2), 397-415.
  • Cantor, R., & Packer, F. (1996). Determinants and İmpact of Sovereign Credit Ratings. Economic Policy Review, 2(2).
  • Cantor, R., & Packer, F. (1996). Multiple Ratings and Credit Standards: Differences of Opinion İn The Credit Rating İndustry (No. 12). Nueva York: Federal Reserve Bank Of New York.
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  • Cantor, R., & Packer, F. (1996-B). Determinants and İmpact of Sovereign Credit Ratings. Economic Policy Review, 2(2).
  • Caporale, G. M., Matousek, R., & Stewart, C. (2011). EU Banks Rating Assignments: Is There Heterogeneity Between New And Old Member Countries?. Review Of International Economics, 19(1), 189-206.
  • Chen, S., Härdle, W. K., & Moro, R. A. (2011). Modeling Default Risk with Support Vector Machines. Quantitative Finance, 11(1), 135-154.
  • Cooper, J. C. (1999). Artificial Neural Networks Versus Multivariate Statistics: An Application from Economics. Journal Of Applied Statistics, 26(8), 909-921.
  • Corsetti, G., Kuester, K., Meier, A., & Müller, G. J. (2013). Sovereign Risk, Fiscal Policy, And Macroeconomic Stability. The Economic Journal, 123(566), F99-F132.
  • Cortes, C. And Vapnik, V. (1995). Support Vector Networks. Machine Learning, 20, 1-25.
  • Cosset, J. C., & Roy, J. (1991). The Determinants of Country Risk Ratings. Journal Of International Business Studies, 22(1), 135-142.
  • Cunha, I., Ferreira, M. A., & Silva, R. (2019). Do Credit Rating Agencies Influence Elections?. Available At SSRN 2748458.
  • Dhonte, P. (1974). Quantitative İndicators And Analysis of External Debt Problems. International Monetary Fund Mimeo, Washington, DC.
  • Diamonte, R. L., Liew, J. M., & Stevens, R. L. (1996). Political Risk İn Emerging and Developed Markets. Financial Analysts Journal, 52(3), 71-76.
  • Doluca, H. (2014). Is There a Bias in Sovereign Ratings Due to Financial Reasons? The Empirical Economics Letters, 13 (7), 801 – 814.
  • Doumpos, M., & Zopounidis, C. (2002). On The Use of A Multi‐Criteria Hierarchical Discrimination Approach For Country Risk Assessment. Journal Of Multi‐Criteria Decision Analysis, 11(4‐5), 279-289.
  • Easton, S. T., & Rockerbie, D. W. (1999). What's İn A Default? Lending To LDCs İn The Face of Default Risk. Journal Of Development Economics, 58(2), 319-332.
  • Edwards, S. (1985). The Pricing of Bonds and Bank Loans İn International Markets: An Empirical Analysis Of Developing Countries & Apos; Foreign Borrowing. NBER Working Paper, (W1689).
  • Erdal, H. I., & Karakurt, O. (2013). Advancing Monthly Streamflow Prediction Accuracy of CART Models Using Ensemble Learning Paradigms. Journal Of Hydrology, 477, 119-128.
  • Feder, G., & Just, R. E. (1977). A Study of Debt Servicing Capacity Applying Logit Analysis. Journal Of Development Economics, 4(1), 25-38.
  • Fix, E., & Hodges, J. L. (1989). Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties. International Statistical Review/Revue Internationale De Statistique, 57(3), 238-247.
  • Frank Jr, C. R., & Cline, W. R. (1971). Measurement Of Debt Servicing Capacity: An Application of Discriminant Analysis. Journal Of İnternational Economics, 1(3), 327-344.
  • Frascaroli, B. F., & Oliveira, J. (2017). Sovereign Risk Ratings, Macroeconomic Fundamentals and Accountability: Evidence from Developing Countries. Advances İn Scientific and Applied Accounting, 304-318.
  • Haan, J. D., Siermann, C. L., & Lubek, E. V. (1997). Political İnstability And Country Risk: New Evidence. Applied Economics Letters, 4(11), 703-707.
  • Han, J. And Kamber, M. (2006). Data Mining Concepts and Techniques (2nd Edition).
  • Haque, N. U., Kumar, M. S., Mark, N., & Mathieson, D. J. (1996). The Economic Content Of İndicators Of Developing Country Creditworthiness. Staff Papers, 43(4), 688-724.
  • Haque, N.U., Mark, N. C., & Mathieson, D. J. (1998). The Relative İmportance Of Political and Economic Variables İn Creditworthiness Ratings.
  • Hernández-Trillo, F. (1995). A Model-Based Estimation of The Probability of Default İn Sovereign Credit Markets. Journal Of Development Economics, 46(1), 163-179.
  • Hilscher, J., & Nosbusch, Y. (2010). Determinants Of Sovereign Risk: Macroeconomics Fundamentals and The Pricing of Sovereign Debt. Review of Finance, 14(2), 235-262.
  • Hoti, S., & Mcaleer, M. (2004). An Empirical Assessment of Country Risk Ratings and Associated Models. Journal Of Economic Surveys, 18(4), 539-588.
  • Ivkin, A. (2018). Country Risk İn International Investment: Its’ Structure and Methods of Estimation. Review Of Business and Economics Studies, 6(1), 56-77.
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Details

Primary Language Turkish
Journal Section Articles
Authors

Seyyide Doğan 0000-0001-7835-7905

Hasan Türe 0000-0002-1975-9063

Publication Date September 14, 2022
Published in Issue Year 2022 Volume: 6 Issue: 3

Cite

APA Doğan, S., & Türe, H. (2022). Makine Öğrenmesi Teknikleri ile Ülke Riski Tahmini. Fiscaoeconomia, 6(3), 1126-1151. https://doi.org/10.25295/fsecon.1098493

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