Ülke Kredi Notlarının Makine Öğrenme Algoritmaları ile Tahmini
Estimating Sovereign Credit Rating by Using Machine Learning Algorithm

Summary/Abstract: Purpose – The main objective of this study is to investigate the success of artificial neural networks, adaptive neuro fuzzy inference system and support vector machines in sovereign credit rating estimation. Design/methodology/approach – The research problem addresses the estimation of countries' credit scores as a classification problem. Selected variables were used as input for mentioned algorithms and classification success of these algorithms was investigated. The data set used is the current three-year data covering the 2016-2018 period of the countries. Numerous attempts were made to select the parameters of the algorithms used and the appropriate parameter sets were tried to be determined. Findings – According to the research results, although the predictive success of all three models is high, support vector machines was determined as the best classifier algorithm which produces the best results. Discussion – It is possible to say that the performance of the prediction of all models is acceptable and they are the models that can be used for sovereign credit rating prediction. For example, in the study of Leshno and Spector, (1996) the predicted performance of ANN model was 72%, in Mohapatra, De, and Ratha, (2010) was 75% and in Blanco et al. (2013) was 92.4%. It is seen that the results of the analysis are successful compared to the studies in the literature.

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