Reducing Overreliance on Sovereign Credit Ratings: Which Model Serves Better?

Huseyin Ozturk, Ersin Namli () and Halil Ibrahim Erdal ()
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Ersin Namli: İstanbul University
Halil Ibrahim Erdal: Turkish Cooperation and Coordination Agency (TIKA)

Computational Economics, 2016, vol. 48, issue 1, No 3, 59-81

Abstract: Abstract Sovereign credit ratings have been a controversial issue since the outbreak of the 2008 financial crisis. Among the debates the inaccuracies stay at the centre. By employing classification and regression trees, multilayer perceptron, support vector machines (SVM), Bayes net, and naïve Bayes; we compare the ability of various learning techniques with the conventional statistical method in predicting sovereign credit ratings. Experimental results suggest that all the techniques excluding SVM have over 90 % accurate prediction. According to within one and two notch accurate prediction measure, the prediction performance of SVM also increases above 90 %. These findings indicate a clear outperformance of AI methods over the conventional statistical method. The results have many implications for the practitioners in credit scoring industry. Amidst the regulatory measures that encourage individual credit scoring for international financial institutions, these findings suggest that up-to-date AI methods serve quite reliable technical tools to predict sovereign credit ratings.

Keywords: Classification and regression trees (CART); Multilayer perceptron (MP); Support vector machines (SVM); Bayes net; and naïve Bayes; Sovereign credit ratings (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s10614-015-9534-3

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