A guided data projection technique for classification of sovereign ratings: The case of European Union 27

• We consider the problem of sovereign rating from an ordinal regression perspective.
• A pairwise class distance based projection is considered together with a SVR algorithm.
• The projected regression estimations are used to visualize and analyse the different countries in a continuous scale.

Sovereign rating has had an increasing importance since the beginning of the financial crisis. However, credit rating agencies opacity has been criticised by several authors highlighting the suitability of designing more objective alternative methods. This paper tackles the sovereign credit rating classification problem within an ordinal classification perspective by employing a pairwise class distances projection to build a classification model based on standard regression techniques. In this work the ϵ-SVR is selected as the regressor tool. The quality of the projection is validated through the classification results obtained for four performance metrics when applied to Standard & Poors, Moody's and Fitch sovereign rating data of U27 countries during the period 2007–2010. This validated projection is later used for ranking visualization which might be suitable to build a decision support system.

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