Sovereign rating has had an increasing importance since the beginning of the
nancial 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 classi cation
problem within an ordinal classi cation perspective by employing a pairwise
class distances projection to build a classi cation model based on standard regression
techniques. In this work the -SVR is selected as the regressor tool.
The quality of the projection is validated through the classi cation 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 syste
