Título Addressing the EU sovereign ratings using an ordinal regression approach
Autores FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, CAMPOY MUÑOZ, MARÍA DEL PILAR, La Paz-Marín M.-De., Hervás-Martínez C., Yao X., CAMPOY MUÑOZ, MARÍA DEL PILAR, FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS
Publicación externa No
Medio IEEE Trans. Cybern.
Alcance Article
Naturaleza Científica
Cuartil JCR 4
Ámbito Internacional
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-84890120267&doi=10.1109%2fTSMCC.2013.2247595&partnerID=40&md5=d49d22636401727aad404b0d55430269
Fecha de publicacion 01/01/2013
ISI 000327647500059
Scopus Id 2-s2.0-84890120267
DOI 10.1109/TSMCC.2013.2247595
Abstract The current European debt crisis has drawn considerable attention to credit-rating agencies' news about sovereign ratings. From a technical point of view, credit rating constitutes a typical ordinal regression problem because credit-rating agencies generally present a scale of risk composed of several categories. This fact motivated the use of an ordinal regression approach to address the problem of sovereign credit rating in this paper. Therefore, the ranking of different classes will be taken into account for the design of the classifier. To do so, a novel model is introduced in order to replicate sovereign rating, based on the negative correlation learning framework. The methodology is fully described in this paper and applied to the classification of the 27 European countries' sovereign rating during the 2007-2010 period based on Standard and Poor's reports. The proposed technique seems to be competitive and robust enough to classify the sovereign ratings reported by this agency when compared with other existing well-known ordinal and nominal methods. © 2013 IEEE.
Palabras clave Country risks; Credit ratings; Different class; European Countries; European Debt Crisis; Negative correlation learning; Ordinal regression; Developing countries; Neural networks; Regression analysis;
Miembros de la Universidad Loyola

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