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

Research areas:

• Methodology - Machine learning

Year:

2014

Type of Publication:

Article

Keywords:

Ordinal regression, Ordinal classification, Country risk, Sovereign risk, Rating agencies, Financial crisis

Authors:

• Sánchez-Monedero, Javier
• Campoy-Muñoz, Pilar
• Gutiérrez, Pedro Antonio
• Hervás-Martínez, César

Journal:

Applied Soft Computing

Volume:

22

Pages:

339-350

Month:

September

BibTex:

@article{SanchezMonedero2014, author = "Javier S{\'a}nchez-Monedero and Pilar Campoy-Mu{\~n}oz and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "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 epsilon-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 {\&}amp; Poors, Moody{\&}#039;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.", awards = "JCR(2014): 2.810 Position: 17/123 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2014): 2.810 Position: 17/123 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1016/j.asoc.2014.05.008", journal = "Applied Soft Computing", keywords = "Ordinal regression, Ordinal classification, Country risk, Sovereign risk, Rating agencies, Financial crisis", month = "September", note = "JCR(2014): 2.810 Position: 17/123 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "339-350", title = "{A} guided data projection technique for classification of sovereign ratings: the case of {E}uropean {U}nion 27", url = "http://dx.doi.org/10.1016/j.asoc.2014.05.008", volume = "22", year = "2014", }

Note:

JCR(2014): 2.810 Position: 17/123 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Abstract:

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 epsilon-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.

Comments:

JCR(2014): 2.810 Position: 17/123 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE