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

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 @e-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.

[1]  Andrea Esuli,et al.  Evaluation Measures for Ordinal Regression , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[2]  Nada Mora,et al.  Sovereign credit ratings: Guilty beyond reasonable doubt? , 2006 .

[3]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[4]  Eyke Hüllermeier,et al.  Is an ordinal class structure useful in classifier learning? , 2008, Int. J. Data Min. Model. Manag..

[5]  Charles R. Frank,et al.  Measurement of debt servicing capacity: An application of discriminant analysis , 1971 .

[6]  Craig A. Depken,et al.  Corruption and Creditworthiness: Evidence from Sovereign Credit Ratings ⁄ , 2006 .

[7]  Fabian Amtenbrink,et al.  Credit Rating Agencies , 2011 .

[8]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[9]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[10]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[11]  Gang Kou,et al.  An empirical study of classification algorithm evaluation for financial risk prediction , 2011, Appl. Soft Comput..

[12]  Chih-Jen Lin,et al.  A Comparison of Methods for Multi-class Support Vector Machines , 2015 .

[13]  Wei-Yang Lin,et al.  Machine Learning in Financial Crisis Prediction: A Survey , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[14]  Bernhard Rauch,et al.  Fact and Fiction in EU-Governmental Economic Data , 2011 .

[15]  Bernard De Baets,et al.  Learning partial ordinal class memberships with kernel-based proportional odds models , 2012, Comput. Stat. Data Anal..

[16]  Antonio Afonso,et al.  Short‐ and long‐run determinants of sovereign debt credit ratings , 2011 .

[17]  Richard Cantor,et al.  Determinants and Impact of Sovereign Credit Ratings , 1996 .

[18]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[19]  Richard Cantor,et al.  Determinants and Impact of Sovereign Credit Ratings , 1996 .

[20]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[21]  Pedro Antonio Gutiérrez,et al.  Metrics to guide a multi-objective evolutionary algorithm for ordinal classification , 2014, Neurocomputing.

[22]  John C. B. Cooper.  Artificial neural networks versus multivariate statistics: An application from economics , 1999 .

[23]  D. Hauner,et al.  Sovereign Risk: Are the EU's New Member States Different? , 2010 .

[24]  Antanas Verikas,et al.  Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey , 2010, Soft Comput..

[25]  KimKyoung-jae,et al.  A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach , 2012 .

[26]  Ian H. Witten,et al.  Data mining - practical machine learning tools and techniques, Second Edition , 2005, The Morgan Kaufmann series in data management systems.

[27]  Ian Witten,et al.  Data Mining , 2000 .

[28]  M. Kendall.  Rank Correlation Methods , 1949 .

[29]  Jaime S. Cardoso,et al.  Measuring the Performance of Ordinal Classification , 2011, Int. J. Pattern Recognit. Artif. Intell..

[30]  Thore Graepel,et al.  Large Margin Rank Boundaries for Ordinal Regression , 2000 .

[31]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[32]  Stephen Thomas,et al.  Modelling sovereign credit ratings: Neural networks versus ordered probit , 2006, Expert Syst. Appl..

[33]  F. Tay,et al.  Application of support vector machines in financial time series forecasting , 2001 .

[34]  Wei Chu,et al.  Support Vector Ordinal Regression , 2007, Neural Computation.

[35]  Lean Yu,et al.  A Modified Least Squares Support Vector Machine Classifier with Application to Credit Risk Analysis , 2009, Int. J. Inf. Technol. Decis. Mak..

[36]  Gerhard Widmer,et al.  Prediction of Ordinal Classes Using Regression Trees , 2001, Fundam. Informaticae.

[37]  O. Gwilym,et al.  Split sovereign ratings and rating migrations in emerging economies , 2010 .

[38]  Kyoung-jae Kim,et al.  A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach , 2012, Comput. Oper. Res..

[39]  Eibe Frank,et al.  Logistic Model Trees , 2003, Machine Learning.

[40]  Robert W. Faff,et al.  Variations in sovereign credit quality assessments across rating agencies , 2010 .

[41]  Ling Li,et al.  Ordinal Regression by Extended Binary Classification , 2006, NIPS.

[42]  Arindam Chaudhuri,et al.  Fuzzy Support Vector Machine for bankruptcy prediction , 2011, Appl. Soft Comput..

[43]  Francisco Javier de Cos Juez,et al.  Bankruptcy forecasting: A hybrid approach using Fuzzy c-means clustering and Multivariate Adaptive Regression Splines (MARS) , 2011, Expert Syst. Appl..

[44]  Kin Keung Lai,et al.  Credit Scoring Models with AUC Maximization Based on Weighted SVM , 2009, Int. J. Inf. Technol. Decis. Mak..

[45]  A. Atiya,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.

[46]  Nicholas P. Sargen.  Economic indicators and country risk appraisal , 1977 .

[47]  W. Perraudin,et al.  The Estimation of Transition Matrices for Sovereign Credit Ratings , 2002 .

[48]  Vadlamani Ravi,et al.  Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review , 2007, Eur. J. Oper. Res..

[49]  Juliana Yim,et al.  Comparison of country risk models: hybrid neural networks, logit models, discriminant analysis and cluster techniques , 2005, Expert Syst. Appl..

[50]  Jaime S. Cardoso,et al.  The unimodal model for the classification of ordinal data , 2008, Neural Networks.

[51]  Wei Chu,et al.  Gaussian Processes for Ordinal Regression , 2005, J. Mach. Learn. Res..

[52]  Emawtee Bissoondoyal-Bheenick,et al.  An analysis of the determinants of sovereign ratings , 2005 .

[53]  E. Altman,et al.  Toward a Bottom-Up Approach to Assessing Sovereign Default Risk , 2011, Corporate Financial Distress, Restructuring, and Bankruptcy.

[54]  Eibe Frank,et al.  A Simple Approach to Ordinal Classification , 2001, ECML.

[55]  Vadlamani Ravi,et al.  Soft computing system for bank performance prediction , 2008, Appl. Soft Comput..

[56]  Soushan Wu,et al.  Credit rating analysis with support vector machines and neural networks: a market comparative study , 2004, Decis. Support Syst..

[57]  P. McCullagh.  Regression Models for Ordinal Data , 1980 .

[58]  Wu Meng,et al.  Application of Support Vector Machines in Financial Time Series Forecasting , 2007 .

[59]  E. Deakin.  Discriminant Analysis Of Predictors Of Business Failure , 1972 .

[60]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[61]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[62]  P. McCullagh.  Analysis of Ordinal Categorical Data , 1985 .

[63]  Willem Waegeman,et al.  An ensemble of Weighted Support Vector Machines for Ordinal Regression , 2007 .

[64]  Pedro Antonio Gutiérrez,et al.  Weighting Efficient Accuracy and Minimum Sensitivity for Evolving Multi-Class Classifiers , 2011, Neural Processing Letters.

[65]  Chih-Fong Tsai,et al.  Credit rating by hybrid machine learning techniques , 2010, Appl. Soft Comput..

[66]  Antonio Afonso,et al.  Ordered response models for sovereign debt ratings , 2006 .

[67]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[68]  Bertrand Candelon,et al.  Sovereign Rating News and Financial Markets Spillovers: Evidence from the European Debt Crisis , 2011, SSRN Electronic Journal.

[69]  Young-Chan Lee,et al.  Application of support vector machines to corporate credit rating prediction , 2007, Expert Syst. Appl..

[70]  Johan A. K. Suykens,et al.  A process model to develop an internal rating system: Sovereign credit ratings , 2006, Decis. Support Syst..

[71]  Ling Li,et al.  Reduction from Cost-Sensitive Ordinal Ranking to Weighted Binary Classification , 2012, Neural Computation.

[72]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.