Author(s)

R. Campos, F.J. Ruiz, N. Agell & C. Angulo

Abstract

Correct default risk classification of an issuer is a critical factor. Practitioners and academics alike agree on this. Thus, under the supervision of financial experts, significant resources of investment advisory companies are used for this task. Researchers, both theoretical and empirical ones, are not the exception either. Nowadays, many methodological and technical advances allow support for the work of classification of issuers. Learning algorithms based on Kernel Machines, particularly Support Vector Machines (SVM), have provided good results in classification problems when data are not linearly separable or noise patterns are employed for training. Moreover, on using kernel s

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