Predicting default risk is important for firms and banks to operate successfully. There are many reasons to use nonlinear techniques for predicting bankruptcy from financial ratios. Here we propose the so-called Support Vector Machine (SVM) to predict the default risk of German firms. Our...

The goal of this work is to introduce one of the most successful among recently developed statistical techniques - the support vector machine (SVM) - to the field of corporate bankruptcy analysis. The main emphasis is done on implementing SVMs for analysing predictors in the form of financial...

This paper introduces a statistical technique, Support Vector Machines (SVM), which is considered by the Deutsche Bundesbank as an alternative for company rating. A special attention is paid to the features of the SVM which provide a higher accuracy of company classification into solvent and...

Los sistemas de evaluación del crédito desarrollados internamente por los bancos centrales nacionales (ICAS) son una fuente importante de valoración del riesgo de crédito dentro del marco de los activos de garantía de política monetaria del Eurosistema. En particular, los ICAS permiten que...

This paper introduces a statistical technique, Support Vector Machines (SVM), which is considered by the Deutsche Bundesbank as an alternative for company rating. A special attention is paid to the features of the SVM which provide a higher accuracy of company classification into solvent and...

The in-house credit assessment systems (ICASs) developed by euro area national central banks (NCBs) are an important source of credit risk assessment within the Eurosystem collateral framework. They allow counterparties to mobilise as collateral the loans (credit claims) granted to non-financial...