Enhanced default risk models with SVM+

Authors:
Bernardete Ribeiro;Catarina Silva;Ning Chen;Armando Vieira;João Carvalho das Neves
Affiliations:
CISUC, Department of Informatics Engineering, University of Coimbra, Portugal;CISUC, Department of Informatics Engineering, University of Coimbra, Portugal and ESTG, School of Technology and Management, Polytechinc Institute of Leiria, Portugal;GECAD, Polytechnic Institute of Porto, Porto, Portugal;GECAD, Polytechnic Institute of Porto, Porto, Portugal;ISEG-School of Economics and Management, Technical University of Lisbon, Portugal
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

Default risk models have lately raised a great interest due to the recent world economic crisis. In spite of many advanced techniques that have extensively been proposed, no comprehensive method incorporating a holistic perspective has hitherto been considered. Thus, the existing models for bankruptcy prediction lack the whole coverage of contextual knowledge which may prevent the decision makers such as investors and financial analysts to take the right decisions. Recently, SVM+ provides a formal way to incorporate additional information (not only training data) onto the learning models improving generalization. In financial settings examples of such non-financial (though relevant) information are marketing reports, competitors landscape, economic environment, customers screening, industry trends, etc. By exploiting additional information able to improve classical inductive learning we propose a prediction model where data is naturally separated into several structured groups clustered by the size and annual turnover of the firms. Experimental results in the setting of a heterogeneous data set of French companies demonstrated that the proposed default risk model showed better predictability performance than the baseline SVM and multi-task learning with SVM.