Forecasting the insolvency of US banks using support vector machines (SVMs) based on local learning feature selection
by Theophilos Papadimitriou; Periklis Gogas; Vasilios Plakandaras; John C. Mourmouris
International Journal of Computational Economics and Econometrics (IJCEE), Vol. 3, No. 1/2, 2013

Abstract: We propose a support vector machine (SVM)-based structural model to forecast the collapse of banking institutions in the USA using publicly disclosed information from their financial statements on a four-year rolling window. In our approach, the optimum input variable set is defined from a large data set using an iterative relevance-based selection procedure. We train an SVM model to classify banks as solvent and insolvent. The resulting model exhibits significant ability in bank default forecasting.

Online publication date: Thu, 05-Sep-2013

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