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Forecasting the insolvency of U.S. banks using Support Vector Machines (SVM) based on Local Learning Feature Selection

Periklis Gogas (), Theophilos Papadimitriou () and Vasilios Plakandaras ()

No 2-2013, DUTH Research Papers in Economics from Democritus University of Thrace, Department of Economics

Abstract: We propose a Support Vector Machine (SVM) based structural model in order to forecast the collapse of banking institutions in the U.S. 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 dataset 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.

Keywords: Bank insolvency; SVM; local learning; feature selection (search for similar items in EconPapers)
JEL-codes: G21 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ban and nep-for
Date: 2013-03-19
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