Forecasting the insolvency of US banks using support vector machines (SVMs) based on local learning feature selection

Theophilos Papadimitriou, Periklis Gogas, Vasilios Plakandaras and John C. Mourmouris

International Journal of Computational Economics and Econometrics, 2013, vol. 3, issue 1/2, 83-90

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.

Keywords: bank insolvency; SVM; support vector machines; local learning; feature selection; insolvency forecasting; US banks; USA; United States; banking industry; financial statements; bank default; banking collapse. (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (5)

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Working Paper: Forecasting the insolvency of U.S. banks using Support Vector Machines (SVM) based on Local Learning Feature Selection (2013)
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