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
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)
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Journal Article: Forecasting the insolvency of US banks using support vector machines (SVMs) based on local learning feature selection (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:ris:duthrp:2013_002
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