Forecasting the insolvency of U.S. banks using Support Vector Machines (SVM) based on Local Learning Feature Selection
AbstractWe 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.
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Bibliographic InfoPaper provided by Democritus University of Thrace, Department of Economics in its series DUTH Research Papers in Economics with number 2-2013.
Length: 8 pages
Date of creation: 19 Mar 2013
Date of revision:
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Postal: Department of Economics, University Campus, Komotini, 69100, Greece
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Bank insolvency; SVM; local learning; feature selection;
Other versions of this item:
- Theophilos Papadimitriou & Periklis Gogas & Vasilios Plakandaras & John C. Mourmouris, 2013. "Forecasting the insolvency of US banks using support vector machines (SVMs) based on local learning feature selection," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 3(1/2), pages 83-90.
- G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
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