Forecasting the Insolvency of U.S. Banks Using Support Vector Machines (SVM) Based on Local Learning Feature Selection
International Journal of Computational Economics and Econometrics, vol. 3(1/2),
11 Pages Posted: 28 Jan 2014
Date Written: November 7, 2013
We propose an 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
JEL Classification: G21
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