Forecasting the insolvency of U.S. banks using Support Vector Machines (SVM) based on Local Learning Feature Selection
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.
|Date of creation:||19 Mar 2013|
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