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

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.