Forecasting the insolvency of US banks using support vector machines (SVMs) based on local learning feature selection
| Autor: | Theophilos Papadimitriou, Vasilios Plakandaras, John C. Mourmouris, Periklis Gogas |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Economics and Econometrics
Insolvency business.industry Computer science Feature selection Machine learning computer.software_genre Computer Science Applications Set (abstract data type) Data set Support vector machine Variable (computer science) Relevance (information retrieval) Artificial intelligence business computer Selection (genetic algorithm) |
| Zdroj: | International Journal of Computational Economics and Econometrics. 3:83 |
| ISSN: | 1757-1189 1757-1170 |
| DOI: | 10.1504/ijcee.2013.056267 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|