EconPapers    
Economics at your fingertips  
 

Forecasting the insolvency of U.S. banks using Support Vector Machines (SVM) based on Local Learning Feature Selection

Periklis Gogas (), Theophilos Papadimitriou () and Vasilios Plakandaras ()

No 2-2013, DUTH Research Papers in Economics from Democritus University of Thrace, Department of Economics

Abstract: 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.

Keywords: Bank insolvency; SVM; local learning; feature selection (search for similar items in EconPapers)
JEL-codes: G21 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ban and nep-for
Date: 2013-03-19
References: Add references at CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
http://utopia.duth.gr/~vplakand/Forecasting%20the% ... ture%20Selection.pdf Full text (application/pdf)
Our link check indicates that this URL is bad, the error code is: 404 Not Found

Related works:
Journal Article: Forecasting the insolvency of US banks using support vector machines (SVMs) based on local learning feature selection (2013) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ris:duthrp:2013_002

Access Statistics for this paper

More papers in DUTH Research Papers in Economics from Democritus University of Thrace, Department of Economics Department of Economics, University Campus, Komotini, 69100, Greece. Contact information at EDIRC.
Bibliographic data for series maintained by Periklis Gogas (). This e-mail address is bad, please contact .

 
Page updated 2019-10-18
Handle: RePEc:ris:duthrp:2013_002