Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/113838
Authors: 
Montero-Romero, Teresa
del Carmen López-Martín, María
Becerra-Alonso, David
Martínez-Estudillo, Francisco José
Year of Publication: 
2012
Citation: 
[Journal:] Revista de Métodos Cuantitativos para la Economía y la Empresa [ISSN:] 1886-516X [Volume:] 13 [Year:] 2012 [Pages:] 3-23
Abstract: 
The level of default in financial institutions is a key piece of information in the activity of these organizations and reveals their level of risk. This in turn explains the growing attention given to variables of this kind, during the crisis of these last years. This paper presents a method to estimate the default rate using the non-linear model defined by standard Multilayer Perceptron (MLP) neural networks trained with a novel methodology called Extreme Learning Machine (ELM). The experimental results are promising, and show a good performance when comparing the MLP model trained with the Leverberg-Marquard algorithm.
Subjects: 
level of default
financial institutions
neural networks
extreme learning machine
JEL: 
G21
G01
C45
Document Type: 
Article

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