| Título |
Extreme Learning Machine to Analyze the Level of Default in Spanish Deposit Institutions |
| Autores |
MONTERO ROMERO, Mª TERESA, LÓPEZ MARTÍN, MARÍA DEL CARMEN, BECERRA ALONSO, DAVID, MARTÍNEZ ESTUDILLO, FRANCISCO JOSÉ |
| Publicación externa |
No |
| Medio |
Rev. Metodos Cuantitativos Econ. Empresa |
| Alcance |
Article |
| Naturaleza |
Científica |
| Cuartil SJR |
3 |
| Impacto SJR |
0.14 |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872566848&partnerID=40&md5=46682a834aae07fc7f9faf851ed4437f |
| Fecha de publicacion |
01/01/2012 |
| Scopus Id |
2-s2.0-84872566848 |
| 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. |
| Palabras clave |
Extreme learning machine; Financial institutions; Level of default; Neural networks |
| Miembros de la Universidad Loyola |
|
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