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, LÓPEZ MARTÍN, MARÍA DEL CARMEN, MARTÍNEZ ESTUDILLO, FRANCISCO JOSÉ, MONTERO ROMERO, Mª TERESA, BECERRA ALONSO, DAVID
Publicación externa No
Medio Rev. Metodos Cuantitativos Econ. Empresa
Alcance Article
Naturaleza Científica
Cuartil SJR 3
Impacto SJR 0.14000
Ámbito Internacional
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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