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Extreme Learning Machine to Analyze the Level of Default in Spanish Deposit Institutions || Análisis de la morosidad de las entidades financieras españolas mediante Extreme Learning Machine

Author

Listed:
  • Montero-Romero, Teresa

    (Department of Management and Quantitative Methods, ETEA, Córdoba (Spain))

  • López-Martín, María del Carmen

    (Department of Economics, Legal Sciences and Sociology, ETEA, Córdoba (Spain))

  • Becerra-Alonso, David

    (Department of Management and Quantitative Methods, ETEA, Córdoba (Spain))

  • Martínez-Estudillo, Francisco José

    (Department of Management and Quantitative Methods, ETEA, Córdoba (Spain))

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. || La morosidad en las entidades financieras es un dato muy importante de la actividad de estas instituciones pues permite conocer el nivel de riesgo asumido por éstas. Esto a su vez explica la creciente atención otorgada a esta variable, especialmente en los últimos años de crisis. Este artículo presenta un método para estimar el nivel de la tasa de morosidad por medio de un modelo no lineal definido por la red neuronal Multilayer Perceptron (MLP) entrenada con una nueva metodología llamada Extreme Learning Machine (ELM). Los resultados experimentales son prometedores, mostrando un buen resultado si se compara con el modelo MLP entrenado con el algoritmo de Leverberg-Marquard.

Suggested Citation

  • Montero-Romero, Teresa & López-Martín, María del Carmen & Becerra-Alonso, David & Martínez-Estudillo, Francisco José, 2012. "Extreme Learning Machine to Analyze the Level of Default in Spanish Deposit Institutions || Análisis de la morosidad de las entidades financieras españolas mediante Extreme Learning Machine," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 13(1), pages 3-23, June.
  • Handle: RePEc:pab:rmcpee:v:13:y:2012:i:1:p:3-23
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    References listed on IDEAS

    as
    1. McNelis, Paul D., 2004. "Neural Networks in Finance," Elsevier Monographs, Elsevier, edition 1, number 9780124859678.
    2. Wong, W.K. & Guo, Z.X., 2010. "A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm," International Journal of Production Economics, Elsevier, vol. 128(2), pages 614-624, December.
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    More about this item

    Keywords

    level of default; financial institutions; neural networks; Extreme Learning Machine; nivel de morosidad; instituciones financieras; redes neuronales; Extreme Learning Machine;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G01 - Financial Economics - - General - - - Financial Crises
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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