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Applying artificial neural networks to bank-decision simulations

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  • Dorota Witkowska

Abstract

Artificial neural networks are nonlinear models that can be trained to extract hidden structures and relationships that govern the data. They can be used for analyzing relationships among economic and financial phenomena. This paper presents research on applying a back propagation algorithm to firm classification. Experiments were provided for three neural network architectures by applying training and testing samples constructed from actual data of the firms that applied for credit in regional banks for the period 1994–97. To study the effect of proportion between the number of firms that obtained and did not obtain credit, three proportions of the training and testing set compositions were created: 1:1, 2:1, and 4:1. Classification accuracy was evaluated in terms of errors made by the neural networks. Copyright International Atlantic Economic Society 1999

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  • Dorota Witkowska, 1999. "Applying artificial neural networks to bank-decision simulations," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 5(3), pages 350-368, August.
    • Handle: RePEc:kap:iaecre:v:5:y:1999:i:3:p:350-368:10.1007/bf02296417
    DOI: 10.1007/BF02296417
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    References listed on IDEAS

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    1. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    2. Iebeling Kaastra & Milton S. Boyd, 1995. "Forecasting futures trading volume using neural networks," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 15(8), pages 953-970, December.
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    Cited by:

    1. Christian A. Johnson, 2005. "Modelos de alerta temprana para pronosticar crisis bancarias: desde la extracción de señales a las redes neuronales," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 20(1), pages 95-121, June.
    2. Christian A. Johnson & Rodrigo Vergara, 2005. "The implementation of monetary policy in an emerging economy: the case of Chile," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 20(1), pages 45-62, June.
    3. Brad S. Trinkle & Amelia A. Baldwin, 2016. "Research Opportunities for Neural Networks: The Case for Credit," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(3), pages 240-254, July.

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