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Application of the Fuzzy ARTMAP neural network architecture to bank failure predictions

  • Luis J. de Miguel
  • Elena Revilla
  • J. Miguel Rodríguez
  • J. Manuel Cano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 686)

Abstract

This paper describes an application of a neural nertwork architecture for bank failure predictions. The experiences of financial instability has motivated a great interest in the question of bank solvency. Although several prediction models have been developed, their reliability is not enough. The paper presents a supervised neural networt model, based on the Adaptive Resonance Theory (ART), which was introduced by Stephen Grossberg in 1976. Also Fuzzy Sets Theory is used as processing scheme. The data sample have been taken from the Spanish bank crisis during 1978–1983. The model obtained is able to predict the complete learning data set in two different cases, data one year prior the failure and data two years prior the failure. The obtained results, using a set of test data, reach a high level of performance, with a low number of training cycles.

Keywords

bank failure predictions Fuzzy Sets Theory Adaptive Resonance Theory supervised neural network 

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Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Luis J. de Miguel
    • 1
  • Elena Revilla
    • 1
  • J. Miguel Rodríguez
    • 1
  • J. Manuel Cano
    • 1
  1. 1.Department of Systems Engineering and Control and Department of Business AdministrationUniversity of ValladolidValladolidSpain

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