Neural Computing & Applications

, Volume 1, Issue 3, pp 193–206 | Cite as

Self-organizing neural networks for the analysis and representation of data: Some financial cases

  • Bonifacio Martín-del-Brío
  • Carlos Serrano-Cinca


Many recent papers have dealt with the application of feedforward neural networks in financial data processing. This powerful neural model can implement very complex nonlinear mappings, but when outputs are not available or clustering of patterns is required, the use of unsupervised models such as self-organizing maps is more suitable. The present work shows the capabilities of self-organizing feature maps for the analysis and representation of financial data and for aid in financial decision-making. For this purpose, we analyse the Spanish banking crisis of 1977–1985 and the Spanish economic situation in 1990 and 1991, making use of this unsupervised model. Emphasis is placed on the analysis of the synaptic weights, fundamental for delimiting regions on the map, such as bankrupt or solvent regions, where similar companies are clustered. The time evolution of the companies and other important conclusions can be drawn from the resulting maps.


Neural networks Unsupervised learning Self-organizing feature maps Data processing Financial data analysis Weight analysis 

Characters and symbols used and their meaning


x dimension of the neuron grid, in number of neurons


y dimension of the neuron grid, in number of neurons


dimension of the input vector, number of input variables

(i, j)

indices of a neuron on the map


index of the input variables


synaptic weight that connects thek input with the (i, j) neuron on the map


weight vector of the (i, j) neuron


input vector


input vector


learning rate


starting learning rate


final learning rate


neighbourhood radius


starting neighbourhood radius


final neighbourhood radius


iteration counter


number of iterations until reachingR f


number of iterations until reaching ∈f


lateral interaction function


standard deviation

for every

d (x, y)

distance between the vectors x and y


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Copyright information

© Springer-Verlag London Limited 1993

Authors and Affiliations

  • Bonifacio Martín-del-Brío
    • 1
  • Carlos Serrano-Cinca
    • 2
  1. 1.Departamento de Ingeniería Eléctrica e Informática, Facultad de CienciasUniversidad de ZaragozaZaragozaSpain
  2. 2.Departamento de Contabilidad y Finanzas, Facultad de Ciencias Económicas y EmpresarialesUniversidad de ZaragozaZaragozaSpain

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