Martín, B. and Serrano Cinca, C. (1993): "Self Organizing Neural Networks for the Analysis and Representation of Data: some Financial Cases", Neural Computing & Applications, Vol. 1, nº 2, diciembre, pp. 193-206, Ed Springer Verlag.

Abstract Many recent papers have dealt with the application of feedforward neural networks in financial data processing. This powerful neural model can implement very complex non-linear 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. Keywords Neural Networks, unsupervised learning, Self-Organizing Feature Maps, data processing, financial data analysis, weight analysis. Download (http://dx.doi.org/10.1007/BF01414948)