From financial information to strategic groups: a self-organizing neural network approach

Serrano Cinca, C. (1996) From financial information to strategic groups: a self-organizing neural network approach (Discussion Papers in Accounting and Management Science, 96-125) Southampton, UK. University of Southampton 19pp.

Abstract

This paper sets out to determine the strategic positioning of Spanish Savings Banks, using data drawn from published financial information. Its starting point is the idea of the strategic group, regularly employed in Business Management to explain the relationships between firms within the same sector, but with the peculiarity that the strategic group is identified using financial information. In this way, groups of firms that follow a similar financial strategy -with similar cost structures, levels of profitability, borrowing, etc.- have been obtained.
As the exploratory data analysis technique used to obtain these strategic groups, a combination of a non-supervised neural network, the Self-Organising Feature Maps (SOFM) with Cluster Analysis (CA) is proposed. This methodology permits the visualisation of similarities between firms in an intuitive manner. The application of the proposed methodology to the financial information published by the totality of Spanish Savings Banks allows for the identification of the existence of profound regional differences in this important sector of the Spanish financial system. Thereafter, a bivariate study of the financial ratios details the aspects that distinguish the Savings Banks that operate in the different Spanish regions.

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