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 , Southampton, UK University of Southampton 19pp. (Discussion Papers in Accounting and Management Science, 96-125).


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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

Item Type: Monograph (Discussion Paper)
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Keywords: Self-organising feature maps, neural networks, kohonen maps, financial statement analysis, strategic groups, savings banks

ePrint ID: 36144
Date :
Date Event
Date Deposited: 30 Apr 2007
Last Modified: 16 Apr 2017 22:07
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