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

✍ Scribed by Carlos Serrano-Cinca

• Publisher: John Wiley and Sons
• Year: 1998
• Tongue: English
• Weight: 204 KB
• Volume: 17
• Category: Article
• ISSN: 0277-6693
• DOI: 10.1002/(sici)1099-131x(1998090)17:5/6<415::aid-for705>3.0.co;2-x

✦ Synopsis

This paper sets out to determine the strategic positioning of Spanish savings banks, using data drawn from published ®nancial information. Its starting point is the idea of the strategic group, regularly employed in business management to explain the relationships between ®rms within the same sector, but with the characteristic that the strategic group is identi®ed using ®nancial information. In this way, groups of ®rms that follow a similar ®nancial strategyÐwith similar cost structures, levels of pro®tability, borrowing, etc.Ðhave been obtained. As the exploratory data analysis technique used to obtain these strategic groups, a combination of a nonsupervised neural network, the Self-Organizing Feature Maps (SOFM) with Cluster Analysis (CA) is proposed. This methodology permits the visualization of similarities between ®rms in an intuitive manner. The application of the proposed methodology to the ®nancial information published by the totality of Spanish savings banks allows for the identi®cation of the existence of profound regional dierences in this important sector of the Spanish ®nancial system. Thereafter, a bivariate study of the ®nancial ratios details the aspects that distinguish the savings banks that operate in the dierent Spanish regions.