Managing complexity in large data bases using self-organizing maps

Abstract

The amount of financial information in today's sophisticated large data bases is substantial and makes comparisons between company performance—especially over time—difficult or at least very time consuming. The aim of this paper is to investigate whether neural networks in the form of self-organizing maps can be used to manage the complexity in large data bases. We structure and analyze accounting numbers in a large data base over several time periods. By using self-organizing maps, we overcome the problems associated with finding the appropriate underlying distribution and the functional form of the underlying data in the structuring task that is often encountered, for example, when using cluster analysis. The method chosen also offers a way of visualizing the results. The data base in this study consists of annual reports of more than 120 world wide pulp and paper companies with data from a five year time period.