Competitive Benchmarking in Large Data Bases Using Self-Organizing Maps

  • 이영찬 (서강대학교 경영학연구원 경영연구소)
  • Published : 1999.10.01

Abstract

The amount of financial information in today's sophisticated large data bases is huge and makes comparisons between company performance difficult or at least very time consuming. The purpose 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. This paper structures and analyzes 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 80 Korean companies with data from the year 1998.

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