COMPETITIVE FINANCIAL BENCHMARKING USING SELF-ORGANIZING MAPS

Competitive benchmarking is a company-internal process in which the ac tivities of a given company are measured against the best practices of other, best-in-class companies. Internal functions are analyzed and m easured using financial and/or non-financial yardsticks. In financial benchmarking, the first step is financial statement analysis to help d etermine which company characteristics to measure and which yardsticks to apply. However, for the task of running computerized benchmarking systems the amount of financial information required is often so large as to render comparison between companies difficult or at least very time consuming. The overall objective of this study is to investigate the potential of neural networks for pre-processing the vast amount of financial data available on companies, and for presenting the approxi mated financial performance position of one company as compared to tha t of others. The study demonstrates how a large annual reports databas e on international pulp and paper companies can be preprocessed, i.e. classified with self-organizing maps that is one form of neural networ ks. The test results are encouraging, and show that self-organizing ma ps are a viable tool for organizing large databases into clusters of c ompanies having similar financial characteristics.