Comparing numerical data and text information from annual reports using self-organizing maps

Abstract More and more companies provide their accounting information in electronic form today. The accounting information in electronic form can be found in large commercial databases or on the web. This information is of great interest for different stakeholders, i.e., stockholders, creditors, auditors, financial analysts, and management. For the stakeholders it is important to be able to extract both quantitative and qualitative information concerning the companies they are interested in. The annual reports contain information both in numerical and symbolic form. So far, only the numerical information has been analyzed with help of computers. However, technology has evolved and in particular neural networks in the form of self-organizing maps (SOMs) provide a new tool for analyzing also text information. In this paper, we compare results on quantitative data with results on qualitative data from annual reports. We use smart encoding, SOMs, and document histograms for comparing the performance of forest companies worldwide. Firstly, we cluster the companies according to, on the one hand, quantitative information, and on the other hand, qualitative information. Secondly, we compare the results produced by the clustering methods. Our results of the comparison show that there is a difference between the results.

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