Using Kohonen networks in the analysis of transport companies in the Czech Republic

Abstract

The transport sector has a significant impact on the performance of the Czech economy. Transport companies, of course, have their own specificities, whether they deal with ecology or the financial and economic situation. It is precisely the economic position of a transport company that needs to be analysed in order to identify the need for change, to predict the further development of such company. For analysis, a variety of methods is used, of which artificial neural networks are a very interesting and effective tool. The aim of this paper is to make a cluster analysis of transport companies operating in the Czech Republic based on this tool. The data of the financial statements of transport companies in the Czech Republic in 2016 are taken into account. Only some items from the financial statements are selected for analysis. The file is then subjected to a cluster analysis, specifically using the Kohonen networks – Statistica software. In accordance with the methodology of the contribution, the data is divided into three sets - training, testing and validation. Companies were divided into clusters in the 10x10 Kohonen Map. Some clusters are significant in terms of number of companies. These clusters are further analysed. Specific conclusions are made: A larger company generates, on average, a higher operating profit, larger companies achieve higher ROE and, in the case of a larger company, the financial leverage acts more positively.

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