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Artificial Intelligence for Cluster Analysis: Case Study of Transport Companies in Czech Republic

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  • Eva Kalinová

    (School of Expertness and Valuation, Institute of Technology and Business in České Budějovice, Okružní 517/10, 37001 České Budějovice, Czech Republic)

Abstract

What is the situation of the transport sector in the Czech Republic and what is its importance for the economy of the Czech Republic? How and to what extent do businesses operating in this sector influence the sector as such, and how many businesses in this sector have such influence? Additionally, what happens if the most important businesses in the transport sector go bankrupt, and which businesses are the most important ones? Searching for the answers to these questions is a subject of this contribution, focusing primarily on the cluster analysis using artificial neural networks (ANN), specifically with Kohonen networks, which represent the main method for processing a large volume of not only accounting data on transport companies. In this research, the dataset consists of the financial statements of transport companies for the years 2015–2018. The research part of the contribution deals mainly with the issue of the transport sector’s development in the years 2015–2018 with the companies operating in this sector and tries to identify the most important companies in terms of their importance for this sector. The results show that the whole transport sector is influenced mainly by the two largest companies, whose potential changes can affect companies themselves but to a great extent also the development of the whole transport sector. For the two companies, financial analysis is carried out using ratios, whose results show that despite the negative values of the important value generators of one of these companies, the company is still able to significantly influence the situation in the transport sector of the CR. This information is a clear guide for experts, development analysts, to determine the further development of the whole sector when focusing on the development of the two specific companies only. A question arises as to how the created model can be applied to other economic sectors, especially in other EU countries.

Suggested Citation

  • Eva Kalinová, 2021. "Artificial Intelligence for Cluster Analysis: Case Study of Transport Companies in Czech Republic," JRFM, MDPI, vol. 14(9), pages 1-36, September.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:9:p:411-:d:627341
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    References listed on IDEAS

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    1. du Jardin, Philippe & Séverin, Eric, 2012. "Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time," European Journal of Operational Research, Elsevier, vol. 221(2), pages 378-396.
    2. Hui Li & Lu-Yao Hong & Qing Zhou & Hai-Jie Yu, 2015. "The assisted prediction modelling frame with hybridisation and ensemble for business risk forecasting and an implementation," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(11), pages 2072-2086, August.
    3. Inès Abdelkafi & Manel Zribi & Rochdi Feki, 2018. "New Classification of Developed and Emerging Countries Based on the Effects of Subprime Crises: Kohonen Map Method," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 9(3), pages 908-927, September.
    4. Maha Bakoben & Tony Bellotti & Niall Adams, 2017. "Identification of Credit Risk Based on Cluster Analysis of Account Behaviours," Papers 1706.07466, arXiv.org.
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    1. Beata Gavurova & Sylvia Jencova & Radovan Bacik & Marta Miskufova & Stanislav Letkovsky, 2022. "Artificial intelligence in predicting the bankruptcy of non-financial corporations," Oeconomia Copernicana, Institute of Economic Research, vol. 13(4), pages 1215-1251, December.

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