Creating a Comprehensive Method for the Evaluation of a Company

Jakub Horak, Tomas Krulicky, Zuzana Rowland and Veronika Machova
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Jakub Horak: The Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 8215/1, 01026 Zilina, Slovakia
Tomas Krulicky: The Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 8215/1, 01026 Zilina, Slovakia
Zuzana Rowland: School of Expertness and Valuation, Institute of Technology and Business, Okruzni 517/10, 37001 Ceske Budejovice, Czech Republic
Veronika Machova: School of Expertness and Valuation, Institute of Technology and Business, Okruzni 517/10, 37001 Ceske Budejovice, Czech Republic

Sustainability, 2020, vol. 12, issue 21, 1-23

Abstract: For investment purposes, the evaluation of a company is not only a matter for a company itself, but also for shareholders and external persons. There are many methods for evaluating a company. This contribution therefore focuses on the creation of a comprehensive method for the evaluation of an industrial enterprise, one that can be used to predict potential future bankruptcies, using a dataset of financial statements of active companies and those in liquidation in the period 2015–2019. Artificial neural networks were used to process the data, specifically logistic regressions from the data processed in the Statistica and Mathematica software programmes. The results showed that the models created using the Mathematica software are not applicable in practice due to the parameters of the obtained results. In contrast, the artificial neural structures obtained using the neural network model in the Statistica software were prospective due to their performance, which is almost always above 0.8, and the logical economic interpretation of the relevant variables. All the generated and retained networks show excellent performance and few errors. However, one of the artificial structures, network no. 4 (MLP 16-16-2), produces better results than the others. Overall, accuracy is almost 81%. In the case of the classification of companies capable of surviving financial distress, accuracy is almost 90%, with that for the classification of companies at risk of going into bankruptcy at nearly 55%.

Keywords: evaluation; bankruptcy; artificial neural networks; financial health; comprehensive method (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:12:y:2020:i:21:p:9114-:d:438941

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