Neural network modelling of non-prosperity of Slovak companies

• Autorzy: Durica Marek , Mazanec Jaroslav , Frnda Jaroslav

Identyfikatory

DOI

10.2478/emj-2023-0016

• Języki publikacji: EN

Abstrakty

EN

Early identification of potential financial problems is among important companies’ risk management tasks. This paper aims to propose individual and ensemble models based on various types of neural networks. The created models are evaluated based on several quantitative metrics, and the best-proposed models predict the impending financial problems of Slovak companies a year in advance. The precise analysis and cleaning of real data from the financial statements of real Slovak companies result in a data set consisting of the values of nine potential predictors of almost 19 thousand companies. Individual and ensemble models based on MLP and RBF-type neural networks and the Kohonen map are created on the training sample. On the other hand, several metrics quantify the predictive ability of the created models on the test sample. Ensemble models achieved better predictive ability compared to individual models. MLP networks achieved the highest overall accuracy of almost 89 %. However, the non-prosperity of Slovak companies was best identified by RBF networks created by the boosting and bagging technique. The sensitivity of these models is about 87 %. The study found that models based on neural networks can be successfully designed and used to predict financial distress in the Slovak economy.

Słowa kluczowe

EN

company; Kohonen map; neural network; non-prosperity; predictive ability

PL

firma; Mapa Kohonena; sieć neuronowa; brak perspektyw; zdolność przewidywania

• Wydawca: Oficyna Wydawnicza Politechniki Białostockiej
• Czasopismo: Engineering Management in Production and Services
• Rocznik: 2023
• Tom: Vol. 15, no. 3
• Strony: 1--13
• Opis fizyczny: Bibliogr. 41 poz., rys., tab.

Twórcy

autor

Durica Marek

• University of Zilina, Slovakia

• https://orcid.org/0000-0002-2118-166X

autor

Mazanec Jaroslav

• University of Zilina, Slovakia

• https://orcid.org/0000-0003-4723-7075

autor

Frnda Jaroslav

• University of Zilina, Slovakia

• https://orcid.org/0000-0001-6065-3087

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