<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

Neural network modelling of non-prosperity of Slovak companies

Engineering Management in Production and Services

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Neural network modelling of non-prosperity of Slovak companies"}

Early identification of potential financial problems is among important companies’ risk management tasks. This paper aims to propose individual and ensemble...

Neural network modelling of non-prosperity of Slovak companies

Online veröffentlicht: 10. Okt. 2023

Seitenbereich: 1 - 13

Eingereicht: 15. Dez. 2022

Akzeptiert: 01. Juni 2023

DOI: https://doi.org/10.2478/emj-2023-0016

Schlüsselwörter
company, Kohonen map, neural network, non-prosperity, predictive ability

© 2023 Marek Durica et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Neural network modelling of non-prosperity of Slovak companies

Online veröffentlicht: 10. Okt. 2023

Seitenbereich: 1 - 13

Eingereicht: 15. Dez. 2022

Akzeptiert: 01. Juni 2023

DOI: https://doi.org/10.2478/emj-2023-0016

Schlüsselwörtercompany, Kohonen map, neural network, non-prosperity, predictive ability

© 2023 Marek Durica et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
company, Kohonen map, neural network, non-prosperity, predictive ability