Analysis of the stability factors of Ukrainian banks during the 2014–2017 systemic crisis using the Kohonen self-organizing neural networks
| Autor: | Tetiana Kolodizieva, Hanna Hlukha, Oleksii Mints, Viktoriya Marhasova, Roman Kurok |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Self-organizing map
Organizational Behavior and Human Resource Management Index (economics) Computer science Ukrainian Stability (learning theory) forecasting 02 engineering and technology bank reliability lcsh:HG1501-3550 Management of Technology and Innovation 0502 economics and business 0202 electrical engineering electronic engineering information engineering Kohonen map Marketing 050208 finance Artificial neural network business.industry 05 social sciences language.human_language bankruptcy crisis language lcsh:Banking 020201 artificial intelligence & image processing Artificial intelligence business self-organizing neural network Law Finance |
| Zdroj: | Banks and Bank Systems, Vol 14, Iss 3, Pp 86-98 (2019) |
| ISSN: | 1991-7074 1816-7403 |
| Popis: | The article proposes an approach to analyzing reliability factors of commercial banks during the 2014–2017 systemic crisis in the Ukrainian banking system, using the Kohonen self-organizing neural networks and maps. As a result of an experimental study, data were obtained on financial factors affecting the stability of a commercial bank in a crisis period. It has been concluded that during the banking crisis in Ukraine in 2014–2017, the resource base of a bank was the main factor of this bank stability. The most preferred sources of resources were funds from other banks (bankruptcy rate of 5.7%) and legal entities (bankruptcy rate of 8%), and the least stable were funds from individuals (bankruptcy rate of 28.5%). The relationship between financial stability and the amount of capital and the structure of bank loans is less pronounced. However, one can say that banks that focused on lending to individuals experienced a worse crisis than banks whose main borrowers were legal entities. The tools considered in the article (the Kohonen self-organizing neural networks and maps) allow for efficiently segmenting data samples according to various criteria, including bank solvency. The “hazardous” zones with a high bankruptcy rate (up to 49.2%) and the “safe” zone with a low rate of bankruptcy (6.3%) were highlighted on the map constructed. These results are of practical value and can be used in analyzing and selecting counterparties in the banking system during a downturn. |
| Databáze: | OpenAIRE |
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