Bankruptcy prediction based on financial ratios using Jordan Recurrent Neural Networks: a case study in Polish companies

Complexity of bankruptcy causes the accurate models of bankruptcy prediction difficult to be achieved. Various prediction models have been developed to improve the accuracy of bankruptcy predictions. Machine learning has been widely used to predict because of its adaptive capabilities. Artificial Ne...

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Bibliographic Details
Published in:Journal of physics. Conference series 2018-05, Vol.1025 (1), p.12098
Main Authors: Hardinata, Lingga, Warsito, Budi, Suparti
Format: Article
Language:English
Subjects:
Artificial neural networks
Bankruptcy
Data mining
Learning theory
Machine learning
Neural networks
Physics
Prediction models
Recurrent neural networks
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Summary:Complexity of bankruptcy causes the accurate models of bankruptcy prediction difficult to be achieved. Various prediction models have been developed to improve the accuracy of bankruptcy predictions. Machine learning has been widely used to predict because of its adaptive capabilities. Artificial Neural Networks (ANN) is one of machine learning which proved able to complete inference tasks such as prediction and classification especially in data mining. In this paper, we propose the implementation of Jordan Recurrent Neural Networks (JRNN) to classify and predict corporate bankruptcy based on financial ratios. Feedback interconnection in JRNN enable to make the network keep important information well allowing the network to work more effectively. The result analysis showed that JRNN works very well in bankruptcy prediction with average success rate of 81.3785%.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1025/1/012098