2018
DOI: 10.1088/1742-6596/1025/1/012098
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Bankruptcy prediction based on financial ratios using Jordan Recurrent Neural Networks: a case study in Polish companies

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Cited by 12 publications
(7 citation statements)
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“…Neural Network (NN) models work very well with regards to bankruptcy prediction, with an average success rate of 81.3785% [12]. Messai and Gallali [13] support their findings in 2015, going on to state that NN models are useful for financial institutions and policy makers.…”
Section: Neural Network Modelsmentioning
confidence: 88%
“…Neural Network (NN) models work very well with regards to bankruptcy prediction, with an average success rate of 81.3785% [12]. Messai and Gallali [13] support their findings in 2015, going on to state that NN models are useful for financial institutions and policy makers.…”
Section: Neural Network Modelsmentioning
confidence: 88%
“…El proceso de predicción, entrenamiento y validación para una RNRE o RNRJ se muestran en la Figura 6, Figura 7 y Figura 8, respectivamente [6], [7], [24].…”
Section: En Launclassified
“…Therefore, banks seek to reduce the risk of default and, on the other hand, pay attention to capital adequacy. Investors also need reliable tools to help them make the right choice for their portfolios [6], [17]. For example, Fadhilah and Kurniawati's (2018) study applied the size of liquidity ratio of working capital to total assets for evaluated bankruptcy prediction in the National Private Banks Foreign Exchange listed in the Indonesian Stock Exchange.…”
Section: Altman Z-score's Modelmentioning
confidence: 99%