Predicting Bank Defaults in Ukraine: A Macro-Micro Perspective

Hlazunov, Anatolii; Verchenko, Olesia

doi:10.26531/vnbu2020.250.03

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…In the case of classification, generally, if the output after sigmoid function is greater than 0.5, the instance is regarded as classified into this category. And if it is less than or equal to 0.5, it is not classified into this category [ 12 ]. The function image is shown in Figure 3 and the formula is shown as follows:

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Section: Bp Neural Network Warning Modelmentioning

confidence: 99%

Construction and Application of the Financial Early-Warning Model Based on the BP Neural Network

Jiang

Wang

2022

Computational Intelligence and Neuroscience

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In order to further improve the early-warning effect of enterprise financial crisis management and reduce the occurrence of enterprise financial crisis, by taking listed companies as examples and combining the operating conditions of listed companies, a financial crisis early-warning indicator system was built from five aspects of profitability, debt-paying ability, development ability, operation ability, and cash flow ability. In addition, a financial management early-warning model based on the BP neural network algorithm was built. Through the experimental prediction, it is showed that the financial crisis early-warning model of listed companies based on the BP neural network algorithm for crisis prediction accuracy was more than 75%. The accuracy of the first three years of model prediction was 93.33% and 72.34%, respectively. The accuracy of model prediction in the first two years was 94.67% and 82.98%, respectively. In the first year, the accuracy rate increased to 100% and 89.36%. Compared with the prediction accuracy of the logistic model (50%), it is fully reflected that the financial early-warning model proposed in the research had a good crisis prediction ability.

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Section: Bp Neural Network Warning Modelmentioning

confidence: 99%