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- W4387247255 abstract "Financial crisis forecasting is essential to prevent financial problems from occurring in companies. Because the dynamic characteristics of financial marketing are nonlinear, it is unclear whether risks can be predicted. This paper aims to explore the predictability of financial situation from financial indicators perspective. This study has taken listed companies as examples and constructed financial crisis prediction systems which include financial and non-financial indicators from financial statements and other table notes information in the Guotaian Database. The adoptable indicators are retained to be used and 136 companies with normal and abnormal financial indicators are selected for empirical research. The original BP neural network early warning model of financial crisis and the optimized BP neural network early warning model of financial crisis are established respectively to run the stimulation test with the retained data. The optimized model can improve the model fit. It can improve the accuracy of the prediction of financial distress effectively, which can reduce financial losses to companies and investors. As information on company financial statements is confusing and complex, traditional mathematical and statistical methods cannot provide accurate predictions of corporate financial crisis. The network framework proposed in this paper has strong horizontal connections between units, powerful memory and stronger generalization ability compared with traditional network structures. The optimization model has significant performance improvements." @default.
- W4387247255 created "2023-10-03" @default.
- W4387247255 creator A5043936824 @default.
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- W4387247255 date "2023-08-17" @default.
- W4387247255 modified "2023-10-03" @default.
- W4387247255 title "Financial Crisis Prediction of Listed Companies Based on Optimized BP Neural Networks" @default.
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- W4387247255 doi "https://doi.org/10.1145/3616131.3616140" @default.
- W4387247255 hasPublicationYear "2023" @default.
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