<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper is based on the use of recurrent neural networks and LSTM deep neural networks to obtain the financial risk prediction feature sequence in the context of big data. The financial risk prediction feature sequence is used as the input value of the input gate of the LSTM deep neural network model after data filtering, normalization and loss function optimization, and then the financial risk prediction for the output gate of the LSTM deep neural network model. Considering the availability of data, small and medium-sized enterprises listed in A-share companies in the Wind database are selected as sample enterprises, and evaluation indexes are constructed and detected at the same time so as to complete the experimental design of enterprise financial risk prediction in the context of big data. The prediction of enterprise financial risk is empirically analyzed using simulation analysis and statistical analysis. The results show that in the model performance analysis, the average value of ten years of data, the highest value is still the result obtained by LSTM training, 0.761, compared with other models of LSTM deep neural network in static financial risk prediction in the overall best performance. In the case study of Yibai Pharmaceutical, the minimum value of the rate of return, return on total assets, and return on assets were -10.02%, 2.56%, -20.72%, which reflects the fact that the private enterprises still have large profitability space to be mined. This study helps investors or financial institutions such as funds to find out the possible financial risk crisis of listed companies as early as possible to avoid the parties from incurring large financial losses.</p>
</abstract>

This paper is based on the use of recurrent neural networks and LSTM deep neural networks to obtain the financial risk prediction feature sequence in the context of big data. The financial risk prediction feature sequence is used as the input value of the input gate of the LSTM deep neural network model after data filtering, normalization and loss function optimization, and then the financial risk prediction for the output gate of the LSTM deep neural network model. Considering the availability of data, small and medium-sized enterprises listed in A-share companies in the Wind database are selected as sample enterprises, and evaluation indexes are constructed and detected at the same time so as to complete the experimental design of enterprise financial risk prediction in the context of big data. The prediction of enterprise financial risk is empirically analyzed using simulation analysis and statistical analysis. The results show that in the model performance analysis, the average value of ten years of data, the highest value is still the result obtained by LSTM training, 0.761, compared with other models of LSTM deep neural network in static financial risk prediction in the overall best performance. In the case study of Yibai Pharmaceutical, the minimum value of the rate of return, return on total assets, and return on assets were -10.02%, 2.56%, -20.72%, which reflects the fact that the private enterprises still have large profitability space to be mined. This study helps investors or financial institutions such as funds to find out the possible financial risk crisis of listed companies as early as possible to avoid the parties from incurring large financial losses.

Financial Risk Prediction Model in the Context of Big Data - Corporate Financial Risk Control Based on LSTM Deep Neural Networks

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper is based on the use of recurrent neural networks and LSTM deep neural networks to obtain the financial risk prediction feature sequence in the...

Financial Risk Prediction Model in the Context of Big Data - Corporate Financial Risk Control Based on LSTM Deep Neural Networks

Online veröffentlicht: 09. Dez. 2023

Seitenbereich: -

Eingereicht: 27. Jan. 2023

Akzeptiert: 19. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01422

Schlüsselwörter
Recurrent neural network, LSTM, Deep neural network, Loss function, Risk prediction model, Corporate financial risk

© 2023 Qiang Du, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Financial Risk Prediction Model in the Context of Big Data - Corporate Financial Risk Control Based on LSTM Deep Neural Networks

Online veröffentlicht: 09. Dez. 2023

Seitenbereich: -

Eingereicht: 27. Jan. 2023

Akzeptiert: 19. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01422

SchlüsselwörterRecurrent neural network, LSTM, Deep neural network, Loss function, Risk prediction model, Corporate financial risk

© 2023 Qiang Du, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Recurrent neural network, LSTM, Deep neural network, Loss function, Risk prediction model, Corporate financial risk