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BP Neural Network Based on Simulated Annealing Algorithm Optimization for Financial Crisis Dynamic Early Warning

Ying Chen1

  • 1School of Accounting, Tongling Univesity, Tongling 244000, Anhui, China.

Computational Intelligence and Neuroscience
|October 18, 2021
PubMed
Summary

This study enhances financial early warning systems using a simulated annealing algorithm-optimized BP neural network. This approach improves prediction accuracy and identifies key financial indicators for stable enterprise operations.

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Area of Science:

  • * Financial Risk Management
  • * Computational Finance
  • * Machine Learning Applications

Background:

  • * Financial early warning mechanisms are crucial for the stability and development of listed companies.
  • * Traditional models like logistic regression have limitations in accurately predicting financial distress.
  • * Optimizing predictive models is essential for timely intervention and risk mitigation.

Purpose of the Study:

  • * To compare the predictive performance of logistic regression and BP neural network early warning models.
  • * To evaluate the effectiveness of a simulated annealing algorithm in optimizing the BP neural network.
  • * To identify key financial indicators with strong discriminatory power for assessing corporate financial health.

Main Methods:

  • * Implementation of logistic regression and BP neural network (backpropagation neural network) models.

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  • * Optimization of the BP neural network using a simulated annealing algorithm.
  • * Comparative analysis of model accuracy and variable importance across different methods.
  • * Integration of multithreading, data compression, and segmentation to enhance algorithm efficiency.

    • Main Results:

    • * The simulated annealing algorithm significantly improves the efficiency and reduces the running time of the BP neural network.
    • * The optimized BP neural network demonstrates superior prediction accuracy compared to the logistic regression model.
    • * Three specific index dimensions within the BP neural network show strong ability to discriminate financial status.
    • * Variable importance analysis highlights key factors influencing financial health predictions.

    Conclusions:

    • * The simulated annealing algorithm offers significant advantages for optimizing financial early warning models.
    • * The optimized BP neural network provides a highly accurate and practically relevant tool for financial risk prediction.
    • * Enhanced computational techniques like multithreading improve the feasibility of complex model deployment.