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Log-periodic power law hybrid model based on BP neural network

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Abstract

Stock market crashes bring about huge economic loss, so predicting the stock market crashes is extremely important for both the investors and practitioners. Back propagation (BP) neural network performed greatly in data adaptability and fitting, it has been widely used for forecasting study in various fields. However, given the low frequency and lack of periodicity of stock market crashes, the BP neural network can hardly predict the intensively fluctuated stock prices accurately during the crashes. To resolve this issue, we introduced BP neural network into Log-periodic power law model, proposed the Log-periodic power law hybrid model based on the BP neural network. This combined approach could further capture the fluctuation trend and predict the volatility during the stock market crashes better. The experimental results showed that this hybrid model could predict the stock prices in different stock markets with a slightly increased accuracy. Moreover, its performance was more stable compared with BP neural network.

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Funding

This work was financially supported by the National Youth Science Foundation of China (No. 61503272), Key Research and Development Plan of Shanxi Province (201903D121151).

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Authors and Affiliations

  1. College of Information and Computer, Taiyuan University of Technology, Taiyuan, Shanxi, China

    Huiqing Wang, Dan Liu, Jingjing Wang, Yue Ma & Yuanyuan Lian

  2. Postal Savings Bank of China, Beijing, China

    Jianhui Zhang

Authors
  1. Huiqing Wang
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  2. Dan Liu
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  3. Jianhui Zhang
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  4. Jingjing Wang
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  5. Yue Ma
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  6. Yuanyuan Lian
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Correspondence to Dan Liu.

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Wang, H., Liu, D., Zhang, J. et al. Log-periodic power law hybrid model based on BP neural network. Evol. Intel. 17, 123–131 (2024). https://doi.org/10.1007/s12065-020-00552-z

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