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Multi-Factor RFG-LSTM Algorithm for Stock Sequence Predicting

Author

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  • Zhi Su

    (Central University of Finance and Economics
    Central University of Finance and Economics)

  • Heliang Xie

    (Central University of Finance and Economics
    Postal Savings Bank of China)

  • Lu Han

    (Central University of Finance and Economics)

Abstract

As has been demonstrated, the long short-term memory (LSTM) algorithm has the special ability to process sequenced data; however, LSTM suffers from high dimensionality, and its structure is too complex, leading to overfitting. In this research, we propose a new method, RFG-LSTM, which uses a rectified forgetting gate (RFG) to restructure the LSTM. The rectified forgetting gate is a function that can limit the boundary of an input sequence, so it can reduce the dimensionality and complexity of a neural network. Through theoretical analysis, we demonstrate that RFG-LSTM is monotonic, just as LSTM is; additionally, the stringency does not change in the new algorithm. Thus, RFG-LSTM also has the ability to process sequenced data. Based on the real trading scenario of China’s A stock market, we construct a multi-factor alpha portfolio with RFG-LSTM. The experimental results show that the RFG-LSTM model can objectively learn the characteristics and rules of the A stock market, and this can contribute to a portfolio investment strategy.

Suggested Citation

  • Zhi Su & Heliang Xie & Lu Han, 2021. "Multi-Factor RFG-LSTM Algorithm for Stock Sequence Predicting," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1041-1058, April.
    • Handle: RePEc:kap:compec:v:57:y:2021:i:4:d:10.1007_s10614-020-10008-2
    DOI: 10.1007/s10614-020-10008-2
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    References listed on IDEAS

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    1. Dixon, Matthew & Klabjan, Diego & Bang, Jin Hoon, 2017. "Classification-based financial markets prediction using deep neural networks," Algorithmic Finance, IOS Press, vol. 6(3-4), pages 67-77.
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    Cited by:

    1. Qin Lu & Jingwen Liao & Kechi Chen & Yanhui Liang & Yu Lin, 2024. "Predicting Natural Gas Prices Based on a Novel Hybrid Model with Variational Mode Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 639-678, February.

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