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A new approach for Trading based on Long-Short Term memory technique
[Une nouvelle approche pour le Trading basée sur la technique Long-Short Term Memory]

Author

Listed:
  • Zineb Lanbouri

    (ENSIAS - Ecole Nationale Supérieure d'Informatique et d'Analyses des Systèmes - UM5 - Université Mohammed V de Rabat [Agdal])

  • Saaid Achchab

    (ENSIAS - Ecole Nationale Supérieure d'Informatique et d'Analyses des Systèmes - UM5 - Université Mohammed V de Rabat [Agdal])

Abstract

The stock market prediction has always been crucial for stakeholders, traders and investors. We developed an ensemble Long Short Term Memory (LSTM) model that includes two-time frequencies (annual and daily parameters) in order to predict the next day's Closing price (one step ahead). Based on a four-step approach, this methodology is a serial combination of two LSTM algorithms. The empirical experiment is applied to 417 New York stock exchange companies. Based on Open High Low Close metrics and other financial ratios, this approach proves that the stock market prediction can be improved.

Suggested Citation

  • Zineb Lanbouri & Saaid Achchab, 2019. "A new approach for Trading based on Long-Short Term memory technique [Une nouvelle approche pour le Trading basée sur la technique Long-Short Term Memory]," Post-Print hal-02396905, HAL.
    • Handle: RePEc:hal:journl:hal-02396905
    Note: View the original document on HAL open archive server: https://hal.science/hal-02396905
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    References listed on IDEAS

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    1. Glantz, Morton & Kissell, Robert, 2013. "Multi-Asset Risk Modeling," Elsevier Monographs, Elsevier, edition 1, number 9780124016903.
    2. Pedro Cosme Vieira & Aurora A. C. Teixeira, 2010. "Are finance, management, and marketing autonomous fields of scientific research? An analysis based on journal citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(3), pages 627-646, December.
    3. 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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