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Financial time series prediction under Covid-19 pandemic crisis with Long Short-Term Memory (LSTM) network

Author

Listed:
  • Mourad Mroua

    (University of Sfax)

  • Ahlem Lamine

    (University of Sfax)

Abstract

In this paper, we design and apply the Long Short-Term Memory (LSTM) neural network approach to predict several financial classes’ time series under COVID-19 pandemic crisis period. We use the S&P GSCI commodity indices and their sub-indices and consider the stock market indices for different regions. Based on the daily prices, the results show that the proposed LSTM network can form a robust prediction model to determine the optimal diversification strategies. Our prediction model achieved RMSEs and MAEs too small for the different selected financial assets, showing the predictive power of our LSTM network especially during the COVID-19 health crisis. In addition, our LSTM network outperforms ARIMA-type models for all selected assets.

Suggested Citation

  • Mourad Mroua & Ahlem Lamine, 2023. "Financial time series prediction under Covid-19 pandemic crisis with Long Short-Term Memory (LSTM) network," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-15, December.
    • Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-02042-w
    DOI: 10.1057/s41599-023-02042-w
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    References listed on IDEAS

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