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Construction of an Ensemble Scheme for Stock Price Prediction Using Deep Learning Techniques

Author

Listed:
  • Justice Kwame Appati

    (University of Ghana, Ghana)

  • Ismail Wafaa Denwar

    (University of Ghana, Ghana)

  • Ebenezer Owusu

    (University of Ghana, Ghana)

  • Michael Agbo Tettey Soli

    (University of Ghana, Ghana)

Abstract
This study proposes a deep learning approach for stock price prediction by bridging the long short-term memory with gated recurrent unit. In its evaluation, the mean absolute error and mean square error were used. The model proposed is an extension of the study of Hossain et al. established in 2018 with an MSE of 0.00098 as its lowest error. The current proposed model is a mix of the bidirectional LSTM and bidirectional GRU resulting in 0.00000008 MSE as the lowest error recorded. The LSTM model recorded 0.00000025 MSE, the GRU model recorded 0.00000077 MSE, and the LSTM + GRU model recorded 0.00000023 MSE. Other combinations of the existing models such as the bi-directional LSTM model recorded 0.00000019 MSE, bi-directional GRU recorded 0.00000011 MSE, bidirectional LSTM + GRU recorded 0.00000027 MSE, LSTM and bi-directional GRU recorded 0.00000020 MSE.

Suggested Citation

  • Justice Kwame Appati & Ismail Wafaa Denwar & Ebenezer Owusu & Michael Agbo Tettey Soli, 2021. "Construction of an Ensemble Scheme for Stock Price Prediction Using Deep Learning Techniques," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 17(2), pages 1-24, April.
    • Handle: RePEc:igg:jiit00:v:17:y:2021:i:2:p:1-24
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