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Intelligent stock prediction: A neural network approach

Author

Listed:
  • Mohamad Hassan Shahrour

    (Université Côte d’Azur, IAE Nice, GRM, Nice, France)

  • Mostafa Dekmak

    (��University of Sunderland, Sunderland, UK)

Abstract

Ever since the existence of financial markets, predicting stocks’ movement has been crucial for investors in order to increase their investment returns. Despite the plethora of research, the outstanding literature provides mixed results concerning the choice of model. Are Artificial Intelligence systems valid techniques in predicting stock prices? Do deep learning models outperform machine learning models? Through developing different machine and deep learning models, the overall findings reveal that deep learning techniques (i.e., ANN and LSTM) outperform machine learning techniques (i.e., SVR) in price prediction. The results are validated using different accuracy measures.

Suggested Citation

  • Mohamad Hassan Shahrour & Mostafa Dekmak, 2023. "Intelligent stock prediction: A neural network approach," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 10(01), pages 1-14, March.
  • Handle: RePEc:wsi:ijfexx:v:10:y:2023:i:01:n:s2424786322500165
    DOI: 10.1142/S2424786322500165
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    More about this item

    Keywords

    Stock prediction; artificial intelligence; deep learning; machine learning; neural networks;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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