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Intelligent Stock Prediction: A Neural Network Approach

Author

Listed:
  • Mohamad Hassan Shahrour

    (GRM - Groupe de Recherche en Management - EA 4711 - UNS - Université Nice Sophia Antipolis (1965 - 2019) - UniCA - Université Côte d'Azur)

  • Mostafa Dekmak

    (University of Sunderland)

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, 2022. "Intelligent Stock Prediction: A Neural Network Approach," Post-Print hal-03884171, HAL.
    • Handle: RePEc:hal:journl:hal-03884171
    DOI: 10.1142/S2424786322500165
    as

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