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Forecasting the Moroccan Stock Market: A Theoretical Approach Integrating Macroeconomic and Sentiment Data through Deep Learning

Author

Listed:
  • Imad Talhartit

    (Université Hassan 1er [Settat], Ecole Nationale de Commerce et Gestion - Settat, Laboratory of Finance, Audit and Organizational Governance Research)

  • Sanae Ait Jillali

    (Université Hassan 1er [Settat], Ecole Nationale de Commerce et Gestion - Settat, Laboratory of Finance, Audit and Organizational Governance Research)

  • Mounime El Kabbouri

    (Université Hassan 1er [Settat], Ecole Nationale de Commerce et Gestion - Settat, Laboratory of Finance, Audit and Organizational Governance Research)

Abstract

In today's data-driven economy, predicting stock market behavior has become a key focus for both finance professionals and academics. Traditionally reliant on historical and economic data, stock price forecasting is now being enhanced by AI technologies, especially Deep Learning and Natural Language Processing (NLP), which allow the integration of qualitative data like news sentiment and investor opinions. Deep Learning uses multi-layered neural networks to analyze complex patterns, while NLP enables machines to interpret human language, making it useful for extracting sentiment from media sources. Though most research has focused on developed markets, emerging economies like Morocco offer a unique context due to their evolving financial systems and data limitations. This study takes a theoretical and exploratory approach, aiming to conceptually examine how macroeconomic indicators and sentiment analysis can be integrated using deep learning models to enhance stock price prediction in Morocco. Rather than building a model, the paper reviews literature, evaluates data sources, and identifies key challenges and opportunities. Ultimately, the study aims to bridge AI techniques with financial theory in an emerging market setting, providing a foundation for future empirical research and interdisciplinary collaboration.

Suggested Citation

  • Imad Talhartit & Sanae Ait Jillali & Mounime El Kabbouri, 2025. "Forecasting the Moroccan Stock Market: A Theoretical Approach Integrating Macroeconomic and Sentiment Data through Deep Learning," Post-Print hal-05094029, HAL.
    • Handle: RePEc:hal:journl:hal-05094029
    DOI: 10.5281/zenodo.15576354
    Note: View the original document on HAL open archive server: https://hal.science/hal-05094029v1
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Stock Price Prediction; Deep Learning; Natural Language Processing (NLP); Sentiment Analysis; Macroeconomic Indicators; Emerging Markets; Moroccan Financial Market;
    All these keywords.

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