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The Application of Machine Learning Techniques to Predict Stock Market Crises in Africa

Author

Listed:
  • Muhammad Naeem

    (Mathematics & Computer Science Department, Modern College of Business and Science, Muscat 133, Oman
    UCP Business School, University of Central Punjab, Lahore 54782, Pakistan)

  • Hothefa Shaker Jassim

    (Mathematics & Computer Science Department, Modern College of Business and Science, Muscat 133, Oman)

  • David Korsah

    (Department of Finance, University of Ghana Business School, Legon, Accra LG78, Ghana)

Abstract

This study sought to ascertain a machine learning algorithm capable of predicting crises in the African stock market with the highest accuracy. Seven different machine-learning algorithms were employed on historical stock prices of the eight stock markets, three main sentiment indicators, and the exchange rate of the respective countries’ currencies against the US dollar, each spanning from 1 May 2007 to 1 April 2023. It was revealed that extreme gradient boosting (XGBoost) emerged as the most effective way of predicting crises. Historical stock prices and exchange rates were found to be the most important features, exerting strong influences on stock market crises. Regarding the sentiment front, investors’ perceptions of possible volatility on the S&P 500 (Chicago Board Options Exchange (CBOE) VIX) and the Daily News Sentiment Index were identified as influential predictors. The study advances an understanding of market sentiment and emphasizes the importance of employing advanced computational techniques for risk management and market stability.

Suggested Citation

  • Muhammad Naeem & Hothefa Shaker Jassim & David Korsah, 2024. "The Application of Machine Learning Techniques to Predict Stock Market Crises in Africa," JRFM, MDPI, vol. 17(12), pages 1-19, December.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:12:p:554-:d:1540423
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    References listed on IDEAS

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    Cited by:

    1. David Korsah, 2025. "Climate, energy, and geopolitical risks in African stock markets: a comparative TVP-VAR and QVAR approach," Future Business Journal, Springer, vol. 11(1), pages 1-16, December.
    2. Teplova, Tamara & Fayzulin, Maksim & Kurkin, Aleksei, 2025. "Early warning system for Russian stock market crises: TCN-LSTM-Attention model using imbalanced data and attention mechanism," Socio-Economic Planning Sciences, Elsevier, vol. 101(C).
    3. David Korsah & Seth Kwadwo Danso, 2025. "Immune or vulnerable? African stock markets’ response to U.S.–China trade wars and geopolitical tensions," Future Business Journal, Springer, vol. 11(1), pages 1-17, December.

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