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

Author

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  • 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

    as
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    3. Sandeep A. Patel & Asani Sarkar, 1998. "Crises in Developed and Emerging Stock Markets," Financial Analysts Journal, Taylor & Francis Journals, vol. 54(6), pages 50-61, November.
    4. Hassan Raza & Zafar Akhtar, 2024. "Predicting stock prices in the Pakistan market using machine learning and technical indicators," Modern Finance, Modern Finance Institute, vol. 2(2), pages 46-63.
    5. Mensi, Walid & Boubaker, Ferihane Zaraa & Al-Yahyaee, Khamis Hamed & Kang, Sang Hoon, 2018. "Dynamic volatility spillovers and connectedness between global, regional, and GIPSI stock markets," Finance Research Letters, Elsevier, vol. 25(C), pages 230-238.
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