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PREDICTING CRISES ON THE AFRICAN FRONTIER STOCK MARKETS WITH INVESTOR SENTIMENT INDICATORS: A MACHINE LEARNING APPROACH

David Korsah & Lord Mensah

Journal of International Technology and Information Management2025https://doi.org/10.58729/1941-6679.1593article
ABDC C
Weight
0.50

What the paper says

This study examined the predictive ability of machine learning algorithms in identifying crises within African stock markets. The study employed seven distinct machine-learning models, analyzing historical stock prices from eight stock markets, three major sentiment indicators, and the exchange rates of local currencies against the US dollar, with each data spanning from May 1, 2007, to April 1, 2023. Extreme Gradient Boosting (XGBoost) emerged as the most effective algorithm for predicting crises. Historical stock prices and exchange rates were identified as the most critical features for prediction. On the sentiment side, investors’ perceptions of potential volatility on the S&P 500, as captured by the CBOE Volatility Index (VIX), and the daily News Sentiment Index were recognized as significant predictors.The study advances the understanding of market sentiment’s role in stock market dynamics and highlights the importance of employing advanced computational techniques for risk management and market stability.

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https://doi.org/https://doi.org/10.58729/1941-6679.1593

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@article{david2025,
  title        = {{PREDICTING CRISES ON THE AFRICAN FRONTIER STOCK MARKETS WITH INVESTOR SENTIMENT INDICATORS: A MACHINE LEARNING APPROACH}},
  author       = {David Korsah & Lord Mensah},
  journal      = {Journal of International Technology and Information Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.58729/1941-6679.1593},
}

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PREDICTING CRISES ON THE AFRICAN FRONTIER STOCK MARKETS WITH INVESTOR SENTIMENT INDICATORS: A MACHINE LEARNING APPROACH

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.