Abstract
Stock price prediction one of the most fascinating challenges for both professionals and academicians, especially in high-volatility and high-complexity environments. Traders employ a variety of strategies to forecast stock values. In this study, we used a combination of technical indicators and the Random Forest (RF) algorithm to forecast Moroccan stock market in several time periods (1, 5 and 10 days), and we compared the findings to those of the Support Vector Machine (SVM) model. For all of the datasets tested, the results demonstrate that the RF technique beats SVM in different time frames. The accuracy, F-Score, Recall, and AUC of the ROC curve were used to assess the robustness of our model.
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Ifleh, A., El Kabbouri, M. (2022). Prediction of Moroccan Stock Price Based on Machine Learning Algorithms. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_68
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DOI: https://doi.org/10.1007/978-3-030-96308-8_68
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