Prediction of Moroccan Stock Price Based on Machine Learning Algorithms

Authors

  • Abdelhadi Ifleh
  • Mounim El Kabbouri

Keywords:

: Moroccan Stock Price, Machine Learning, RF, SVM, ROC curves

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 (TI) 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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Published

2022-01-01

How to Cite

Abdelhadi Ifleh, & Mounim El Kabbouri. (2022). Prediction of Moroccan Stock Price Based on Machine Learning Algorithms . Journal of Network and Innovative Computing, 10, 9. Retrieved from https://cspub-jnic.org/index.php/jnic/article/view/152

Issue

Vol. 10 (2022)

Section

Original Article

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