This paper suggests an enhanced machine-learning-based system to guide future stock price decisions. In reality, most existing machine learning systems, such as SEA (Stream Ensemble Algorithm), VFDT (Very Fast Decision Tree ), and online bagging and boosting, keep models updated with only new data and reduce training timeframes to allow working rapidly with the most recent model. However, limited learning times and the exclusion of essential information from previous data may result in a bad performance. When it comes to learning models, our system takes a different approach. It builds several models based on random selections of historical data from the main stock as well as related stocks. The best models are then combined to generate a final, performant model. We performed an empirical study on five Islamic stock market indices. We can say from the results that our system outperforms the existing published algorithms. This framework can contribute then to having an enhanced system that will enable different stakeholders to make rapid decisions based on the forecasted trend of indices.

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We study in this paper the presence of long memory of four Mediterranean stock markets namely Morocco, Turkey, Spain, and France, over the period 2000-2020. The presence of long memory propriety has tested by using the R/S analysis approach. Results show that the four processes have a long memory. furthermore, ARFIMA-FIGARCH, under different distribution assumptions as Normal, Student-t, and Skewed Student- t, was estimated in order to test the feature of long memory in the return and volatility of the stock markets simultaneously. Results show strong evidence of long memory in both returns and volatility for the Moroccan and French stock markets and only in volatility for The Spanish and Turkish ones. The long memory in returns indicates that their behavior is predictable implying the rejection of the efficient market hypothesis. The long memory in volatility shows that risk is an important parameter of the behavior of the future returns in the four stock markets.