دراسات اقتصادية
Volume 5, Numéro 2, Pages 279-300
2018-12-21
Authors : Mokrani Ahlem . Cherabi Abdelaziz .
The objective of the present paper is to predict the future evolution of the Moroccan and the Tunisian stock markets using Artificial Neural Networks namely, the Multilayer Perceptron with Back-propagation, and the Auto Regressive Integrated Moving Average with Conditional Heteroskedasticity (ARIMA-GARCH). Data consisted of daily closing stock prices from 2013 to 2016 (785 observations). Results showed that artificial neural networks have produced a much lower prediction error compared to ARIMA-GARCH. It was concluded that ANNs are much more powerful than ARIMA-GARCH. However, their predictive ability is closely related to how well they are designed
Artificial Neural Networks; ARIMA-GARCH; Prediction; Stock Markets; Morocco and Tunisia
Zahouani Marwa
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Bouguerra Imane
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pages 492-509.
Hellal Aouatef
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Djeddou Messaoud
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Loukam Imed
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A. Hameed Ibrahim
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Al Dallal Jehad
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Shawaqfah Moayyad
.
pages 69-83.
Aberkane Salah
.
pages 98-103.
Sahed Abdelkader
.
Toul Hamza
.
pages 388-400.