Extraction of economic variables stimulating the activity of the Algiers stock exchange using machine learning algorithms

  • Fadhila Ferahi Université d'Alger 3, Algérie
Keywords: Supervised Machine Learning, Support Vector Machine, Python Software, Algeria Stock Exchange, Market Value


Since two decades of the establishment of Algiers Stock Exchange, it has not yet reached the required addition to the national economy. So, in the context of the reform policies to enhance the pillars of economic recovery, this study came to examine the mechanisms that activate the role of this stock exchange, examining the economic variables that have stimulated its activity, and explore solutions that can be adopted to meet the challenges basing on experiences of other countries and using modern techniques of machine learning such that classification algorithms used in the Python software. Based on economic database on Algeria, during the period 1999-2020, this research reached to extract and classify three variables with positive effect on the performance of the Algiers Stock Exchange, and nominated the Support Vector Machine (SVM) algorithm to determine the optimal model which can predict the positive development of this stock exchange with highest accuracy and best recall.


Journal Identifiers

eISSN: 1012-0009
print ISSN: 2437-0568