Unsupervised Learning Diversification Applied on the Tunisian Stock Market Before and During the COVID-19 Crisis

Abstract

Financial data, related to companies listed on the Tunisian stock exchange, were collected and analyzed according to the methodology applied in machine learning on over 2 different time periods. A particular interest was focused on the periods before and during the COVID-19 crisis. The results obtained in this article show, on the one hand, that an empirical diversification based on unsupervised learning algorithms is possible and on the other hand, a good coherence with the corporates financial state in Tunisia. This article shows, for instance, that the kmeans algorithm makes it possible to segment companies according to several criteria and to discover the aberrant behavior of certain companies with an abnormal financial situation. These results were confirmed by other outlier detection algorithms.