EMoS Published: Machine Learning for Stock Market

EMoS students Alban Manishimwe, Happyness Alexander & Hillary Kaluuma have made a notable contribution by publishing their work on development of an integrated mobile based application on machine learning for East Africa stock market. The paper was supervised by Dr. Mussa Ally, the Deputy Centre Leader.

The financial markets have been identified as a key driver for common market and customs union in the East African Community. The challenge however is that the East African countries run the most expensive stocks on the African Continent and this has greatly influenced trade volumes and investor appetite. When these markets are integrated it allows collective bargaining thereby increasing their market share across the region other than a given country running its own exchange.

This paper presents a software platform that will enable an investor to purchase and sell stocks irrespective of any location in the East African Region thus spur the development of stock market in terms of volume and competitiveness.