Application of Markov chain to model and forecast stock market trend: A study of Safaricom shares in Nairobi Securities Exchange, Kenya

Abstract

Wealth creation is the goal for every investor. The stock market is one attractive area for investment. Nairobi Securities Exchange being an emerging market in the region, it is considered that both foreign and local investors will seize the opportunity and invest in the stock market. However, this has not been the case for many potential investors due to inability to make informed investment decisions based on future expectations of the stock market. An understanding of the stock market trend in terms of predicting price movements is important for investment decisions. Markov Chain model has been widely applied in predicting stock market trend. In many applications, it has been applied in predicting stock index for a group of stock but little has been done Moreover, the model has had limited application in emerging stock markets. The overall objective of this study therefore, was to apply Markov Chain to model and forecast trend of Safaricom shares trading in Nairobi Securities Exchange, Kenya. The study was conducted through a longitudinal case study design. Secondary quantitative data on the daily closing share prices of Safaricom was obtained from NSE website over a period covering 1st April 2008 to 30 rading data panel. A markov chain model was determined based on probability transition matrix and initial state vector. In the long run, irrespective of the current state of share price, the model predicted that the Safaricom share prices would depreciate, maintain value or appreciate with a probability of 0.3, 0.1 and 0.5 respectively.