To counter strong features of disorder and randomness of stock market fluctuation in Nigeria, this paper introduces a Markov process models for the stock market trend forecasting, which is a useful complement for an existing technical analysis. 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 for a single stock. The overall objective of this study is to apply Markov chain to model and forecast trend of Dangote Flour shares trading in the Nigerian Stock Exchange. The study was conducted through longitudinal case study design. Secondary quantitative data on the daily closing shares prices was obtained from the Nigerian Stock Exchange website over a period covering 1st January, 2018 to 31st December, 2019 forming 464 days trading 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 shares prices, the model predicted that the Dangote Flour would remain stable, increase and decrease at 0.31, 0.33 and 0.33 respectively