Application of Markov Chain to Model the Monthly Stock Prices of Nestle Foods Nigeria PLC

Abstract

This study applies Markov chain modelling to analyze and forecast the stock price behaviour of Nestle Foods Nigeria PLC. Using daily closing price data from February 2021 to March 2024, a discrete-state Markov chain model was developed with three states representing price increases, decreases, and stagnation. Transition probabilities between states were estimated from the historical data to construct a 3x3 transition probability matrix. The steady-state vector was calculated to determine long-run equilibrium probabilities for each state. Key findings include equal long-run probabilities of approximately 33.3% for each of the three price movement states, suggesting a balanced, random walk-like behaviour consistent with the efficient market hypothesis. The transition matrix provided insights into short-term price movement tendencies. The results also indicated that Nestle Foods stock prices follow an efficient market process where past movements do not significantly predict future changes. This Markov chain analysis offers a probabilistic framework for understanding Nestle Foods' stock price dynamics, with implications for investment strategies and market efficiency. The methodology demonstrates the applicability of Markov models for quantitative stock analysis in the Nigerian market context.