Stock Market Trend Analysis and Prediction Using Markov Chain on National Stock Exchange

Abstract

In developing economies, the stock market plays a vital role in capital formation and economic development. Forecasting stock
price movements remains a challenging yet crucial task for investors and researchers due to the inherent stochastic nature and volatility of stock prices. This study employs a Markov Chain (MC) model to analyze and predict the price trend of ITC Ltd. shares traded on the National Stock Exchange (NSE) of India. A two-state Markov model was constructed using 700 days of historical daily closing prices, with states defined as “Increase” and “Decrease” based on day-to-day price changes. Initial and transition probabilities were computed, and the long-term behavior of the stock was evaluated through steady-state probabilities and n-step transition matrices. The analysis reveals that the stock has approximately a 50% probability of increasing and a 49% probability of decreasing in the long run. The study further calculates the expected number of visits and return times for each state. The results demonstrate that the Markov Chain model can serve as a reliable probabilistic framework for stock trend forecasting and can assist investors in making informed decisions. This approach offers significant utility for portfolio management and market risk analysis in the Indian equity context.