Application of Markov Chains in Stock Price Trend Forecasting

In this work we explore the application of Markov chains in forecasting stock price trends. Markov chains let a stochastic process that transitions from one state to another are based on probabilities offer a valuable framework for analyzing sequential data such as stock prices. By modeling the state transitions of stock prices, we aim to predict future price movements and identify potential trends. Through empirical analysis and evaluation, we demonstrate the effectiveness of Markov chains in stock price trend forecasting across various stocks. This work is a contributionin the growing body of literature on quantitative methods in financial forecasting and provides insights into the practical application of Markov chains in the stock market domain.