This study aims to investigate the application of Markov Chain models to historical stock market data, with a specific focus on the S&P 500 index. By transforming continuous closing prices into discrete categorical states such as Increase, Decrease, and Same, the Markov model allows us to analyze stock market trends in terms of probabilistic transitions. The primary goal is to determine the extent to which the future state of the market can be inferred from its current state using the principles of stochastic processes. This project demonstrates the practicality of using Markov Chains to forecast short-term market movements and highlights its potential as a tool for investors and analysts to identify market momentum and turning points.