Accurate prediction of stock market behavior remains a critical challenge due to the inherent
volatility and complexity of financial markets. Reliable forecasting models are essential for
investors, analysts, and policymakers to support informed decision-making and effective risk
management. This study investigates the application of statistical modeling techniques, with a
primary focus on the Autoregressive Integrated Moving Average (ARIMA) model, for forecasting
stock price movements. Historical stock price data were collected from publicly available financial
sources and subjected to systematic preprocessing, including data cleaning, normalization, and
transformation, to ensure analytical accuracy.