This paper explores the application of the Kalman filter algorithm in estimating the true value of stocks in finance. By filtering out noise from stock price data, the Kalman filter provides insights into the intrinsic worth of stocks, aiding investment decision-making. Combining the Kalman filter (KF) with the Rolling Windows technique enhances trading strategies based on market conditions. The study highlights the simplicity and effectiveness of the Kalman filter and suggests future enhancements, such as incorporating financial models and adapting to market volatility. These directions aim to refine the algorithm’s capabilities in capturing complex financial dynamics and improving prediction accuracy in uncertain market environments.