<![CDATA[General Background: Stock price prediction remains a central concern in financial economics due to its role in guiding investment decisions and managing market risk. Specific Background: Conventional forecasting techniques often inadequately address uncertainty, adaptability to new information, and the integration of prior knowledge, whereas Bayesian analysis offers a probabilistic framework that updates beliefs using historical and incoming data. Knowledge Gap: Despite growing interest in Bayesian methods within finance, empirical evidence demonstrating their practical efficiency in closely matching predicted and actual stock prices remains limited. Aims: This study aims to examine the efficiency of Bayesian analysis as a technical tool for predicting stock prices and supporting investor decision-making. Results: The findings indicate that Bayesian analysis generates predictions with minimal deviation from actual stock prices, confirming the robustness and reliability of the model. Novelty: The study reinforces the applied value of Bayesian analysis by empirically demonstrating its predictive efficiency within a real stock market context. Implications: These results suggest that Bayesian-based forecasting can enhance investors’ analytical independence, reduce reliance on costly external financial analysts, and promote more informed, data-driven investment strategies in dynamic financial markets.Keywords : Bayesian Analysis, Stock Price Prediction, Bayesian Forecasting Models, Financial Time Series, Investment Decision MakingHighlight : Forecast outputs closely matched observed market values, indicating minimal deviation across tested periods. Continuous updating with incoming information improved adaptability under changing market conditions. Empirical application demonstrated practical usefulness for investment decision-making without reliance on external advisors.]]>
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