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Section Business and Economics

The Efficacy of Bayesian Analysis in Predicting Stock Prices

Vol. 11 No. 1 (2026): June :
DOI : https://doi.org/10.21070/acopen.11.2026.13156
Published : Dec 27, 2025

Haider Abbas abdullah Aljanabi (1)

(1) Collage Of Administration & Economics, Kerbala University, Iraq
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Abstract:

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 Making
Highlight :








  • 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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