Hybrid Bayesian and Multiple Regression Models for Stock Market Prediction: A Comparative Analysis of Predictive Accuracy and Risk Management

In financial markets, forecasting stock price movements is critical for informed decision-making. Probabilistic models, particularly Bayesian methods, have grown in popularity due to their flexibility and ability to manage uncertainty. Multiple regression analysis, which focuses on the relationships between stock prices and independent variables like economic indicators, has also proven beneficial. However, traditional regression methods have limitations when dealing with nonlinear relationships in stock markets. This study explores the integration of Bayesian and multiple regression models, leveraging the strengths of both methods. By applying these models to daily stock prices from the S&P 500 index over ten years and incorporating macroeconomic variables such as interest rates, inflation, and GDP growth, we evaluate the predictive accuracy of each approach. The hybrid model, which combines Bayesian and multiple regression techniques, outperforms both individual models. It offers improved forecasts and risk management strategies. These findings provide significant insights for financial analysts and investors aiming to enhance their forecasting accuracy and mitigate risks.