Examining Nasdaq Market Data and Presenting an Optimized Model by Extreme Gradient Boosting Regression and Artificial Bee Colony

Stock price prediction is a critical task in the financial sector due to its profound implications for traders and investors. This paper presents a comparative analysis of machine learning models applied to stock price prediction using historical data from the Nasdaq stock index spanning the years 2015 to 2023. The study introduces an Extreme Gradient Boosting Regression (XGBR) model optimized with three distinct metaheuristic algorithms: Battle Royal Optimization (BRO), Moth Flame Optimization (MFO), and Artificial Bee Colony (ABC). These optimization techniques aim to enhance the model's predictive performance by improving parameter tuning and model generalization. Among the optimized models, the ABC-XGBR demonstrated superior performance due to its strong balance between exploration and exploitation and its effective search capability in high-dimensional feature spaces. The experimental results show significant improvements over the baseline XGBR model, with R² values of 0.9721 for BRO-XGBR, 0.9885 for MFO-XGBR, and 0.9936 for ABC-XGBR. These outcomes underscore the effectiveness of combining machine learning with nature-inspired optimization algorithms to produce more accurate stock price forecasts. This research contributes valuable insights into the practical application of hybrid models for financial forecasting, emphasizing their utility in enhancing predictive accuracy. It also offers decision-makers—such as investors, analysts, and financial institutions—a robust framework for incorporating data-driven strategies into risk assessment and portfolio management. Future work may explore additional datasets, real-time prediction capabilities, and further refinement of optimization algorithms to extend the applicability of these methods to broader financial contexts.