EvoBagNet: Efficient Ensemble Bagging Learning Model Enhanced with Evolutionary Algorithm in IT Stock Prediction

The rapid economic growth in recent years has led to a surge in stock market participation, necessitating accurate stock price prediction to mitigate investment risks and maximize returns. However, stock price’s dynamic nature and intrinsic volatility pose significant challenges to traditional statistical and machine learning (ML) models, which often struggle with overfitting, poor robustness, and limited generalization. To address these challenges, this study introduces EvoBagNet, an evolutionary Bagging ensemble learning model specifically designed for robust and high-accuracy stock price prediction. The proposed framework integrates nine state-of-the-art ML techniques, encompassing tree-based methods, neural networks, and both boosting and bagging ensemble approaches, applied to recent datasets from nine prominent IT sector companies. Extensive experimentation was conducted using multiple train-test split ratios to evaluate the scalability and adaptability of the models under diverse scenarios. The performance of EvoBagNet was assessed against six evaluation metrics, revealing its superior accuracy and robustness compared to alternative models. EvoBagNet demonstrated exceptional prediction accuracy across all datasets, achieving performance scores of 97.0%±0.7, 98.3%±0.5, 97.3%±0.8, 97.4%±0.6, 97.0%±1.0, 98.6%±0.4, 98.8%±0.4, 91.7%±1.2, and 98.4%±0.3 for Tech Mahindra, Mindtree, Infosys, Wipro, TCS, Mphasis, L&T Tech, HCL, and Coforge, respectively. These results highlight EvoBagNet’s potential as a powerful tool for stock price forecasting, offering significant implications for informed investment strategies and financial decision-making. This study underscores EvoBagNet’s effectiveness in addressing the limitations of traditional ML models, paving the way for its application in dynamic and high-stakes financial markets.