Optimizing Stock Price Prediction Using a Hybrid Model Integrating Meta-Heuristics and Machine Learning

Abstract

Predicting stock prices in financial markets is a formidable challenge due to the multifaceted nature of market dynamics, influenced by various factors. This study presents a comprehensive hybrid methodology that combines meta-heuristics and intelligent evolutionary search algorithms to forecast BSE Sensex stock prices. The methodology begins with rigorous data collection and preprocessing, followed by feature selection using meta-heuristics, which optimize the feature subset for predictive accuracy. Diverse predictive models, encompassing traditional time series models and advanced machine learning algorithms, are selected and integrated through meta-heuristics, enabling an ensemble approach that capitalizes on the strengths of each model. Intelligent evolutionary search techniques fine-tune model hyperparameters, ensuring optimal performance. Risk management strategies are embedded in the system, and ethical considerations are addressed to promote responsible AI usage in finance. Continuous model adaptation and comprehensive documentation are maintained to facilitate reliability and transparency. The methodology undergoes extensive evaluation, including backtesting and robustness testing, to validate its effectiveness in forecasting BSE Sensex stock prices. The study aims to empower investors and financial professionals with more accurate predictions and enhanced risk management capabilities, fostering informed decision-making in the dynamic and uncertain realm of stock market investment.