Advancements in Stock Market Forecasting with Machine Learning: A Comprehensive Review and Future Prospects

Machine learning (ML) techniques have emerged as promising tools for enhancing market forecasting compared to traditional methods. This research conducts a systematic literature review to delineate current trends and future trajectories in ML-driven stock market prediction studies. Peer-reviewed journal articles spanning the last twenty years were classified into four primary categories: artificial neural networks, support vector machines, genetic algorithms in conjunction with other methods, and hybrid or alternative AI approaches. Each category underwent scrutiny to unveil commonalities, distinctive perspectives, constraints, and areas necessitating further exploration. The outcomes furnish valuable insights and suggestions for forthcoming research endeavours in this field.