Artificial Intelligence for Financial Time-Series Forecasting: A Systematic Review of Stock Market Prediction Models
TL;DR
The findings indicate that Long Short-Term Memory is the most widely adopted Deep Learning model, XGBoost and Random Forest are the dominant Machine Learning algorithms, while Hybrid models consistently achieve the highest prediction accuracy and lower forecasting errors.
Abstract
Stock market prediction has become an important research area due to its significant role in supporting investment decisions, risk management, and financial planning. The highly dynamic and nonlinear nature of financial markets makes accurate forecasting a challenging task, encouraging researchers to adopt Artificial Intelligence-based techniques. In recent years, Machine Learning, Deep Learning, and Hybrid approaches have demonstrated remarkable improvements over conventional statistical methods in predicting stock prices and market trends. This paper presents a comprehensive Systematic Literature Review of recent advancements in AI-based stock market prediction published between 2020 and 2026. The review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology to systematically identify, screen, evaluate, and synthesize relevant peer-reviewed studies collected from major scientific databases. A total of 30 high-quality studies were selected for detailed analysis and categorized into Machine Learning, Deep Learning, and Hybrid approaches. The selected studies were comparatively analyzed based on prediction models, datasets, evaluation metrics, forecasting performance, publication trends, and application domains. The findings indicate that Long Short-Term Memory is the most widely adopted Deep Learning model, XGBoost and Random Forest are the dominant Machine Learning algorithms, while Hybrid models consistently achieve the highest prediction accuracy and lower forecasting errors. The review also identifies key research challenges, including market volatility, non-stationary financial data, model interpretability, and computational complexity, and highlights emerging research directions such as Explainable Artificial Intelligence, multimodal data integration, Transformer-based architectures, Graph Neural Networks, and adaptive learning frameworks. This review provides a comprehensive reference for researchers and practitioners by summarizing recent developments, identifying existing research gaps, and outlining future opportunities for developing more accurate, robust, and intelligent stock market prediction systems.