Anticipating Financial Turmoil: A Review of Machine Learning Approaches for Stock Market Crash Prediction and a Proposed Framework - Dr. David R. Klein; Dr. Lin Yue (Emerging Frontiers Library for The American Journal of Engineering and Technology, 2025)
Accurate prediction of stock market crashes is a longstanding challenge in financial analysis, with significant implications for investors, regulators, and policymakers. This paper presents a comprehensive review of existing machine learning (ML) approaches used in the prediction of financial crises and market crashes. It evaluates a wide range of techniques—including supervised learning, unsupervised learning, ensemble models, and deep learning—highlighting their strengths, limitations, and performance in various market conditions. Key challenges such as data imbalance, feature selection, temporal dependencies, and the interpretability of predictions are discussed. Building on the insights from existing literature, the paper proposes a novel hybrid framework that integrates multi-source financial indicators, sentiment analysis, and time-series modeling to enhance crash prediction accuracy and reliability. The study contributes to the growing field of AI-driven financial forecasting and provides a foundation for future research on robust early warning systems.