Stock Market Movements Using Advanced Deep Learning Architectures and Hybrid Approaches.

Abstract

Predicting short-term stock market movements is a challenging yet highly sought-after goal in financial markets. This research investigates the effectiveness of advanced machine learning techniques, specifically deep learning architectures, ensemble methods, and hybrid models, for forecasting daily stock price changes and directional movements. Leveraging historical financial data, including price, volume, technical indicators, and incorporating natural language processing (NLP) for potential sentiment analysis (as implied by library usage), the study employs a quantitative, empirical research design. We implement and evaluate Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Transformer models to capture temporal dependencies and patterns. Furthermore, stacking ensembles and hybrid models combining deep learning with technical analysis and rule-based strategies are explored. Model performance is rigorously evaluated using comprehensive regression, classification, and financial/trading metrics on a held-out test set. Explainable AI techniques, including SHAP, LIME, and attention visualization, are applied to interpret model predictions and understand feature importance. The findings provide insights into the capabilities of various advanced ML techniques for stock market prediction, highlighting the potential benefits of integrating diverse data sources and model architectures. While the results section presents hypothetical outcomes requiring experimental validation, the methodology establishes a robust framework for future quantitative analysis.