Beyond Traditional Models: Revolutionizing Stock Market Prediction with a Deep Learning Framework

Stock market prediction is a crucial yet challenging task for investors and financial analysts. Traditional approaches, including RF, SVM, NB, ELM, and RNN, are limited by their computational expense, sensitivity to parameter tuning, poor performance with complex data, limited interpretability, and difficulty in training. To overcome these drawbacks, a novel approach has been proposed, leveraging LSTM and GRU with attention mechanism architectures to enhance stock market prediction accuracy. By incorporating historical stock market data, economic indicators, and sentiment analysis, the model captures intricate patterns and dynamics within financial markets. The attention mechanism improves prediction accuracy by focusing on relevant historical data points, capturing long-term dependencies, and reducing noise. This enables the model to identify key features and patterns driving stock prices, resulting in more accurate predictions and improved interpretability. The proposed approach's performance is evaluated across various market conditions and time horizons, providing actionable insights for investors and enhancing portfolio performance.