Enhanced Stock Market Prediction Analysis Using LSTM with Attention, XGBoost, and Reinforcement Learning

Abstract

Stock market prediction is inherently complex due to its non-linearity, volatility, and dependency on multiple factors. Traditional models like ARIMA and LSTM improve forecasting accuracy but struggle with long-term dependencies and non-linear relationships. This research proposes a hybrid framework integrating LSTM with an Attention Mechanism, XGBoost for residual error correction, and Reinforcement Learning (DQN) for optimizing trading strategies. The LSTM-Attention module enhances sequential learning by prioritizing critical time steps, while XGBoost refines predictions by addressing residual errors. A Deep Q-Network (DQN)-based trading agent optimizes buy/sell decisions, ensuring profit maximization and risk management. Using Alibaba (BABA) stock data (2016-2021), the model is evaluated based on MSE, MAE, R² score, and Sharpe Ratio, outperforming traditional approaches. The cloud-based deployment enables real-time decision-making, making this hybrid model a robust solution for algorithmic trading and financial forecasting.