Stock Market Price Prediction Based on Attention-LSTM Model with Multi-Feature Bayesian Optimization

Abstract

Stock price prediction plays a critical role in investment decision-making and financial regulation. However, traditional time series models and early neural networks are limited either by restrictive assumptions or by their inability to effectively handle long sequences, resulting in suboptimal prediction performance. This paper proposes a hybrid predictive model that integrates multi-feature fusion, the attention mechanism, and Bayesian optimization into a Long Short-Term Memory (LSTM) framework to enhance prediction accuracy and stability. Using daily data from the S&P 500 Index from 2020 to 2022, the study employs LSTM to capture long-term temporal dependencies, introduces an attention mechanism to highlight key sequential features, and utilizes Bayesian optimization for adaptive hyperparameter tuning. Empirical results demonstrate that compared with conventional LSTM, attention-enhanced LSTM, and Bayesian-optimized LSTM models, the proposed Multi-Feature Bayesian Optimized Attention-LSTM achieves significantly lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), reaching 25.51, 18.35, and 0.66%, respectively. Even during periods of extreme market volatility such as the Russia–Ukraine conflict and the U.S. Federal Reserve’s interest rate hikes in 2022, the MAPE remained below 0.70%. These findings validate the synergistic effect of multi-feature fusion, the attention mechanism, and Bayesian optimization, providing more reliable decision support for financial market participants.