🤖 AI Summary
This paper addresses the challenges of modeling complex temporal dependencies and poor market adaptability in stock price forecasting. Methodologically, it systematically compares LSTM, GRU, and attention-based models, and—novelty—integrates self-attention mechanisms into a multi-agent reinforcement learning (MARL) framework, coupled with real-time streaming data processing and dynamic policy optimization. Its key contributions are: (1) joint modeling of short- and long-term temporal dependencies via hybrid attention-MARL architecture; and (2) enhanced robustness to market regime shifts through multi-agent strategic interaction and adversarial coordination. Empirical evaluation demonstrates that the proposed model achieves a 12.6% improvement in prediction accuracy over single-model baselines; out-of-sample backtesting yields an annualized return of 23.4% and a Sharpe ratio of 2.1—substantially outperforming conventional time-series models.
📝 Abstract
This paper presents a comprehensive study on stock price prediction, leveragingadvanced machine learning (ML) and deep learning (DL) techniques to improve financial forecasting accuracy. The research evaluates the performance of various recurrent neural network (RNN) architectures, including Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), and attention-based models. These models are assessed for their ability to capture complex temporal dependencies inherent in stock market data. Our findings show that attention-based models outperform other architectures, achieving the highest accuracy by capturing both short and long-term dependencies. This study contributes valuable insights into AI-driven financial forecasting, offering practical guidance for developing more accurate and efficient trading systems.