Enhancing Stock Market Predictions with Granger and Reinforcement Learning: An Adaptive Approach

Traditional graph neural networks (GNNs) construct static graph structures, which are unable to dynamically adapt to market changes. To address this challenge, this paper proposes an integrated prediction model combining GNNs and reinforcement learning (RL), enabling dynamic adjustment of graph structures to enhance the model's adaptability and predictive ability in volatile markets. Firstly, we use GNNs to extract complex inter-stock relationships from historical stock data, constructing a dynamic market structure. Secondly, through RL strategies within the RL model, we continuously adjust trading decisions to maximize long-term returns and adapt to market changes. This combined model not only captures market trading information but also responds promptly to market dynamics, achieving more accurate predictions. Experimental results on multiple datasets of Taiwan 50 constituent stocks demonstrate that our proposed GNN-RL model outperforms traditional methods in terms of accuracy and other metrics, proving its potential and superiority in practical applications.