A Dynamic-Causal Lens of Stock Interactions: Graph Modeling From Symbolic Movement Patterns

Haotian Liu, Bowen Hu, Yadong Zhou, Yuxun Zhou

Abstract: In modern quantitative finance and portfolio-based investment, modeling latent interactions among stocks is paramount for prediction and decision-making on profit, risk management, hedging, etc. While previous studies have constructed complex stock graphs for applying sophisticated variants of graph neural networks (GNNs), existing graph modeling approaches still face two limitations: 1) Correlation-based statistical relationships fail to unveil nuanced stock interactions effectively and determine directional influence. 2) Rigid and static graphs overlook the evolving graph structure of stocks in volatile financial systems. In this paper, we propose a dynamic-causal graph neural network (DC-GNN) to discover causal interactions from the non-stationary price time series and dynamically model graph structures for stock movement prediction. More specifically, we identify the pattern prototypes of all directed stock pairs from long-term price movement knowledge to quantify their causal interactions. These prototypes capture the pattern-to-pattern correspondence across time series based on symbolic dynamics. By inferring real-time stock networks from the prototypes, we encapsulate neighbor-induced causal impacts within heterogeneous edges to model bullish, bearish, and neutral effects among stocks. Extensive experiments conducted on real-world trading data demonstrate the superiority of the proposed framework over various state-of-the-art baselines and its effectiveness, robustness, and interpretability in Fintech.