Keywords
Causal Inference
Abstract
Stock movement prediction is a problem of enduring interest in quantitative finance and machine learning. While correlation-based graph neural networks have demonstrated promising results by modeling relationships among financial assets, these approaches fundamentally rely on statistical associations rather than causal mechanisms. Correlation-based models are inherently limited in their ability to generalize under market regime shifts and structural breaks, as they capture spurious co-movements that do not reflect true causal relationships. In this paper, we propose CausalKG (Causal Knowledge Graph), a novel framework that constructs a causally-structured financial knowledge graph from historical event data, corporate fundamentals, and macroeconomic indicators, and applies counterfactual reasoning to predict stock movements under intervention scenarios. Our approach distinguishes between correlation and causation by modeling the underlying causal DAG (Directed Acyclic Graph) of financial markets, enabling more robust predictions when market conditions change. We develop a Causal Graph Attention Network (CGAT) that operates over the causal graph and a Counterfactual Prediction Engine (CPE) that estimates how a stock would behave if certain causal factors were intervened upon. Extensive experiments on three major stock datasets (S&P 500, FTSE 100, and Nikkei 225) show that CausalKG outperforms ten baseline models including S3G (Lu, Hu, and Zhang, 2026), the state-of-the-art stock state space graph model, with an average improvement of 5.2% in directional accuracy and 9.1% in Matthews Correlation Coefficient (MCC). Notably, CausalKG exhibits significantly superior generalization during market crisis periods (2020 COVID-19 crash and 2022 rate-hike cycle), where correlation-based models suffer severe performance degradation.
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