How CausalStock Rethinks Multi-Stock Prediction in the Age of LLMs

Causality in Financial Markets via Deep Learning and Denoised News Signals

Predicting stock movement has always sat at the intersection of noise and information. Prices shift because the world shifts, and the world shifts through events that are reported, interpreted, and amplified through news cycles. Traditional quantitative models tend to treat news as a source of sentiment or context, while correlation-based systems try to capture the intricate relationships among stocks. Yet both approaches overlook a fundamental truth of markets: relationships are directional. A dominant platform influences its ecosystem more than the reverse. In other words, many relationships that move stock prices are causal.

A research (arXiv:2411.06391v1) framework called CausalStock tackles this challenge directly. It proposes an end-to-end deep learning system that discovers temporal causal relations among stocks and uses these insights to improve news-driven multi-stock movement prediction. At the same time, it introduces an LLM-powered news encoder designed to extract meaningful signals from noisy financial text. The combination leads to more accurate predictions, clearer interpretability, and stronger real-world trading performance.

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Why Causality Matters More Than Correlation

Most modern multi-stock prediction models attempt to model relationships using attention mechanisms or graph neural networks. These architectures identify correlations between stocks, which can certainly improve predictive accuracy, but correlations rarely explain how information flows through a market.

Causal relationships reflect asymmetry. A major regulatory event affecting a chip manufacturer will likely spill into downstream device makers. A global retailer may influence shipping companies, but the causal effect in the opposite direction is weaker. Markets are networks of asymmetric influence, and capturing this directionality is essential for understanding not only what will move, but why it will move.