Spatial-temporal stock movement prediction and portfolio selection based on the semantic company relationship graph

Chang Luo, He (Heather) He, Mihai Cucuringu and Tiejun Ma

Quantitative Finance, 2026, vol. 26, issue 1, 99-117

Abstract: In this paper, we approach stock price movements as a spatial-temporal prediction task, advancing beyond the traditional view of stocks as standalone entities. We first represent companies as vector embeddings, utilizing company name co-occurrence statistics from a large financial news corpus, and then construct a Semantic Company Relationship Graph (SCRG) using cosine similarities between vectors to define the mutual relationships. To tackle the financial prediction task, we introduce a novel Non-Independent and Identically Distributed Spatial-Temporal Graph Neural Network (NIST-GNN). It is specifically designed to propagate features from both neighboring companies and internal historical data while effectively handling the inherent temporal non-IIDness in stock sequences. This innovative aspect of our NIST-GNN allows for a more nuanced understanding and processing of temporal data, setting it apart from traditional spatial-temporal approaches. Our experimental results demonstrate that this methodology significantly outperforms benchmark models, yielding superior profitability and enhancing the Sharpe Ratio by 0.61 compared to the best-performing baseline, with statistical significance. Importantly, our findings provide valuable theoretical insights into the effect of information diffusion within the US market, revealing that public information from cross-correlated companies typically experiences a minimum one-day lag before diffusion, challenging conventional perceptions of market efficiency.

Date: 2026
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DOI: 10.1080/14697688.2025.2548897

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