Zhiyu Xu


2025

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Modeling Interactions Between Stocks Using LLM-Enhanced Graphs for Volume Prediction
Zhiyu Xu | Yi Liu | Yuchi Wang | Ruihan Bao | Keiko Harimoto | Xu Sun
Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal)

Accurate trading volume prediction is essential for portfolio optimization, market regulation, and financial risk control. An effective method for predicting trading volume involves building a graph to model relations between stock. Recent research has enhanced these models by integrating stock news to improve forecasting ability. However, existing approaches primarily integrate news data as auxiliary features for nodes in Graph Neural Networks (GNNs), overlooking the relational information between stocks embedded in news. To address this, we propose LLM-Enhanced Dynamic Graph Neural Network (LED-GNN), a framework that constructs dynamic graphs using inter-stock relationships extracted from news via a large language model (LLM)-centered pipeline, combined with graphs learned from historical price-volume data. A dynamic GNN then processes these graphs to generate predictions. Evaluated on a real-world dataset, TOPIX, with Reuters Financial News, LED-GNN consistently outperformed all baseline models, achieving a 2% improvement over the strongest baseline.