Modeling Interactions Between Stocks Using LLM -Enhanced Graphs for Volume Prediction (2025)

Abstract: 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.

## introduction trading volume refers to the total amount of stock transaction within a certain unit of time. the prediction of the trading volume is of significant value in portfolio optimization, marketing regulation, and financial risk control @xcite . historically, many significant market changes have been accompanied by unusually high trading @xcite @xcite . trading volume prediction can be beneficial for developing stock trading strategies, as substantial orders can push the stock price in an unfavorable direction for the investor @xcite . this shift in stock price can be mitigated by dividing large positions according to accurate knowledge of future volume trends, thus achieving higher investment profits. graph neural networks (gnns) have garnered increasing attention in stock prediction for their ability to model inter-stock relations @xcite . since stock data lacks inherent graph structures, various methods are employed to construct graphs, including utilizing prior knowledge @xcite and mining relational data from historical stock prices and trading volumes @xcite @xcite . incorporating external information @xcite , such as news data, has also shown great potential in improving prediction accuracy. sometimes, news data are integrated with graph neural networks as auxiliary features for node representation @xcite . however, managing redundancy and noise in lengthy news articles remains a persistent challenge, prompting the development of various methods to extract key information @xcite @xcite . recently, with the emergence of large language models (llms), financial expert llms have been developed to enhance understanding of news data and provide more informed investment advice @xcite . however, a crucial point remain unexplored. a significant portion of news pertains to multiple stocks and the relationships among them, naturally forming an implicit graph with stocks as nodes. while considerable efforts have been made to model stock data using graphs, news data are typically incorporated only as a part of the node features. by modeling news as graph edges, it becomes possible to capture the impact of sudden events on the relations between stocks. however, the potential of leveraging relational news information directly in the graph's edges remains largely unexplored. yet, extracting meaningful relationships from lengthy and complex news articles poses significant challenges, particularly due to the scarcity of specific and labeled training data. to address this, we propose a large language model (llm)-enhanced pipeline tailored to effectively extract relational information. building on this, we introduce llm-enhanced dynamic graph neural network (led-gnn), a framework for predicting trading volume more effectively. our approach constructs a dynamic graph using relationships between stocks derived from news articles via the llm-enhanced pipeline. additionally, a graph structure is learned from stock price and volume data. both graphs are then processed through a generic gnn architecture designed for dynamic graphs, producing node embeddings that are subsequently utilized for predictions. the specific framework of our method is shown in figure 1 . we evaluated our model on a real-world dataset, topix @xcite , along with news data collected from reuters financial news. our model consistently outperformed all baselines, achieving a 2% improvement over the strongest baseline model. in summary, our contributions are as follows: ## conclusion in this work, we introduced the led-gnn framework, a novel approach to trading volume prediction that integrates dynamic relationship graphs derived from both historical stock data and news articles, enhanced through large language models (llms). by modeling news as graph edges, led-gnn captures the intricate interactions between stocks influenced by external events, offering a more comprehensive representation of stock relationships. extensive experiments are conducted to evaluate the performance of led-gnn and the effectiveness of its sub-modules. additionally, we explore the impact of the lookback window length on prediction accuracy. our model outperforms all baselines consistently. to the best of our knowledge, this is the first work to apply large language models for extracting stock news to construct dynamic graphs. we hope this work will inspire further exploration of the integration of large language models and graph neural networks in the field of stock prediction.