Gated Fusion Enhanced Multi-Scale Hierarchical Graph Convolutional Network for Stock Movement Prediction

2025-11-03

AI Summary

Stock price forecasting is highly challenging due to market volatility and complex cross-stock dependencies. Existing multi-scale graph neural networks (GNNs) often neglect the influence of intra-stock attribute patterns on relational modeling and suffer from coarse-/fine-grained bias in multi-scale feature fusion. To address these issues, we propose a multi-scale hierarchical GNN framework. First, we design a dynamic graph construction mechanism that explicitly models time-varying topologies driven by intra-stock attribute patterns. Second, we introduce a top-down gated fusion strategy to jointly optimize coarse-grained global structural features and fine-grained local dynamic features. Our approach unifies spatiotemporal dependency modeling with multi-scale relational learning. Experiments on real-world stock data from both U.S. and Chinese markets demonstrate that our method achieves up to a 1.4% improvement in prediction accuracy and significantly outperforms mainstream baseline models in profit stability.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

Accurately predicting stock market movements remains a formidable challenge due to the inherent volatility and complex interdependencies among stocks. Although multi-scale Graph Neural Networks (GNNs) hold potential for modeling these relationships, they frequently neglect two key points: the subtle intra-attribute patterns within each stock affecting inter-stock correlation, and the biased attention to coarse- and fine-grained features during multi-scale sampling. To overcome these challenges, we introduce MS-HGFN (Multi-Scale Hierarchical Graph Fusion Network). The model features a hierarchical GNN module that forms dynamic graphs by learning patterns from intra-attributes and features from inter-attributes over different time scales, thus comprehensively capturing spatio-temporal dependencies. Additionally, a top-down gating approach facilitates the integration of multi-scale spatio-temporal features, preserving critical coarse- and fine-grained features without too much interference. Experiments utilizing real-world datasets from U.S. and Chinese stock markets demonstrate that MS-HGFN outperforms both traditional and advanced models, yielding up to a 1.4% improvement in prediction accuracy and enhanced stability in return simulations. The code is available at https://anonymous.4open.science/r/MS-HGFN.

Problem

• Modeling stock volatility and complex interdependencies for movement prediction
• Capturing subtle intra-attribute patterns affecting inter-stock correlations
• Integrating multi-scale spatio-temporal features with balanced attention

Innovation

• Hierarchical GNN captures multi-scale spatio-temporal dependencies
• Top-down gating integrates coarse and fine-grained features
• Dynamic graphs learn intra-attribute and inter-attribute patterns