mlsanityData to 14 Oct 2026

Stock Type Prediction Model Based on Hierarchical Graph Neural Network

arXiv 2412.06862 ↗First posted 9 Dec 2024cs.LG q-fin.CPcited by 5

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Abstract (arXiv)

This paper introduces a novel approach to stock data analysis by employing a Hierarchical Graph Neural Network (HGNN) model that captures multi-level information and relational structures in the stock market. The HGNN model integrates stock relationship data and hierarchical attributes to predict stock types effectively. The paper discusses the construction of a stock industry relationship graph and the extraction of temporal information from historical price sequences. It also highlights the design of a graph convolution operation and a temporal attention aggregator to model the macro market state. The integration of these features results in a comprehensive stock prediction model that addresses the challenges of utilizing stock relationship data and modeling hierarchical attributes in the stock market.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 14 of its 25 references that are in the map. First posted on arXiv 9 Dec 2024.

Builds on

14 of 25 references.

References · 14

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2 more
  1. 2024Research on Disease Prediction Model Construction Based on Computer AI deep Learning Technologycited by 6
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Cited by year

Citing papers per year: 2024: 3; 2025: 2.

5 papers.

  1. 2024Leveraging Convolutional Neural Network-Transformer Synergy for Predictive Modeling in Risk-Based Applicationscited by 9
  2. 2025A Deep Learning Framework Integrating CNN and BiLSTM for Financial Systemic Risk Analysis and Predictioncited by 9
  3. 2024Machine Learning Techniques for Pattern Recognition in High-Dimensional Data Miningcited by 8
  4. 2024Dynamic Scheduling Strategies for Resource Optimization in Computing Environmentscited by 6
  5. 2025Dynamic Adaptation of LoRA Fine-Tuning for Efficient and Task-Specific Optimization of Large Language Modelscited by 4