Stock Type Prediction Model Based on Hierarchical Graph Neural Network

ArXiv ID: 2412.06862 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Hierarchical Graph Neural Network (HGNN), Graph Convolution, Temporal Attention Aggregator, Stock Relationship Graph, Stock Prediction, Equities (Stocks)

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 5.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced hierarchical graph neural networks with temporal attention and LSTM-based feature extraction, representing high mathematical density. While the methodology is data-heavy (requiring historical price sequences, trading curb indicators, and industry relationship graphs), the summary lacks specific details on backtesting metrics, dataset sizes, or implementation performance, suggesting moderate empirical rigor.

  flowchart TD
    A["Research Goal<br>Predict Stock Types via<br>Hierarchical Graph Neural Network"] --> B["Data Collection & Construction"]
    B --> C["Key Methodology Steps"]
    C --> D["Computational Processes"]
    D --> E["Findings & Outcomes"]

    B --> B1["Stock Relationship Graph<br>Macro-level connections"]
    B --> B2["Temporal Price Sequences<br>Micro-level historical data"]
    B --> B3["Hierarchical Attributes<br>Multi-level stock info"]

    C --> C1["Graph Convolution<br>Extract spatial features"]
    C --> C2["Temporal Attention Aggregator<br>Model market state dynamics"]

    D --> D1["Hierarchical GNN Integration<br>Spatial + Temporal fusion"]
    D --> D2["Stock Type Prediction<br>Classification output"]

    E --> E1["Effective Stock Prediction<br>Addresses relationship data challenges"]
    E --> E2["Macro-Market State Modeling<br>Captures multi-level information"]