Higher-order Graph Attention Network for Stock Selection with Joint Analysis

ArXiv ID: 2306.15526 “View on arXiv”

Authors: Unknown

Abstract

Stock selection is important for investors to construct profitable portfolios. Graph neural networks (GNNs) are increasingly attracting researchers for stock prediction due to their strong ability of relation modelling and generalisation. However, the existing GNN methods only focus on simple pairwise stock relation and do not capture complex higher-order structures modelling relations more than two nodes. In addition, they only consider factors of technical analysis and overlook factors of fundamental analysis that can affect the stock trend significantly. Motivated by them, we propose higher-order graph attention network with joint analysis (H-GAT). H-GAT is able to capture higher-order structures and jointly incorporate factors of fundamental analysis with factors of technical analysis. Specifically, the sequential layer of H-GAT take both types of factors as the input of a long-short term memory model. The relation embedding layer of H-GAT constructs a higher-order graph and learn node embedding with GAT. We then predict the ranks of stock return. Extensive experiments demonstrate the superiority of our H-GAT method on the profitability test and Sharp ratio over both NSDAQ and NYSE datasets

Keywords: Graph Neural Networks, Stock Selection, Fundamental Analysis, Technical Analysis, Higher-Order Structures

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced graph neural network architectures (higher-order GAT with LSTM) with dense mathematical notation and definitions, while presenting backtest-ready experiments with concrete profitability metrics (Sharpe ratio, IRR) on multiple real-world datasets (NYSE, NASDAQ).

  flowchart TD
    A["Research Goal: Develop H-GAT for stock selection<br>incorporating higher-order structures and joint analysis"] --> B["Data Preparation"]
    B --> B1["NASDAQ & NYSE Datasets"]
    B --> B2["Inputs: Fundamental & Technical Factors"]
    B --> B3["Graph Construction"]
    
    B3 --> C["Methodology: H-GAT Model Architecture"]
    C --> C1["Sequential Layer<br>LSTM for Temporal Features"]
    C --> C2["Relation Embedding Layer<br>Higher-order Graph + GAT"]
    
    C1 & C2 --> D["Computational Process<br>Learn Stock Embeddings & Predict Returns"]
    
    D --> E["Key Findings / Outcomes"]
    E --> E1["Superior Profitability"]
    E --> E2["Higher Sharpe Ratio"]
    E --> E3["Effectiveness of Higher-order & Joint Analysis"]