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

翻译：选股对于投资者构建盈利投资组合至关重要。图神经网络因其强大的关系建模与泛化能力，正日益受到股票预测研究者的关注。然而，现有图神经网络方法仅聚焦于简单的成对股票关系，未能捕捉涉及两个以上节点的复杂高阶结构关系。此外，这些方法仅考虑技术分析因素，而忽略了可能显著影响股票走势的基本面分析因素。受此启发，我们提出了一种联合分析下的高阶图注意力网络（H-GAT）。H-GAT能够捕捉高阶结构，并将基本面分析因素与技术分析因素进行联合整合。具体而言，H-GAT的序列层将两类因素同时作为长短期记忆模型的输入；其关系嵌入层构建高阶图，并利用图注意力网络学习节点嵌入。随后我们对股票收益进行排序预测。大量实验表明，在纳斯达克和纽交所数据集上，我们的H-GAT方法在盈利能力测试和夏普比率方面均展现出显著优越性。