Higher-order Graph Attention Network for Stock Selection with Joint
  Analysis
Yang Qiao Yiping Xia Xiang Li Zheng Li Yan Ge
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