  Predicting the future price trends of stocks is a challenging yet intriguing
problem given its critical role to help investors make profitable decisions. In
this paper, we present a collaborative temporal-relational modeling framework
for end-to-end stock trend prediction. The temporal dynamics of stocks is
firstly captured with an attention-based recurrent neural network. Then,
different from existing studies relying on the pairwise correlations between
stocks, we argue that stocks are naturally connected as a collective group, and
introduce the hypergraph structures to jointly characterize the stock
group-wise relationships of industry-belonging and fund-holding. A novel
hypergraph tri-attention network (HGTAN) is proposed to augment the hypergraph
convolutional networks with a hierarchical organization of intra-hyperedge,
inter-hyperedge, and inter-hypergraph attention modules. In this manner, HGTAN
adaptively determines the importance of nodes, hyperedges, and hypergraphs
during the information propagation among stocks, so that the potential
synergies between stock movements can be fully exploited. Extensive experiments
on real-world data demonstrate the effectiveness of our approach. Also, the
results of investment simulation show that our approach can achieve a more
desirable risk-adjusted return. The data and codes of our work have been
released at https://github.com/lixiaojieff/HGTAN.
