DR-GAT: Dynamic routing graph attention network for stock recommendation

Abstract

Investors are increasingly interested in using financial technology to guide investment decisions. The phenomenon of “stock linkage” or “leading-lag” is often observed between related stocks on the stock market. Based on this feature of the stock market, complex relationships between stocks influence investment decisions. Existing methods use static prior relational data (e.g., industry relations and Wiki relations) to build corporate relation graph, while learning relational features using the same graph for all stocks. This description and use of corporate relationships ignored the dynamic nature of association relationships in time and space, limiting the ability to learn latent relationships between stocks. To address these issues, we propose a Dynamic Routing Graph Attention Network (DR-GAT) for stock recommendation. We propose a novel similarity measurement method called stock price trend similarity. To track the evolution of intercorporate relationships over time, we dynamically construct relation graph attention networks using prior knowledge and stock price trend similarity. Moreover, a newly designed relation graph router (RGR) can route each stock to an optimal relationship graph based on its volatility. Extensive experiments demonstrate the superiority of our DR-GAT method. It outperforms state-of-the-art methods achieving an average return ratio of 152% and 162% on NASDAQ and NYSE, respectively.