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Stock Movement Prediction (SMP) aims at predicting listed companies' stockfuture price trend, which is a challenging task due to the volatile nature offinancial markets. Recent financial studies show that the momentum spillovereffect plays a significant role in stock fluctuation. However, previous studiestypically only learn the simple connection information among related companies,which inevitably fail to model complex relations of listed companies in thereal financial market. To address this issue, we first construct a morecomprehensive Market Knowledge Graph (MKG) which contains bi-typed entitiesincluding listed companies and their associated executives, andhybrid-relations including the explicit relations and implicit relations.Afterward, we propose DanSmp, a novel Dual Attention Networks to learn themomentum spillover signals based upon the constructed MKG for stock prediction.The empirical experiments on our constructed datasets against nine SOTAbaselines demonstrate that the proposed DanSmp is capable of improving stockprediction with the constructed MKG.

Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks