Before revision:  Specifically, we first use RNN to model the temporal dependency of each related stock' price movement based on their own information of the past time slices, then we employ GCN to model the influence from involved stock based on three novel graphs which represent the shareholder relationship, industry relationship and concept relationship among stocks based on investment decisions . Experiments on two stock indexes in China market show that our model outperforms other baselines.
After revision:  Specifically, we first encode multiple relationships among stocks into graphs based on financial domain knowledge and utilize GCN to extract the cross effect based on these pre-defined graphs. To further get rid of prior knowledge, we explore an adaptive relationship learned by data automatically. The cross-correlation features produced by GCN is concatenated with historical records and fed into GRU to model the influence from involved stock based on three novel graphs which represent the shareholder relationship, industry relationship and concept relationship among stocks based on investment decisions . Experiments on two stock indexes in China market show that our model outperforms other baselines.