Stock price movement prediction is commonly accepted as a very challenging task due to the volatile nature of financial markets. Previous works typically predict the stock price mainly based on its own information, neglecting the cross effect among involved stocks. However, it is well known that an individual stock price is correlated with prices of other stocks in complex ways. To take the cross effect into consideration, we propose a deep learning framework, called Multi-GCGRU, which comprises graph convolutional network (GCN) and gated recurrent units (GRU) to predict stock movement. 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 the pre-defined graphs. The cross-correlation features produced by GCN are concatenated with historical records and fed into GRU to model the temporal pattern in stock price. To further get rid of prior knowledge, we explore an adaptive stock graph learned by data automatically. Experiments on two stock indexes in China market show that our model outperforms other baselines. Note that our model is rather feasible to incorporate more effective pre-defined stock relationships. What's more, it can also learn a data-driven relationship without any domain knowledge.