  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 unit (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
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 are concatenated with historical records and then fed
into GRU to model the temporal dependency of stock prices. 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 stock
relationships containing expert knowledge, as well as learn data-driven
relationship.
