Stock price movement prediction is commonly accepted as a very challenging task due to the extremely volatile nature of financial markets. Previous works typically focus on understanding the temporal dependency of stock price movement based on the history of individual stock movement, but they do not take the complex relationships among involved stocks into consideration. However it is well known that an individual stock price is correlated with prices of other stocks . To address that , we propose a deep learning-based framework, which utilizes recurrent neural network (RNN) and graph convolutional network (GCN)  to predict stock movement. 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. To our best knowledge, it is the first time to incorporate multi-relationships among involved stocks into a GCN based deep learning framework for predicting stock price movement .
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 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 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 relationship on the basis of data dynamically .
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extremely
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focus on understanding the temporal dependency of stock price movement based on the history of individual stock movement, but they do not take the complex relationships
predict the stock price mainly based on its own information, neglecting the cross effect
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into consideration. However
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. To address that
in complex ways. To take the cross effect into consideration
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learning-based framework, which utilizes recurrent neural network (RNN) and
learning framework, called Multi-GCGRU, which comprises
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and gated recurrent units (GRU)
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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
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
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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
temporal dependency of stock prices
R
To our best knowledge, it is the first time to incorporate multi-relationships among involved stocks into a GCN based deep learning framework for predicting stock price movement
Note that our model is rather feasible to incorporate more effective stock relationships containing expert knowledge as well as learn relationship on the basis of data dynamically