We develop hybrid-attention dynamic graph neural network (HAD-GNN), an inductive graph representation learning framework for dynamic stock graphs to incorporate temporal and graph attention mechanisms. This paper represents the first effort to leverage such phenomena to construct dynamic stock graphs for stock predictions. As a result, firms’ stock prices often feature synchronous co-movement patterns. In today’s economy, individual firms are increasingly connected via different types of relationships.

Summary of Contribution: Accurate predictions of stock prices have important implications for financial decisions. The proposed method can have important implications for the management of financial portfolios and investment risk. Extensive experiments on stocks from different markets and trading simulations demonstrate that the proposed method significantly improves stock predictions. We also extended mini-batch gradient descent to inductive representation learning on dynamic stock graphs so that the model can update parameters over mini-batch stock graphs with higher training efficiency. To learn node representations from such dynamic graphs for better stock predictions, we propose the hybrid-attention dynamic graph neural network, an inductive graph representation learning method. This paper proposes a novel method to leverage such relationships for stock price predictions by adopting inductive graph representation learning on dynamic stock graphs constructed based on historical stock price co-movement. Co-movement among individual firms’ stock prices can reflect complex interfirm relationships.

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