Multi-Relational Graph Convolution Network for Stock Movement Prediction

Zihao Zhou, Le Zhang, Rui Zha, Qiming Hao, Tong Xu, Di Wu, Enhong Chen

Abstract: Stock movement prediction aims at predicting the future price trends of stocks, which plays an important role in quantitative investing. Existing approaches toward this direction mainly focus on modeling the historical sequential information, while the fine-grained relationships among stocks (e.g., belonging to the same industry or concept) were largely neglected. To tackle this limitation, in this paper, we propose a Multi-Relational Graph Convolution Network (MRGCN) framework for stock movement prediction, which incorporates the fine-grained multiple relationships into stock representation. Specifically, we first extract the temporal and static information for each stock from the historical series and corporation descriptions respectively. Then, we construct two pre-defined graphs based on domain knowledge and a self-adaptive graph to capture both explicit and implicit relationships among stocks. Along this line, the graph convolution network with attention mechanism is adopted on the multi-relational graph to generate the structural representation for each stock, and an embedding reconstruction module is further designed to refine the representation. Finally, we make predictions by integrating both temporal and structural embedding of stocks. Experiments on real-world China A-share market evince the superior performance of MRGCN compared to other baselines.