MGCN-SP: A Motif-based Graph Convolutional Network for Stock Prediction

The prediction of stock market price trends has always been a challenging issue, attracting widespread attention from both economists and computer scientists. Recently, integrating stock prices with news data has been shown to be an effective strategy for enhancing the accuracy of the prediction task. Yet, many current methods fail to fully leverage the intricate inter-stock relationships inherent in stock news.Applying deep learning, especially Graph Convolutional Networks (GCNs), to predict stock trends has demonstrated advanced performance. This method employs a message-passing architecture, enabling nodes to progressively aggregate information from neighboring nodes across multiple layers. In this paper, we propose a novel approach: the Motif-based Graph Convolutional Network for Stock Prediction (MGCN-SP). This model mitigates the over-smoothing problem by incorporating network motifs into the layer propagation process. Specifically, we first generate a motif graph by correlating stocks with stock news. Then, we encode the stock price information and stock news into features using the scaled dot product attention adapted from the transformer architecture. After that, we apply the motif-based graph convolutional network. This framework is designed to jointly refine the embeddings of both stock news and stock time series data using a transformer encoder and to estimate the likelihood of target movements. Finally, we conducted extensive implementation in the U.S. stock market, and the experimental results demonstrate that our method outperforms some state-of-the-art approaches.