Dynamic graph construction via motif detection for stock prediction

Abstract

Stock trend prediction is crucial for recommending high-investment value stocks and can strongly assist investors in making decisions. In recent years, the significance of stock relationships has been gradually recognized for trend prediction, and graph neural networks (GNNs) have been introduced to capture useful features from relationships. However, applying GNNs to stock relationship analysis still faces numerous challenges, including inappropriate distance algorithms, non-dynamic stock graphs, and over-fitting. To address these challenges, we propose a dynamic graph construction module. The module offers the following advantages: (1) A dynamic graph construction module is introduced. (2) A novel stock distance algorithm based on motif detection is proposed to reduce the distance between stocks with similar trends. (3) A dynamic graph-based LSTM is proposed to aggregate the changes in historical graphs. We have conduct numerous experiments on 4503 Chinese A-share stocks, spanning 1218 trading days. Our model demonstrates 8.65% and 1.02% relative improvements in accumulated return and accuracy, respectively. In addition, the trading simulation validates that our algorithm outperforms the state-of-the-art (SOTA) algorithms in terms of profitability.