Implicit-Causality-Exploration-Enabled Graph Neural Network for Stock Prediction

Accurate stock prediction plays an important role in financial markets and can aid investors in making well-informed decisions and optimizing their investment strategies. Relationships exist among stocks in the market, leading to high correlation in their prices. Recently, several methods have been proposed to mine such relationships in order to enhance forecasting results. However, previous works have focused on exploring the correlations among stocks while neglecting the causal characteristics, thereby restricting the predictive performance. Furthermore, due to the diversity of relationships, existing methods are unable to handle both dynamic and static relationships simultaneously. To address the limitations of prior research, we introduce a novel stock trend forecasting framework capable of mining the causal relationships that affect changes in companies’ stock prices and simultaneously extracts both dynamic and static features to enhance the forecasting performance. Extensive experimental results in the Chinese stock market demonstrate that the proposed framework achieves obvious improvement against multiple state-of-the-art approaches.