Graph-Driven Insights: Enhancing Stock Market Prediction with Relational Temporal Dynamics

Renjun Jia, Kaiming Yang, Dawei Cheng, Li Han, Yuqi Liang

Abstract: This paper proposes a novel approach to predicting stock returns based on Relational Temporal Graph Neural Networks (RTGNN). This method provides insights into the dynamic and asymmetric relationships that exist both among different stocks and within individual stocks over time, addressing the limitations of existing methods in modeling asymmetric relationships, as well as the neglect of market dynamics. The proposed method employs a temporal attention mechanism to process continuous financial data, with the objective of adaptively constructing asymmetric graphs of the dynamics of the stock relationship based on historical stock data and changes in momentum of each stock. The experimental results, which were performed on the NASDAQ 100 and CSI 300 datasets, demonstrate that the proposed model outperforms traditional methods in terms of investment returns and information coefficients. In the 2023 investment backtesting experiment, the proposed methodology exhibited superior performance, achieving over 35% excess return in both datasets. Our sources are released at https://github.com/tj-lg100/RTGNN.

External IDs:dblp:conf/icassp/JiaYC0L25