A Dynamic Dual-Graph Neural Network for Stock Price Movement Prediction

Kelvin Du, Rui Mao, Frank Xing, Erik Cambria

Abstract: The prediction of stock price movements is challenging due to the inherently dynamic and complex characteristics of financial markets. A current research gap is the lack of exploration into the complex interrelationships inherent in stock price dynamics, often analyzing predictions in isolation with an implicit presumption that solely the historical data of a given stock influences its future trend. However, stock prices are impacted by a diverse array of driving factors that extend beyond the traditionally examined historical prices, encompassing influences such as inter-stock correlations. In this paper, we present a predictive approach using a dynamic dual-graph neural network. The network combines textual data and quantitative metrics to capture multiple dynamic relationships. Specifically, We have developed a price relationship graph (PRG) and a semantic relationship graph (SRG), which are later integrated using a graph attention neural network. The effectiveness of our neural architecture is validated through extensive testing on two benchmark datasets for stock movement prediction, illustrating its superior performance compared to other graph-based networks for stock market prediction.