DGDNN: Decoupled Graph Diffusion Neural Network for Stock Movement Prediction
This work addresses the problem of forecasting stock trends for financial analysts and investors by offering a novel graph learning approach that improves accuracy over existing methods, though it is incremental in the domain of graph neural networks for finance.
The paper tackles stock movement prediction by proposing a decoupled graph diffusion neural network that automatically constructs dynamic stock graphs and captures hierarchical intra-stock features, achieving substantial improvements over state-of-the-art baselines on real-world datasets.
Forecasting future stock trends remains challenging for academia and industry due to stochastic inter-stock dynamics and hierarchical intra-stock dynamics influencing stock prices. In recent years, graph neural networks have achieved remarkable performance in this problem by formulating multiple stocks as graph-structured data. However, most of these approaches rely on artificially defined factors to construct static stock graphs, which fail to capture the intrinsic interdependencies between stocks that rapidly evolve. In addition, these methods often ignore the hierarchical features of the stocks and lose distinctive information within. In this work, we propose a novel graph learning approach implemented without expert knowledge to address these issues. First, our approach automatically constructs dynamic stock graphs by entropy-driven edge generation from a signal processing perspective. Then, we further learn task-optimal dependencies between stocks via a generalized graph diffusion process on constructed stock graphs. Last, a decoupled representation learning scheme is adopted to capture distinctive hierarchical intra-stock features. Experimental results demonstrate substantial improvements over state-of-the-art baselines on real-world datasets. Moreover, the ablation study and sensitivity study further illustrate the effectiveness of the proposed method in modeling the time-evolving inter-stock and intra-stock dynamics.