  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.
