Multi-relational Graph Diffusion Neural Network with Parallel Retention
  for Stock Trends Classification
Zinuo You Pengju Zhang Jin Zheng John Cartlidge
Stock trend classification remains a fundamental yet challenging task, owing
to the intricate time-evolving dynamics between and within stocks. To tackle
these two challenges, we propose a graph-based representation learning approach
aimed at predicting the future movements of multiple stocks. Initially, we
model the complex time-varying relationships between stocks by generating
dynamic multi-relational stock graphs. This is achieved through a novel edge
generation algorithm that leverages information entropy and signal energy to
quantify the intensity and directionality of inter-stock relations on each
trading day. Then, we further refine these initial graphs through a stochastic
multi-relational diffusion process, adaptively learning task-optimal edges.
Subsequently, we implement a decoupled representation learning scheme with
parallel retention to obtain the final graph representation. This strategy
better captures the unique temporal features within individual stocks while
also capturing the overall structure of the stock graph. Comprehensive
experiments conducted on real-world datasets from two US markets (NASDAQ and
NYSE) and one Chinese market (Shanghai Stock Exchange: SSE) validate the
effectiveness of our method. Our approach consistently outperforms
state-of-the-art baselines in forecasting next trading day stock trends across
three test periods spanning seven years. Datasets and code have been released
(https://github.com/pixelhero98/MGDPR).