DGDNN: Decoupled Graph Diffusion Neural Network for Stock Movement Prediction

Zinuo You, Zijian Shi, Hongbo Bo, Hongbo Bo, John Cartlidge, Li Zhang, Yan Ge

2024

Abstract

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.

Paper Citation

in Harvard Style

You Z., Shi Z., Bo H., Cartlidge J., Zhang L. and Ge Y. (2024). DGDNN: Decoupled Graph Diffusion Neural Network for Stock Movement Prediction. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 431-442. DOI: 10.5220/0012406400003636

in Bibtex Style

@conference{icaart24,
author={Zinuo You and Zijian Shi and Hongbo Bo and John Cartlidge and Li Zhang and Yan Ge},
title={DGDNN: Decoupled Graph Diffusion Neural Network for Stock Movement Prediction},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2024},
pages={431-442},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012406400003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - DGDNN: Decoupled Graph Diffusion Neural Network for Stock Movement Prediction
SN - 978-989-758-680-4
AU - You Z.
AU - Shi Z.
AU - Bo H.
AU - Cartlidge J.
AU - Zhang L.
AU - Ge Y.
PY - 2024
SP - 431
EP - 442
DO - 10.5220/0012406400003636
PB - SciTePress