MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive
  and Dynamic Stock Investment Prediction
Hao Qian Hongting Zhou Qian Zhao Hao Chen Hongxiang Yao Jingwei Wang Ziqi Liu Fei Yu Zhiqiang Zhang Jun Zhou
The stock market is a crucial component of the financial system, but
predicting the movement of stock prices is challenging due to the dynamic and
intricate relations arising from various aspects such as economic indicators,
financial reports, global news, and investor sentiment. Traditional sequential
methods and graph-based models have been applied in stock movement prediction,
but they have limitations in capturing the multifaceted and temporal influences
in stock price movements. To address these challenges, the Multi-relational
Dynamic Graph Neural Network (MDGNN) framework is proposed, which utilizes a
discrete dynamic graph to comprehensively capture multifaceted relations among
stocks and their evolution over time. The representation generated from the
graph offers a complete perspective on the interrelationships among stocks and
associated entities. Additionally, the power of the Transformer structure is
leveraged to encode the temporal evolution of multiplex relations, providing a
dynamic and effective approach to predicting stock investment. Further, our
proposed MDGNN framework achieves the best performance in public datasets
compared with state-of-the-art (SOTA) stock investment methods.