MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Prediction

TL;DR

This paper introduces MDGNN, a novel dynamic graph neural network that models multifaceted and evolving relations among stocks over time, significantly improving stock investment prediction accuracy.

Contribution

The paper presents a multi-relational dynamic graph neural network that captures complex, temporal stock relations using a discrete dynamic graph and Transformer encoding, advancing stock prediction methods.

Findings

Achieves state-of-the-art performance on public datasets.

Effectively models multifaceted and evolving stock relations.

Outperforms existing methods in prediction accuracy.

Abstract

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.