# Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction

arXiv: https://arxiv.org/abs/2005.04955 · HTML: https://mlsanity.com/paper/2005.04955/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2020.

## Built on by (most cited first, 13 of 13)

- [MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs](https://mlsanity.com/paper/2312.03731/) (2023)
- [Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks](https://mlsanity.com/paper/2201.04965/) (2022)
- [Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction](https://mlsanity.com/paper/2309.00073/) (2023)
- [Graph-Based Learning for Stock Movement Prediction with Textual and Relational Data](https://mlsanity.com/paper/2107.10941/) (2021)
- [DGDNN: Decoupled Graph Diffusion Neural Network for Stock Movement Prediction](https://mlsanity.com/paper/2401.01846/) (2024)
- [Stock Movement Prediction with Multimodal Stable Fusion via Gated Cross-Attention Mechanism](https://mlsanity.com/paper/2406.06594/) (2024)
- [A Distillation-based Future-aware Graph Neural Network for Stock Trend Prediction](https://mlsanity.com/paper/2502.10776/) (2025)
- [Dynamic graph neural networks for enhanced volatility prediction in financial markets](https://mlsanity.com/paper/2410.16858/) (2024)
- [Higher-order Graph Attention Network for Stock Selection with Joint Analysis](https://mlsanity.com/paper/2306.15526/) (2023)
- [Multivariate Realized Volatility Forecasting with Graph Neural Network](https://mlsanity.com/paper/2112.09015/) (2021)
- [Financial Assets Dependency Prediction Utilizing Spatiotemporal Patterns](https://mlsanity.com/paper/2406.11886/) (2024)
- [SETN: Stock Embedding Enhanced with Textual and Network Information](https://mlsanity.com/paper/2408.02899/) (2024)
- [GRU-PFG: Extract Inter-Stock Correlation from Stock Factors with Graph Neural Network](https://mlsanity.com/paper/2411.18997/) (2024)

## Abstract (arXiv)

Stock price movement prediction is commonly accepted as a very challenging task due to the volatile nature of financial markets. Previous works typically predict the stock price mainly based on its own information, neglecting the cross effect among involved stocks. However, it is well known that an individual stock price is correlated with prices of other stocks in complex ways. To take the cross effect into consideration, we propose a deep learning framework, called Multi-GCGRU, which comprises graph convolutional network (GCN) and gated recurrent unit (GRU) to predict stock movement. Specifically, we first encode multiple relationships among stocks into graphs based on financial domain knowledge and utilize GCN to extract the cross effect based on these pre-defined graphs. To further get rid of prior knowledge, we explore an adaptive relationship learned by data automatically. The cross-correlation features produced by GCN are concatenated with historical records and then fed into GRU to model the temporal dependency of stock prices. Experiments on two stock indexes in China market show that our model outperforms other baselines. Note that our model is rather feasible to incorporate more effective stock relationships containing expert knowledge, as well as learn data-driven relationship.