# GCNET: graph-based prediction of stock price movement using graph convolutional network

arXiv: https://arxiv.org/abs/2203.11091 · HTML: https://mlsanity.com/paper/2203.11091/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 54 references that are in the map.
- First posted on arXiv 19 Feb 2022.

## Builds on (most cited first, 12 of 12)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction](https://mlsanity.com/paper/1704.02971/) (2017)
- [Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction](https://mlsanity.com/paper/1712.02136/) (2017)
- [Graph Neural Networks in TensorFlow and Keras with Spektral](https://mlsanity.com/paper/2006.12138/) (2020)
- [CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images](https://mlsanity.com/paper/1909.01068/) (2019)
- [HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction](https://mlsanity.com/paper/1908.07999/) (2019)
- [Price graphs: Utilizing the structural information of financial time series for stock prediction](https://mlsanity.com/paper/2106.02522/) (2021)
- [Predicting stock market movements using network science: An information theoretic approach](https://mlsanity.com/paper/1705.07980/) (2017)
- [Embedding Ranking-Oriented Recommender System Graphs](https://mlsanity.com/paper/2007.16173/) (2020)

## Built on by (most cited first, 6 of 6)

- [AI in Investment Analysis: LLMs for Equity Stock Ratings](https://mlsanity.com/paper/2411.00856/) (2024)
- [Stock Type Prediction Model Based on Hierarchical Graph Neural Network](https://mlsanity.com/paper/2412.06862/) (2024)
- [Time Series Foundation Models and Deep Learning Architectures for Earthquake Temporal and Spatial Nowcasting](https://mlsanity.com/paper/2408.11990/) (2024)
- [NETpred: Network-based modeling and prediction of multiple connected market indices](https://mlsanity.com/paper/2212.05916/) (2022)
- [Understanding Key Features of Time Series Foundation Models from Epidemic Forecasting](https://mlsanity.com/paper/2606.19560/) (2026)
- [STN-TGAT: Top-K Portfolio Construction via Prior-Guided Graph Attention with Learnable Soft-Threshold Sparsification](https://mlsanity.com/paper/2607.19385/) (2026)

## Abstract (arXiv)

The importance of considering related stocks data for the prediction of stock price movement has been shown in many studies, however, advanced graphical techniques for modeling, embedding and analyzing the behavior of interrelated stocks have not been widely exploited for the prediction of stocks price movements yet. The main challenges in this domain are to find a way for modeling the existing relations among an arbitrary set of stocks and to exploit such a model for improving the prediction performance for those stocks. The most of existing methods in this domain rely on basic graph-analysis techniques, with limited prediction power, and suffer from a lack of generality and flexibility. In this paper, we introduce a novel framework, called GCNET that models the relations among an arbitrary set of stocks as a graph structure called influence network and uses a set of history-based prediction models to infer plausible initial labels for a subset of the stock nodes in the graph. Finally, GCNET uses the Graph Convolutional Network algorithm to analyze this partially labeled graph and predicts the next price direction of movement for each stock in the graph. GCNET is a general prediction framework that can be applied for the prediction of the price fluctuations of interacting stocks based on their historical data. Our experiments and evaluations on a set of stocks from the NASDAQ index demonstrate that GCNET significantly improves the performance of SOTA in terms of accuracy and MCC measures.