# Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis

arXiv: https://arxiv.org/abs/1909.10660 · HTML: https://mlsanity.com/paper/1909.10660/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2019.

## Built on by (most cited first, 27 of 27)

- [Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook](https://mlsanity.com/paper/2310.10196/) (2023)
- [Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvature](https://mlsanity.com/paper/2211.15779/) (2022)
- [A Review on Graph Neural Network Methods in Financial Applications](https://mlsanity.com/paper/2111.15367/) (2021)
- [HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information](https://mlsanity.com/paper/2110.13716/) (2021)
- [REST: Relational Event-driven Stock Trend Forecasting](https://mlsanity.com/paper/2102.07372/) (2021)
- [Graph Deep Learning for Time Series Forecasting](https://mlsanity.com/paper/2310.15978/) (2023)
- [SoK: Differential Privacy on Graph-Structured Data](https://mlsanity.com/paper/2203.09205/) (2022)
- [From Deep Learning to LLMs: A survey of AI in Quantitative Investment](https://mlsanity.com/paper/2503.21422/) (2025)
- [Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-series](https://mlsanity.com/paper/2203.03991/) (2022)
- [FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks](https://mlsanity.com/paper/2106.10159/) (2021)
- [Graph Neural Networks with Parallel Neighborhood Aggregations for Graph Classification](https://mlsanity.com/paper/2111.11482/) (2021)
- [E2EAI: End-to-End Deep Learning Framework for Active Investing](https://mlsanity.com/paper/2305.16364/) (2023)
- [Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey](https://mlsanity.com/paper/2308.04947/) (2023)
- [GraphCNNpred: A stock market indices prediction using a Graph based deep learning system](https://mlsanity.com/paper/2407.03760/) (2024)
- [Dynamic Graph Representation with Contrastive Learning for Financial Market Prediction: Integrating Temporal Evolution and Static Relations](https://mlsanity.com/paper/2412.04034/) (2024)
- [Enterprise Analytics using Graph Database and Graph-based Deep Learning](https://mlsanity.com/paper/2108.02867/) (2021)
- [TwERC: High Performance Ensembled Candidate Generation for Ads Recommendation at Twitter](https://mlsanity.com/paper/2302.13915/) (2023)
- [Stock Price Prediction Using a Hybrid LSTM-GNN Model: Integrating Time-Series and Graph-Based Analysis](https://mlsanity.com/paper/2502.15813/) (2025)
- [Improving COVID-19 Forecasting using eXogenous Variables](https://mlsanity.com/paper/2107.10397/) (2021)
- [NETpred: Network-based modeling and prediction of multiple connected market indices](https://mlsanity.com/paper/2212.05916/) (2022)
- [Higher Order Transformers: Enhancing Stock Movement Prediction On Multimodal Time-Series Data](https://mlsanity.com/paper/2412.10540/) (2024)
- [Can we imitate the principal investor's behavior to learn option price?](https://mlsanity.com/paper/2105.11376/) (2021)
- [SETN: Stock Embedding Enhanced with Textual and Network Information](https://mlsanity.com/paper/2408.02899/) (2024)
- [Stock Market Telepathy: Graph Neural Networks Predicting the Secret Conversations between MINT and G7 Countries](https://mlsanity.com/paper/2506.01945/) (2025)
- [Structure Over Signal: A Globalized Approach to Multi-relational GNNs for Stock Prediction](https://mlsanity.com/paper/2510.10775/) (2025)
- [A Hybrid Model for Stock Market Forecasting: Integrating News Sentiment and Time Series Data with Graph Neural Networks](https://mlsanity.com/paper/2512.08567/) (2025)
- [Fraud Detection in Cryptocurrency Markets with Spatio-Temporal Graph Neural Networks](https://mlsanity.com/paper/2604.24590/) (2026)

## Abstract (arXiv)

Recently, there has been a surge of interest in the use of machine learning to help aid in the accurate predictions of financial markets. Despite the exciting advances in this cross-section of finance and AI, many of the current approaches are limited to using technical analysis to capture historical trends of each stock price and thus limited to certain experimental setups to obtain good prediction results. On the other hand, professional investors additionally use their rich knowledge of inter-market and inter-company relations to map the connectivity of companies and events, and use this map to make better market predictions. For instance, they would predict the movement of a certain company's stock price based not only on its former stock price trends but also on the performance of its suppliers or customers, the overall industry, macroeconomic factors and trade policies. This paper investigates the effectiveness of work at the intersection of market predictions and graph neural networks, which hold the potential to mimic the ways in which investors make decisions by incorporating company knowledge graphs directly into the predictive model. The main goal of this work is to test the validity of this approach across different markets and longer time horizons for backtesting using rolling window analysis. In this work, we concentrate on the prediction of individual stock prices in the Japanese Nikkei 225 market over a period of roughly 20 years. For the knowledge graph, we use the Nikkei Value Search data, which is a rich dataset showing mainly supplier relations among Japanese and foreign companies. Our preliminary results show a 29.5% increase and a 2.2-fold increase in the return ratio and Sharpe ratio, respectively, when compared to the market benchmark, as well as a 6.32% increase and 1.3-fold increase, respectively, compared to the baseline LSTM model.