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

Author

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  • Daiki Matsunaga
  • Toyotaro Suzumura
  • Toshihiro Takahashi

Abstract

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.

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  • Daiki Matsunaga & Toyotaro Suzumura & Toshihiro Takahashi, 2019. "Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis," Papers 1909.10660, arXiv.org, revised Nov 2019.
    • Handle: RePEc:arx:papers:1909.10660
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    References listed on IDEAS

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    1. Fuli Feng & Xiangnan He & Xiang Wang & Cheng Luo & Yiqun Liu & Tat-Seng Chua, 2018. "Temporal Relational Ranking for Stock Prediction," Papers 1809.09441, arXiv.org, revised Jan 2019.
    2. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    3. Raehyun Kim & Chan Ho So & Minbyul Jeong & Sanghoon Lee & Jinkyu Kim & Jaewoo Kang, 2019. "HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction," Papers 1908.07999, arXiv.org, revised Nov 2019.
    4. Dev Shah & Haruna Isah & Farhana Zulkernine, 2019. "Stock Market Analysis: A Review and Taxonomy of Prediction Techniques," IJFS, MDPI, vol. 7(2), pages 1-22, May.
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    Citations

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    Cited by:

    1. Alireza Jafari & Saman Haratizadeh, 2022. "NETpred: Network-based modeling and prediction of multiple connected market indices," Papers 2212.05916, arXiv.org.
    2. Edward Turner, 2021. "Graph Auto-Encoders for Financial Clustering," Papers 2111.13519, arXiv.org, revised Dec 2021.
    3. Pei-Fen Tsai & Cheng-Han Gao & Shyan-Ming Yuan, 2023. "Stock Selection Using Machine Learning Based on Financial Ratios," Mathematics, MDPI, vol. 11(23), pages 1-18, November.
    4. Xianchao Wu, 2020. "Event-Driven Learning of Systematic Behaviours in Stock Markets," Papers 2010.15586, arXiv.org.
    5. Wentao Xu & Weiqing Liu & Lewen Wang & Yingce Xia & Jiang Bian & Jian Yin & Tie-Yan Liu, 2021. "HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information," Papers 2110.13716, arXiv.org, revised Jan 2022.
    6. Zikai Wei & Bo Dai & Dahua Lin, 2023. "E2EAI: End-to-End Deep Learning Framework for Active Investing," Papers 2305.16364, arXiv.org.
    7. Yuanrong Wang & Tomaso Aste, 2022. "Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-series," Papers 2203.03991, arXiv.org.
    8. Wentao Xu & Weiqing Liu & Chang Xu & Jiang Bian & Jian Yin & Tie-Yan Liu, 2021. "REST: Relational Event-driven Stock Trend Forecasting," Papers 2102.07372, arXiv.org, revised Feb 2021.
    9. Jianian Wang & Sheng Zhang & Yanghua Xiao & Rui Song, 2021. "A Review on Graph Neural Network Methods in Financial Applications," Papers 2111.15367, arXiv.org, revised Apr 2022.
    10. Jinan Zou & Qingying Zhao & Yang Jiao & Haiyao Cao & Yanxi Liu & Qingsen Yan & Ehsan Abbasnejad & Lingqiao Liu & Javen Qinfeng Shi, 2022. "Stock Market Prediction via Deep Learning Techniques: A Survey," Papers 2212.12717, arXiv.org, revised Feb 2023.
    11. Dragos Gorduza & Xiaowen Dong & Stefan Zohren, 2022. "Understanding stock market instability via graph auto-encoders," Papers 2212.04974, arXiv.org.
    12. Rohitash Chandra & Yixuan He, 2021. "Bayesian neural networks for stock price forecasting before and during COVID-19 pandemic," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-32, July.
    13. Soroush Omranpour & Guillaume Rabusseau & Reihaneh Rabbany, 2024. "Higher Order Transformers: Enhancing Stock Movement Prediction On Multimodal Time-Series Data," Papers 2412.10540, arXiv.org.
    14. Mittal, Varun & Schaposnik, Laura, 2022. "Housing market forecasts via stock market indicators," MPRA Paper 115009, University Library of Munich, Germany.

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