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Graph-Based Learning for Stock Movement Prediction with Textual and Relational Data

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  • Qinkai Chen
  • Christian-Yann Robert

Abstract

Predicting stock prices from textual information is a challenging task due to the uncertainty of the market and the difficulty understanding the natural language from a machine's perspective. Previous researches focus mostly on sentiment extraction based on single news. However, the stocks on the financial market can be highly correlated, one news regarding one stock can quickly impact the prices of other stocks. To take this effect into account, we propose a new stock movement prediction framework: Multi-Graph Recurrent Network for Stock Forecasting (MGRN). This architecture allows to combine the textual sentiment from financial news and multiple relational information extracted from other financial data. Through an accuracy test and a trading simulation on the stocks in the STOXX Europe 600 index, we demonstrate a better performance from our model than other benchmarks.

Suggested Citation

  • Qinkai Chen & Christian-Yann Robert, 2021. "Graph-Based Learning for Stock Movement Prediction with Textual and Relational Data," Papers 2107.10941, arXiv.org, revised Dec 2021.
    • Handle: RePEc:arx:papers:2107.10941
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    References listed on IDEAS

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    Cited by:

    1. Qinkai Chen & Christian-Yann Robert, 2021. "Multivariate Realized Volatility Forecasting with Graph Neural Network," Papers 2112.09015, arXiv.org, revised Dec 2021.
    2. Hao Qian & Hongting Zhou & Qian Zhao & Hao Chen & Hongxiang Yao & Jingwei Wang & Ziqi Liu & Fei Yu & Zhiqiang Zhang & Jun Zhou, 2024. "MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Prediction," Papers 2402.06633, arXiv.org.

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