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Related Experiment Videos

A graph neural network-based stock forecasting method utilizing multi-source heterogeneous data fusion.

Xiaohan Li1, Jun Wang1, Jinghua Tan1

  • 1School of Economic Information Engineering, Southwestern University of Finance and Economics, 610000 Cheng Du, China.

Multimedia Tools and Applications
|June 7, 2022
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel method for predicting stock market volatility by integrating heterogeneous data using graph neural networks. The approach significantly improves prediction accuracy, offering better financial risk management.

Area of Science:

  • Financial Engineering
  • Data Science
  • Computational Finance

Background:

  • Stock market volatility prediction is crucial for risk management and investment.
  • Existing methods struggle with complex, multi-source heterogeneous stock market data.
  • Intelligent algorithms face limitations in processing diverse financial data.

Purpose of the Study:

  • To develop a novel method for stock market volatility prediction.
  • To effectively fuse and analyze multi-source heterogeneous financial data.
  • To enhance the accuracy of financial forecasting models.

Main Methods:

  • Utilized edge weight and information transmission for subgraph node screening.
  • Employed Gate Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) for subgraph node aggregation.
Keywords:
Graph dataGraph neural networkMulti-source dataStock prediction

Related Experiment Videos

  • Combined metapath attention mechanisms with graph neural networks for data classification.

    • Main Results:

    • Achieved a 16.64% higher accuracy compared to dimensional reduction indices.
    • Outperformed other heterogeneous data fusion methods by 14.48%.
    • Demonstrated the feasibility of fusing heterogeneous stock market data and mining implicit semantic information.

    Conclusions:

    • The proposed graph neural network approach effectively handles multi-source heterogeneous stock market data.
    • This method offers a significant improvement in stock market volatility prediction accuracy.
    • The findings provide a more robust framework for financial risk control and investment strategies.