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Related Experiment Videos

Trading network predicts stock price.

Xiao-Qian Sun1, Hua-Wei Shen1, Xue-Qi Cheng1

  • 1Institute of Computing Technology, Chinese Academy of Sciences, No.6 Kexueyuan South Road Zhongguancun, Haidian District, 100190, Beijing, China.

Scientific Reports
|January 17, 2014
PubMed
Summary
This summary is machine-generated.

This study predicts stock prices using investor trading behavior, not just historical data. Analyzing trading networks and investor roles significantly improves stock price prediction accuracy.

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Area of Science:

  • Financial Markets
  • Computational Finance
  • Network Science

Background:

  • Traditional stock price prediction relies on historical stock data or company performance.
  • Predicting stock prices remains a significant challenge in financial market analysis.

Purpose of the Study:

  • To propose a novel method for stock price prediction utilizing investor trading behavior.
  • To investigate the relationship between investor trading networks and stock price movements.

Main Methods:

  • Constructing daily investor trading networks for individual stocks.
  • Classifying network nodes into three distinct roles based on connectivity patterns.
  • Calculating trading relationship indices representing the fraction of trades between different investor roles.
  • Integrating these indices into a neural network model alongside historical stock price time series.

Main Results:

  • A strong Granger causality was observed between stock prices and the derived trading relationship indices.
  • Incorporating trading relationship indices into the neural network model significantly enhanced stock price prediction accuracy.
  • The model demonstrated improved performance on 51 stocks across two Chinese Stock Exchanges.

Conclusions:

  • Investor trading behavior, when analyzed through network structures and role classifications, provides valuable predictive information for stock prices.
  • The proposed method offers a significant improvement over traditional approaches by incorporating network-based financial behavior.
  • This research highlights the potential of network science in understanding and predicting financial market dynamics.