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Knowledge-based multiplex network reconstruction and influential substructure identification of stock time series: An application to the Chinese A-share market

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Listed:
  • Zhang, Xiaoqi
  • Du, Peilin
  • Zheng, Yanqiao
  • Zhang, Zexuan
  • Yao, Jiayi

Abstract

We propose a novel and easy-to-implement algorithm to reconstruct the hidden network from stock time series with the assist of prior knowledge in finance. Compared with the data-oriented approaches, our knowledge-based approach gives the first attempt to utilize the pricing knowledge in factor model, which results naturally in a multiplex structure of stock network. Each of the multi-layers in the network encodes the structural feature of the corresponding pricing factor into a core–periphery subnetwork, suggesting the influential channels for certain types of risk propagation. We apply the algorithm to Chinese A-share market and reveal the advantage of the knowledge add-in.

Suggested Citation

  • Zhang, Xiaoqi & Du, Peilin & Zheng, Yanqiao & Zhang, Zexuan & Yao, Jiayi, 2025. "Knowledge-based multiplex network reconstruction and influential substructure identification of stock time series: An application to the Chinese A-share market," Finance Research Letters, Elsevier, vol. 75(C).
    • Handle: RePEc:eee:finlet:v:75:y:2025:i:c:s1544612325000868
    DOI: 10.1016/j.frl.2025.106821
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    References listed on IDEAS

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