IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v65y2025i5d10.1007_s10614-024-10657-7.html
   My bibliography  Save this article

Stock Returns Prediction Based on Implied Volatility Spread Under Network Perspective

Author

Listed:
  • Hairong Cui

    (Nanjing University of Information Science and Technology)

  • Xurui Wang

    (Nanjing University of Information Science and Technology)

  • Xiaojun Chu

    (Nanjing University of Information Science and Technology)

Abstract

Using 50 ETF options data from the Shanghai Stock Exchange as samples, this paper explores the predictive power of option implied volatility spread (IVS) on stock market returns, mainly from a network perspective. In this paper, we first construct a multi-scale data series by wavelet decomposition of the data, and then build a corresponding dynamic complex network on this basis. We analyze the topological features of the network to reveal the dynamic relationship between variables. At the same time, the topological features are used as input variables for machine learning to quantitatively explore the return information contained in the IVS. The conclusions show not only that IVS has the strongest correlation with stock market returns in the medium and long-term, but that the accuracy of IVS prediction is also highest at this time. Furthermore, the GBDT machine learning model is more effective in predicting future stock market returns when using IVS as an indicator.

Suggested Citation

  • Hairong Cui & Xurui Wang & Xiaojun Chu, 2025. "Stock Returns Prediction Based on Implied Volatility Spread Under Network Perspective," Computational Economics, Springer;Society for Computational Economics, vol. 65(5), pages 2829-2852, May.
    • Handle: RePEc:kap:compec:v:65:y:2025:i:5:d:10.1007_s10614-024-10657-7
    DOI: 10.1007/s10614-024-10657-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-024-10657-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-024-10657-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:65:y:2025:i:5:d:10.1007_s10614-024-10657-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.