IDEAS home Printed from https://ideas.repec.org/a/mes/emfitr/v57y2021i8p2259-2270.html
   My bibliography  Save this article

Risk Early Warning Research on China’s Futures Company

Author

Listed:
  • Wang Ping
  • Feng Wang
  • Aihua Wang
  • Yuncheng Huang

Abstract

Having effective and reliable risk management is essential for the development of futures trading companies in China. Research analyzing early warnings on related risks for futures trading companies in China have important theoretical and empirical value. In this paper, we take the period during which fluctuation in extreme market risk occurred as the verification period and design overall risk indicators and equations to measure market risk for futures trading companies. We built an early risk warning model using extreme learning machine technology. We tested our model’s validity using statistics from China’s futures market. Empirical evidence shows that our model is more accurate than models based on the support vector machine, logistic regression, and the back-propagation neural network.

Suggested Citation

  • Wang Ping & Feng Wang & Aihua Wang & Yuncheng Huang, 2021. "Risk Early Warning Research on China’s Futures Company," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(8), pages 2259-2270, June.
    • Handle: RePEc:mes:emfitr:v:57:y:2021:i:8:p:2259-2270
    DOI: 10.1080/1540496X.2019.1689355
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/1540496X.2019.1689355
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/1540496X.2019.1689355?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.

    More about this item

    Statistics

    Access and download statistics

    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:mes:emfitr:v:57:y:2021:i:8:p:2259-2270. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/MREE20 .

    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.