IDEAS home Printed from https://ideas.repec.org/a/hin/complx/5588018.html
   My bibliography  Save this article

Research on Risk Identification System Based on Random Forest Algorithm-High-Order Moment Model

Author

Listed:
  • Li-Jun Liu
  • Wei-Kang Shen
  • Jia-Ming Zhu
  • Huihua Chen

Abstract

With the continuous development of the stock market, designing a reasonable risk identification tool will help to solve the irrational problem of investors. This paper first selects the stocks with the most valuable investment value in the future through the random forest algorithm in the nine-factor model and then analyzes them by using the higher-order moment model to find that different investors’ preferences will make the weight of the portfolio change accordingly, which will eventually make the optimal return and risk set of the composition of the portfolio change. The risk identification system designed in this paper can provide an effective risk identification tool for investors and help them make rational judgments.

Suggested Citation

  • Li-Jun Liu & Wei-Kang Shen & Jia-Ming Zhu & Huihua Chen, 2021. "Research on Risk Identification System Based on Random Forest Algorithm-High-Order Moment Model," Complexity, Hindawi, vol. 2021, pages 1-10, April.
    • Handle: RePEc:hin:complx:5588018
    DOI: 10.1155/2021/5588018
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/complexity/2021/5588018.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/complexity/2021/5588018.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2021/5588018?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
    ---><---

    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:hin:complx:5588018. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.