IDEAS home Printed from https://ideas.repec.org/a/mes/emfitr/v58y2022i8p2368-2387.html
   My bibliography  Save this article

A Novel Credit Rating Model: Empirical Analysis from Chinese Small Enterprises

Author

Listed:
  • Bin Meng
  • Haibo Kuang
  • Liang Lv
  • Lidong Fan
  • Hongyu Chen

Abstract

This article establishes a novel credit rating model for small enterprises, thereby solving the problem that commercial banks cannot accurately obtain financial information about small enterprises or reasonably evaluate the credit risk of small enterprise loans. Through the identification of default status and removal of redundant information for indicator screening, this article adopts a weighting method that can be used to classify small enterprises. The empirical results show that the discriminant precision of default status by the credit rating system of China’s small enterprises, constructed by this article, is up to 91.9%. The weighting results show that in the credit rating of small enterprises, financial indicators cannot reflect all the liabilities, and the role of qualitative indicators in credit ratings is more important. This article empowers 30 indicators based on the principle of distinguishing different types of customers, classifies customers into nine different levels, and avoids the unreasonable phenomenon that default customers have higher credit scores than non-default customers. The results can help commercial banks to distinguish customers of different significance levels.

Suggested Citation

  • Bin Meng & Haibo Kuang & Liang Lv & Lidong Fan & Hongyu Chen, 2022. "A Novel Credit Rating Model: Empirical Analysis from Chinese Small Enterprises," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 58(8), pages 2368-2387, June.
  • Handle: RePEc:mes:emfitr:v:58:y:2022:i:8:p:2368-2387
    DOI: 10.1080/1540496X.2021.1984226
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/1540496X.2021.1984226
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/1540496X.2021.1984226?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liang Lv & Lidong Fan & Bin Meng & Mohammad Zoynul Abedin & Haoyue Feng, 2023. "A Combined Evaluation Method of Corporate Social Responsibility Based on the Difference and Similarity: A Case Study of Transportation Industry in China," Sustainability, MDPI, vol. 15(6), pages 1-25, March.

    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:58:y:2022:i:8:p:2368-2387. 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.