IDEAS home Printed from https://ideas.repec.org/a/mes/chinec/v49y2016i3p161-172.html
   My bibliography  Save this article

Risks of P2P Lending Platforms in China: Modeling Failure Using a Cox Hazard Model

Author

Listed:
  • Jianjun Li
  • Sara Hsu
  • Zhang Chen
  • Yang Chen

Abstract

P2P lending platforms in China have risen since 2006 but have already experienced problems with fraud and liquidity. In this article, we describe the P2P lending platforms and their associated risks, and discuss and analyze a dataset on failed and nonfailed P2P companies. We find that an increase in the registered capital results in a decrease in the hazard ratio, while an increase in the interest rate results in an increase in the hazard ratio. We discuss policy implications for the P2P lending sector, which can help to reduce risk in the sector while allowing innovation to arise.

Suggested Citation

  • Jianjun Li & Sara Hsu & Zhang Chen & Yang Chen, 2016. "Risks of P2P Lending Platforms in China: Modeling Failure Using a Cox Hazard Model," Chinese Economy, Taylor & Francis Journals, vol. 49(3), pages 161-172, May.
  • Handle: RePEc:mes:chinec:v:49:y:2016:i:3:p:161-172
    DOI: 10.1080/10971475.2016.1159904
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/10971475.2016.1159904
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/10971475.2016.1159904?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. Zhao Wang & Cuiqing Jiang & Huimin Zhao, 2022. "Know Where to Invest: Platform Risk Evaluation in Online Lending," Information Systems Research, INFORMS, vol. 33(3), pages 765-783, September.
    2. Ajay Byanjankar & József Mezei & Markku Heikkilä, 2021. "Data‐driven optimization of peer‐to‐peer lending portfolios based on the expected value framework," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(2), pages 119-129, April.
    3. Nigmonov, Asror & Shams, Syed & Alam, Khorshed, 2024. "Liquidity risk in FinTech lending: Early impact of the COVID-19 pandemic on the P2P lending market," Emerging Markets Review, Elsevier, vol. 58(C).
    4. Kerry Liu, 2020. "Chinese consumer finance: a primer," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-22, December.

    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:chinec:v:49:y:2016:i:3:p:161-172. 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/MCES20 .

    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.