IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v40y2021ics1544612319312772.html
   My bibliography  Save this article

Can credit ratings predict defaults in peer-to-peer online lending? Evidence from a Chinese platform

Author

Listed:
  • Wu, Yu
  • Zhang, Tong

Abstract

By investigating a Chinese peer-to-peer online lending platform, Renrendai, we find that the credit ratings of new borrowers do not accurately predict their default. Moreover, we find that on this platform the probability of default by new borrowers is 56%. These findings indicate that in China, in the absence of authoritative credit agencies, platforms’ assigning credit ratings themselves, not only induces high investment risk for lenders, but also high systemic risk for platforms since most of these platforms guarantee the loan principal. Our results might explain why over 86% of Chinese lending platforms experience operational difficulties.11This percentage is calculated based on data published on p2peye.com.

Suggested Citation

  • Wu, Yu & Zhang, Tong, 2021. "Can credit ratings predict defaults in peer-to-peer online lending? Evidence from a Chinese platform," Finance Research Letters, Elsevier, vol. 40(C).
    • Handle: RePEc:eee:finlet:v:40:y:2021:i:c:s1544612319312772
    DOI: 10.1016/j.frl.2020.101724
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612319312772
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2020.101724?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.

    References listed on IDEAS

    as
    1. Ding, Jie & Huang, Jinbo & Li, Yong & Meng, Meichen, 2019. "Is there an effective reputation mechanism in peer-to-peer lending? Evidence from China," Finance Research Letters, Elsevier, vol. 30(C), pages 208-215.
    2. Iyer, Rajkamal & Khwaja, Asim Ijaz & Luttmer, Erzo F. P. & Shue, Kelly, 2009. "Screening in New Credit Markets: Can Individual Lenders Infer Borrower Creditworthiness in Peer-to-Peer Lending?," Working Paper Series rwp09-031, Harvard University, John F. Kennedy School of Government.
    3. Chen, Xiao & Huang, Bihong & Ye, Dezhu, 2018. "The role of punctuation in P2P lending: Evidence from China," Economic Modelling, Elsevier, vol. 68(C), pages 634-643.
    4. Xuchen Lin & Xiaolong Li & Zhong Zheng, 2017. "Evaluating borrower’s default risk in peer-to-peer lending: evidence from a lending platform in China," Applied Economics, Taylor & Francis Journals, vol. 49(35), pages 3538-3545, July.
    5. Juanjuan Chen & Yabin Zhang & Zhujia Yin, 2018. "Education Premium In The Online Peer-To-Peer Lending Marketplace: Evidence From The Big Data In China," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 63(01), pages 45-64, March.
    6. Dorfleitner, Gregor & Priberny, Christopher & Schuster, Stephanie & Stoiber, Johannes & Weber, Martina & de Castro, Ivan & Kammler, Julia, 2016. "Description-text related soft information in peer-to-peer lending – Evidence from two leading European platforms," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 169-187.
    7. Riza Emekter & Yanbin Tu & Benjamas Jirasakuldech & Min Lu, 2015. "Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending," Applied Economics, Taylor & Francis Journals, vol. 47(1), pages 54-70, January.
    8. Mingfeng Lin & Nagpurnanand R. Prabhala & Siva Viswanathan, 2013. "Judging Borrowers by the Company They Keep: Friendship Networks and Information Asymmetry in Online Peer-to-Peer Lending," Management Science, INFORMS, vol. 59(1), pages 17-35, August.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Sha, Yezhou, 2022. "Rating manipulation and creditworthiness for platform economy: Evidence from peer-to-peer lending," International Review of Financial Analysis, Elsevier, vol. 84(C).
    2. Wang, Shaoda & Ye, Dezhu & Liao, Junyun, 2024. "Politeness matters: The role of polite languages in online peer-to-peer lending," Journal of Business Research, Elsevier, vol. 171(C).
    3. Liu, Yiting & Baals, Lennart John & Osterrieder, Jörg & Hadji-Misheva, Branka, 2024. "Network centrality and credit risk: A comprehensive analysis of peer-to-peer lending dynamics," Finance Research Letters, Elsevier, vol. 63(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Davaadorj, Zagdbazar & Enkhtaivan, Bolortuya & Lu, Wenling, 2024. "The role of job titles in online peer-to-peer lending: An empirical investigation on skilled borrowers," Journal of Behavioral and Experimental Finance, Elsevier, vol. 41(C).
    2. Wang, Qi & Xiong, Xiong & Zheng, Zunxin, 2021. "Platform Characteristics and Online Peer-to-Peer Lending: Evidence from China," Finance Research Letters, Elsevier, vol. 38(C).
    3. Yeujun Yoon & Yu Li & Yan Feng, 2019. "Factors affecting platform default risk in online peer-to-peer (P2P) lending business: an empirical study using Chinese online P2P platform data," Electronic Commerce Research, Springer, vol. 19(1), pages 131-158, March.
    4. Pankaj Kumar Maskara & Emre Kuvvet & Gengxuan Chen, 2021. "The role of P2P platforms in enhancing financial inclusion in the United States: An analysis of peer‐to‐peer lending across the rural–urban divide," Financial Management, Financial Management Association International, vol. 50(3), pages 747-774, September.
    5. Mengyin Li & Phillip H. Phan & Xian Sun, 2021. "Business Friendliness: A Double-Edged Sword," Sustainability, MDPI, vol. 13(4), pages 1-22, February.
    6. Wang, Shaoda & Ye, Dezhu & Liao, Junyun, 2024. "Politeness matters: The role of polite languages in online peer-to-peer lending," Journal of Business Research, Elsevier, vol. 171(C).
    7. Jianrong Yao & Jiarui Chen & June Wei & Yuangao Chen & Shuiqing Yang, 2019. "The relationship between soft information in loan titles and online peer-to-peer lending: evidence from RenRenDai platform," Electronic Commerce Research, Springer, vol. 19(1), pages 111-129, March.
    8. Li, Jianwen & Hu, Jinyan, 2019. "Does university reputation matter? Evidence from peer-to-peer lending," Finance Research Letters, Elsevier, vol. 31(C), pages 66-77.
    9. Wang, Chao & Wang, Junbo & Wu, Chunchi & Zhang, Yue, 2023. "Voluntary disclosure in P2P lending: Information or hyperbole?," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    10. Qun Chen & Ji-Wen Li & Jian-Guo Liu & Jing-Ti Han & Yun Shi & Xun-Hua Guo, 2021. "Borrower Learning Effects: Do Prior Experiences Promote Continuous Successes in Peer-to-Peer Lending?," Information Systems Frontiers, Springer, vol. 23(4), pages 963-986, August.
    11. Qun Chen & Ji-Wen Li & Jian-Guo Liu & Jing-Ti Han & Yun Shi & Xun-Hua Guo, 0. "Borrower Learning Effects: Do Prior Experiences Promote Continuous Successes in Peer-to-Peer Lending?," Information Systems Frontiers, Springer, vol. 0, pages 1-24.
    12. Samuel Ribeiro-Navarrete & Juan Piñeiro-Chousa & M. Ángeles López-Cabarcos & Daniel Palacios-Marqués, 2022. "Crowdlending: mapping the core literature and research frontiers," Review of Managerial Science, Springer, vol. 16(8), pages 2381-2411, November.
    13. Chen, Shiyi & Gu, Yan & Liu, Qingfu & Tse, Yiuman, 2020. "How do lenders evaluate borrowers in peer-to-peer lending in China?," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 651-662.
    14. Xueru Chen & Xiaoji Hu & Shenglin Ben, 2021. "How do reputation, structure design and FinTech ecosystem affect the net cash inflow of P2P lending platforms? Evidence from China," Electronic Commerce Research, Springer, vol. 21(4), pages 1055-1082, December.
    15. Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
    16. Ligang Zhou & Chao Ma, 2023. "A Comparison of Different Rules on Loans Evaluation in Peer-to-Peer Lending by Gradient Boosting Models Under Moving Windows with Two Timestamps," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1481-1504, December.
    17. Yufei Xia & Lingyun He & Yinguo Li & Nana Liu & Yanlin Ding, 2020. "Predicting loan default in peer‐to‐peer lending using narrative data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 260-280, March.
    18. Qizhi Tao & Yizhe Dong & Ziming Lin, 2017. "Who can get money? Evidence from the Chinese peer-to-peer lending platform," Information Systems Frontiers, Springer, vol. 19(3), pages 425-441, June.
    19. Ma, Qianli & Xu, Lei & Anwar, Sajid & Lu, Zenghua, 2023. "Banking competition and the use of shadow credit: Evidence from lending marketplaces," Global Finance Journal, Elsevier, vol. 58(C).
    20. Wu, Bao & Liu, Zijia & Gu, Qiuyang & Tsai, Fu-Sheng, 2023. "Underdog mentality, identity discrimination and access to peer-to-peer lending market: Exploring effects of digital authentication," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 83(C).

    More about this item

    Keywords

    Peer-to-peer online lending; Default risk; Credit rating; New borrowers;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • G29 - Financial Economics - - Financial Institutions and Services - - - Other

    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:eee:finlet:v:40:y:2021:i:c:s1544612319312772. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    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.