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

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  • 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
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    References listed on IDEAS

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    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.
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    3. 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.
    4. 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.
    5. 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.
    6. 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.
    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.
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    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).

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    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

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