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Asymmetric information in peer-to-peer lending: empirical evidence from China

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  • Wang, Jin
  • Li, Rui

Abstract

This paper examines how asymmetric information affects peer-to-peer lending in China. We find that default rates rise significantly with interest rates. Specifically, borrowers who select interest rates above the legal maximum private lending rate of 15.4% are more likely to default. A lack of interest rate caps to prevent adverse selection is responsible for the market failure of Chinese peer-to-peer lending. However, there is no strong evidence of the moral hazard effect in relation to interest rate and loan size. In addition, the credit scoring based on applicant characteristics can mitigate the asymmetric information problem, but not eradicate it completely.

Suggested Citation

  • Wang, Jin & Li, Rui, 2023. "Asymmetric information in peer-to-peer lending: empirical evidence from China," Finance Research Letters, Elsevier, vol. 51(C).
    • Handle: RePEc:eee:finlet:v:51:y:2023:i:c:s1544612322006298
    DOI: 10.1016/j.frl.2022.103452
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    References listed on IDEAS

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    1. William Adams & Liran Einav & Jonathan Levin, 2009. "Liquidity Constraints and Imperfect Information in Subprime Lending," American Economic Review, American Economic Association, vol. 99(1), pages 49-84, March.
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    3. Gao, M. & Yen, J. & Liu, M., 2021. "Determinants of defaults on P2P lending platforms in China," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 334-348.
    4. 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).
    5. Deng, Jiapin, 2022. "The crowding-out effect of formal finance on the P2P lending market: An explanation for the failure of China's P2P lending industry," Finance Research Letters, Elsevier, vol. 45(C).
    6. Jiaqi Yan & Wayne Yu & J. Leon Zhao, 2015. "How signaling and search costs affect information asymmetry in P2P lending: the economics of big data," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-11, December.
    7. Stiglitz, Joseph E & Weiss, Andrew, 1981. "Credit Rationing in Markets with Imperfect Information," American Economic Review, American Economic Association, vol. 71(3), pages 393-410, June.
    8. Chen, Xiao & Chong, Zhaohui & Giudici, Paolo & Huang, Bihong, 2022. "Network centrality effects in peer to peer lending," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
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    Cited by:

    1. Davaadorj, Zagdbazar, 2024. "The influence of the digital divide on peer-to-peer lending outcomes," Finance Research Letters, Elsevier, vol. 61(C).
    2. Gao, Hongming & Zhu, Hui & Ma, Haiying, 2024. "Peer effect and funding success: Analyzing friendship networks in online credit markets," Finance Research Letters, Elsevier, vol. 66(C).

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    More about this item

    Keywords

    Asymmetric information; Peer-to-peer lending; Adverse selection; Moral hazard; China;
    All these keywords.

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

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