IDEAS home Printed from https://ideas.repec.org/a/sae/sagope/v13y2023i4p21582440231201378.html
   My bibliography  Save this article

Relationship Between Interest Rate and Risk of P2P Lending in China Based on the Skew-Normal Panel Data Model

Author

Listed:
  • Rendao Ye
  • Ya Lin

Abstract

This study examines the relationship between interest rate and defunct platform risk of China’s peer-to-peer (P2P) lending platforms. P2P lending provides an alternative funding source for individuals and micro-enterprises and offers a new investment tool for households. But the frequent collapses of many platforms were huge losses to market participants and even led to a decline in China’s P2P lending industry. In this study, weekly data of 76 platforms from December 3, 2017, to October 6, 2019, are employed, and empirical research based on the normal and skew-normal panel data model respectively is conducted. Statistical indicators prove that the skew-normal panel data model is preferable to another one in modeling the data set of interest rates. The empirical results show that China’s P2P market is efficient overall. But the positive correlation between the interest rate and risk is not significant for platforms with excessively high interest rates, whose interest rates are more determined by the types of ownership. The findings and implications are provided in the end.

Suggested Citation

  • Rendao Ye & Ya Lin, 2023. "Relationship Between Interest Rate and Risk of P2P Lending in China Based on the Skew-Normal Panel Data Model," SAGE Open, , vol. 13(4), pages 21582440231, October.
    • Handle: RePEc:sae:sagope:v:13:y:2023:i:4:p:21582440231201378
    DOI: 10.1177/21582440231201378
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/21582440231201378
    Download Restriction: no
    File URL: https://libkey.io/10.1177/21582440231201378?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
    ---><---

    References listed on IDEAS

    as
    1. A. Azzalini & A. Capitanio, 1999. "Statistical applications of the multivariate skew normal distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 579-602.
    2. GUO, Jianfeng & LIU, Xiaojie & CUI, Changnan & GU, Fu, 2021. "Influence of nonspecific factors on the interest rate of online peer-to-peer microloans in China," Finance Research Letters, Elsevier, vol. 41(C).
    3. 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.
    4. Sune Karlsson & Jimmy Skoglund, 2004. "Maximum-likelihood based inference in the two-way random effects model with serially correlated time effects," Empirical Economics, Springer, vol. 29(1), pages 79-88, January.
    5. Bachmann, Alexander & Funk, Burkhardt, 2011. "Online Peer-to-Peer Lending – A Literature Review," Journal of Internet Banking and Commerce, , vol. 16(2), pages 01-18, August.
    6. Frederick J. Riggins & David M. Weber, 2017. "Information asymmetries and identification bias in P2P social microlending," Information Technology for Development, Taylor & Francis Journals, vol. 23(1), pages 107-126, January.
    7. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    8. Daowen Zhang & Marie Davidian, 2001. "Linear Mixed Models with Flexible Distributions of Random Effects for Longitudinal Data," Biometrics, The International Biometric Society, vol. 57(3), pages 795-802, September.
    9. Jefferson Duarte & Stephan Siegel & Lance Young, 2012. "Trust and Credit: The Role of Appearance in Peer-to-peer Lending," The Review of Financial Studies, Society for Financial Studies, vol. 25(8), pages 2455-2484.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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).
    3. Zhenhua Wu & Lin Hu & Zhijie Lin & Yong Tan, 2021. "Competition and Distortion: A Theory of Information Bias on the Peer-to-Peer Lending Market," Information Systems Research, INFORMS, vol. 32(4), pages 1140-1154, December.
    4. Baumöhl, Eduard & Lyócsa, Štefan & Vašaničová, Petra, 2024. "Macroeconomic environment and the future performance of loans: Evidence from three peer-to-peer platforms," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    5. Qizhi Tao & Yizhe Dong & Ziming Lin, 0. "Who can get money? Evidence from the Chinese peer-to-peer lending platform," Information Systems Frontiers, Springer, vol. 0, pages 1-17.
    6. Huang, Jin & Sena, Vania & Li, Jun & Ozdemir, Sena, 2021. "Message framing in P2P lending relationships," Journal of Business Research, Elsevier, vol. 122(C), pages 761-773.
    7. 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.
    8. Peng Wang & Haichao Zheng & Dongyu Chen & Liangchao Ding, 2015. "Exploring the critical factors influencing online lending intentions," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-11, December.
    9. Liu, Aiping & Urquía-Grande, Elena & López-Sánchez, Pilar & Rodríguez-López, Ángel, 2023. "Research into microfinance and ICTs: A bibliometric analysis," Evaluation and Program Planning, Elsevier, vol. 97(C).
    10. Jing Zhang & Wei Zhang & Yuelei Li & Shuxing Yin, 2021. "Seeking excess returns under a posted price mechanism: Evidence from a peer‐to‐peer lending market," Manchester School, University of Manchester, vol. 89(5), pages 486-506, September.
    11. Cai, Wanxiang & Polzin, Friedemann & Stam, Erik, 2021. "Crowdfunding and social capital: A systematic review using a dynamic perspective," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    12. Andreas Hoegen & Dennis M. Steininger & Daniel Veit, 2018. "How do investors decide? An interdisciplinary review of decision-making in crowdfunding," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(3), pages 339-365, August.
    13. 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.
    14. Manconi, Alberto & Braggion, Fabio & Zhu, Haikun, 2018. "Can Technology Undermine Macroprudential Regulation? Evidence from Peer-to-Peer Credit in China," CEPR Discussion Papers 12668, C.E.P.R. Discussion Papers.
    15. Ye, Rendao & Wang, Tonghui & Gupta, Arjun K., 2014. "Distribution of matrix quadratic forms under skew-normal settings," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 229-239.
    16. 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.
    17. Biancini, Sara & Verdier, Marianne, 2023. "Bank-platform competition in the credit market," International Journal of Industrial Organization, Elsevier, vol. 91(C).
    18. Teply, Petr & Polena, Michal, 2020. "Best classification algorithms in peer-to-peer lending," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    19. Carla Martínez-Climent & Ana Zorio-Grima & Domingo Ribeiro-Soriano, 2018. "Financial return crowdfunding: literature review and bibliometric analysis," International Entrepreneurship and Management Journal, Springer, vol. 14(3), pages 527-553, September.
    20. Galit Klein & Zeev Shtudiner & Moti Zwilling, 2023. "Why do peer-to-peer (P2P) lending platforms fail? The gap between P2P lenders' preferences and the platforms’ intentions," Electronic Commerce Research, Springer, vol. 23(2), pages 709-738, June.

    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:sae:sagope:v:13:y:2023:i:4:p:21582440231201378. 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: SAGE Publications (email available below). General contact details of provider: .

    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.