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Factors affecting platform default risk in online peer-to-peer (P2P) lending business: an empirical study using Chinese online P2P platform data

Author

Listed:
  • Yeujun Yoon

    (Kyung Hee University)

  • Yu Li

    (Peking University HSBC Business School)

  • Yan Feng

    (Peking University HSBC Business School)

Abstract

Most previous literatures focus on the micro level default risk of individual borrowers whereas the platform default risk has not been rigorously studied yet. In this paper, we investigate the factors affecting platform default risk by employing the Chinese online P2P platform data. We find significant evidence that severe competition among platforms can increase risky behaviors of platforms by allowing riskier borrowers into the system. Some of the risk management devices could alleviate the default risk of platforms; however, others are not effective at alleviating the default risks. In addition, we find evidence that macro environment such as stock market condition or increases in speculative investment opportunities plays critical roles to increase the platform default rate. Our study sheds light on the platforms’ default risk issues and verifies key factors that influence their risky behaviors.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:elcore:v:19:y:2019:i:1:d:10.1007_s10660-018-9291-1
    DOI: 10.1007/s10660-018-9291-1
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    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. Dongwoo Kim, 2023. "Can investors’ collective decision-making evolve? Evidence from peer-to-peer lending markets," Electronic Commerce Research, Springer, vol. 23(2), pages 1323-1358, June.
    3. Yeh, Jen-Yin & Chiu, Hsin-Yu & Huang, Jhih-Huei, 2024. "Predicting failure of P2P lending platforms through machine learning: The case in China," Finance Research Letters, Elsevier, vol. 59(C).
    4. Yanyan Cui & Lixin Liu, 2022. "Investor sentiment-aware prediction model for P2P lending indicators based on LSTM," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-17, January.
    5. Na Sun & Liangrong Song & Yan Sun, 2021. "Fuze Effect: A Landmine in the Way of Sustainable Development of FinTech—The Lessons from the Peer-To-Peer Risk Outbreak," Sustainability, MDPI, vol. 13(4), pages 1-21, February.
    6. 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.
    7. Ying Liu & Rui Wang & Jin Qin, 2021. "CEO influence on P2P platform survival: Education and experience do matter!," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(3), pages 622-634, April.
    8. Wang, Chao & Zhang, Yue & Zhang, Weiguo & Gong, Xue, 2021. "Textual sentiment of comments and collapse of P2P platforms: Evidence from China's P2P market," Research in International Business and Finance, Elsevier, vol. 58(C).
    9. Jen-Yin Yeh & Hsin-Yu Chiu & Jhih-Huei Huang, 2023. "Predicting Failure of P2P Lending Platforms through Machine Learning: The Case in China," Papers 2311.14577, arXiv.org.
    10. Evangelos Katsamakas & J. Manuel Sanchez-Cartas, 2024. "A computational model of the effects of borrower default on the stability of P2P lending platforms," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 14(3), pages 597-618, September.

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