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Can Listing Information Indicate Borrower Credit Risk in Online Peer-to-Peer Lending?

Author

Listed:
  • Yi Liu
  • Quanli Zhou
  • Xuan Zhao
  • Yudong Wang

Abstract

Effective assessment of borrower credit risk is the greatest challenge for peer-to-peer (P2P) lenders, especially in the Chinese market, where borrowers lack widely recognized credit scores. In this study, based on credit data from 2012 to 2015 from the website Renrendai.com, a logit model was used to assess borrower credit risk and predict the probability of default in every out-of-sample listing. The predicted probability of default was then compared with the actual default observation of default. The empirical results show that the logit model can evaluate the credit risk of P2P borrowers, and the model reduces the default rate to 9.5%, compared with the total sample default rate of 16.5%.

Suggested Citation

  • Yi Liu & Quanli Zhou & Xuan Zhao & Yudong Wang, 2018. "Can Listing Information Indicate Borrower Credit Risk in Online Peer-to-Peer Lending?," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(13), pages 2982-2994, October.
    • Handle: RePEc:mes:emfitr:v:54:y:2018:i:13:p:2982-2994
    DOI: 10.1080/1540496X.2018.1427061
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    Citations

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    Cited by:

    1. Gaigalienė Asta & Česnys Dovydas, 2018. "Determinants of Default in Lithuanian Peer-To-Peer Platforms," Management of Organizations: Systematic Research, Sciendo, vol. 80(1), pages 19-36, December.
    2. Liu, Yi & Yang, Menglong & Wang, Yudong & Li, Yongshan & Xiong, Tiancheng & Li, Anzhe, 2022. "Applying machine learning algorithms to predict default probability in the online credit market: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 79(C).
    3. 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).
    4. 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.
    5. Chen, Pei-Fen & Lo, Shihmin & Tang, Hai-Yuan, 2022. "What if borrowers stop paying their loans? Investors’ rates of return on a peer-to-peer lending platform," International Review of Economics & Finance, Elsevier, vol. 77(C), pages 359-377.

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