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Forward looking vs backward looking

Author

Listed:
  • Yanyan Gao
  • Jun Sun
  • Qin Zhou

Abstract

Purpose - The purpose of this paper is to estimate the effectiveness of the credit evaluation system using the borrowing data from China’s leading P2P lending platform, Renrendai.com. Design/methodology/approach - The current credit valuation systems are classified into the forward-looking mechanism, which judges the borrowers’ credit levels based on their uploaded information, and the backward-looking mechanism, which judges the borrowers’ credit levels based on their historical repayment performance. Probit models and Tobit models are used to examine the effectiveness of credit evaluation mechanisms. Findings - The results show that only the “hard” information reflecting borrowers’ credit ability can explain the default risk on the platform under the forward-looking credit evaluation mechanism. The backward-looking credit evaluation mechanism (BCEM) based on the repeated borrowings produces both promise-enhancing and “fishing” incentives and thus fails to explain the default risk, and weakens the effectiveness of forward-looking credit indicators in explaining the default risk because it encourages borrowers to invest in forging forward-looking credit indicators. Additional information such as the interest rate and the repayment periods reveals borrowers’ credit and thus can also be used as a predictor of borrowers’ default risk. Practical implications - The findings suggest that currentex antescreening based on the information collected from the borrowers or repeated borrowings is inadequate to control the default risk in P2P lending markets and thus needs be improved.Ex postmonitoring and sharing on defaulter’s information should be strengthened to increase the default cost and thus to deter potential bad borrowers. Originality/value - To the authors’ knowledge, this is the first paper classifying the credit evaluation system in online P2P lending market into the forward-looking type and the backward-looking type, which is important since they provide different incentives to borrowers. The paper also investigates and provides evidence on the promise-enhancing and “fishing” incentives of BCEMs.

Suggested Citation

  • Yanyan Gao & Jun Sun & Qin Zhou, 2017. "Forward looking vs backward looking," China Finance Review International, Emerald Group Publishing Limited, vol. 7(2), pages 228-248, May.
  • Handle: RePEc:eme:cfripp:cfri-07-2016-0089
    DOI: 10.1108/CFRI-07-2016-0089
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    Citations

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    Cited by:

    1. Chen, Rongda & Zhou, Hanxian & Jin, Chenglu & Zheng, Wei, 2019. "Modeling of recovery rate for a given default by non-parametric method," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
    2. Chen, Rongda & Chen, Xinhao & Jin, Chenglu & Chen, Yiyang & Chen, Jiayi, 2020. "Credit rating of online lending borrowers using recovery rates," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 204-216.

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