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P2P Network Lending, Loss Given Default and Credit Risks

Author

Listed:
  • Guangyou Zhou

    (School of Economics, Fudan University, Shanghai 200433, China)

  • Yijia Zhang

    (School of Economics, Fudan University, Shanghai 200433, China)

  • Sumei Luo

    (School of Finance, Shanghai University of Finance and Economics, Shanghai 200433, China)

Abstract

Peer-to-peer (P2P) network lending is a new mode of internet finance that still holds credit risk as its main risk. According to the internal rating method of the New Basel Accord, in addition to the probability of default, loss given default is also one of the important indicators of evaluation credit risks. Proceeding from the perspective of loss given default (LGD), this paper conducts an empirical study on the probability distribution of LGDs of P2P as well as its influencing factors with the transaction data of Lending Club. The results show that: (1) the LGDs of P2P loans presents an obvious unimodal distribution, the peak value is relatively high and tends to concentrate with the decrease of the borrower’s credit rating, indicating that the distribution of LGDs of P2P lending is similar to that of unsecured bonds; (2) The total asset of the borrower has no significant impact on LGD, the credit rating and the debt-to-income ratio exert a significant negative impact, while the term and amount of the loan produce a relatively strong positive impact. Therefore, when evaluating the borrower’s repayment ability, it is required to pay more attention to its assets structure rather than the size of its total assets. When carrying out risk control for the P2P platform, it is necessary to give priority to the control of default rate.

Suggested Citation

  • Guangyou Zhou & Yijia Zhang & Sumei Luo, 2018. "P2P Network Lending, Loss Given Default and Credit Risks," Sustainability, MDPI, vol. 10(4), pages 1-15, March.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:4:p:1010-:d:138653
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    References listed on IDEAS

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    1. Mild, Andreas & Waitz, Martin & Wöckl, Jürgen, 2015. "How low can you go? — Overcoming the inability of lenders to set proper interest rates on unsecured peer-to-peer lending markets," Journal of Business Research, Elsevier, vol. 68(6), pages 1291-1305.
    2. Frontczak, Robert & Rostek, Stefan, 2015. "Modeling loss given default with stochastic collateral," Economic Modelling, Elsevier, vol. 44(C), pages 162-170.
    3. Xuchen Lin & Xiaolong Li & Zhong Zheng, 2017. "Evaluating borrower’s default risk in peer-to-peer lending: evidence from a lending platform in China," Applied Economics, Taylor & Francis Journals, vol. 49(35), pages 3538-3545, July.
    4. Riza Emekter & Yanbin Tu & Benjamas Jirasakuldech & Min Lu, 2015. "Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending," Applied Economics, Taylor & Francis Journals, vol. 47(1), pages 54-70, January.
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    Cited by:

    1. Li, Aimin & Li, Zhiyong & Bellotti, Anthony, 2023. "Predicting loss given default of unsecured consumer loans with time-varying survival scores," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    2. Zhou Rongxi & Xiong Yahui & Wang Ning & Wang Xizu, 2019. "Coupling Degree Evaluation of China’s Internet Financial Ecosystem Based on Entropy Method and Principal Component Analysis," Journal of Systems Science and Information, De Gruyter, vol. 7(5), pages 399-421, October.
    3. Serena Gallo, 2021. "Fintech platforms: Lax or careful borrowers’ screening?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-33, December.
    4. 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).
    5. Yuting Tu & Xin Yan & Huan Wang, 2023. "Game Theory Analysis of Chinese DC/EP Loan and Internet Loan Models in the Context of Regulatory Goals," Sustainability, MDPI, vol. 15(9), pages 1-15, April.
    6. Xia, Yufei & Zhao, Junhao & He, Lingyun & Li, Yinguo & Yang, Xiaoli, 2021. "Forecasting loss given default for peer-to-peer loans via heterogeneous stacking ensemble approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1590-1613.
    7. Shen, Feng & Zhang, Xin & Wang, Run & Lan, Dao & Zhou, Wei, 2022. "Sequential optimization three-way decision model with information gain for credit default risk evaluation," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1116-1128.
    8. Yang, Yi & Guo, Yuxuan & Chang, Xiangyu, 2021. "Angle-based cost-sensitive multicategory classification," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    9. Wang, Qian & Su, Zhongnan & Chen, Xinyang, 2021. "Information disclosure and the default risk of online peer-to-peer lending platform," Finance Research Letters, Elsevier, vol. 38(C).
    10. Lkhagvadorj Munkhdalai & Tsendsuren Munkhdalai & Oyun-Erdene Namsrai & Jong Yun Lee & Keun Ho Ryu, 2019. "An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments," Sustainability, MDPI, vol. 11(3), pages 1-23, January.

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    Keywords

    P2P network lending; LGD; credit risks; Lending Club;
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