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What if borrowers stop paying their loans? Investors’ rates of return on a peer-to-peer lending platform

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  • Chen, Pei-Fen
  • Lo, Shihmin
  • Tang, Hai-Yuan

Abstract

This research uses over 750,000 loan records from LendingClub issued from 2015 through 2018 to estimate the gross rates of return (ROR) for each loan by assuming the borrowers stop paying their loans once a default occurs. We find the probability of earning a positive return is 75.6% in terms of the total number of loans. By our calculation, the median of ROR ranges from 4.7% to 10.3% by lending to grade A through D borrowers, but ROR plummets to −6.6% to grade E-and-below borrowers. Regression analysis shows that borrowers' credit rating, loan interest rate, loan status, and paid-month are the most critical factors to influence investors’ ROR. Lastly, we offer a few investment implications and suggestions regarding participation in LendingClub.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reveco:v:77:y:2022:i:c:p:359-377
    DOI: 10.1016/j.iref.2021.10.011
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    References listed on IDEAS

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    1. Guo, Yanhong & Zhou, Wenjun & Luo, Chunyu & Liu, Chuanren & Xiong, Hui, 2016. "Instance-based credit risk assessment for investment decisions in P2P lending," European Journal of Operational Research, Elsevier, vol. 249(2), pages 417-426.
    2. 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.
    3. Zhiyong Li & Ke Li & Xiao Yao & Qing Wen, 2019. "Predicting Prepayment and Default Risks of Unsecured Consumer Loans in Online Lending," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(1), pages 118-132, January.
    4. Lu, Yunlin & Guo, Haifeng & Kao, Erin H. & Fung, Hung-Gay, 2015. "Shadow banking and firm financing in China," International Review of Economics & Finance, Elsevier, vol. 36(C), pages 40-53.
    5. Julapa Jagtiani & Catharine Lemieux, 2019. "The roles of alternative data and machine learning in fintech lending: Evidence from the LendingClub consumer platform," Financial Management, Financial Management Association International, vol. 48(4), pages 1009-1029, December.
    6. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
    7. 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.
    8. Juanjuan Zhang & Peng Liu, 2012. "Rational Herding in Microloan Markets," Management Science, INFORMS, vol. 58(5), pages 892-912, May.
    9. Xiao-hong Chen & Fu-jing Jin & Qun Zhang & Li Yang, 2016. "Are investors rational or perceptual in P2P lending?," Information Systems and e-Business Management, Springer, vol. 14(4), pages 921-944, November.
    10. Zhang, Zan & Hu, Wenjun & Chang, Tsangyao, 2019. "Nonlinear effects of P2P lending on bank loans in a Panel Smooth Transition Regression model," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 468-473.
    11. Carlos Serrano-Cinca & Begoña Gutiérrez-Nieto & Luz López-Palacios, 2015. "Determinants of Default in P2P Lending," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    12. Chen, Jia & Jiang, Jiajun & Liu, Yu-jane, 2018. "Financial literacy and gender difference in loan performance," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 307-320.
    13. 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.
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    Cited by:

    1. 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).

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    More about this item

    Keywords

    Credit grades; Investors' return; LendingClub; Loan rates; Peer-to-peer lending;
    All these keywords.

    JEL classification:

    • O35 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Social Innovation
    • M13 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - New Firms; Startups
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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