IDEAS home Printed from https://ideas.repec.org/p/jrp/jrpwrp/2016-023.html
   My bibliography  Save this paper

Determinants of borrowers' default in P2P lending under consideration of the loan risk class

Author

Listed:
  • Michal Polena

    (School of Economics and Business Administration, Friedrich-Schiller-University Jena)

  • Tobias Regner

    (School of Economics and Business Administration, Friedrich-Schiller-University Jena)

Abstract

We study the determinants of borrowers' default in P2P lending with a new data set consisting of 70,673 loan observations from Lending Club. Previous research identified a number of default determining variables but did not distinguish between different loan risk levels. We define four loan risk classes and test the significance of the default determining variables within each loan risk class. Our findings suggest that the significance of most variables depends on the loan risk class. Only few variables are consistently significant across all risk classes. The debt-to-income ratio, inquiries in the past 6 months and a loan intended for a small business are positively correlated with the default rate. Annual income and credit card as loan purpose are negatively correlated.

Suggested Citation

  • Michal Polena & Tobias Regner, 2016. "Determinants of borrowers' default in P2P lending under consideration of the loan risk class," Jena Economic Research Papers 2016-023, Friedrich-Schiller-University Jena.
  • Handle: RePEc:jrp:jrpwrp:2016-023
    as

    Download full text from publisher

    File URL: http://www2.wiwi.uni-jena.de/Papers/jerp2016/wp_2016_023.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Freedman, Seth & Jin, Ginger Zhe, 2017. "The information value of online social networks: Lessons from peer-to-peer lending," International Journal of Industrial Organization, Elsevier, vol. 51(C), pages 185-222.
    2. Adam Nowak & Amanda Ross & Christopher Yencha, 2018. "Small Business Borrowing And Peer‐To‐Peer Lending: Evidence From Lending Club," Contemporary Economic Policy, Western Economic Association International, vol. 36(2), pages 318-336, April.
    3. Nataliya Barasinska & Dorothea Schäfer, 2014. "Is Crowdfunding Different? Evidence on the Relation between Gender and Funding Success from a German Peer-to-Peer Lending Platform," German Economic Review, Verein für Socialpolitik, vol. 15(4), pages 436-452, November.
    4. Herzenstein, Michal & Dholakia, Utpal M. & Andrews, Rick L., 2011. "Strategic Herding Behavior in Peer-to-Peer Loan Auctions," Journal of Interactive Marketing, Elsevier, vol. 25(1), pages 27-36.
    5. Iyer, Rajkamal & Khwaja, Asim Ijaz & Luttmer, Erzo F. P. & Shue, Kelly, 2009. "Screening in New Credit Markets: Can Individual Lenders Infer Borrower Creditworthiness in Peer-to-Peer Lending?," Working Paper Series rwp09-031, Harvard University, John F. Kennedy School of Government.
    6. 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.
    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. Mingfeng Lin & Nagpurnanand R. Prabhala & Siva Viswanathan, 2013. "Judging Borrowers by the Company They Keep: Friendship Networks and Information Asymmetry in Online Peer-to-Peer Lending," Management Science, INFORMS, vol. 59(1), pages 17-35, August.
    9. Karen Mills & Brayden McCarthy, 2014. "The State of Small Business Lending: Credit Access during the Recovery and How Technology May Change the Game," Harvard Business School Working Papers 15-004, Harvard Business School.
    10. Juanjuan Zhang & Peng Liu, 2012. "Rational Herding in Microloan Markets," Management Science, INFORMS, vol. 58(5), pages 892-912, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Teply, Petr & Polena, Michal, 2020. "Best classification algorithms in peer-to-peer lending," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    2. Lu, Haitian & Wang, Bo & Wang, Haizhi & Zhao, Tianyu, 2020. "Does social capital matter for peer-to-peer-lending? Empirical evidence," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    3. Marta Kłosok & Marcin Chlebus, 2020. "Towards better understanding of complex machine learning models using Explainable Artificial Intelligence (XAI) - case of Credit Scoring modelling," Working Papers 2020-18, Faculty of Economic Sciences, University of Warsaw.
    4. 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.
    5. Yan Wang & Xuelei Sherry Ni, 2020. "Improving Investment Suggestions for Peer-to-Peer (P2P) Lending via Integrating Credit Scoring into Profit Scoring," Papers 2009.04536, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lu, Haitian & Wang, Bo & Wang, Haizhi & Zhao, Tianyu, 2020. "Does social capital matter for peer-to-peer-lending? Empirical evidence," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    2. 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.
    3. Andreas Hoegen & Dennis M. Steininger & Daniel Veit, 2018. "How do investors decide? An interdisciplinary review of decision-making in crowdfunding," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(3), pages 339-365, August.
    4. Teply, Petr & Polena, Michal, 2020. "Best classification algorithms in peer-to-peer lending," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    5. Wang, Qi & Xiong, Xiong & Zheng, Zunxin, 2021. "Platform Characteristics and Online Peer-to-Peer Lending: Evidence from China," Finance Research Letters, Elsevier, vol. 38(C).
    6. Wang, Congcong & Tong, Lin, 2020. "Lender rationality and trade-off behavior: Evidence from Lending Club and Renrendai," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 55-66.
    7. Adam Nowak & Amanda Ross & Christopher Yencha, 2018. "Small Business Borrowing And Peer‐To‐Peer Lending: Evidence From Lending Club," Contemporary Economic Policy, Western Economic Association International, vol. 36(2), pages 318-336, April.
    8. Demir, Tolga & Mohammad, Ali & Shafi, Kourosh, 2019. "Crowdfunding as Gambling: Evidence from Repeated Natural Experiments," Working Paper Series in Economics and Institutions of Innovation 481, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies.
    9. Jianrong Yao & Jiarui Chen & June Wei & Yuangao Chen & Shuiqing Yang, 2019. "The relationship between soft information in loan titles and online peer-to-peer lending: evidence from RenRenDai platform," Electronic Commerce Research, Springer, vol. 19(1), pages 111-129, March.
    10. Yingxiu Zhao & Wei Zhang & Xiangyu Kong, 2019. "Dynamic Cross-Correlations between Participants’ Attentions to P2P Lending and Offline Loan in the Private Lending Market," Complexity, Hindawi, vol. 2019, pages 1-8, December.
    11. Qun Chen & Ji-Wen Li & Jian-Guo Liu & Jing-Ti Han & Yun Shi & Xun-Hua Guo, 0. "Borrower Learning Effects: Do Prior Experiences Promote Continuous Successes in Peer-to-Peer Lending?," Information Systems Frontiers, Springer, vol. 0, pages 1-24.
    12. Wolfgang Pointner & Burkhard Raunig, 2018. "A primer on peer-to-peer lending: immediate financial intermediation in practice," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue Q3/18, pages 36-51.
    13. Carla Martínez-Climent & Ana Zorio-Grima & Domingo Ribeiro-Soriano, 2018. "Financial return crowdfunding: literature review and bibliometric analysis," International Entrepreneurship and Management Journal, Springer, vol. 14(3), pages 527-553, September.
    14. Kräussl, Roman & Kräussl, Zsofia & Pollet, Joshua & Rinne, Kalle, 2018. "The performance of marketplace lenders: Evidence from lending club payment data," CFS Working Paper Series 598, Center for Financial Studies (CFS).
    15. Gregor Dorfleitner & Eva-Maria Oswald & Rongxin Zhang, 2021. "From Credit Risk to Social Impact: On the Funding Determinants in Interest-Free Peer-to-Peer Lending," Journal of Business Ethics, Springer, vol. 170(2), pages 375-400, May.
    16. Ge Gao & Mustafa Caglayan & Yuelei Li & Oleksandr Talavera, 2020. "Expert Imitation in P2P Markets," Discussion Papers 20-10, Department of Economics, University of Birmingham.
    17. Benjamin Käfer, 2016. "Peer-to-Peer Lending – A (Financial Stability) Risk Perspective," MAGKS Papers on Economics 201622, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    18. Andrew Grant & Luke Deer, 2020. "Consumer marketplace lending in Australia: Credit scores and loan funding success," Australian Journal of Management, Australian School of Business, vol. 45(4), pages 607-623, November.
    19. 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.
    20. Dongyu Chen & Xiaolin Li & Fujun Lai, 2017. "Gender discrimination in online peer-to-peer credit lending: evidence from a lending platform in China," Electronic Commerce Research, Springer, vol. 17(4), pages 553-583, December.

    More about this item

    Keywords

    crowdfunding; peer-to-peer lending; P2P; credit grade; FICO score; default risk;
    All these keywords.

    JEL classification:

    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • E41 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Demand for Money
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jrp:jrpwrp:2016-023. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: http://www.wiwiss.uni-jena.de/ .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Markus Pasche (email available below). General contact details of provider: http://www.wiwiss.uni-jena.de/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.