IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v306y2023i2p968-985.html
   My bibliography  Save this article

The profitability of online loans: A competing risks analysis on default and prepayment

Author

Listed:
  • Li, Zhiyong
  • Li, Aimin
  • Bellotti, Anthony
  • Yao, Xiao

Abstract

Traditional credit scoring models help lenders to make informed decisions in identifying those borrowers most likely to default. We analyse over one million online loans and find that the rates for both default and prepayment are relatively high compared to traditional bank loans. A preliminary nonparametric life-table estimate shows that loans with different terms exhibit varying patterns of hazards. We use a proportional hazard model with competing risks to predict the time to default and prepayment, and parameterise those covariates affecting the time to both events. Two dimensions of predictive performance, the discriminant power and the probability calibration, are then examined. To further support the primacy of profit-driven decisions, we propose a framework based on competing risks survival analysis to estimate the profitability of loans and the return of loan portfolios. Profitability forecasts incorporating both the time to default and prepayment are compared to the profitability of real assets, and finally a penalty is suggested to compensate for those losses incurred by prepayment.

Suggested Citation

  • Li, Zhiyong & Li, Aimin & Bellotti, Anthony & Yao, Xiao, 2023. "The profitability of online loans: A competing risks analysis on default and prepayment," European Journal of Operational Research, Elsevier, vol. 306(2), pages 968-985.
  • Handle: RePEc:eee:ejores:v:306:y:2023:i:2:p:968-985
    DOI: 10.1016/j.ejor.2022.08.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221722006567
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2022.08.013?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Cole, Rebel A. & Gunther, Jeffery W., 1995. "Separating the likelihood and timing of bank failure," Journal of Banking & Finance, Elsevier, vol. 19(6), pages 1073-1089, September.
    2. Fitzpatrick, Trevor & Mues, Christophe, 2021. "How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments," European Journal of Operational Research, Elsevier, vol. 294(2), pages 711-722.
    3. Lijia Mo & James Yae, 2022. "Lending Club meets Zillow: local housing prices and default risk of peer-to-peer loans," Applied Economics, Taylor & Francis Journals, vol. 54(35), pages 4101-4112, July.
    4. Kirkby, J. Lars & Mitra, Sovan & Nguyen, Duy, 2020. "An analysis of dollar cost averaging and market timing investment strategies," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1168-1186.
    5. Anderson, Raymond, 2007. "The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation," OUP Catalogue, Oxford University Press, number 9780199226405, Decembrie.
    6. 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.
    7. Rajkamal Iyer & Asim Ijaz Khwaja & Erzo F. P. Luttmer & Kelly Shue, 2016. "Screening Peers Softly: Inferring the Quality of Small Borrowers," Management Science, INFORMS, vol. 62(6), pages 1554-1577, June.
    8. 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.
    9. Maximilian Schmeiser & Matthew Gross, 2016. "The Determinants of Subprime Mortgage Performance Following a Loan Modification," The Journal of Real Estate Finance and Economics, Springer, vol. 52(1), pages 1-27, January.
    10. Agarwal, Sumit & Ambrose, Brent W. & Liu, Chunlin, 2006. "Credit Lines and Credit Utilization," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(1), pages 1-22, February.
    11. Bastos, João A., 2010. "Forecasting bank loans loss-given-default," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2510-2517, October.
    12. Deng, Yongheng & Gabriel, Stuart, 2006. "Risk-Based Pricing and the Enhancement of Mortgage Credit Availability among Underserved and Higher Credit-Risk Populations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(6), pages 1431-1460, September.
    13. O. Emre Ergungor & Stephanie Moulton, 2014. "Beyond the Transaction: Banks and Mortgage Default of Low‐Income Homebuyers," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(8), pages 1721-1752, December.
    14. Steinbuks, Jevgenijs, 2015. "Effects of prepayment regulations on termination of subprime mortgages," Journal of Banking & Finance, Elsevier, vol. 59(C), pages 445-456.
    15. Tony Bellotti & Jonathan Crook, 2014. "Retail credit stress testing using a discrete hazard model with macroeconomic factors," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 340-350, March.
    16. Beltratti, Andrea & Benetton, Matteo & Gavazza, Alessandro, 2017. "The role of prepayment penalties in mortgage loans," Journal of Banking & Finance, Elsevier, vol. 82(C), pages 165-179.
    17. Dorfleitner, Gregor & Priberny, Christopher & Schuster, Stephanie & Stoiber, Johannes & Weber, Martina & de Castro, Ivan & Kammler, Julia, 2016. "Description-text related soft information in peer-to-peer lending – Evidence from two leading European platforms," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 169-187.
    18. Bauer, Julian & Agarwal, Vineet, 2014. "Are hazard models superior to traditional bankruptcy prediction approaches? A comprehensive test," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 432-442.
    19. Dumitrescu, Elena & Hué, Sullivan & Hurlin, Christophe & Tokpavi, Sessi, 2022. "Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1178-1192.
    20. Bellotti, Anthony & Brigo, Damiano & Gambetti, Paolo & Vrins, Frédéric, 2021. "Forecasting recovery rates on non-performing loans with machine learning," International Journal of Forecasting, Elsevier, vol. 37(1), pages 428-444.
    21. Yao, Xiao & Crook, Jonathan & Andreeva, Galina, 2017. "Enhancing two-stage modelling methodology for loss given default with support vector machines," European Journal of Operational Research, Elsevier, vol. 263(2), pages 679-689.
    22. Yongheng Deng & John M. Quigley & Robert Van Order, 2000. "Mortgage Terminations, Heterogeneity and the Exercise of Mortgage Options," Econometrica, Econometric Society, vol. 68(2), pages 275-308, March.
    23. Nazemi, Abdolreza & Fatemi Pour, Farnoosh & Heidenreich, Konstantin & Fabozzi, Frank J., 2017. "Fuzzy decision fusion approach for loss-given-default modeling," European Journal of Operational Research, Elsevier, vol. 262(2), pages 780-791.
    24. Nailong Zhang & Qingyu Yang & Aidan Kelleher & Wujun Si, 2019. "A new mixture cure model under competing risks to score online consumer loans," Quantitative Finance, Taylor & Francis Journals, vol. 19(7), pages 1243-1253, July.
    25. T Bellotti & J Crook, 2009. "Credit scoring with macroeconomic variables using survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1699-1707, December.
    26. 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.
    27. Finlay, Steven, 2010. "Credit scoring for profitability objectives," European Journal of Operational Research, Elsevier, vol. 202(2), pages 528-537, April.
    28. M Malik & L C Thomas, 2010. "Modelling credit risk of portfolio of consumer loans," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 411-420, March.
    29. Mark Thackham & Jun Ma, 2022. "On maximum likelihood estimation of competing risks using the cause-specific semi-parametric Cox model with time-varying covariates – An application to credit risk," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(1), pages 5-14, January.
    30. Ellen Tobback & David Martens & Tony Van Gestel & Bart Baesens, 2014. "Forecasting Loss Given Default models: impact of account characteristics and the macroeconomic state," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 376-392, March.
    31. Morgan J. Rose, 2013. "Geographic Variation in Subprime Loan Features, Foreclosures, and Prepayments," The Review of Economics and Statistics, MIT Press, vol. 95(2), pages 563-590, May.
    32. J Banasik & J N Crook & L C Thomas, 1999. "Not if but when will borrowers default," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(12), pages 1185-1190, December.
    33. 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.
    34. Andreeva, Galina & Ansell, Jake & Crook, Jonathan, 2007. "Modelling profitability using survival combination scores," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1537-1549, December.
    35. Cuiqing Jiang & Zhao Wang & Ruiya Wang & Yong Ding, 2018. "Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending," Annals of Operations Research, Springer, vol. 266(1), pages 511-529, July.
    36. M Stepanova & L C Thomas, 2001. "PHAB scores: proportional hazards analysis behavioural scores," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(9), pages 1007-1016, September.
    37. Maria Stepanova & Lyn Thomas, 2002. "Survival Analysis Methods for Personal Loan Data," Operations Research, INFORMS, vol. 50(2), pages 277-289, April.
    38. Do, Hung Xuan & Rösch, Daniel & Scheule, Harald, 2018. "Predicting loss severities for residential mortgage loans: A three-step selection approach," European Journal of Operational Research, Elsevier, vol. 270(1), pages 246-259.
    39. Mayer, Chris & Piskorski, Tomasz & Tchistyi, Alexei, 2013. "The inefficiency of refinancing: Why prepayment penalties are good for risky borrowers," Journal of Financial Economics, Elsevier, vol. 107(3), pages 694-714.
    40. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    41. 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.
    42. Raquel Florez-Lopez & Juan Manuel Ramon-Jeronimo, 2014. "Modelling credit risk with scarce default data: on the suitability of cooperative bootstrapped strategies for small low-default portfolios," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 416-434, March.
    43. J-K Im & D W Apley & C Qi & X Shan, 2012. "A time-dependent proportional hazards survival model for credit risk analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(3), pages 306-321, March.
    44. Scott R. Brown, 2016. "The Influence of Homebuyer Education on Default and Foreclosure Risk: A Natural Experiment," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 35(1), pages 145-172, January.
    45. Ateca-Amestoy, Victoria & Prieto-Rodriguez, Juan, 2013. "Forecasting accuracy of behavioural models for participation in the arts," European Journal of Operational Research, Elsevier, vol. 229(1), pages 124-131.
    46. Roberto Quercia & Jonathan Spader, 2008. "Does homeownership counseling affect the prepayment and default behavior of affordable mortgage borrowers?," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 27(2), pages 304-325.
    47. Varli, Yusuf & Yildirim, Yildiray, 2015. "Default and prepayment modelling in participating mortgages," Journal of Banking & Finance, Elsevier, vol. 61(C), pages 81-88.
    48. 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.
    49. 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.
    50. Bellotti, Tony & Crook, Jonathan, 2012. "Loss given default models incorporating macroeconomic variables for credit cards," International Journal of Forecasting, Elsevier, vol. 28(1), pages 171-182.
    51. Zhang, Jie & Thomas, Lyn C., 2012. "Comparisons of linear regression and survival analysis using single and mixture distributions approaches in modelling LGD," International Journal of Forecasting, Elsevier, vol. 28(1), pages 204-215.
    52. Jefferson Duarte & Stephan Siegel & Lance Young, 2012. "Trust and Credit: The Role of Appearance in Peer-to-peer Lending," Review of Financial Studies, Society for Financial Studies, vol. 25(8), pages 2455-2484.
    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. 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).

    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. 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. 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.
    3. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
    4. Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
    5. Kellner, Ralf & Nagl, Maximilian & Rösch, Daniel, 2022. "Opening the black box – Quantile neural networks for loss given default prediction," Journal of Banking & Finance, Elsevier, vol. 134(C).
    6. Liu, He & Qiao, Han & Wang, Shouyang & Li, Yuze, 2019. "Platform Competition in Peer-to-Peer Lending Considering Risk Control Ability," European Journal of Operational Research, Elsevier, vol. 274(1), pages 280-290.
    7. Hurlin, Christophe & Leymarie, Jérémy & Patin, Antoine, 2018. "Loss functions for Loss Given Default model comparison," European Journal of Operational Research, Elsevier, vol. 268(1), pages 348-360.
    8. Qizhi Tao & Yizhe Dong & Ziming Lin, 2017. "Who can get money? Evidence from the Chinese peer-to-peer lending platform," Information Systems Frontiers, Springer, vol. 19(3), pages 425-441, June.
    9. Qizhi Tao & Yizhe Dong & Ziming Lin, 0. "Who can get money? Evidence from the Chinese peer-to-peer lending platform," Information Systems Frontiers, Springer, vol. 0, pages 1-17.
    10. Lore Dirick & Gerda Claeskens & Bart Baesens, 2017. "Time to default in credit scoring using survival analysis: a benchmark study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(6), pages 652-665, June.
    11. Marc Gürtler & Marvin Zöllner, 2023. "Heterogeneities among credit risk parameter distributions: the modality defines the best estimation method," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 251-287, March.
    12. Jiang, Cuiqing & Wang, Zhao & Zhao, Huimin, 2019. "A prediction-driven mixture cure model and its application in credit scoring," European Journal of Operational Research, Elsevier, vol. 277(1), pages 20-31.
    13. Thi Mai Luong, 2020. "Selection Effects of Lender and Borrower Choices on Risk Measurement, Management and Prudential Regulation," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 3-2020.
    14. Starosta, Wojciech, 2021. "Loss given default decomposition using mixture distributions of in-default events," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1187-1199.
    15. Sanchez-Barrios, Luis Javier & Andreeva, Galina & Ansell, Jake, 2016. "“Time-to-profit scorecards for revolving credit”," European Journal of Operational Research, Elsevier, vol. 249(2), pages 397-406.
    16. Samuel Ribeiro-Navarrete & Juan Piñeiro-Chousa & M. Ángeles López-Cabarcos & Daniel Palacios-Marqués, 2022. "Crowdlending: mapping the core literature and research frontiers," Review of Managerial Science, Springer, vol. 16(8), pages 2381-2411, November.
    17. Salvatore D. Tomarchio & Antonio Punzo, 2019. "Modelling the loss given default distribution via a family of zero‐and‐one inflated mixture models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1247-1266, October.
    18. Yufei Xia & Lingyun He & Yinguo Li & Nana Liu & Yanlin Ding, 2020. "Predicting loan default in peer‐to‐peer lending using narrative data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 260-280, March.
    19. Li, Jianwen, 2023. "MSMEs meet FinTech: Chance or challenge?," Finance Research Letters, Elsevier, vol. 57(C).
    20. Betz, Jennifer & Kellner, Ralf & Rösch, Daniel, 2018. "Systematic Effects among Loss Given Defaults and their Implications on Downturn Estimation," European Journal of Operational Research, Elsevier, vol. 271(3), pages 1113-1144.

    More about this item

    Keywords

    OR in banking; Competing risks; Credit scoring; Profitability; Survival analysis;
    All these keywords.

    JEL classification:

    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    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:eee:ejores:v:306:y:2023:i:2:p:968-985. See general information about how to correct material in RePEc.

    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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.