IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v68y2020icp204-216.html

Credit rating of online lending borrowers using recovery rates

Author

Listed:
  • Chen, Rongda
  • Chen, Xinhao
  • Jin, Chenglu
  • Chen, Yiyang
  • Chen, Jiayi

Abstract

A core issue of the rapid developing online lending is to establish a sound credit rating method for borrowers. When examining 14,558 recovery rates of defaulted assets corresponding to each borrower of Renrendai platform from 2011 to 2016, we find that the current credit rating system (seven levels from AA, A, B, C, D, E and HR) cannot distinguish the distribution of recovery rates. Accordingly, this study proposes a credit rating approach for online lending platform using a Chinese sample, given that online lending has developed far more in China than in other countries and regions. Firstly, by referencing to the Sesame Credit and US FICO Credit systems, 15 indices are selected. Secondly, the K-Means clustering credit rating method using recovery rate is used to reclassify borrowers, solving the problem that Renrendai’s credit rating system cannot distinguish borrowers with assets of different recovery rates. However, this simple reclassification lead to another issue that borrowers with high credit rating are more than borrowers with low credit rating (the so-called "inverted pyramid" problem). Therefore, finally, an augmented credit rating method is developed to reclassify borrowers, which integrates factor analysis and K-Means clustering using recovery rate. By using this finalized method, borrowers with different recovery rates are distinguished clearly to different credit levels.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reveco:v:68:y:2020:i:c:p:204-216
    DOI: 10.1016/j.iref.2020.04.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1059056020300721
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.iref.2020.04.003?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Shi, Baofeng & Chi, Guotai & Li, Weiping, 2020. "Exploring the mismatch between credit ratings and loss-given-default: A credit risk approach," Economic Modelling, Elsevier, vol. 85(C), pages 420-428.
    2. 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).
    3. Angilella, Silvia & Mazzù, Sebastiano, 2015. "The financing of innovative SMEs: A multicriteria credit rating model," European Journal of Operational Research, Elsevier, vol. 244(2), pages 540-554.
    4. Liran Einav & Chiara Farronato & Jonathan Levin, 2016. "Peer-to-Peer Markets," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 615-635, October.
    5. Livingston, Miles & Poon, Winnie P.H. & Zhou, Lei, 2018. "Are Chinese credit ratings relevant? A study of the Chinese bond market and credit rating industry," Journal of Banking & Finance, Elsevier, vol. 87(C), pages 216-232.
    6. Chen, Xiao & Huang, Bihong & Ye, Dezhu, 2018. "The role of punctuation in P2P lending: Evidence from China," Economic Modelling, Elsevier, vol. 68(C), pages 634-643.
    7. Gürtler, Marc & Hibbeln, Martin, 2013. "Improvements in loss given default forecasts for bank loans," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2354-2366.
    8. Shen, Chung-Hua & Lin, Shih-Jie & Tang, De-Piao & Hsiao, Yu-Jen, 2016. "The relationship between financial disputes and financial literacy," Pacific-Basin Finance Journal, Elsevier, vol. 36(C), pages 46-65.
    9. 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.
    10. Treacy, William F. & Carey, Mark, 2000. "Credit risk rating systems at large US banks," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 167-201, January.
    11. Li, Zhiyong & Zhang, Haiyang & Yu, Mei & Wang, Hairan, 2019. "Too long to be true in the description? Evidence from a Peer-to-Peer platform in China," Research in International Business and Finance, Elsevier, vol. 50(C), pages 246-251.
    12. Lawrence J. White, 2010. "Markets: The Credit Rating Agencies," Journal of Economic Perspectives, American Economic Association, vol. 24(2), pages 211-226, Spring.
    13. Huan Tang, 2019. "Peer-to-Peer Lenders Versus Banks: Substitutes or Complements?," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1900-1938.
    14. Daniel Paravisini & Veronica Rappoport & Enrichetta Ravina, 2017. "Risk Aversion and Wealth: Evidence from Person-to-Person Lending Portfolios," Management Science, INFORMS, vol. 63(2), pages 279-297, February.
    15. 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.
    16. Hou, Xiaohui & Gao, Zhixian & Wang, Qing, 2016. "Internet finance development and banking market discipline: Evidence from China," Journal of Financial Stability, Elsevier, vol. 22(C), pages 88-100.
    17. Hartmann-Wendels, Thomas & Miller, Patrick & Töws, Eugen, 2014. "Loss given default for leasing: Parametric and nonparametric estimations," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 364-375.
    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. Liu, Yi & Yang, Menglong & Wang, Yudong & Li, Yongshan & Xiong, Tiancheng & Li, Anzhe, 2022. "Applying machine learning algorithms to predict default probability in the online credit market: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 79(C).
    2. Dang, Chao & Chen, Xinyang & Yu, Shengjie & Chen, Rongda & Yang, Yifan, 2022. "Credit ratings of Chinese households using factor scores and K-means clustering method," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 309-320.
    3. Li, Yanru & Wang, Haijun & Gao, Huikun & Li, Qinghai & Sun, Guanglin, 2024. "Credit rating, repayment willingness and farmer credit default," International Review of Financial Analysis, Elsevier, vol. 93(C).
    4. Chen, Dangxing & Ye, Jiahui & Ye, Weicheng, 2023. "Interpretable selective learning in credit risk," Research in International Business and Finance, Elsevier, vol. 65(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. 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. Li, Jianwen & Zhang, Bo & Jiang, Mingming & Hu, Jinyan, 2023. "Homophilous intensity in the online lending market: Bidding behavior and economic effects," Journal of Banking & Finance, Elsevier, vol. 152(C).
    3. Wang, Chao & Wang, Junbo & Wu, Chunchi & Zhang, Yue, 2023. "Voluntary disclosure in P2P lending: Information or hyperbole?," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    4. 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).
    5. 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.
    6. Chrysovalantis Gaganis & Panagiota Papadimitri & Menelaos Tasiou, 2021. "A multicriteria decision support tool for modelling bank credit ratings," Annals of Operations Research, Springer, vol. 306(1), pages 27-56, November.
    7. 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.
    8. Štefan Lyócsa & Petra Vašaničová & Branka Hadji Misheva & Marko Dávid Vateha, 2022. "Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    9. Wenming Xu & Yan Liu, 2021. "Does reputational capital affect credit rating agencies?: empirical evidence from a natural experiment in China," European Journal of Law and Economics, Springer, vol. 51(3), pages 433-468, June.
    10. Jérémy Leymarie & Christophe Hurlin & Antoine Patin, 2018. "Loss Functions for LGD Models Comparison," Post-Print hal-01923050, HAL.
    11. D'Acunto, Francesco & Ghosh, Pulak & Jain, Rajiv & Rossi, Alberto G., 2022. "How costly are cultural biases?," LawFin Working Paper Series 34, Goethe University, Center for Advanced Studies on the Foundations of Law and Finance (LawFin).
    12. 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.
    13. 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.
    14. Zhang, Xuan & Ouyang, Ruolan & Liu, Ding & Xu, Liao, 2020. "Determinants of corporate default risk in China: The role of financial constraints," Economic Modelling, Elsevier, vol. 92(C), pages 87-98.
    15. Wu, Yu & Zhang, Tong, 2021. "Can credit ratings predict defaults in peer-to-peer online lending? Evidence from a Chinese platform," Finance Research Letters, Elsevier, vol. 40(C).
    16. Ma, Qianli & Xu, Lei & Anwar, Sajid & Lu, Zenghua, 2023. "Banking competition and the use of shadow credit: Evidence from lending marketplaces," Global Finance Journal, Elsevier, vol. 58(C).
    17. Khoo, Shee-Yee & Klein, Paul-Olivier, 2025. "Islamic bonds ratings and the price of risk," Journal of Corporate Finance, Elsevier, vol. 93(C).
    18. 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).
    19. Hu, Xiaolu & Huang, Haozhi & Pan, Zheyao & Shi, Jing, 2019. "Information asymmetry and credit rating: A quasi-natural experiment from China," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 132-152.
    20. Hui-Ching Chuang & Jau-er Chen, 2023. "Exploring Industry-Distress Effects on Loan Recovery: A Double Machine Learning Approach for Quantiles," Econometrics, MDPI, vol. 11(1), pages 1-20, February.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:reveco:v:68:y:2020:i:c:p:204-216. 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/inca/620165 .

    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.