IDEAS home Printed from https://ideas.repec.org/a/eee/pacfin/v88y2024ics0927538x24003020.html
   My bibliography  Save this article

Using machine learning to investigate the determinants of loan default in P2P lending: Are there differences between before and during COVID-19?

Author

Listed:
  • Xu, Qi
  • Liu, Caixia
  • Luo, Jing
  • Liu, Feng

Abstract

The coronavirus disease (COVID-19) has led to a persistent increase in the volatility of the credit market and triggered a series of financial distress and bankruptcy. To investigate whether there are differences in loan default determinants before and during COVID-19 and to identify the most effective predictors of loan default during COVID-19, this study employs machine learning methods to establish a comprehensive loan default prediction model for Peer-to-peer (P2P) lending based on four perspectives: loan characteristics, credit transaction history, personal information, and macroeconomic environment. The results show that the EXtreme Gradient Boosting (XGBoost) outperforms the other models and that credit transaction history plays a vital role in forecasting loan default over the two periods. We also find discrepancies between the effects of consumer price index, purchasing manager’ index, and the number of bidders on loan default before and during the pandemic. Our study contributes to related research fields on loan default prediction by identifying loan default determinants that are more applicable to unstable periods and investigating the impact of COVID-19 on default predictions. Meanwhile, our findings can provide P2P lending investors, platforms, and policymakers with practical implications to reduce uncertainty and losses that result from similar black swan events.

Suggested Citation

  • Xu, Qi & Liu, Caixia & Luo, Jing & Liu, Feng, 2024. "Using machine learning to investigate the determinants of loan default in P2P lending: Are there differences between before and during COVID-19?," Pacific-Basin Finance Journal, Elsevier, vol. 88(C).
    • Handle: RePEc:eee:pacfin:v:88:y:2024:i:c:s0927538x24003020
    DOI: 10.1016/j.pacfin.2024.102550
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0927538X24003020
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.pacfin.2024.102550?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. 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. Nigmonov, Asror & Shams, Syed & Alam, Khorshed, 2022. "Macroeconomic determinants of loan defaults: Evidence from the U.S. peer-to-peer lending market," Research in International Business and Finance, Elsevier, vol. 59(C).
    3. 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.
    4. Wenda Song & Haiyang Zhang, 2020. "Monetary Policy and Borrowers’ Loan Defaults: Research Based on Data from Renrendai," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 28(1), pages 94-121, January.
    5. 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.
    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. He, Qing & Li, Xiaoyang, 2021. "The failure of Chinese peer-to-peer lending platforms: Finance and politics," Journal of Corporate Finance, Elsevier, vol. 66(C).
    8. Chen, Shiyi & Gu, Yan & Liu, Qingfu & Tse, Yiuman, 2020. "How do lenders evaluate borrowers in peer-to-peer lending in China?," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 651-662.
    9. 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.
    10. Ghosh, Amit, 2015. "Banking-industry specific and regional economic determinants of non-performing loans: Evidence from US states," Journal of Financial Stability, Elsevier, vol. 20(C), pages 93-104.
    11. Li, Xingjian & Feng, Hongrui & Zhao, Sebastian & Carter, David A., 2021. "The effect of revenue diversification on bank profitability and risk during the COVID-19 pandemic," Finance Research Letters, Elsevier, vol. 43(C).
    12. 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.
    13. Jiang, Cuixia & Xu, Qifa & Zhang, Weiming & Li, Mengting & Yang, Shanlin, 2018. "Does automatic bidding mechanism affect herding behavior? Evidence from online P2P lending in China," Journal of Behavioral and Experimental Finance, Elsevier, vol. 20(C), pages 39-44.
    14. Huang, Yiping & Li, Xiang & Wang, Chu, 2021. "What does peer-to-peer lending evidence say about the Risk-Taking Channel of monetary policy?," Journal of Corporate Finance, Elsevier, vol. 66(C).
    15. Liu, Yun & Zhang, Yun & Zhang, Yifei & Xiao, He, 2022. "Small business owners’ Fintech credit in crises: Theory and evidence from farmers under the COVID-19," Pacific-Basin Finance Journal, Elsevier, vol. 71(C).
    16. Luca Barbaglia & Sebastiano Manzan & Elisa Tosetti, 2023. "Forecasting Loan Default in Europe with Machine Learning," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 569-596.
    17. Wimboh Santoso & Irwan Trinugroho & Tastaftiyan Risfandy, 2020. "What Determine Loan Rate and Default Status in Financial Technology Online Direct Lending? Evidence from Indonesia," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(2), pages 351-369, January.
    18. Li, Jianwen & Hu, Jinyan, 2019. "Does university reputation matter? Evidence from peer-to-peer lending," Finance Research Letters, Elsevier, vol. 31(C), pages 66-77.
    19. Salima Smiti & Makram Soui, 2020. "Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE," Information Systems Frontiers, Springer, vol. 22(5), pages 1067-1083, October.
    20. Deng, Jiapin, 2022. "The crowding-out effect of formal finance on the P2P lending market: An explanation for the failure of China's P2P lending industry," Finance Research Letters, Elsevier, vol. 45(C).
    21. Boyd, John H. & Levine, Ross & Smith, Bruce D., 2001. "The impact of inflation on financial sector performance," Journal of Monetary Economics, Elsevier, vol. 47(2), pages 221-248, April.
    22. 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).
    23. Jiaqi Yan & Wayne Yu & J. Leon Zhao, 2015. "How signaling and search costs affect information asymmetry in P2P lending: the economics of big data," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-11, December.
    24. 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).
    25. 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).
    26. Prystav, Fabian, 2016. "Personal information in peer-to-peer loan applications: Is less more?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 9(C), pages 6-19.
    Full references (including those not matched with items on IDEAS)

    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. 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).
    2. Dongwoo Kim, 2023. "Can investors’ collective decision-making evolve? Evidence from peer-to-peer lending markets," Electronic Commerce Research, Springer, vol. 23(2), pages 1323-1358, June.
    3. Li, Jianwen, 2023. "MSMEs meet FinTech: Chance or challenge?," Finance Research Letters, Elsevier, vol. 57(C).
    4. 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).
    5. 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.
    6. Wang, Shaoda & Ye, Dezhu & Liao, Junyun, 2024. "Politeness matters: The role of polite languages in online peer-to-peer lending," Journal of Business Research, Elsevier, vol. 171(C).
    7. Xueru Chen & Xiaoji Hu & Shenglin Ben, 2021. "How do reputation, structure design and FinTech ecosystem affect the net cash inflow of P2P lending platforms? Evidence from China," Electronic Commerce Research, Springer, vol. 21(4), pages 1055-1082, December.
    8. Käfer Benjamin, 2018. "Peer-to-Peer Lending – A (Financial Stability) Risk Perspective," Review of Economics, De Gruyter, vol. 69(1), pages 1-25, April.
    9. Zhao Wang & Cuiqing Jiang & Huimin Zhao, 2022. "Know Where to Invest: Platform Risk Evaluation in Online Lending," Information Systems Research, INFORMS, vol. 33(3), pages 765-783, September.
    10. Ligang Zhou & Chao Ma, 2023. "A Comparison of Different Rules on Loans Evaluation in Peer-to-Peer Lending by Gradient Boosting Models Under Moving Windows with Two Timestamps," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1481-1504, December.
    11. 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.
    12. Xin Li & Xiujuan Tian, 2022. "Research on SMEs’ Reputation Mechanism and Default Risk Based on Investors’ Financial Participation," Sustainability, MDPI, vol. 14(21), pages 1-17, November.
    13. Jin Huang & Jun Li & Vania Sena, 2023. "Psychological distancing and language intensity in Peer‐to‐Peer lending," Journal of Consumer Affairs, Wiley Blackwell, vol. 57(3), pages 1281-1303, July.
    14. Jianwen Li & Jinyan Hu, 2022. "Migrants and default: Evidence from China," International Review of Finance, International Review of Finance Ltd., vol. 22(3), pages 472-505, September.
    15. 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.
    16. Natnara Chulawate & Supaporn Kiattisin, 2023. "Success Factors Influencing Peer-to-Peer Lending to Support Financial Innovation," Sustainability, MDPI, vol. 15(5), pages 1-16, February.
    17. 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.
    18. Caglayan, Mustafa & Talavera, Oleksandr & Xiong, Lin, 2022. "Female small business owners in China: Discouraged, not discriminated," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    19. 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).
    20. Jie Li & Quanyun Song & Yu Wu & Bihong Huang, 2021. "The effects of online consumer credit on household consumption level and structure: Evidence from China," Journal of Consumer Affairs, Wiley Blackwell, vol. 55(4), pages 1614-1632, December.

    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:pacfin:v:88:y:2024:i:c:s0927538x24003020. 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/pacfin .

    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.