Evaluating borrower’s default risk in peer-to-peer lending: evidence from a lending platform in China

Citations

Cited by:

1. Li, ZhouPing & Ge, RuYi & Guo, XiaoShuang & Cai, Lingfei, 2021. "Can individual investors learn from experience in online P2P lending? Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
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3. Davaadorj, Zagdbazar & Enkhtaivan, Bolortuya & Lu, Wenling, 2024. "The role of job titles in online peer-to-peer lending: An empirical investigation on skilled borrowers," Journal of Behavioral and Experimental Finance, Elsevier, vol. 41(C).
4. repec:zbw:bofitp:2019_023 is not listed on IDEAS
5. 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).
6. Jin, Ming & Yin, Mingmei & Chen, Zhongfei, 2021. "Do investors prefer borrowers from high level of trust cities? Evidence from China’s P2P market," Research in International Business and Finance, Elsevier, vol. 58(C).
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8. Funke, Michael & Li, Xiang & Tsang, Andrew, 2019. "Monetary policy shocks and peer-to-peer lending in China," BOFIT Discussion Papers 23/2019, Bank of Finland Institute for Emerging Economies (BOFIT).
   • Funke, Michael & Li, Xiang & Tsang, Andrew, 2019. "Monetary policy shocks and peer-to-peer lending in China," BOFIT Discussion Papers 23/2019, Bank of Finland, Institute for Economies in Transition.
9. 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.
10. Ji-Yoon Kim & Sung-Bae Cho, 2019. "Towards Repayment Prediction in Peer-to-Peer Social Lending Using Deep Learning," Mathematics, MDPI, vol. 7(11), pages 1-17, November.
11. Gero Friedrich Bone-Winkel & Felix Reichenbach, 2024. "Improving credit risk assessment in P2P lending with explainable machine learning survival analysis," Digital Finance, Springer, vol. 6(3), pages 501-542, September.
12. 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).
13. Arif Perdana & Pearpilai Jutasompakorn & Sunghun Chung, 2023. "Shaping crowdlending investors’ trust: Technological, social, and economic exchange perspectives," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-17, December.
14. Tianzi Bao & Yi Ding & Ram Gopal & Mareike Möhlmann, 2024. "Throwing Good Money After Bad: Risk Mitigation Strategies in the P2P Lending Platforms," Information Systems Frontiers, Springer, vol. 26(4), pages 1453-1473, August.
15. Chen, Cathy W.S. & Dong, Manh Cuong & Liu, Nathan & Sriboonchitta, Songsak, 2019. "Inferences of default risk and borrower characteristics on P2P lending," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
16. Zhang, Man & Zhang, Zhiying & Tian, Xiujuan, 2023. "Social identity of civil servants and online peer-to-peer lending: Evidence from China," Economics Letters, Elsevier, vol. 229(C).
17. Pankaj Kumar Maskara & Emre Kuvvet & Gengxuan Chen, 2021. "The role of P2P platforms in enhancing financial inclusion in the United States: An analysis of peer‐to‐peer lending across the rural–urban divide," Financial Management, Financial Management Association International, vol. 50(3), pages 747-774, September.
18. 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).
19. 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).
20. Tian, Geran & Wang, Xiaowen & Wu, Weixing, 2021. "Borrow low, lend high: Credit arbitrage by sophisticated investors," Pacific-Basin Finance Journal, Elsevier, vol. 67(C).
21. Mengyin Li & Phillip H. Phan & Xian Sun, 2021. "Business Friendliness: A Double-Edged Sword," Sustainability, MDPI, vol. 13(4), pages 1-22, February.
22. Kim Sia Ling & Siti Suhana Jamaian & Syahira Mansur & Alwyn Kwan Hoong Liew, 2023. "Modeling Tenant’s Credit Scoring Using Logistic Regression," SAGE Open, , vol. 13(3), pages 21582440231, August.
23. Chen, Shou & Jiang, Xiangqian & He, Hongbo & Zhou, Xi, 2020. "A pricing model with dynamic repayment flows for guaranteed consumer loans," Economic Modelling, Elsevier, vol. 91(C), pages 1-11.
24. Jong Wook Lee & So Young Sohn, 2021. "Evaluating borrowers’ default risk with a spatial probit model reflecting the distance in their relational network," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-11, December.
25. Zhang, Yun & Liu, Yun & Zhang, Yifei & Chen, Xin, 2022. "Globalization blueprint and households’ fintech debt: Evidence from China’s One Belt One Road initiative," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 38-55.
26. Mendelson Haim & Zhu Mingxi, 2024. "Optimal Information Acquisition Strategies: The Case of Online Lending," Papers 2410.05539, arXiv.org.
27. 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).
28. Guangyou Zhou & Yijia Zhang & Sumei Luo, 2018. "P2P Network Lending, Loss Given Default and Credit Risks," Sustainability, MDPI, vol. 10(4), pages 1-15, March.