Can Listing Information Indicate Borrower Credit Risk in Online Peer-to-Peer Lending?. Issue 13 (21st October 2018)

• Record Type: Journal Article
• Authors: Liu, Yi; Zhou, Quanli; Zhao, Xuan; Wang, Yudong
• Is Part Of: Emerging markets finance & trade. Volume 54:Issue 13(2018)
• Page Start: 2982
• Page End: 2994
• Publication Date: 2018-10-21
• Subjects: credit-risk identification -- credit score -- peer-to-peer lending; Balkan Peninsula -- Commerce -- Periodicals; Balkan Peninsula -- Foreign economic relations -- Periodicals; Europe, Central -- Commerce -- Periodicals; Europe, Central -- Foreign economic relations -- Periodicals; Europe, Eastern -- Commerce -- Periodicals; Europe, Eastern -- Foreign economic relations -- Periodicals; Turkey -- Commerce -- Periodicals; Turkey -- Foreign economic relations -- Periodicals; Asia -- Commerce -- Periodicals; Asia -- Foreign economic relations -- Periodicals; Eurasia -- Commerce -- Periodicals; Eurasia -- Foreign economic relations -- Periodicals; 382.05
• DOI: 10.1080/1540496X.2018.1427061
• Languages: English
• ISSNs: 1540-496X

Abstract

ABSTRACT: Effective assessment of borrower credit risk is the greatest challenge for peer-to-peer (P2P) lenders, especially in the Chinese market, where borrowers lack widely recognized credit scores. In this study, based on credit data from 2012 to 2015 from the website Renrendai.com, a logit model was used to assess borrower credit risk and predict the probability of default in every out-of-sample listing. The predicted probability of default was then compared with the actual default observation of default. The empirical results show that the logit model can evaluate the credit risk of P2P borrowers, and the model reduces the default rate to 9.5%, compared with the total sample default rate of 16.5%.