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

Lin, Xuchen; Li, Xiaolong; Zhong, Zheng

doi:10.1080/00036846.2016.1262526

Cited by 133 publications

(56 citation statements)

References 16 publications

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“…On P2P lending, a great number of studies focus on investigating the factors that determine funding success and default risk. The factors include borrowers’ financial capacity (Serrano‐Cinca et al ., ), borrowers’ characteristics such as gender, age, marital status, educational level and appearance (e.g., Duarte et al ., ; Lin et al ., ), social network capitals (Gomez and Santor, ; Freedman and Jin, ; Lin et al ., ), soft information created by borrowers (Iyer et al ., ), and lenders’ behaviors (Galak et al ., ; Herzenstein et al ., ). Empirical studies of platforms in other countries (e.g., Prosper, LendingClub, and Kiva) and China (e.g., PaipaiDai, RenrenDai, and myc4.com) have obtained similar findings for most influential factors on borrowers’ default behavior.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Venture Capital Investment on the Performance of Peer‐to‐Peer Lending Platforms: Evidence from China

Luo

et al. 2019

Asia-Pac J of Fin Stud

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The Chinese peer-to-peer (P2P) lending market is expanding quickly but has a relatively poor level of operation. Using panel-structured data from a leading P2P lending portal in China, we investigate the effects of venture capital (VC) investment on the performance of P2P lending platforms. Specifically, we identify a short-term signaling effect and a long-term governance effect of VC investment on platform performance in terms of transaction volume, and numbers of lenders and borrowers. However, we only find a decrease in average interest rates after venture capitalists' (VCs') entry in the long run. Moreover, we verify both the effects of investment from listed VCs, but no signaling effect and a weak governance effect of investment from non-listed VCs. Our analysis provides new insights into how VC investment improves the performance of target firms.

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Section: Introductionmentioning

confidence: 99%

The Impact of Venture Capital Investment on the Performance of Peer‐to‐Peer Lending Platforms: Evidence from China

Luo

et al. 2019

Asia-Pac J of Fin Stud

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“…Transaction fees are usually charged at origination so that platforms can make profits. In recent years, the peer‐to‐peer lending market has shown a substantial increase in popularity (Bachmann et al ., ; Berger and Gleisner, 2009; Lin et al ., ; Milne and Parboteeah, 2016; Wang et al ., 2009). According to PriceWaterhouseCoopers (2015) in 2014 peer‐to‐peer lending generated approximately $5.5 billion worth of loans in the USA.…”

Section: Introductionmentioning

confidence: 99%

“…There are a few recent studies that analyse the determinants of default in the peer-to-peer platforms, e.g. Guo et al (2016), Dorfleitner et al (2016), Lin et al (2017) and Serrano-Cinca et al (2015). Knowing whether a borrower has a defaulted payment in the last 7 years, we can first analyse the dependence between peer-to-peer and bank loan defaults.…”

Section: Introductionmentioning

confidence: 99%

“…Knowing whether a borrower has a defaulted payment in the last 7 years, we can first analyse the dependence between peer-to-peer and bank loan defaults. As the information on peer-to-peer default is sequential to that from credit bureaus, peer-to-peer platforms usually use univariate logits to build scoring models that consider as explanatory variable whether the borrower has been listed in default or not in credit bureaus (Lin et al, 2017). Instead, we suggest discriminating between good and bad borrowers in the peer-to-peer platform by using the peer-to-peer default probability estimated conditionally on whether a loan is or is not in default in credit bureaus.…”

Section: Introductionmentioning

confidence: 99%

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A Joint Scoring Model for Peer-to-Peer and Traditional Lending: A Bivariate Model with Copula Dependence

Calabrese

Osmetti

Zanin

2019

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary We analyse the dependence between defaults in peer‐to‐peer lending and credit bureaus. To achieve this, we propose a new flexible bivariate regression model that is suitable for binary imbalanced samples. We use different copula functions to model the dependence structure between defaults in the two credit markets. We implement the model in the R package BivGEV and we explore the empirical properties of the proposed fitting procedure by a Monte Carlo study. The application of this proposal to a comprehensive data set provided by Lending Club shows a significant level of dependence between the defaults in peer‐to‐peer and credit bureaus. Finally, we find that our model outperforms the bivariate probit and univariate logit models in predicting peer‐to‐peer default, in estimating the value at risk and the expected shortfall.

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“…The research results of He Qizhi, et al (2016) [16] suggest that the fluctuations of interest rates of network lending have the effects of agglomeration and risk accumulation instead of leverage effect and have generally consistent response to bull and bear information, which means that the risk of the network lending market is strong while the risk awareness of market participants is not strong. The research of Xuchen Lin, et al (2017) [17] Empirical results reveal that gender, age, marital status, educational level, working years, company size, monthly payment, loan amount, debt to income ratio and delinquency history play a significant role in loan defaults. Empirical results reveal that gender, age, marital status, educational level, working years, company size, monthly payment, loan amount, debt to income ratio and delinquency history play a significant role in loan defaults.…”

Section: Literature Reviewmentioning

confidence: 99%

P2P Network Lending, Loss Given Default and Credit Risks

2018

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Peer-to-peer (P2P) network lending is a new mode of internet finance that still holds credit risk as its main risk. According to the internal rating method of the New Basel Accord, in addition to the probability of default, loss given default is also one of the important indicators of evaluation credit risks. Proceeding from the perspective of loss given default (LGD), this paper conducts an empirical study on the probability distribution of LGDs of P2P as well as its influencing factors with the transaction data of Lending Club. The results show that: (1) the LGDs of P2P loans presents an obvious unimodal distribution, the peak value is relatively high and tends to concentrate with the decrease of the borrower's credit rating, indicating that the distribution of LGDs of P2P lending is similar to that of unsecured bonds; (2) The total asset of the borrower has no significant impact on LGD, the credit rating and the debt-to-income ratio exert a significant negative impact, while the term and amount of the loan produce a relatively strong positive impact. Therefore, when evaluating the borrower's repayment ability, it is required to pay more attention to its assets structure rather than the size of its total assets. When carrying out risk control for the P2P platform, it is necessary to give priority to the control of default rate.

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

Cited by 133 publications

References 16 publications

The Impact of Venture Capital Investment on the Performance of Peer‐to‐Peer Lending Platforms: Evidence from China

The Impact of Venture Capital Investment on the Performance of Peer‐to‐Peer Lending Platforms: Evidence from China

A Joint Scoring Model for Peer-to-Peer and Traditional Lending: A Bivariate Model with Copula Dependence

P2P Network Lending, Loss Given Default and Credit Risks

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