Soft information in online peer-to-peer lending: Evidence from a leading platform in China

Wang, Chao; Zhang, Weiguo; Zhao, Xuejin; Wang, Junbo

doi:10.1016/j.elerap.2019.100873

Cited by 26 publications

(11 citation statements)

References 31 publications

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“…Different from the third-party credit system certification in developed countries, P2P platforms in China need to evaluate borrowers' credit conditions by themselves [3]. At present, empirical research on P2P platform default mainly focuses on the influencing factors of borrower default risk [16] and quantitative research [17]. Stiglitz and Weiss first confirmed in 1981 that information asymmetry and imperfection in the credit market would greatly reduce the efficiency and capital liquidity of the credit market [18].…”

Section: Literature Reviewmentioning

confidence: 99%

How Does the Urgency of Borrowing in Text Messages Affect Loan Defaults? Evidence from P2P Loans in China

Hong

Yuan

Zhou

2021

Security and Communication Networks

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In P2P loans with information asymmetry, the text information described by the borrower plays an important role in alleviating the information asymmetry between borrowers and lenders. To explore the borrowing described in text information and its relationship with default behavior, this article selects credits from April 2014 to October 2016 as the repayment period and studies default data. This is performed based on the length of the excavated text, purpose of the loan, repayment ability, willingness to reimburse, five text variables, and degree of loan urgency. The empirical results show that text length has a significant negative correlation with the default probability of borrowers. Different loan purposes have different default risks. Interestingly, the more urgent a loan is, the more likely the borrower is to default. However, repayment ability information and repayment willingness information have no significant effect on default behavior. In addition, the Nagelkerke R2 improved by nearly 3% in the logistic regression model with the addition of text variables. In short, fully excavating loan description information is helpful in reducing the risk of loan default.

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Section: Literature Reviewmentioning

confidence: 99%

How Does the Urgency of Borrowing in Text Messages Affect Loan Defaults? Evidence from P2P Loans in China

Hong

Yuan

Zhou

2021

Security and Communication Networks

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“…Loan amount and interest rate have a negative impact on the funding success ratio (Puro et al, 2010, Zhang & Liu, 2012. Information such as loan description and borrower's picture can alleviate information asymmetry (Wang et al, 2019, Liang & He, 2020. The other research stream focuses on the behavior of lenders in P2P lending.…”

Section: Background and Motivationmentioning

confidence: 99%

The Impact of Automated Investment on Peer-to-Peer Lending

Chen

Fan

et al. 2021

Journal of Global Information Management

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In the face of fierce competition, many peer-to-peer (P2P) lending platforms have introduced automated investment tools to serve customers better. Based on a large sample of data from PPdai.com, the authors studied the impact of automated investment on lenders’ investment behavior and platform performance. Using the propensity score matching (PSM) method, this article checks the differences of funding duration and loan performance with and without participation of automated investment tools in P2P lending. Our empirical results show that automated investment in P2P lending can significantly weaken investors’ herding behavior. The authors also found that automated investment prolongs the average funding duration of loans and undermines the platform efficiency. Furthermore, this study indicates that usage of automated investment does not affect the return on investment (ROI) in general.

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“…In P2P loans, while factors similar to those affecting repayment of conventional loans, such as demographic, financial, and credit information (Chen et al ., 2017; Chen et al ., 2020; Ding et al ., 2019; Hu et al ., 2019; Pope and Sydnor, 2011; Tao et al ., 2017; Wang et al ., 2019) are important, factors not considered in assessing a borrower's creditworthiness in making conventional loans, such as perceived appearance (Duarte et al ., 2012), social network (Freedman and Jin, 2017), geographic location (Jiang et al ., 2019), language (Jiang et al ., 2019), and friendships (Lin et al ., 2013), are found to have a significant impact on repayment performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Distinctive features of student borrowers and suboptimal investor decision‐making: Evidence from the P2P lending market

Kim¹,

Kim

2020

Asia-Pac J of Fin Stud

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This study attempts to identify the characteristics and behavioral features of student borrowers, and to investigate how well individual investors incorporate the uniqueness of these borrowers in their funding decisions. We find that student borrowers are distinct from other types of borrowers, not only with respect to factors that existed prior to joining the market, but also in terms of information generated during loan requests, subsequent cancellation behavior, repayment performance, and even in the composition and predictive accuracy of loan repayment models. However, individual investors are not fully aware of the unique attributes of student borrowers in their funding decisions.

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Soft information in online peer-to-peer lending: Evidence from a leading platform in China

Cited by 26 publications

References 31 publications

How Does the Urgency of Borrowing in Text Messages Affect Loan Defaults? Evidence from P2P Loans in China

How Does the Urgency of Borrowing in Text Messages Affect Loan Defaults? Evidence from P2P Loans in China

The Impact of Automated Investment on Peer-to-Peer Lending

Distinctive features of student borrowers and suboptimal investor decision‐making: Evidence from the P2P lending market

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