Can Listing Information Indicate Borrower Credit Risk in Online Peer-to-Peer Lending?

Liu, Yi; Zhou, Quanli; Zhang, Xuan; Wang, Yudong

doi:10.1080/1540496x.2018.1427061

Cited by 26 publications

(14 citation statements)

References 23 publications

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“…Ge et al [10] test empirically the influence of social medium information shown by the borrower itself to the online loan default rate. Liu et al [11] test empirically the forecasting effect of lender's information on default rates of items using data from the Renrendai website.…”

Section: Literature Reviewsmentioning

confidence: 99%

“…(10) Year (y): the age of the borrower, and it is required to be from 22 to 70 in the Renrendai platform. (11) Education (e): the borrower's educational background, and it is divided into four grades-high school (or below), college, undergraduate, and postgraduate (above)-is and expressed, respectively, by 0, 1, 2, and 3, and thus, the bigger e means the higher the educational background. ( 12) Marriage (m): The borrower's marriage situation-1 if married and 0 if not married.…”

Section: Variable Selection and Sample Characteristicsmentioning

confidence: 99%

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Evaluating Investors’ Recognition Abilities for Risk and Profit in Online Loan Markets Using Nonlinear Models and Financial Big Data

Xia

et al. 2021

Journal of Function Spaces

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Financial big data are obtained by web crawler, and investors’ recognition abilities for risk and profit in online loan markets are researched using heteroskedastic Probit models. The conclusions are obtained as follows: First, the preference for the item is reflected directly in the time and indirectly in the number of participants for being full, and the larger the preference, the shorter the time and the fewer the participants. Second, investors can discriminate the default risk not reflected by the interest rate, and the bigger the default risk, the longer the time and the more participants being full. Third, investors can discriminate the pure return rate deducted from the maturity term and credit risk, and the higher the return, the shorter the time and the fewer the participants being full. Fourth, default risks are reflected well by online loan platform interest rates, and inventors do not choose the item blindly according to the interest rate but consider comprehensively the profit and the risk. In the future, interest rate liberalization should be deepened, the choosing function of interest rates should be played better, and the information disclosure, investor education, and investor effective usage of other information should be strengthened.

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Section: Literature Reviewsmentioning

confidence: 99%

Section: Variable Selection and Sample Characteristicsmentioning

confidence: 99%

Evaluating Investors’ Recognition Abilities for Risk and Profit in Online Loan Markets Using Nonlinear Models and Financial Big Data

Xia

et al. 2021

Journal of Function Spaces

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“…Most P2P loan platforms are concentrated in developed financial markets. 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].…”

Section: Literature Reviewmentioning

confidence: 99%

“…With the continuous integration of finance and Internet technology, a new type of online lending model called peerto-peer online lending, which is a fast-lending model with no mortgage and no guarantee, has emerged [1][2][3]. P2P lending is regarded as a revolution in the lending market and can be regarded as an alternative to credit loans by local lending institutions [3,4]. Compared with traditional financial institutions, the P2P network lending model faces a more serious problem of information asymmetry [2,5].…”

Section: Introductionmentioning

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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“…e study in [14] finds similar results by classifying the borrower information into loan characteristics, borrower credit, and personal details. e authors in [15] test the strong signals affecting the probability of borrowing success for PaiPaiDai.…”

Section: Introductionmentioning

confidence: 62%

The Prediction Analysis of Peer-to-Peer Lending Platforms Default Risk Based on Comparative Models

Guo

Peng

et al. 2020

Scientific Programming

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This paper examines the determinants of platform default risk using machine learning methods, including comprehensive models, and thus compares these models’ predictive abilities. To test platform’s default risk, this paper constructs three types of variables, which reflect a platform’s operating characteristics, customer feedback, and compliance capability. We find that the abnormal return tends to trigger default risk significantly. However, the default risk can be minimized if a platform has positive recommendations from customers and more transparent information disclosure or is affiliated as the member of the National Internet Finance Association of China. Empirical results indicate that the CART model outperforms the Random Forests model and Logit regression in predicting platform default risk. Our study sheds light on default risk prediction and thus can improve the government regulation ability.

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Can Listing Information Indicate Borrower Credit Risk in Online Peer-to-Peer Lending?

Cited by 26 publications

References 23 publications

Evaluating Investors’ Recognition Abilities for Risk and Profit in Online Loan Markets Using Nonlinear Models and Financial Big Data

Evaluating Investors’ Recognition Abilities for Risk and Profit in Online Loan Markets Using Nonlinear Models and Financial Big Data

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

The Prediction Analysis of Peer-to-Peer Lending Platforms Default Risk Based on Comparative Models

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