Networking with Peers: Evidence from a P2P Lending Platform (original) (raw)
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Despite the lack of delegated monitor and of collateral guarantees P2P lending platforms exhibit relatively low loan and delinquency rates. The adverse selection is indeed mitigated by a new screening technology (information processing through machine learning) that provides costless public signals. Using data from Prosper and Lending Club we show that loans' spreads, proxing asymmetric information, decline with credit scores or hard information indicators and with indications from "group ties" (soft information from social networks). Also an increase in the risk of bank run in the traditional banking sector increases participation in the P2P markets and reduces their rates (substitution effect). We rationalize this evidence with a dynamic general equilibrium model where lenders and borrowers choose between traditional bank services (subject to the risk of bank runs and early liquidation) and P2P markets (which clear at a pooling price due to asymmetric information, bu...
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2009 International Conference on Computational Science and Engineering, 2009
Increasingly, economic transactions are taking place over social networks. We study the static and dynamic characteristics of a peer-to-peer lending network through 350,000 loan listings and accompanying member profiles from the online marketplace Prosper.com. Our results imply that social factors such as participation in affinity groups and descriptive profile text are correlated with financial indicators; at the same time, we see evidence of suboptimal lending decisions, minimal learning, and herding behavior in the network. We discuss implications and suggest possible improvements to the online peer-to-peer lending model.
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The advancement of financial technology has given rise to the online peer-to-peer lending (P2P lending) platform to provide loans. P2P lending provides loans without the role of financial intermediaries such as banks. Further, P2P lending is expected to increase access to financing for business, especially for micro and small businesses, which typically find it difficult to obtain funding from formal financial institutions. However, P2P lending platforms include several concerns to lenders, as they can't always find the same guaranteed security in P2P lending as conventional banks provide. Thus, we predict that platform reputation is critical in this sector. Specifically, this study aims to determine what factors form investors' perceptions on P2P lending platform reputation, evaluate the relationship between platform reputation and lenders' willingness to provide loans, and evaluate whether trust will strengthen the relationship. Using 160 lenders as respondents, this study was conducted using the regression analysis method with the help of SPSS 23 software to test the proposed models. The results show that security and protection have the greatest influence on platform reputation. Reputation itself was found to have a positive impact on lenders' willingness to lend; however, trust is found to have no moderating effect and, instead, has a positive influence on the lenders' investment decisions as an independent variable. Considering that P2P lending is able to increase SME's access to finance, the collaboration of conventional financial institutions and technology will create a relationship that benefits both parties. With collaboration, P2P lending platforms can generate a good reputation quickly and economically, while conventional financial institutions are able to increase customer bases to a wider range of borrowers from the SME sector.
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Journal of Financial and Quantitative Analysis, 2021
How does social capital affect trust? Evidence from a Chinese peer-to-peer lending platform shows that regional social capital affects the trustee’s trustworthiness and the trustor’s trust propensity. Ceteris paribus, borrowers from regions with higher social capital receive larger bids from individual lenders and have higher funding success, larger loan sizes, and lower default rates, especially for low-quality borrowers. Lenders from regions with higher social capital take higher risks and have higher default rates, especially for inexperienced lenders. Cross-regional transactions are most (least) likely to be realized between parties from regions with high (low) social capital.
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This paper studies P2P lending and the factors explaining loan default. This is an important issue because in P2P lending individual investors bear the credit risk, instead of financial institutions, which are experts in dealing with this risk. P2P lenders suffer a severe problem of information asymmetry, because they are at a disadvantage facing the borrower. For this reason, P2P lending sites provide potential lenders with information about borrowers and their loan purpose. They also assign a grade to each loan. The empirical study is based on loans’ data collected from Lending Club (N = 24,449) from 2008 to 2014 that are first analyzed by using univariate means tests and survival analysis. Factors explaining default are loan purpose, annual income, current housing situation, credit history and indebtedness. Secondly, a logistic regression model is developed to predict defaults. The grade assigned by the P2P lending site is the most predictive factor of default, but the accuracy of the model is improved by adding other information, especially the borrower’s debt level.
A Comparative Study of online P2P Lending in the USA andChina
The Journal of Internet Banking and Commerce, 2012
Peer-to-Peer (P2P) lending provides online users an innovative loaning and investment vehicle without the intermediation of financial institutions. However, the research on online P2P lending is still scarce. In this study we review relevant literature and conduct a comparative study of online P2P lending practices in the USA and China. We find that two categories of credit information, “hard” and “soft” information, may have profound influences on lending outcomes in both countries, but lenders in China is more reliable on “soft” information. This study provides valuable insights for future research and practices and enriches the understanding of online P2P lending across countries.
Springer New York LLC, 2019
Most previous literatures focus on the micro level default risk of individual borrowers whereas the platform default risk has not been rigorously studied yet. In this paper, we investigate the factors affecting platform default risk by employing the Chinese online P2P platform data. We find significant evidence that severe competition among platforms can increase risky behaviors of platforms by allowing riskier borrowers into the system. Some of the risk management devices could alleviate the default risk of platforms; however, others are not effective at alleviating the default risks. In addition, we find evidence that macro environment such as stock market condition or increases in speculative investment opportunities plays critical roles to increase the platform default rate. Our study sheds light on the platforms' default risk issues and verifies key factors that influence their risky behaviors.
Lessons from the rise and fall of Chinese peer-to-peer lending
Journal of Banking Regulation, 2020
This paper reviews the development and assesses the future of Peer-to-Peer (P2P) lending in China. Chinese P2P lending has expanded by a factor of 60 over the four years from 2013 and 2017. Consequently, it is now much greater, both in absolute terms and relative to the size of the economy, than in any other country. The industry though has been plagued by problematic often fraudulent business models in what was, until 2015, effectively a regulatory vacuum. A strict new regulatory regime is currently being introduced. However, by its introduction, especially the requirements on capital requirements and registration, are substantially reducing the volume of P2P lending. We consider the future of P2P lending concluding it's facing substantial uncertainties.