Adverse Selection in P2P Lending: Does Peer Screening Work Efficiently?—Empirical Evidence from a P2P Platform (original) (raw)
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Financial Innovation
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Emergence of Financial Intermediaries in ElectronicMarkets: The Case of Online P2P Lending
We analyze the role of intermediaries in electronic markets using detailed data of more than 14,000 originated loans on an electronic P2P (peer-to-peer) lending platform. In such an electronic credit market, lenders bid to supply a private loan. Screening of potential borrowers and the monitoring of loan repayment can be delegated to designated group leaders. We find that these market participants act as financial intermediaries and significantly improve borrowers' credit conditions by reducing iriformation asymmetries, predominantly for borrowers with less attractive risk characteristics. Our findings may be surprising given the replacement of a bank by an electronic marketplace.
How Does P2P Lending Fit into the Consumer Credit Market?
SSRN Electronic Journal, 2016
Research Question In recent years, we have begun to observe the growth of the internet economy, which has progressively led to "crowd-based" platforms and the direct matching of lenders and borrowers. Via peer-to-peer (P2P) lending platforms the decision process of loan origination is given into the hands of private lenders and borrowers. This paper investigates how the P2P lending market fits into the credit market and specifically aims to answer the following questions: Why do retail consumers look for P2P financial intermediation? Are the interest rates charged by P2P lenders in Germany higher than those of banks? Are P2P loans more risky than bank loans? Are internet-based peer-to-peer loans substitutes for or complementary to bank loans? Contribution and Results The paper shows that loans channelled via P2P platforms involve higher interest rates than loans channelled via the traditional banking sector. They are also riskier than those of banks. However, when adjusted for risk, the interest rates are comparable. Moreover, analysis of the different segments of the bank credit market and P2P lending shows that, after having controlled for interest rate and risk differences, the bank lending volumes are negatively correlated with the P2P lending volumes. Our finding suggests that high-risk borrowers substitute bank loans for P2P loans since banks are unwilling or unable to supply this slice of the market.
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.
Enhancing investment decisions in P2P lending: an investor composition perspective
2011
P2P lending, as a novel economic lending model, has imposed new challenges about how to make effective investment decisions. Indeed, a key challenge along this line is how to align the right information with the right people. For a long time, people have made tremendous efforts in establishing credit records for the borrowers. However, information from investors is still under-explored for improving investment decisions in P2P lending. To that end, we propose a data driven investment decision-making framework, which exploits the investor composition of each investment for enhancing decisions making in P2P lending. Specifically, we first build investor profiles based on quantitative analysis of past performances, risk preferences, and investment experiences of investors. Then, based on investor profiles, we develop an investor composition analysis model, which can be used to select valuable investments and improve the investment decisions. To validate the proposed model, we perform extensive experiments on the real-world data from the world's largest P2P lending marketplace. Experimental results reveal that investor composition can help us evaluate the profit potential of an investment and the decision model based on investor composition can help investors make better investment decisions.