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Ginger Zhe Jin, Maryland Print
Tuesday, 27 April 2010, 14:00 - 15:30

Learning by Doing with Asymmetric Information: Evidence from

Ginger Zhe Jin, University of Maryland

Abstract:This paper examines the nature of information asymmetry in online peer-to-peer (P2P) lending markets. These markets use the Internet to match individual borrowers and lenders of consumer loans without financial institutions as intermediaries. Like other anonymous interactions, P2P lending may face additional information asymmetry as compared to offline because P2P lenders have less access to .hard. information such as borrower credit history, income, or employment. However, the shortage of hard information could be mitigated by soft information via online social networks. We examine this tradeoff using data from all requested and funded loans between June 1, 2006 and July 31, 2008 on

We have three main findings. First, Prosper lenders understand the ordinal difference across credit grades but the incomplete disclosure of a borrower.s credit history leads to additional adverse selection relative to traditional markets. Second, some social networks help to mitigate information asymmetry and others do not, depending on the institutional incentives. Third, lenders, especially those who joined Prosper early, did not fully understand the market risk. We estimate, on average, lenders would have expected an annualized internal rate of return of -0.62% to -1.38% on a dollar invested if they had correctly understood the risk distribution of Prosper loans. However, lender learning is effective in reducing the riskof funded loans over time. As a result, the market has excluded more and more sub-prime borrowers and evolved towards the population served by traditional credit markets.

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