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Auctions for Social Lending: A Theoretical Analysis

Author

Listed:
  • Chen, Ning

    (Nanyang Technological University)

  • Ghosh, Arpita

    (Yahoo! Research)

  • Lambert, Nicolas S.

    (Stanford University)

Abstract

Prosper, the largest online social lending marketplace with over a million members and $207 million in funded loans, uses an auction amongst lenders to finance each loan. In each auction, the borrower specifies D, the amount he wants to borrow, and a maximum acceptable interest rate R. Lenders specify the amounts a[subscript i] they want to lend, and bid on the interest rate, b[subscript i], they're willing to receive. Given that a basic premise of social lending is cheap loans for borrowers, how does the Prosper auction do in terms of the borrower's payment, when lenders are strategic agents with private true interest rates? We first provide an analysis of the complete information game and fully characterize the Nash equilibria of the Prosper mechanism. Next, we show that while the borrower's payment in the VCG mechanism is always within a factor of O(logD) of the payment in any equilibrium of Prosper, even the cheapest Nash equilibrium of the Prosper mechanism can be as large as a factor D of the VCG payment; both factors are tight. Thus, while the Prosper mechanism is a simple uniform price mechanism, it can lead to much larger payments for the borrower than the VCG mechanism. Finally, we consider an incomplete information setting and derive the Bayesian optimal auction for the borrower, which, perhaps surprisingly, may prefer to assign the loan to lenders with high interest-rates over lenders with lower interest rates, when lenders' budgets and interest-rates are correlated.

Suggested Citation

  • Chen, Ning & Ghosh, Arpita & Lambert, Nicolas S., 2011. "Auctions for Social Lending: A Theoretical Analysis," Research Papers 2078, Stanford University, Graduate School of Business.
  • Handle: RePEc:ecl:stabus:2078
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    2. Sirong Luo & Radha Mookerjee & Dengpan Liu, 2021. "The Effects of Auction‐based Pricing Mechanisms and Social Characteristics on Microloan Performance," Production and Operations Management, Production and Operations Management Society, vol. 30(2), pages 311-329, February.
    3. Dorfleitner, Gregor & Rad, Jacqueline & Weber, Martina, 2017. "Pricing in the online invoice trading market: First empirical evidence," Economics Letters, Elsevier, vol. 161(C), pages 56-61.
    4. Belleflamme, Paul & Omrani, Nessrine & Peitz, Martin, 2015. "The economics of crowdfunding platforms," Information Economics and Policy, Elsevier, vol. 33(C), pages 11-28.
    5. Soumajyoti Sarkar & Hamidreza Alvari, 2020. "Mitigating Bias in Online Microfinance Platforms: A Case Study on Kiva.org," Papers 2006.12995, arXiv.org.
    6. Ivelin Elenchev & Aleksandar Vasilev, 2019. "Forecasting the Success Rate of Reward Based Crowdfunding Projects," Managing Global Transitions, University of Primorska, Faculty of Management Koper, vol. 17(1 (Spring), pages 51-77.
    7. Zaiyan Wei & Mingfeng Lin, 2017. "Market Mechanisms in Online Peer-to-Peer Lending," Management Science, INFORMS, vol. 63(12), pages 4236-4257, December.
    8. Dorfleitner, Gregor & Priberny, Christopher & Schuster, Stephanie & Stoiber, Johannes & Weber, Martina & de Castro, Ivan & Kammler, Julia, 2016. "Description-text related soft information in peer-to-peer lending – Evidence from two leading European platforms," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 169-187.
    9. Jianrong Yao & Jiarui Chen & June Wei & Yuangao Chen & Shuiqing Yang, 2019. "The relationship between soft information in loan titles and online peer-to-peer lending: evidence from RenRenDai platform," Electronic Commerce Research, Springer, vol. 19(1), pages 111-129, March.
    10. Xiong Xiong & Zhang Jin & Feng Xu & Jin Xi, 2016. "Review on Financial Innovations in Big Data Era," Journal of Systems Science and Information, De Gruyter, vol. 4(6), pages 489-504, December.
    11. Andreas Dietrich & Reto Rey, 2020. "What Matters to Individual Investors: Price Setting in Online Auctions of P2P Consumer Loans," Papers 2003.11347, arXiv.org, revised Dec 2022.
    12. Zhenhua Wu & Lin Hu & Zhijie Lin & Yong Tan, 2021. "Competition and Distortion: A Theory of Information Bias on the Peer-to-Peer Lending Market," Information Systems Research, INFORMS, vol. 32(4), pages 1140-1154, December.
    13. Bertsch, Christoph & Hull, Isaiah & Zhang, Xin, 2016. "Fed Liftoff and Subprime Loan Interest Rates: Evidence from the Peer-to-Peer Lending Market," Working Paper Series 319, Sveriges Riksbank (Central Bank of Sweden).
    14. Kai Lu & Zaiyan Wei & Tat Y. Chan, 2022. "Information Asymmetry Among Investors and Strategic Bidding in Peer-to-Peer Lending," Information Systems Research, INFORMS, vol. 33(3), pages 824-845, September.
    15. Bertsch, Christoph & Hull, Isaiah & Qi, Yingjie & Zhang, Xin, 2020. "Bank misconduct and online lending," Journal of Banking & Finance, Elsevier, vol. 116(C).

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    More about this item

    JEL classification:

    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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