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Expert Imitation in P2P Markets


  • Ge Gao

    () (University of Birmingham)

  • Mustafa Caglayan

    () (Heriot-Watt University)

  • Yuelei Li

    () (Tianjin University)

  • Oleksandr Talavera

    () (University of Birmingham)


This paper investigates expert bidding imitation in peer-to-peer lending platforms. We employ data from, which contains information about 169,779 investors who placed 3,947,996 bids on 111,284 loan listings from 2010 to 2018. The experts are defined as investors who either have more central roles or who spend more time or money on the network. We find that an average investor mimics the bids of expert lenders. Inactive lenders learn top investors' lending behaviour through observational learning and then follow their actions, although they do not know the experts' identity. Finally, we show that experts rarely imitate other experts, yet they exhibit herding behaviour.

Suggested Citation

  • Ge Gao & Mustafa Caglayan & Yuelei Li & Oleksandr Talavera, 2020. "Expert Imitation in P2P Markets," Discussion Papers 20-10, Department of Economics, University of Birmingham.
  • Handle: RePEc:bir:birmec:20-10

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    References listed on IDEAS

    1. Herzenstein, Michal & Dholakia, Utpal M. & Andrews, Rick L., 2011. "Strategic Herding Behavior in Peer-to-Peer Loan Auctions," Journal of Interactive Marketing, Elsevier, vol. 25(1), pages 27-36.
    2. Raghuram Iyengar & Christophe Van den Bulte & Thomas W. Valente, 2011. "Opinion Leadership and Social Contagion in New Product Diffusion," Marketing Science, INFORMS, vol. 30(2), pages 195-212, 03-04.
    3. Chen, Xiao & Huang, Bihong & Ye, Dezhu, 2018. "The role of punctuation in P2P lending: Evidence from China," Economic Modelling, Elsevier, vol. 68(C), pages 634-643.
    4. Carlos Serrano-Cinca & Begoña Gutiérrez-Nieto & Luz López-Palacios, 2015. "Determinants of Default in P2P Lending," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    5. Sylwester, Kevin, 2000. "Income inequality, education expenditures, and growth," Journal of Development Economics, Elsevier, vol. 63(2), pages 379-398, December.
    6. Giulia Menichetti & Daniel Remondini & Pietro Panzarasa & Raúl J Mondragón & Ginestra Bianconi, 2014. "Weighted Multiplex Networks," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-8, June.
    7. Arieli, Itai, 2017. "Payoff externalities and social learning," Games and Economic Behavior, Elsevier, vol. 104(C), pages 392-410.
    8. Juanjuan Zhang & Peng Liu, 2012. "Rational Herding in Microloan Markets," Management Science, INFORMS, vol. 58(5), pages 892-912, May.
    9. Duncan J. Watts & Peter Sheridan Dodds, 2007. "Influentials, Networks, and Public Opinion Formation," Journal of Consumer Research, Oxford University Press, vol. 34(4), pages 441-458, May.
    10. Kahle, Lynn R & Homer, Pamela M, 1985. "Physical Attractiveness of the Celebrity Endorser: A Social Adaptation Perspective," Journal of Consumer Research, Oxford University Press, vol. 11(4), pages 954-961, March.
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    More about this item


    Peer-to-Peer Lending; Network Analysis; Expert Imitation; Big Data; Financial Technology;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G40 - Financial Economics - - Behavioral Finance - - - General
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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