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Geographical-Proximity Bias in P2B Crowdlending Strategies

Author

Listed:
  • Carole Gresse

    (DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

  • Hugo Marin

    (DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

Abstract

Using data from a peer-to-business crowdlending platform that exploits an auction-driven system to fund corporate loans, we show that non-professional investors are subject to a geographical-proximity bias. They are more likely to win the auctions of borrowers located close to their place of residence notwithstanding that they are not better informed about their creditworthiness. Unexpectedly, this behavioral bias distorts the loan rate discovery processby increasing the cost of funding for borrowers. This adverse effect results from the greaterability of local investors to submit winning bids at an early stage. This ability is gained from their experience in previous auctions of geographically close borrowers. This suggests that the familiarity feeling stemming from geographical closeness strengthens investor attention,and thereby improves lenders' knowledge about the dynamics of the order flow in local borrowers' auctions.

Suggested Citation

  • Carole Gresse & Hugo Marin, 2021. "Geographical-Proximity Bias in P2B Crowdlending Strategies," Working Papers hal-03338244, HAL.
    • Handle: RePEc:hal:wpaper:hal-03338244
    Note: View the original document on HAL open archive server: https://hal.science/hal-03338244
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    References listed on IDEAS

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    1. Karen K. Lewis, 1999. "Trying to Explain Home Bias in Equities and Consumption," Journal of Economic Literature, American Economic Association, vol. 37(2), pages 571-608, June.
    2. Frieder, Laura & Subrahmanyam, Avanidhar, 2005. "Brand Perceptions and the Market for Common Stock," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 40(1), pages 57-85, March.
    3. Rajkamal Iyer & Asim Ijaz Khwaja & Erzo F. P. Luttmer & Kelly Shue, 2016. "Screening Peers Softly: Inferring the Quality of Small Borrowers," Management Science, INFORMS, vol. 62(6), pages 1554-1577, June.
    4. Harald Hau & Helene Rey, 2008. "Home Bias at the Fund Level," American Economic Review, American Economic Association, vol. 98(2), pages 333-338, May.
    5. Ali Hortaçsu & F. Asís Martínez-Jerez & Jason Douglas, 2009. "The Geography of Trade in Online Transactions: Evidence from eBay and MercadoLibre," American Economic Journal: Microeconomics, American Economic Association, vol. 1(1), pages 53-74, February.
    6. Mingfeng Lin & Siva Viswanathan, 2016. "Home Bias in Online Investments: An Empirical Study of an Online Crowdfunding Market," Management Science, INFORMS, vol. 62(5), pages 1393-1414, May.
    7. Calebe de Roure & Loriana Pelizzon & Anjan Thakor, 2022. "P2P Lenders versus Banks: Cream Skimming or Bottom Fishing? [Loan officer incentives, internal rating models and default rates]," The Review of Corporate Finance Studies, Society for Financial Studies, vol. 11(2), pages 213-262.
    8. Anne-Célia Disdier & Keith Head, 2008. "The Puzzling Persistence of the Distance Effect on Bilateral Trade," The Review of Economics and Statistics, MIT Press, vol. 90(1), pages 37-48, February.
    9. Holger C. Wolf, 2000. "Intranational Home Bias In Trade," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 555-563, November.
    10. Massimo Massa & Andrei Simonov, 2006. "Hedging, Familiarity and Portfolio Choice," The Review of Financial Studies, Society for Financial Studies, vol. 19(2), pages 633-685.
    11. Alexandra Moritz & Joern H. Block, 2016. "Crowdfunding: A Literature Review and Research Directions," FGF Studies in Small Business and Entrepreneurship, in: Dennis Brüntje & Oliver Gajda (ed.), Crowdfunding in Europe, edition 1, pages 25-53, Springer.
    12. Ahearne, Alan G. & Griever, William L. & Warnock, Francis E., 2004. "Information costs and home bias: an analysis of US holdings of foreign equities," Journal of International Economics, Elsevier, vol. 62(2), pages 313-336, March.
    13. Huan Tang, 2019. "Peer-to-Peer Lenders Versus Banks: Substitutes or Complements?," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1900-1938.
    14. Boris Vallée & Yao Zeng, 2019. "Marketplace Lending: A New Banking Paradigm?," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1939-1982.
    15. Juanjuan Zhang & Peng Liu, 2012. "Rational Herding in Microloan Markets," Management Science, INFORMS, vol. 58(5), pages 892-912, May.
    16. Gilles Chemla & Katrin Tinn, 2021. "How Wise are Crowds on Crowdfunding Platforms?," Springer Books, in: Raghavendra Rau & Robert Wardrop & Luigi Zingales (ed.), The Palgrave Handbook of Technological Finance, pages 397-406, Springer.
    17. Huberman, Gur, 2001. "Familiarity Breeds Investment," The Review of Financial Studies, Society for Financial Studies, vol. 14(3), pages 659-680.
    18. Shlomo Benartzi, 2001. "Excessive Extrapolation and the Allocation of 401(k) Accounts to Company Stock," Journal of Finance, American Finance Association, vol. 56(5), pages 1747-1764, October.
    19. Mark S. Seasholes & Ning Zhu, 2010. "Individual Investors and Local Bias," Journal of Finance, American Finance Association, vol. 65(5), pages 1987-2010, October.
    20. Heath, Chip & Tversky, Amos, 1991. "Preference and Belief: Ambiguity and Competence in Choice under Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 4(1), pages 5-28, January.
    21. Ajay K. Agrawal & Christian Catalini & Avi Goldfarb, 2011. "The Geography of Crowdfunding," NBER Working Papers 16820, National Bureau of Economic Research, Inc.
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    More about this item

    Keywords

    peer-to-business crowdlending; crowdfunding; behavioral finance; loan performance; price discovery process;
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