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Herding in Equity Crowdfunding

Author

Listed:
  • Astebro, Thomas B.

  • Lovo, Stefano

  • Fernandez Sierra, Manuel

  • Vulkan, Nir

Abstract

Equity crowdfunding has recently become available and is quickly expanding. Concerns have been raised that investors ('backers') may be following the crowd 'too much' and making investments ('pledges') based on past investments rather than private information. We construct a model of equilibrium rational herding where uninformed investors follow signals generated by in formed investors with private information and a public belief generated by all past pledges. We show that large investments provide positive public information about the project's quality, whereas periods of absence of investment provide negative information. An information cascade is shown to occur only if not enough positive signals are generated. We then empirically analyse a large number of pledges from a leading European equity crowdfunding platform. We show that a pledge is strongly affected by both the size of the most recent pledge, and the time elapsed since the most recent pledge. For pledges that are not adjacent in the order of arrivals, the correlation between their sizes is still positive, but after being separated by two or more intervening pledges the correlation is no longer statistically significant. The effects are strongest for less-informed investors, and in some specifications the effects are strongest at the early stage of a campaign. We find similar results in IV analysis. Results are consistent with our model and inconsistent with some alternative models.

Suggested Citation

  • Astebro, Thomas B. & Lovo, Stefano & Fernandez Sierra, Manuel & Vulkan, Nir, 2017. "Herding in Equity Crowdfunding," HEC Research Papers Series 1245, HEC Paris, revised 04 Jun 2018.
  • Handle: RePEc:ebg:heccah:1245
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    Cited by:

    1. Thomas Hellmann & Ilona Mostipan & Nir Vulkan, 2019. "Be Careful What You Ask For: Fundraising Strategies in Equity Crowdfunding," NBER Working Papers 26275, National Bureau of Economic Research, Inc.
    2. Kazem Mochkabadi & Christine K. Volkmann, 2020. "Equity crowdfunding: a systematic review of the literature," Small Business Economics, Springer, vol. 54(1), pages 75-118, January.
    3. Zaggl, Michael A. & Block, Joern, 2019. "Do small funding amounts lead to reverse herding? A field experiment in reward-based crowdfunding," Journal of Business Venturing Insights, Elsevier, vol. 12(C).
    4. Kim, Jin-Hyuk & Newberry, Peter & Qiu, Calvin, 2022. "The role of information signals in determining crowdfunding outcomes," Journal of Empirical Finance, Elsevier, vol. 67(C), pages 168-181.
    5. Irene Comeig & Ernesto Mesa-Vázquez & Pau Sendra-Pons & Amparo Urbano, 2020. "Rational Herding in Reward-Based Crowdfunding: An MTurk Experiment," Sustainability, MDPI, vol. 12(23), pages 1-21, November.
    6. Martin Walther & Marco Bade, 2020. "Observational learning and willingness to pay in equity crowdfunding," Business Research, Springer;German Academic Association for Business Research, vol. 13(2), pages 639-661, July.
    7. Hasnan Baber, 2019. "Subjective Norms and Intention- A Study of Crowdfunding in India," Research in World Economy, Research in World Economy, Sciedu Press, vol. 10(3), pages 136-146, December.
    8. Ferretti, Riccardo & Venturelli, Valeria & Pedrazzoli, Alessia, 2021. "Do multiple competing offerings on a crowdfunding platform influence investment behavior?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).

    More about this item

    Keywords

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    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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