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Rates of return for crowdfunding portfolios: theoretical derivation and implications

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  • Paul Vroomen
  • Subhas Desa

Abstract

Crowdfunding (CF) is emerging as a fast-growing new class of private equity investment. But research on the rates of return that CF investments should yield is sparse. In this paper, we analyse CF investments from the perspective of an investor that exhibits non-satiation behaviour (seeks the maximum return for a given investment risk). Using internal rate of return (IRR) as the return metric, we apply modern portfolio theory, efficient market theory, the Central Limit Theorem and historical returns data for three private equity asset classes – equity funds (1112 funds), venture capital funds (1,474 funds) and Angel investments (1137 exited investments) – to find the efficient frontier of the private equity market. Applying the efficient frontier to CF investments enables us to show with 99% confidence that the target IRR for an efficient CF portfolio is at least 28% if the CF asset class is 10% riskier than Angel investments. We further show that the set of companies that qualify for CF collapses to that small subset that can achieve the revenue growth, and/or can accept the capital structure required to achieve the target IRR.

Suggested Citation

  • Paul Vroomen & Subhas Desa, 2018. "Rates of return for crowdfunding portfolios: theoretical derivation and implications," Venture Capital, Taylor & Francis Journals, vol. 20(3), pages 261-283, July.
  • Handle: RePEc:taf:veecee:v:20:y:2018:i:3:p:261-283
    DOI: 10.1080/13691066.2018.1480265
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    Cited by:

    1. Muneer M. Alshater & Mayank Joshipura & Rim El Khoury & Nohade Nasrallah, 2023. "Initial Coin Offerings: a Hybrid Empirical Review," Small Business Economics, Springer, vol. 61(3), pages 891-908, October.
    2. Santautė Venslavienė & Jelena Stankevičienė & Agnė Vaiciukevičiūtė, 2021. "Assessment of Successful Drivers of Crowdfunding Projects Based on Visual Analogue Scale Matrix for Criteria Weighting Method," Mathematics, MDPI, vol. 9(14), pages 1-18, July.
    3. Ronald Setty & Yuval Elovici & Dafna Schwartz, 2024. "Cost‐sensitive machine learning to support startup investment decisions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(1), March.

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