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ICOs success drivers: a textual and statistical analysis


  • Paola Cerchiello

    (Department of Economics and Management, University of Pavia)

  • Anca Mirela Toma

    (Department of Economics and Management, University of Pavia)


Initial coin offering (aka ICOs) represents one of the several by-product of the cryptocurrencies world. New generation start-up and existing businesses in order to avoid rigid and long money raising protocols imposed by classical channels like banks or venture capitalists, offer the inner value of their business by selling tokens, i.e. units of the chosen cryptocurrency, like a regular firm would do with and IPO. The investors of course hope in a value increasing of the tokens in the near future, provided a solid and valid business idea typically described by the ICO issuers in a white paper, both a descriptive and technical report of the proposed business. However, fraudulent activities perpetrated by unscrupulous start-up happen quite often and it would be crucial to highlight in advance clear signs of illegal money raising. In this paper, we employ a statistical approach to detect which characteristics of an ICO are significantly related to fraudulent behaviours. We leverage a number of differen t variables like: entrepreneurial skills, number of people chatting on Telegram on the given ICO and relative sentiment, type of business, country issuing, token pre-sale price. Through logistic regression, classifcation tree we are able to shed a light on the riskiest ICOs.

Suggested Citation

  • Paola Cerchiello & Anca Mirela Toma, 2018. "ICOs success drivers: a textual and statistical analysis," DEM Working Papers Series 164, University of Pavia, Department of Economics and Management.
  • Handle: RePEc:pav:demwpp:demwp0164

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    References listed on IDEAS

    1. Adhami, Saman & Giudici, Giancarlo & Martinazzi, Stefano, 2018. "Why do businesses go crypto? An empirical analysis of initial coin offerings," Journal of Economics and Business, Elsevier, vol. 100(C), pages 64-75.
    2. Raffaella Calabrese & Giampiero Marra & Silvia Angela Osmetti, 2016. "Bankruptcy prediction of small and medium enterprises using a flexible binary generalized extreme value model," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(4), pages 604-615, April.
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    1. Paola Cerchiello & Anca Mirela Toma & Marco Caluzzi, 2021. "ICOs White Papers: identity card or lark mirror?," DEM Working Papers Series 197, University of Pavia, Department of Economics and Management.
    2. Perez, Charles & Sokolova, Karina & Konate, Malick, 2020. "Digital social capital and performance of initial coin offerings," Technological Forecasting and Social Change, Elsevier, vol. 152(C).

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    ICOs; cryptocurrencies; fundraising; classification models; text analysis;
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