ICOs success drivers: a textual and statistical analysis

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.