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Ico definition cryptocurrency

ico definition cryptocurrency

ICO stands for “initial coin offering,” and refers to a formerly popular method of fundraising capital for early-stage cryptocurrency projects. What is an Initial Coin Offering (ICO)? An event in which a cryptocurrency project “goes public”, selling early coins in exchange for funds. Initial Coin Offering (ICO) is an innovative approach for raising funds through the use of digital currencies (cryptocurrencies). Such a strategy is more. CRYPTOCURRENCIES EXCHANGE WEBSITES

In other words, if the ICO has a Twitter account the probability of success is almost 12 times higher almost 4 times higher for the white paper. Regarding the sentiment, we notice a particularly high positive value, stressing the importance of the perception of possible investors which interact with the ICO proposer by means of a social media, namely Telegram.

To further evaluate such configuration, we have explored the VIF index that accounts for the level of multicollinearity brought by each and every variable. The VIF results for the two model configurations are reported in Table 3 logistic and 5 multinomial , with useful insights in defining the lack of multicollinearity 2. Therefore, in Table 3 we can see low values for the VIF index associated to the estimated logistic model given in Table 2.

The reader can easily notice that there is not any multicollinearity effect, making robust the model. VIF index for logistic regression model. In Table 4 , we report results for fraudulent and scam ICOs compared to successful ones, on the basis of a multilogit regression. Looking at the estimated parameters, we can infer that the patterns are different. The presence of a website has a positive impact on the probability of being a successful ICO and not a scam. In other words, the absence of this characteristic is a driver of scam activity suspects.

Instead the website does not differentiate successful from failures ones. With regards to the presence of advisors and of a white paper, both the variables are significant in differentiating fraudulent from successful ICO, confirming results of logistic regression.

No statistical significance for fraudulent ICOs. Lastly, variable on the sentiment score is relevant and with negative sign for both the classes, in other words an increasing in the sentiment causes an increasing in the probability of success when we consider both failed and fraudulent ICOs. Results from multilogit regression: failure and scam compared to success. In this regard, we should stress that the incidence of scam ICOs in our database is extremely low, this due to the fact that collecting information about such ICOs is particularly complex.

The overall model performance, assessed again in terms of AIC and pseudo R2, is pretty good although inferior to the previous one. In Table 5 , we also report VIF index, so to check the absence of multicollinearity in the reported model.

Please note that, multilogit model reported in Table 4 is a final configuration obtained through stepwise selection. The full models are available in the Appendix Supplementary Material 3. VIF index for multilogit regression model. Discussion and Conclusions Initial coin offerings ICOs are one of the several by-products of the cryptocurrencies world. IPOStart-ups and existing businesses are turning to alternative sources of capital as opposed to classical channels like banks or venture capitalists.

The investors, of course, hope for an increase in the value of the token in the short term, provided a solid and valid business idea typically described by the ICO issuers in a white paper. However, fraudulent activities perpetrated by unscrupulous actors are frequent and it would be crucial to highlight in advance clear signs of illegal money raising.

In this perspective, ICOs analysis can be considered a very particular type of fraud detection activity. However, in our opinion fraud detection presents some specificity that prevent us from entailing ICOs related problems as a proper instance of fraud detection. In particular, our data are not flowing in such huge amount from an on-line system as typically happens with credit card payments or banks transactions.

Typical fraud detection approaches, as in Maheshwara Reddy et al. ICOs instead are characterized by a slow process of engagement of the prospect clients and establishment of consensus that goes through Telegram chats if available , white paper and website.

That being the case, we would suggest to label this specific stream of research as FinTech Fraud detection with all the relative specificity. While analyzing success vs failure dynamic with a classification model is relatively easy since the incidence of the two classes is almost equal 50—50 , it is much more complicated to highlight the key aspects that could witness a fraudulent activity since, in the last 3 years, only few scam events have been reported.

In our sample made of ICOs data collection still active we have 10 scam ICOs and we fit a multilogit regression model for comparing scam and failed ICOs toward successful ones. Results tell us that the presence of a website has a positive impact on the probability of not being a scam but does not have any impact on failed ones. In terms of sentiment expressed on Telegram chats, the impact appears to be negative both on the scam and failed ICOs.

This suggests that monitoring in real time Telegram chats could represent a valid mean for collecting signs of possible problems within the ICOs. If instead, we compare Successful ICOs against Failed ones, we find that the presence of a White Paper and of a Twitter account show positive coefficients.

Regarding the three continuous variables, number of elements of the team, number of advisors, and sentiment score based on NRC lexicon, they are all highly significant and positive suggesting that increasing people in the team and advisors has a positive impact.

Regarding the sentiment, we notice a particularly high positive value, stressing the importance of the perception of possible investors which interact with the ICO proposer by means of a social media. The paper will be improved in the future by increasing the size of the sample and exploring alternative approaches for textual analysis with specific attention to sentiment analysis.

We aim at producing a more refined and tailored sentiment score for each ICO, improving and increasing the vocabulary of words. Specifically regarding the textual analysis an alternative approach that we could use is the combination of words as in Bolasco and Pavone As a final remark, authors are aware of the limits of the paper mainly due to the size of the sample. However, given the still limited literature in this field with no reference to the power of textual information collectable through Telegram chats, this contribution represents a step ahead in the process of understanding the ICOs phenomenon.

Furthermore a different approach would be to study the trends of the ICOs by combining the available information from specialized websites on fraudulent activities such as cyphertrace. Data Availability Statement The datasets generated for this study are available on request to the corresponding author.

Author Contributions The paper is the product of full collaboration among the authors, however PC has inspired the idea, the methodology and wrote sections 1, 3 and 6, AT run the analysis and wrote sections Keywords, 2, 4 and 5. The current version of the work is substantially based on refining a working paper not reviewed published by the same authors in DEM Working Paper Series ICOs success drivers: a textual and statistical analysis.

Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Acknowledgments We acknowledge Federico Campasso that technically helped us in collecting the data and Prof. Silvio Vismara for the useful advises on the work. References Adhami, S. Why Do Businesses Go Crypto? Cryptocurrencies: Looking Beyond the Hype. Failure to abide by these regulations could result in costly penalties and may threaten to derail a project.

Therefore, a business must meet all of its regulatory obligations. Once the token is properly classified, a wide variety of applications are permitted, the most promising being the ability to issue tokens that represent shares of company stock.

Online retailer Overstock. Overstock announced that tZERO, one of its portfolio companies, would hold an ICO to fund the development of a licensed security token trading platform. Many industry observers, including Mr. Byrne, believe that mainstream companies will one day issue shares through ICOs, either in place of or in addition to, traditional public offerings.

He believes that cryptocurrencies are being deployed in irresponsible ways that are causing harm to individuals and damaging the ecosystem of developers and organizations. Ito is concerned that regulators of IPOs have not yet caught up with ICOs, thereby allowing issuers to get rich by taking advantage of unwitting investors who are buying tokens of questionable value. Ultimately, Mr. Ito believes the regulatory intervention will need to be much more sophisticated and technically informed.

In the meantime, a long list of people will read about skyrocketing prices of Bitcoin and decide to buy into one of the myriad ICOs being introduced to the public, perhaps without fully understanding the risks associated with such an investment.

This token can be used in two ways, either with a utility function or a security function. A utility token is generally unregulated and used by startups to gain capital to fund their projects in exchange for future access to the service in development. On the other hand, a security token is generally treated like a stock, a tradable asset with ownership qualities, and is regulated by the SEC.

ICOs are a new concept and some raise concerns with the actual value of the tokens and how easy it is for the issuer to get rich. Share via:.

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