Initial Coin Offerings: Risk or Opportunity?

initial coin offerings ( ICOs ) are one of the several by-products in the global of the cryptocurrencies. Start-ups and existing businesses are turning to option sources of capital as opposed to classical channels like banks or guess capitalists. They can offer the inner value of their business by selling “ tokens, ” i.e., units of the choose cryptocurrency, like a regular firm would do by means of an IPO. The investors, of run, hope for an increase in the value of the token in the short term, provided a solid and valid commercial enterprise mind typically described by the ICO issuers in a white paper. however, deceitful activities perpetrated by unscrupulous actors are patronize and it would be crucial to highlight in advance clear signs of illegal money raising. In this paper, we employ statistical approaches to detect what characteristics of ICOs are significantly related to deceitful behavior. We leverage a number of different variables like : entrepreneurial skills, Telegram chats, and relative sentiment for each ICO, type of business, issuing area, team characteristics. Through logistic arrested development, polynomial logistic regression, and text analysis, we are able to shed light on the riskiest ICOs .

1. Introduction

initial Coin Offerings ( ICOs ) can be considered as an innovative means of obtaining fund, promoted by entrepreneurial companies that base their commercial enterprise projects on a new technology known as blockchain. Up to the award date, more than 1,700 cryptocurrencies have been created but not all of them are successful or characterized by a significant impact. ICOs topic “ tokens, ” i, the unit of a choose cryptocurrency, in exchange of a bland cryptocurrency, in decree to participate in the crowd-funding of the party. Tokens can be bought directly on the web platform of the company, at different stages of the ICO normally referred as pre-sale and sale. Later, the total of bribe tokens can be sold or used in the future to obtain products or services. The portal vein has estimated that until 2017 ICOs raised arsenic much as $ 5.3 billion around the world ; if we consider speculation capitalist, in 2016, they invested $ 71.8 billion in the United States and $ 4.3 billion in Europe ( National Venture Capital Association and Invest Europe ). Start-ups and existing businesses are turning to option sources of capital as opposed to classical channels like banks or speculation capitalists. They can offer the inner value of their business by selling “ tokens, ” i.e., units of the choose cryptocurrency, like a regular firm would do by means of an initial public Offering ( IPO ). When we say cryptocurrency, we refer to a digital currentness, a new means of switch over, the most popular examples of which are Bitcoin and Ethereum. Blockchain ( chain of blocks ) is the technology at the footing of a cryptocurrency ; it is a distribute Ledger Technology defined as a circulate, shared, encrypted database that serves as an irreversible and incorruptible depository of information ( Wright and De Filippi, 2015 ). Bitcoin is presently the largest blockchain network followed by, Ethereum, XRP, Litecoin, EOS and Bitcoin Cash ( Coinmarketcap, 2018 ). ICOs favor open-source project development and decentralize business, generating a built-in customer floor and positive network effects. They besides create a secondary commercialize where tokens can be employed as rewards for using the app of the company or the offered services ( Subramanian, 2018 ). This work aims at addressing the particular characteristics of ICOs using relevant variables that play a key role in determining the success of the ICO .
As it stands there is no database with the information we are looking for, thus we have been building and constantly maintaining a dataset that is presently composed of 196 ICOs that occurred between October 2017 and November 2018 ( Cerchiello et al., 2019 ). The database comprises companies from european countries namely France, Germany, Switzerland, Estonia, Latvia, and not european countries such as Russia, United Kingdom, United States, Japan, Singapore, and Australia. The most common sectors in which ICOs operate are : high-tech services, fiscal services, ache contract, gambling platforms, marketplaces, and exchanges .

2. Initial Coin Offerings

Most of the ICOs projects are related to the development of a blockchain, the issue of newly cryptocurrencies or somehow related fintech services. ICOs tokens accord contributors the right to access platform services in 68.0 % of the cases, government powers in 24.9 % of the cases and profit rights in 26.1 % of the cases. The secondary market for ICOs tokens is quite liquid on the first day of deal, and the initial return is large ( mean value +919.9 % compared to the offer price, median value +24.7 % ). The achiever of such decentralized technology lays on the fact that it works without the commitment and the control of a cardinal authority : the blockchain is a Peer-to-Peer technology. A Peer-to-Peer ( P2P ) system represents a way of structuring distributed applications such that the individual nodes can act as both a node and a server. A key concept for P2P systems is to allow any two peers to communicate with each other in such a way that either ought to be able to initiate the contact ( Peer-to-Peer Research Group, 2013 ). then, the more a P2P network is distributed, scalable, autonomous, and dependable, the more is valuable.

All of these valued features have enabled the fast growth of cryptocurrencies not fair per se but besides as a tool for crow-funding purposes, giving parturition to the alleged Initial Coin Offerings. furthermore, what is further fueling the development of ICOs, according to BIS Annual Economic Report ( 2018 ) is the absence of regulation ( even if many countries are presently working on it ) and, at the consequence, there are barely a few examples of banning acts ( namely China, India, South Korea ). Investors buy ICO tokens in the promise of identical high returns, sometimes even before the commercial enterprise is put in place, since the match cryptocurrencies ( typically Ethereum ) can be immediately traded. In the first base 6 months of 2018, there have been 440 ICOs, with a top out in May ( 125 ) raising more than 10 billion US, where Telegram ICO ( Pre-sale 1 and 2 ) is by far the most paraphrase one with 1.7 billion US ( Coinschedule, 2018 ). In 2017, the total total raised by 210 ICOs was about 4 billion US and overwhelm guess capital funneled toward high technical school initiatives in the same period. The first token sale was held by Mastercoin in July 2013 but one of the most successful and even running is Ethereum which raised 3,700 BTC in its first 12 planck’s constant in 2014, adequate to approximately 2.3 million dollars at that clock time .
recently, there has been a growing literature studying the ICOs drivers aiming to predict their future consequence. A previous study offers an exploratory empirical classification of ICOs and the dynamics of volunteer disclosures. It examines to what extent the handiness and quality of the information disclosed can explain the characteristics of achiever and failure among ICOs and the equate projects ( Blaseg, 2018 ). Another important research focuses on the effectiveness of signaling ventures and ICOs projects technological capabilities to attract higher amounts of fund ( Fisch, 2019 ). Momtaz aims at identifying the likelihood and possible timeframe of value initiation for investors by combining respective factors ( fiscal tax return, amount of capital raised, list, and delisting alternatives, diligence events study etc. ) to analyse the ICOs success drivers ( Momtaz, 2018a ) .
other streams of research boil down on the impingement of managers quality on the ICOs. Momtaz studies the impact of CEOs loyalty disposition and the magnitude of asymmetry of information between managers and investors on ICOs performance ( Momtaz, 2018b ). furthermore, to remain in the management area, an interesting discharge comes from a research specifically directed on CEOs function and effects on ICOs results ( Momtaz, 2018c ). ultimately, another area of studies focuses on the drive factors impacting the fluidity and trade volume of crypto tokens listed after the ICOs. Among those factors have been identified the quality level of disclosed documentation ( beginning code public on Github, blank paper published, an intended budget published for use of proceeds ), the community engagement ( measured by the number of Telegram group members ), the degree of training of the management ( using as proxy the entrepreneurial master background of the lead founder or CEO ), and other outcomes of concern ( i.e., the sum raised in the ICO, outright failure—delisting or disappearance, abnormal returns, and volatility ) ( Howell et al., 2018 ) .
Despite the interest that has been peaked by ICOs and the constantly growing trends, it is worth mentioning that about half of ICOs sold in 2017 fail by February 2018 ( Hankin, 2018 ). In fact, what should drive more attention to ICOs is the reproducible presence of victimize activities only devoted to raising money in a deceitful way. According to Cointelegraph, the Ethereum net ( the prevailing blockchain platform for ICOs ) has experienced considerable phishing, Ponzi schemes, and other scam events, accountancy for about 10 % of ICOs ( Ethereumscamdb, 2018 ). On the other hired hand, it is interesting to assess what factors affect the probability of success of an ICO. Adhami et aluminum. ( 2017 ), based on the analysis of 253 ICOs, showed that the play along characteristics contribute : the handiness of the code source, the administration of a token presale and the possibility for contributors to access to a specific service ( or to plowshare profits ) .
The boom of the ICOs projects and their matter to characteristic brought an important ascend of interest from the general audience, many scientific studies have been conducted and published in the last years. Besides the aforesaid Adhami et aluminum. ( 2017 ), we should mention the working paper by Zetzsche et aluminum. ( 2017 ), that is focused on legal and fiscal risk aspects of ICOs, furthermore a taxonomy is provided and some extra data on ICOs that the authors claim to be continuously updated. recently, Subramanian in 2018 quoted the ICOs as an example of the decentralized blockchain-based electronic market. The main informant of information about blockchain, tokens, and ICOs is obviously the Web. here we can find sites enabling to explore the diverse blockchains associated to the main cryptocurrencies, including Ethereum ‘s matchless. We can besides find websites giving extensive fiscal information on prices of all the main cryptocurrencies and tokens, sites specialized in listing the existing ICOs and giving information about them. Often, these sites evaluate the soundness and likelihood of achiever of the listed ICOs. One of the most popular among these sites is, which evaluates all the listed ICOs and provides an API ( Application Programming Interface ) to automatically gather information on them. ICOs are normally characterized by the follow features : a business mind, most of the time explained in a white newspaper, a proposed team, a target sum to be collected, a given count of tokens to be given to subscribers according to a bias exchange rate with one or more existent cryptocurrencies. Nowadays, a high percentage of ICOs are managed through Smart Contracts running on Ethereum blockchain, and in particular through ERC-20 Token Standard Contract ( Fenu et al., 2018 ) .
On top of all the characteristics explained sol far, there is a far and not yet explored point of concern : the Telegram chats. Telegram is a cloud-based instant messaging and voice over IP service developed by Telegram Messenger founded by the russian entrepreneur Pavel Durov. In March 2018, Telegram stated that it has 200 million monthly active users— “ This is an harebrained numeral by any standards. If Telegram were a country, it would have been the sixth largest country in the world ( Telegram, 2018 ). ” Telegram is completely free and has no ads, users can send any kind of media or documents and can program messages to self-destruct after a certain menstruation of time. Some characteristics are imposing Telegram among the first social networks, indeed it intentionally does not collect data about where its clients live and what they use the platform for. This is one of the independent reason why, according to AppAnnie rankings, Telegram is peculiarly popular in countries like Uzbekistan, Ukraine, and Russia, where Internet access may be limited or closely monitored by the government. As of October 2017, Telegram was by far the most democratic official discussion platform for current and approaching ICOs, with 75 % + of these projects employing it. This means that retrieving Telegram discussions associated with each and every ICO would produce a huge total of textual data potentially useful for understanding the gamble of success and more interestingly possible signs of deceitful activities .

3. Methodology

In this paper we leverage two kinds of data : structured and amorphous ones. Regarding the erstwhile, we take advantage of classical music statistical classification models to distinguish the stauts of an ICO that made up of 3 classes, intended as follows :
Success : the ICO collects the predefined hard capital within the time horizon of the campaign ;
Failure : the ICO does not collect the predefined heavily cap within the time horizon of the campaign ;
Scam : the ICO is discovered to be a deceitful activity with malicious captive during the campaign and described as such by all the platforms we use for data gather ( namely ICObench and Telegram ). A robustness check for the scam labeling come by checking if regulative bodies announced legal actions against the issuers ( for example, official SEC announcements of legal violation ) .
logistic regression aims at classifying the dependent variable into two groups, characterized by a different status [ 1 = victimize vanadium 0 = achiever or 1 = achiever volt 0 = failure ] according to the watch model :
ln(pi1-pi)=α+∑jβjxij,    (1)

where p i is the probability of the event of interest, for ICO i, x i = ( x i 1, …, x ij, …, x iJ ) is a vector of ICOs-specific explanatory variables, the intercept argument α, a well as the arrested development coefficients β j, for j = 1, …, J, are to be estimated from the available data. It follows that the probability of achiever ( or scam ) can be obtained as :
pi=11+exp-(α+∑jβjxij),    (2)

Since the target variable star is naturally categorized according to three classes, success, bankruptcy, and scam we extend the aforesaid binary logistic regression to a polynomial matchless. such model assesses all the categories of interest at the lapp time as follows :
ln(pk1-pK)=αk+∑jβkxij,    (3)

where p k is the probability of kth classify for k = 1, …, K given the constraint that ∑Kpk=1 .
Considering the textual analysis of Telegram chats, we take advantage of quantitative analysis of human languages to discover coarse features of written text. In finical the analysis of relatively short textbook messages like those appearing on micro-blogging chopine presents a number of challenges. Some of these are, the informal conversation ( for example, slang words, repeated letters, emoticons ) and the floor of imply cognition necessary to understand the topics of discussion. furthermore, it is authoritative to consider the high level of make noise contained in the chats, witnessed by the fact that entirely a fraction of them with respect to the entire number available is employed in our opinion analysis .
We have applied a Bag of Word ( BoW ) approach, according to which a textbook is represented as an disordered collection of words, considering only their counts in each comment of the old world chat. The word and document vectorization has been carried out by collecting all the parole frequencies in a Term Document Matrix ( TDM ). Afterwards, such matrix has been weighted by employing the popular TF-IDF ( Term Frequency Inverse Document Frequency ) algorithm. classical textbook cleaning procedures have been put in position like stop-words, punctuation, unnecessary symbols and space removal, specific topic words summation. For descriptive purposes we have used word-clouds for each and every Telegram chat according to the general content and to specific subcategories like sentiments and expressed moods. The most critical part of the psychoanalysis relies on the opinion classification. In general, two different approaches can be used :
• Score dictionary based : the sentiment score is based on the number of matches between predefined list of positive and negative words and terms contained in each text source ( a tweet, a sentence, a whole paragraph ) ;
• Score classifier based : a proper statistical classifier is trained on a large enough dataset of pre-labeled examples and then used to predict the sentiment course of a newfangled model .
however, the second option is rarely feasible because in order to fit a good classifier, a huge sum of pre-classified examples is needed and this represents a particularly complicated task when dealing with short and extremely not conventional text like micro-blogging chats ( Cerchiello and Nicola, 2018 ). Insofar, we decided to focus on a dictionary based access, adapting allow lists of incontrovertible and negative words relevant to ICOs topics in english lyric. We employ three vocabularies from the R package “ tidytext ” :
• AFINN from Finn Årup Nielsen ;
• BING from Bing Liu and collaborators ;
• NRC from Saif Mohammad and Peter Turney .
These lexicons are based on unigrams, i.e., single words, they contain many English words and the words are labeled with scores for positive/negative sentiment and besides possibly emotions like joy, wrath, gloominess, and therefore forth. The NRC vocabulary categorizes words in a binary fashion ( “ yes ” / “ no ” ) into categories of positive, negative, anger, prediction, disgust, fear, joy, sadness, surprise, and trust. The BING vocabulary categorizes words into a binary star manner into positivist and negative categories. The AFINN dictionary assigns words with a mark that runs between −5 and 5, with negative scores indicating veto sentiment and positive scores indicating convinced sentiment. By applying the above described lexicons, we produce for each and every ICO a sentiment score ampere well as counts for cocksure and negative words. All these indexes are used as extra predictors within the logistic models .

4. Data

In this wallpaper, we examine 196 ICOs starting from January 2017 public treasury November 2018. For each project, we gather information from web-based sources, chiefly military rank platforms such as,, ICO, and undertaking ‘s websites. The process of building up the ICOs datum adjust reflects the main phases that an ICO follows to be launched : from the birth of the business estimate, the team build, the purpose of the tokens, the technical requirements ( white paper ), the promotion and the execution phase .

4.1. Collection of Structured Data

The first step in collecting data about each project is to gather information from the most use ICO associate platforms as Icobench, TokenData, Coinschedule, or similar. During such phase, we look for general characteristics such as the name, the nominal symbol, beginning, and end dates of the crowdfunding, the nation of origin, fiscal data such as the total number of publish token, the initial price of the keepsake, the platform used, data on the team proposing the ICO, datum on the advisory board, data on the handiness of the web site, handiness of blank paper and social channels .
Some of these data, such as short and long description, and milestones are textual descriptions. Others are categoric variables, such as the nation, the platform, and variables related to the team members ( identify, function, group ). The remaining variables are numeric, with different degrees of discretization. unfortunately, not all ICOs record all variables, so there are several missing data. The ICO network databases that we use are amply checked in club to minimize the missing values of one of the platforms, therefore we validate the information check for the details on the web site and on the white wallpaper. As a solution, the complete set of authentic information comes from the match between the web site and the white composition .
The variables set, continuous and categorical data, show us that the main area of origin of the projects is Europe with the highest share in Switzerland and Germany. The Switzerland peak is due to the national regulator approach—FINMA ( the swiss Financial Market Supervisory Authority ) —which on 16 February 2018 issued clear steering on the condition of ICOs. FINMA does not categorize payment or utility tokens ( provided they are not used for investment ) as securities. All other tokens are categorized as securities and are subject to securities regulation. To legally issue an equity/asset token, authority from FINMA should be sought, and appropriate conformity measures [ know your customers ( KYC ) and anti-money launder ( AML ) ] must be taken. If a debt token can be classified as a deposit, then unless specific exceptions apply, a bank license is needed prior to the ICO. In the disconnected regulative framework, this is one of the alleged “ crypto-friendly ” countries, that attract cosmopolitan investors .
The presence of a team of experts as a figure of “ advisors ” that follows the stages of growth are helpful in qualifying the ICO as more authentic. On the growth of the dataset the inquiry focused besides on assessing the number of advisors for each ICO, checking their educational background and score as a variable of interest the presence of a Ph.D. that attests a high academic degree of education .
The evolution of the classic Business design that we observe when we analyse the estimate of a start-up, is called White Paper. The business plan is the document that illustrates the strategic intentions and the management of competitive strategies of the company, the development of key value drivers and the economic and fiscal results. The drawing up of the operational plan has the aim of achieving different subjects involved in the commercial enterprise. The capacity of the business plan should not be overlooked, it must be the most possible conventional and of intuitive interpretation. The feature that distinguishes a good plan is the clarity, the synthesis and the professional description of the stick out work flow. The WP ( blank newspaper ) consequently fulfills these functions and, in our analysis, played a full of life function in the statistical analysis in terms of presence or absence of it. The graphics quality with which it is produced is besides crucial, the data contained within it and the description of the team ‘s components .

4.2. Collection of Unstructured Data

Social channels are more personal than every database, military rank platform or websites, so they are a means to reach a wide-eyed range of users, to update them constantly about the development of the project and in the end to create a trusty environment that can finalize in a successful crowdfunding activity. In regulate to conduct the textual analysis, we enrich our database with the social channels data, such as the presence of a channel, the numbers of users as a proxy of the community betrothal and as mentioned in the initiation the textual chat, retrieved in reverse until the universe of the new world chat. The most secondhand social channels are Telegram, Twitter, Facebook, Bitcointlak, Medium, while Linkedin, Reddit, and Slack are not frequently used .
In crowdfunding projects the entrepreneur and the community in which is embedded works as a potent operate for the attraction of a clientele. Some studies have investigated the social network community and the entrepreneurial bodily process finding out that the amount of capital collected in crowdfunding is heavily dependent on the range of social networks the entrepreneurs belong to ( Mollick, 2014 ) .
With regards to the entrepreneurial dimension, we investigate the team components, pointing out that the members checked until now are about 1,000, with a medial size of 7 for plan. For each team extremity we checked general information related to the sociable date, looking for the Linkedin channel activeness ( 48 % of them do not have an person foliate ), the numbers of connections, the job position in the project and the academic background. furthermore, the presence of advisors can play a all-important function in ensuring the dependability of an ICO, provided a wise choice of such advisors. The like applies to institutional investors doing due diligence on a likely speculation. In collecting our data, we focused on the academic background and the current area of expertness of the declared advisors .
As it concerns the unstructured data, insightful information can be derived by the white papers in terms of choice of the technical report and specific subject. A white paper is a summary report that provides detailed information about the project, its originality and the benefits it can give to investors and users, about the technical features, team behind the project, project ‘s background and future plans. Besides all the above information, we collect Telegram chats associated to each ICO ( if available ) and apply all the text analytic techniques to produce a sentiment based score .
In Table 1 we report the arrant list of collected and employed variables .

table 1

Table 1. explanatory variables .

5. Empirical Evidence

In this section we report our independent results obtained from classification analysis and textual analysis. In this involve, in Tables 2, 4 we report results respectively for logistic regression on Success/Failure ( class 2 variable ) and for multilogit arrested development estimated on failure ( fluorine ) and victimize ( south carolina ) compared to success as service line. Regarding the beginning model, in board 2 we report the final shape after several stepwise survival steps. The reader can see that the only two relevant dummy variables are : the presence of a white paper ( Paper_du ) and of a Twitter account ( tw ). Both present positive coefficients showing their impact on increasing the probability of success of an ICO. It should be stressed that the influence of Twitter duct is much higher than the presence of a white paper, indeed if we calculate the consort odds proportion we would get, respectively 11.94 and 3.85. In early words, if the ICO has a Twitter history the probability of success is about 12 times higher ( about 4 times higher for the white newspaper ). Regarding the three continuous variables, number of elements of the team ( Nr_team ), phone number of advisors ( Nr_adv ), and scaled sentiment score based on NRC vocabulary ( Sent_NRC_sc ), they are all highly significant and again positive propose that increasing people and advisors in the team has a positivist impingement. Regarding the sentiment, we notice a particularly gamey plus value, stressing the importance of the sensing of possible investors which interact with the ICO proposer by means of a social media, namely Telegram .

table 2

Table 2. logistic regression results on success/failure ICOs .

To far 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 mannequin configurations are reported in table 3 ( logistic ) and 5 ( polynomial ), with useful insights in defining the lack of multicollinearity. consequently, in board 3 we can see low values for the VIF index associated to the estimated logistic model ( given in Table 2 ). The reader can well notice that there is not any multicollinearity impression, making robust the model. furthermore, reported operation indexes, namely AIC and pseudo R 2, present good values above 50 % .

table 3

Table 3. VIF index for logistic regression model.

In Table 4, we report results for deceitful and victimize ICOs compared to successful ones, on the basis of a multilogit regression. Looking at the calculate parameters, we can infer that the patterns are different. The bearing of a web site has a positivist affect on the probability of being a successful ICO and not a victimize. In other words, the absence of this characteristic is a driver of victimize activeness suspects. rather the web site does not differentiate successful from failures ones. With regards to the bearing of advisors and of a white paper, both the variables are significant in differentiating deceitful from successful ICO, confirming results of logistic regression. No statistical significance for deceitful ICOs. last, variable star on the sentiment seduce 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 deceitful ICOs .

table 4

Table 4. Results from multilogit regression : failure and victimize compared to success .

In this see, we should stress that the incidence of scam ICOs in our database is extremely first gear, this due to the fact that collecting information about such ICOs is particularly complex. Most of the data is completely deleted from the Web adenine soon as the bodily process is recognized as illegal and/or deceitful. The overall model performance, assessed again in terms of AIC and pseudo R 2, is reasonably good although subscript to the former one .
In Table 5, we besides report VIF index, so to check the absence of multicollinearity in the report model. Please note that, multilogit model reported in postpone 4 is a final shape obtained through bit-by-bit excerpt. The full models are available in the Appendix ( Supplementary Material ) .

mesa 5

Table 5. VIF exponent for multilogit regression model .

6. 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 alternate sources of capital as opposed to classical channels like banks or venture capitalists. They can offer the inside value of their business by selling “ tokens, ” i.e., units of the choose cryptocurrency, like a regular firm would do by means of an. The investors, of course, hope for an addition in the respect of the token in the inadequate term, provided a solid and valid clientele mind typically described by the ICO issuers in a white wallpaper. however, deceitful activities perpetrated by unscrupulous actors are patronize and it would be crucial to highlight in advance absolved signs of illegal money raising .
In this position, ICOs psychoanalysis can be considered a very detail type of fraud detection activity. however, in our public opinion fraud detection presents some specificity that prevent us from entailing ICOs relate problems as a proper exemplify of imposter detection. In particular, our data are not flowing in such huge measure from an on-line system as typically happens with credit calling card payments or banks transactions. distinctive fraud detection approaches, as in Maheshwara Reddy et aluminum. ( 2019 ), calculate at learn, about in real times, deceitful fiscal activities based on transactional data that ideally should be blocked ampere soon as potential. ICOs rather 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 web site. That being the sheath, we would suggest to label this specific stream of research as FinTech Fraud detection with all the relative specificity .
While analyzing success v failure dynamic with a classification model is relatively comfortable since the incidence of the two classes is about equal ( 50–50 ), it is much more complicate to highlight the key aspects that could witness a deceitful action since, in the last 3 years, only few scam events have been reported. In our sample made of 196 ICOs ( data solicitation silent 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 web site has a positive impact on the probability of not being a scam but does not have any impact on fail ones. In terms of opinion expressed on Telegram chats, the affect appears to be negative both on the scam and failed ICOs. This suggests that monitoring in real time Telegram chats could represent a valid bastardly for collecting signs of possible problems within the ICOs. If alternatively, we compare successful ICOs against Failed ones, we find that the presence of a White Paper and of a Twitter account appearance plus coefficients .
Regarding the three continuous variables, number of elements of the team, total of advisors, and opinion sexual conquest based on NRC dictionary, they are all highly significant and positive suggest that increasing people in the team and advisors has a cocksure affect. Regarding the sentiment, we notice a particularly high positive prize, stressing the importance of the percept of potential investors which interact with the ICO suggester by means of a social media .
The newspaper will be improved in the future by increasing the size of the sample distribution and exploring alternative approaches for textual analysis with specific care to opinion analysis. We aim at producing a more refine and tailor opinion mark for each ICO, improving and increasing the vocabulary of words. specifically regarding the textual analysis an alternate approach path that we could use is the combination of words as in Bolasco and Pavone ( 2017 ) .
As a final comment, authors are mindful of the limits of the paper chiefly due to the size of the sample. however, given the still limited literature in this airfield with no reference to the power of textual information collectible through Telegram chats, this contribution represents a tone ahead in the process of understanding the ICOs phenomenon. Furthermore a different access would be to study the trends of the ICOs by combining the available data from specialized websites on deceitful activities ( such as and ) and evaluation websites for the active projects .

Data Availability Statement

The datasets generated for this report are available on request to the corresponding generator .

Author Contributions

The newspaper is the product of full collaboration among the authors, however personal computer 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 .


This research has received fund from the European Union ‘s Horizon 2020 inquiry and invention program FIN-TECH : A Financial supervision and Technology submission training program under the concession agreement No. 825215 ( subject : ICT-35-2018, Type of action : CSA ). The current version of the bring is well based on refining a work newspaper ( not reviewed ) published by the lapp authors in DEM Working Paper Series ICOs success drivers : a textual and statistical psychoanalysis .

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or fiscal relationships that could be construed as a likely conflict of interest .


We acknowledge Federico Campasso that technically helped us in collecting the data and Prof. Silvio Vismara for the useful advises on the work .

Supplementary Material

The Supplementary Material for this article can be found on-line at : hypertext transfer protocol : // # supplementary-material



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