FACTORS INFLUENCING THE SUCCESS OF AN

-THE IMPACT OF LOCAT ION ON THE PROBABILITY OF REACHING AN ICO`S FUNDING GOAL-

Diaconu, Stefan (11842008) Business Administration Finance Specialization Supervisor: Feher, Adam Date: 30th June 2020

Abstract

The thesis analyzes whether the geographical location of a project influences the probability that an ICO is going to achieve its funding target. Data on 100 ICO campaigns was selected from 20 different locations between 2017 and 2018. The hypothesis tested whether ICOs based in the US are more likely to reach their funding target compared to ICOs in different countries. A binary logistic regression model was implemented to test the hypothesis. The results confirmed that US-based companies increase the likelihood of reaching the funding goal by 50%, while other location did not have a significant effect. Moreover, the model predicts a higher probability of success with higher experts rating and increased funding targets. Having a Twitter account, an increased team size or shorter campaign duration did not affect the likelihood of reaching the target. The study concludes with emphasizing the importance of developing a general regulatory framework and risk-assessment measures for ICOs.

Statement of Originality

This document is written by student Diaconu Stefan who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

Content:

1. Introduction

2. Literature Review

3. Methodology

4. Results & Discussion

5. Conclusion

6. Appendix & References

1.Introduction

An Initial Coin Offering is the process through which a digital asset becomes listed on a exchange and subsequently can be publicly traded. Similar to IPOs, ICOs are primarily used as crowdfunding instruments for raising capital. As have many distinctive features compared to other asset classes, ICOs seem more appropriate for raising capital than the traditional forms of financing. The first Initial Coin Offering campaign was conducted in 2013 by Omnicoin, and achieved 5,000 Bitcoing in funding-the equivalent of $500,000 at the time (Hofer, 2018). Ever since, the phenomenon of ICOs been encompassing tremendous growth. Between January 2016 and August 2019, $31 billion have been raised by means of an ICO campaign. Over $15 billion have been obtained solely in the first two quarters of 2018, while the first quarter of 2019 achieved a remarkable $8.4 million in averaged raised funds (Yu, 2019). Moreover, 20 coins managed to obtain more than $100 million in funds (Howell et al., 2018). The most successful ICO is represented by EOS, who was able to raise over $4.23 billion within six months. EOS is a software that provides the infrastructure necessary for building decentralized applications on its network, similar to Ethereum. Likewise, Open Network coin (the token listing of the social platform Telegram) was able to reach the $1.7 billion mark in less than three months (Tokendata.io, 2020).

Despite many successful ICOs, several tokens have failed in completing their process and never became listed on an exchange. The most resounding example is represented by the Decentralized Autonomous Organization (DAO), who was aiming to create a decentralized business model for both commercial and non- profit organizations. As the project steered a lot of excitement in the community, DAO managed to acquire $150 million only in the first month of the ICO. However, a cyber-attack exploited a vulnerability in the company's smart-contracts, which led to a loss of more than $50 million. The investors started liquidating all of their holdings once they became aware of the attack, sending the price of DAO coin into a free fall (U.today, 2020). Other failures are attributed to either poor development of token`s price (Gems coin) or simply for being a scam (Droplex and SwissCoin).

Following a significant increase in popularity and funding capital, ICOs could become powerful tools for financing and developing new projects. Yet, the former can also be used as instruments for facilitating fraud and other illegal activities. Absent any form of laws who protect investors from losing their capital, the success of an ICO could be significantly diminished. (Lipusch, 2018). Also, vulnerabilities in the software of a digital platform could lead to cyber-attacks, and consequently cause millions of dollars in losses for investors. Therefore, an unsuccessful ICO campaign entails serious consequences for every party involved. Given the novelty of the phenomenon, it is important to discover the factors that maximize the success of a token-sale campaign. As such, the goal of this thesis is to study the underlying factors who contribute to the success of an Initial Coin Offering. More specifically, the relationship between a company`s location and the ICO`s probability of success will be analyzed. The thesis defines success as reaching 100% of the targeted amount of funds during the campaign. Consequently, the research question investigates whether the geographical location of a project influences the probability that an ICO will achieve its capital target. One hundred tokens were selected from 20 different locations, encompassing the main continents in which ICO campaigns took place -the United States, Europe, and Asia.

The focus of this thesis is empirical by nature. The paper attempts to provide empirical support for the impact of location on the outcome of an ICO. In other words, the thesis analyzes whether certain countries are more successful in raising funds via ICOs than other countries. Governments around the world have taken various approaches regarding Initial Coin Offerings. On the one hand, there are countries who implemented laws that accommodate ICOs, such as USA and Switzerland. On the other hand, there are countries who strictly forbid ICOs to take place on their territories (China and Korea). The remaining countries position themselves in the middle- governments simply warn investors about the riskiness of ICOs, without implementing any measures to prevent it. The type of a government`s approach regarding ICOs is expected to be reflected in the results.

Furthermore, the thesis contributes to current academic literature by studying the relationship between an ICO`s funding target and the probability of achieving that target. No study to date made the distinction between the company`s location and the project`s propensity to reach its goal. The results attempt to provide an empirical basis for researchers to continue studying the impact of the location on the outcome of an ICO, as well as convince policy-makers on the importance of developing a legal framework. The following sections will provide more details regarding this approach.

Section 2 is separated into three subsections. The first subsection introduces the concept of technology and its applications. The second subsection explains the inner-workings of an ICO campaign and the advantages thereof. The last subsection provides an overview of the different legal frameworks around the world. The section is concluded with a review of the results obtained by several academic papers. Section 3 describes the sampling method, the variables included in the analysis, as well as the regression model. Section 4 analyzes the results of the logistic regression. The section also reflects on the limitations of the study, while also answering the research question. Section 5 concludes the paper by offering a summary of the results and their implications.

2. Literature Review

2.1 Blockchain Technology

Prior to studying the factors influencing an ICO`s success, it is important to understand the underlying technology behind the digital assets. Token who aim to become listed on exchanges are products of blockchain technology. According to Boreiko, Ferrarini, and Giudici (2019), blockchain is a database that combines the data records into a block. Subsequently, a ledger is formed by sequentially chaining all of the blocks via a cryptographic signature. Afterward, the records of the ledgers are distributed on the network and synchronized in the computers of the users. Furthermore, Wright and De Fillippi (2015) offer an extensive analysis of the distributed ledger technology and the benefits of decentralization. The authors explain that blockchain facilitates the creation of self-executing contracts (also known as smart contracts), intelligent assets that can be owned all over the internet (smart property) as well as decentralized currencies.

The property of decentralization and synchronization represent the core functions of blockchain technology, which highlights the latter`s potential benefit- bypassing the middlemen and simply relying on two parties for creating a contract. Once recorded on the blockchain, the contracts cannot be modified, nor removed by any party. This allows for greater transparency and control compared to the current status quo. Furthermore, contract negotiation is performed via the means of a probabilistic consensus mechanism, which enables the transition from one contract to the other on the network (Boreiko, Ferrarini & Giudici, 2019). The consensus mechanism is based upon certain protocols and complex algorithms situated at the core of each block in the blockchain system (Cong and He, 2019).

The first direct application for blockchain is represented by Bitcoin. The platform uses the consensus mechanism to process and validate transactions. Upon solving complex computational problems, bitcoin miners are rewarded a certain number of tokens- as if the coins were extracted out of a mine (Cong and He, 2019). This system relies on the assumption that every participant has the option to enter the mining process and be rewarded. Consequently, there can be no cartels who take away the entire computational power and fully capture the rewards, unlike the case of oil production.

Similarly, the Ethereum platform was developed to improve Bitcoin`s ecosystem (Fenu et al., 2018). As the authors explain, the former has had the first successful crowdfunding campaign in the world of cryptocurrencies in terms of organizing the ICO. A Swiss foundation was established to collect the funds, as well as for tax purposes. Shortly after, the platform launched its coin, “Ether”, who later became listed on various exchanges. Moreover, Ethereum created the web-space that allowed decentralized applications (dapps) to be developed on top of its infrastructure. To date, the Ethereum platform is the most popular environment for developing these types of applications. As such, the Initial Coin Offerings analyzed in this paper would have primarily been developed on the Ethereum platform.

Moreover, with the emergence of the Ethereum platform, the development of smart contracts also increased. The latter refers to a set of rules governing the transaction of the digital assets (Howell, Niessner & Yermack, 2018). The contracts are automatically executed when certain conditions are met. Likewise, Ethereum miners are rewarded upon solving pending executions or pending transactions on the Ethereum network. This way, network bottlenecks are avoided and its efficiency is increased (Fisch, 2019). Furthermore, Ethereum is the first platform to have created a standardized smart contract template, called ERC20. The protocol facilitates a standard approach towards token`s issuance, distribution, and control (Howell et al, 2018). This in turn smoothed the path of creating an ICO, as the code could simply be downloaded from the website of Ethereum and adapted for each project. In 2019, more than 80% of the tokens were created based on the ERC20 protocol (Momatz, 2019).

Furthermore, cryptocurrencies are not only popular for their innovative way of securing payments, but also as means for financing ICO campaigns (Hacker & Thomale, 2018). It is no coincidence that in 2017, the prices of Ethereum and Bitcoin reached an all-time high, while ICOs also secured more than $3 million in funding capital. Many projects solely accept cryptocurrencies as a form of finance to the detriment of fiat currencies, typically requesting payment in Ethereum. One disadvantage of this strategy is the increased fee charged by the exchange for the opportunity to purchase and trade tokens. Either way, there is a preference for crypto-funding compared to fiat funding. Despite the highly fluctuating prices of the cryptocurrencies, receiving funding in forms of cryptocurrencies has proved to be an effective strategy. Kaal & Dell’Erva (2017) discovered that volatility in the market for cryptocurrencies does not affect the volume of funds received by an ICO. Likewise, ICOs financing continued to grow regardless of a sharp decline in the price of Bitcoin after December 2017 (Coinmarketcap, 2017).

Lastly, for more risk-averse entrepreneurs, there is also an alternative: Tether coin is the equivalent of the US dollar on the blockchain. With minimum fluctuations, Tether is considered a stable coin, as it is pegged 1:1 to the USD currency. Moreover, during a period of turmoil across the global markets, Tether managed to reach a market capitalization of $7 billion in April 2020 (Cong et al.,2018). Having said that, we will turn our attention to the ICO process, as well as the reasons why an entrepreneur would choose this funding measure.

2.2 The process of an ICO

Prior to the official token sale, the entrepreneur has the option to submit a whitepaper (Fisch, 2019). The latter represents a document (similar to a business plan), containing a brief description of the token, a roadmap of the objectives, as well as details about the team. It also specifies the token`s pricing strategy and the type of (crypto)currencies accepted during the ICO (Lipusch, 2018). After the whitepaper has been completed, a communication channel is established. This typically comes in the form of an official website and several social media platforms. A Twitter account and a Telegram group are typically the channels employed for communication (Howell et al.,2019), but frequently extended to other social platforms as well.

Furthermore, according to Masiak et al. (2019), an entrepreneur discloses an advisory team responsible for conducting the campaign prior to the campaign itself. Likewise, a team of experts is hired for conducting market research and legal counseling. Thereafter, the project is pitched to potential investors in order to determine the willingness of the latter to invest in the project. This is what the authors identify as the Pre-ICO phase. Subsequently, during the ICO, a batch of tokens is sold, typically at a discount price compared to the market price. An important feature during this phase is that the venture itself sets the duration of the ICO, and could also extend this period (Howell et al., 2018). Upon completing the campaign, the token becomes listed on a cryptocurrency exchange. This thesis will however disregard the Post-ICO phase and mostly focus on attributing the success of an ICO during the Pre-ICO and Main-ICO phases.

Following up, the reasons why an entrepreneur would use an ICO as a means of funding as opposed to traditional financing instruments will be considered. Firstly, ICOs offer the possibility to bypass certain financial intermediaries and directly reach the consumer (Yu, 2019). As a result, platforms can overcome coordination problems through the means of an ICO. On top of that, Cong et al (2018) explain that more users join the platform as the price of the token appreciates. Likewise, when ICOs offer a stake for early investors (sometimes at a significant discount), the former is able to increase the platform’s networking effect, thereby overcoming any coordination problem. This particular feature is important as the project could easily be replicated in its early stages. Secondly, in order to prevent investors from “hoarding” its coins, the platform releases a fraction of its total supply during the ICO (Masiak et al, 2019).

Moreover, token platforms are able to determine the elasticity of demand via the use of smart contracts, as well as optimize the token`s initial price prior to the funding campaign. Also, smart contracts allow the developing team to receive users` feedback regarding the latter`s experience with the platform (Catalini & Gans, 2018). Provided with these tools, entrepreneurs can optimize their projects before or during an ICO campaign. In addition, transactions are known to perform faster and incur lower fees, thereby minimizing transaction costs. Moreover, customer`s commitment is secured from the early stages by encouraging users to actively participate in the development of the platform. (Howell, Niessner & Yermack, 2019). Also, minimal fees and rapid transfer facilitate the access to international financing compared to current international funding methods (Momatz, 2020). Larger access to funding implies that a project has higher chances to achieve its targeted funds, and therefore have a successful ICO campaign.

Apart from their features, tokens become more present in every industry. Howell, Niessner & Yermack (2019) developed a model that categorizes tokens into 12 different industry sectors, with the largest sectors revolving around non-crypto marketplaces and asset management. For the first category, Paragon is a relevant example- a project aiming at worldwide legalization of cannabis, which managed to raise over $70 million. Subsequently, Bloom is a project representing the second category, focusing on the assessment of risk and credit rating. The platform ultimately acquired $41.4 million in funds (Tokendata.io, 2020). By comparison, the highest amount raised during a crowdfunding campaign totaled $20.3 million, raised by Pebble Smartwatch (Lipusch, 2018). Additionally, a report from Goldman Sachs shows that, in June, Venture Capital funding achieved $300 million in funding, while ICOs achieved $550 million (Khrapal, 2017). Moreover, according to Dell’Erba (2017), Initial Coin Offerings contain most features from VC, crowdfunding, and other traditional manners for raising funds. Therefore, ICOs are the main tool used for financing blockchain startups since the benefits of the former overcome any benefit from classical financing measures. In spite of that, many governments have taken a negative stance against cryptocurrencies and ICOs.

2.3 Regulatory Framework

In the absence of a well-defined regulatory framework, each country has been implementing its own laws for conducting an ICO. The Security and Exchange Commission in the United States issued a report in July 2017 stating that certain ICOs could fall under current securities regulation. Consequently, the entrepreneurs have to obtain a license in order to operate legally, as well as disclose specific information. Following the USA, several other countries required ICOs to comply with securities regulation or apply for an exemption. Such countries include: Canada, United Kingdom, Hong Kong and Abu Dhabi (Hartley, 2018). Moreover, the US took a step further in terms of regulations by allowing Bitcoin Futures contracts to be traded on the NASDAQ in 2019 (Garcia, 2018). Similarly, Jackson (2018) argues that bitcoin futures could make the market more professional by increasing transparency and facilitating risk transfer methods. However, Corbet et al. (2018) highlighted that Bitcoin futures are not effective instruments for hedging risk, as spot prices are driven mainly by uninformed investors.

Furthermore, the Financial Conduct Authority in the UK announced that ICOs may be subject to regulation as well, depending on the latter`s structure. However, to date, no specific guidelines for conducting an ICO have been stated. (Chiu, 2018). On the other hand, China classified Initial Coin Offerings as illegal activity in September 2017, therefore banning any attempt to raise funds by means of an ICO (Momatz, 2018). Despite that, several articles reported that ICO activity in China has intensified following the ban, with many companies simply shifting headquarters (Smith, 2018). This proves that an ICO ban is not a guaranteed for the cessation of a token, but might affect the latter`s success.

On the other hand, Japan`s Financial Services Agency determined that current legislation regarding payment and exchange services also applies to ICOs (Hartley, 2018). Likewise, The Swiss Financial Market Supervisory Authority published an extensive guideline explaining how Swiss financial market law applies in the context of ICOs. The document includes assistance for structuring the process of a token sales occurring on Swiss territory (Favre, 2018). While the general trend showcases that governments attempt to provide some form of regulation, there are no laws specifically designed for ICOs. In general, governments` regulation who accommodate ICOs comes in two forms: either through adapting the current laws in order to fit ICOs or providing general guidelines for setting up a token sale. As the ICO industry continues to grow, it is not yet known whether new laws have to emerge, or whether the current laws in place are sufficient.

Before concluding this section, it is relevant to review the results of different academic papers in relation to the topic of interest. Current literature analyzes the contribution of three main factors to the success of an ICO: The characteristics of the token, the amount of funds raised during the campaign and Post-ICO returns. As the thesis will not analyze the Post-ICO phase and focus solely on the funds raised during the ICO campaign, the last category will not be mentioned.

For the first category, Momatz (2018) discovered that the quality of management team (measured based on the rating from ICObench.com) positively influences the token`s return in the first day of the campaign. Similarly, Amsden & Schweizer (2018) explain that the size of the team positively influences the amount of funding a token can receive. The thesis will use both quality and size of the management team as control variables. Also, Fisch (2018) discovered that token based on the Ethereum platform benefit from higher initial valuations compared to projects originating on different platforms. This research will focus solely on tokens based on the Ethereum platform. Finally, Xiao et al. (2014) explained how actively communicating with platform users and visitors could lead to a successful funding campaign.

Regarding the second category, Chanson, Risius and Wortmann (2018) analyzed the relationship between market sentiment and the amount of capital raised during the campaign. The researchers discovered that, tokens who have an increased number of Twitter followers positively affect the total amount raised during the ICO. Furthermore, Howell et al. (2018) found a positive relationship between abnormal returns and Twitter followers, while Fisch (2019) explains that the presence of a Twitter page influences the profitability of an ICO. This study will also consider whether the existence of an actively-used Twitter account influences the success of an ICO. Lastly, Hunag, Meoli and Vismara (2019) studied the impact of the ICO`s location on several macroeconomic factors. The study concluded that ICOs occur more frequently in countries with advanced digital technologies, developed financial markets and large public equity firms. The thesis aims to complement the results of the above-mentioned researchers by investigating the impact of location on the probability of reaching the ICO`s funding target.

By now the thesis has hopefully managed to explain the process and ICO has to undergo, as well as the benefits of conducting an ICO campaign. For this reason, the above-mentioned advantages should prove the importance of understanding the factors which determine the success of an ICO. We will now turn our attention to the methods used for answering the research questions.

3. Methodology

3.1 Data collection and sampling method

This section aims at developing a model for understanding the country-specific factors that contribute to a successful ICO campaign. As such, the research question examines whether the location of an ICO influences its probability success. In other words, the thesis attempts to understand the reasons certain countries are able to raise more capital during an ICO compared to other nations. Subsequently, the study will analyze whether the tokens based in the USA have a higher probability of reaching the funding target compared to other countries.

In line with current existing studies (Howell et al., 2018, Amsden & Schweizer, 2018), data on 100 ICOs was selected from ICObench.com. The website is believed to be one of the most reliable sources of information regarding Initial Coin Offerings (Huang et al., 2018, Momatz, 2018). Despite its popularity, ICObench.com has several limitations. According to Lyandres, Palazzo and Rabetti (2018), the website covers only about half of the total tokens that underwent an ICO campaign. Moreover, certain datasets are outdated and yield several errors regarding a project`s financial information. For example, Pentagon reported to have raised $ 335,000 on Icobench, $370,000 on Icorating and $385,000 on Cryptocompare. Also, certain projects were duplicated with similar sources. In order to overcome these problems, the authors proposed to use Alexa Traffic Rank12 for identifying popular websites. Upon doing so, the data was crosschecked with another 3 popular websites (icoholder.com, icodrops.com and icomarks.com). This way, the limitations of the initial website were mitigated. The data was selected only upon reporting consistent information on at least two different websites.

2 Alexa Traffic Rank is a tool for measauring and comparing popular websites, see: https: //www.alexa.com/siteinfo.

Moreover, as several campaigns achieved their targeted fund during several years, the thesis only considered campaign that lasted a maximum of one year in order to make the data less error prone. Hence, the campaigns selected had to start and subsequently end either in 2017 or 2018. In line with the study from Huang et al. (2019), this specific timeframe was selected due to having the highest amount of funds raised ($6.2 billion in 2017 and $7.8 billion in 2018 according to icodata.io), as well as significant number of projects and accurate data. While 2019 also achieved remarkable amounts in funding, it was not included for two reasons. Firstly, there are many ongoing projects that cannot be included in the sample, as the focus is only on past- ICOs that have already completed their campaign. Secondly, many contradictory figures can be found on multiple websites, making the available data error-prone. Similarly, most of the 2016 projects lasted more than one year, and were able to achieve their targets mostly in 2017 rather than 2016. In line with the study from Huang, Meoli and Vismara (2019), only projects from January 2017-December 2017 and January 2018- December 2018 were included in the sample.

The ICO projects were divided based on the country of origin and randomly selected based on the clustered random sampling technique. The sample includes 100 ICO campaigns originating from 20 different countries. Table 1 provides an overview of the location where an ICO was set, as well as the number of ICO campaigns included in the sample. Moreover, as ICOs can be set in multiple countries the study only considered the country of origin where the token was developed. Any form of international expansion was disregarded. As such, tokens who originated in the US would fall into the US category, even if the same project can be found in several other countries.

3.2 Hypotheses development and Regression Model

In order to determine whether the location influences the success of an ICO, the following hypothesis will be tested: “US-based ICO campaigns have a higher probability to achieve their target compared to other locations”. Initially, the targeted funds in the USA was going to be compared with the cumulative amounts raised in the other countries. However, after correcting for outliers, the US-based tokens had a total of $7 million in targeted funds while the other tokens targeted over $20 millions in funding. The funding gap would have led to the rejection of the hypothesis and made the comparison unfruitful. Consequently, three categories have been created: The target of US tokens was separated from the target of Swiss tokens into two different categories. Subsequently, the third category was named “G18”. It comprises the amount of funds raised by the tokens from the other 18 countries included in the sample. While the last category contains 64 projects, the US and Switzerland only have 31 and 15 projects. This problem was mitigated by attributing a 0 to the missing values. Moreover, Swiss tokens had an average target of $6.85 million in funds, while the G18 tokens aimed to reached $13 million in funding. This segregation allowed for a smaller funding gap between the three categories. Likewise, the following sub-hypotheses have been developed:

H1a: Companies located in USA are more likely to reach their funding target compared to Swiss companies.

H1b: Companies located in USA are more likely to achieve their funding target compared to G18 countries.

The USA and Switzerland were chosen for comparison as the two countries have the most developed legal framework with regards to conducting an ICO. On the other hand, the G18 countries included in the sample do not have any legal framework in place directed at ICOs, with some even forbidding the campaigns on their territories. Consequently, from a legal perspective, a clear regulatory framework would make USA and Switzerland more ICO-friendly. Hence, the first hypothesis. In order to test the hypotheses, the Wald test statistic will be implemented. The test will check whether the ICOs based in the USA bring a different contribution to the probability of reaching the target compared to the other countries. Alternatively, the null hypothesis states that, there is no difference in the probabilities of reaching the funding target between USA, Switzerland or the G18 countries.

Moving further, a binary logistic regression model will be implemented as the dependent variable comes in the form of a dummy variable. For calculating the significance of coefficients, the Z-test score to interpret the significance as well as the p-value. Moreover, the model of fit will be determined by the value of Pseudo R-squared and the p-value of the Wald Chi-squared test. The following regression equation will be used:

푃(푌) 퐸(푌) = ln ( ) = 훽 + 훽 푇푎푟푔푒푡 + 훽 푌푒푎푟 + 훽 푇푤푖푡푡푒푟 + 훽 퐷푢푟푎푡푖표푛 + 훽 퐶표푛푡푟표푙푠 1 − 푃(푌) 0 1 2 3 4 5

The dependent variable Success is a dummy variable who returns a value of 1 if the token achieved 100% of its targeted funds within one year and 0 otherwise. Roughly 58% of successful campaigns have been included in the sample, while the other 42% were not able to achieve their target by the end of the campaign. Moreover, only one outlier was found in the sample (EOS) who had a significantly higher target than any other tokens included in the sample ($4.2 billion). Consequently, the token was replaced with a different project who had a target within the normal range. The probability of reaching the targets depends primarily on the way in which the target was established in the first place. For that, the following independent variables were developed: The variable Target contains the targeted amount of funds a company aims to raise. The projects included in the sample had an average target of $60.6 million. Subsequently, three new variables have been created. The variable Target_US includes the funding targeted for each US company. Similarly, the variables Target_Swiss and Target_Others contains the targets for Swiss campaign, and the G18 campaigns respectively. Table 3 in the Appendix provides a summary of the descriptive statistics.

Furthermore, the variable Year accounts for the year in which the campaign took place, returning a value of 1 if the campaign happened in 2017 and 0 for 2018. This differentiation will showcase whether the success of ICOs was differentiated between 2017 and 2018. About 62% of the campaigns in the sample happened in 2017, while 38% occurred in 2018. Moreover, the variable Duration counts the number of days an ICO lasted. The range varied from 25 days to 365, as only campaigns lasting a maximum of one year were included in the sample. The variable was transformed into a dummy variable, returning a 1 if the campaign lasted less than 6 months and 0 if the campaign took longer. The initial goal of including this variable was to determine whether campaigns lasting less or more than 6 months have any impact on the success of a token sale. About 94% of the variables in the sample had a duration longer than 6 months. As there was less data available on ICOs who ended in less than 6 months and simultaneously met the criteria for being selected, the results related to location are most likely biased. Likewise, the variable Location is also a dummy variable, returning a 1 if the ICO was established in USA and 0 otherwise. About 30% of the tokens originated in the USA, while 70% occurred in several different countries.

Lastly, the variable Twitter was created in order to determine whether the active use of Twitter has an impact on the Success of an ICO. It comes in the form of a dummy variable, returning a value of 1 if the platform actively used Twitter to communicate news during the ICO and 0 otherwise. About 44% of the tokens had an actively used Twitter account during the ICO. Twitter was chosen as the main social platform due to the fact that it is the most frequently used platform by tokens for communication. Also, prospective investors follow news based on tweets rather than Facebook or Instagram posts. While Chanson, Risius and Wortmann (2018) attributed higher amounts raised to an increased number of followers on Twitter, this thesis will simply consider whether the company has actively used the above-mentioned platform prior and during the ICO for communicating with investors. The number of followers was not included as the former can be artificially obtained via the use of certain services, for which one pays a premium to gain a certain number of followers. As it is not possible to determine which projects made us of such services, the results would have been biased.

Similarly, several control variables were included in the model. The variable team_size includes the number of the team members on the project. It comes in the form of a dummy variable, returning a 1 if the team is larger than 10 people and a 0 if the size of the team is smaller than 10. The variable was designed for the purpose of controlling for the differences in team sizes, as larger team might be more prone to create successful tokens. Similarly, the variable experts_rating comes in the form of a dummy variable as well, returning 1 if the rating is above 3.5 and 0 otherwise. The variable controls for the differences in the quality of each project. Industry experts have developed an algorithm for rating each ICO on a scale from 0 to 5 based on several criteria. Such criteria include: quality of management, social media statistics, as well as degree of transparency. In line with the study of Myalo and Glukhov (2019), the rating was taken from ICObench.com. Lastly, the GDP of each country was considered in order to control for differences in the economic factors. The variable GDP_x takes the average GDP of each individual country included the sample, between 2017 and 2018. The values are calculated in trillions. The mean GDP for all the countries is $2.33 trillion. The value was calculated in order to distinguishing between countries who are well above or well below the average GDP. Likewise, control variable mitigates the differences in economic factors, thereby allowing us to focus directly on the impact of the location on achieving the targeted amount of capital. Table 2 contains a summary of all the variables included in the model.

To summarize the model, the data contains 13 successful campaign who took place in the USA and 17 unsuccessful campaigns. Similarly, 29 campaigns form several other countries were unsuccessful, while 41 ICOs were successful. Likewise, 56 successful campaigns lasted longer than 6 months, while 2 successful campaigns lasted less than 6 months. For the failed campaigns, 13 took longer than 6 months while 29 lasted less than 6 months. Also, 23 successful ICOs actively used a Twitter account to communicate with investors, while the other 35 did not make us of a twitter account and still managed to achieve the targeted funds. On the other hand, 21 failed campaigns made use of a twitter account, while 22 unsuccessful tokens did not. Related to the timeframe, 62% of the tokens were successful in 2017, and 38% in 2018. Furthermore, 30 successful tokens had a team size larger than 10, while 28 successful campaigns did not. As such, 20 failed campaigns had a team size smaller larger than 10, while 22 used a smaller team. Consequently, 50% of the successful campaigns included in the sample had more than 10 members in their team, while the other 50% did not. Lastly, 21 successful campaigns had an expert rating of above 3.5, while 37 received a rating below 3.5. On the other hand, 16 failed tokens received a rating below 3.5, while 26 had a rating above 3.5. Interestingly enough, only 47% of the campaigns received a rating above 3.5. The following section will perform a regression analysis on the above-mentioned variables, as well as test the hypotheses.

4. Results & Conclusion

This section is divided into 4 sub-sections. The first section checks whether all the assumptions for logistic regression are met and reports the results. The second section analyses the results and assesses the latter`s implication for future policies. Subsequently, the section attempts to provide an answer to the research questions. The third section describes the limitations of the dataset. The last section discusses the limitations of the overall approach towards the topic and offers advises on how the research`s limitations could be overcome.

4.1 Assumptions & Results

In order to run a logistic regression, four assumptions have to be met: Firstly, the outcome of a variable has to be dichotomous. This assumption is met, as the outcome of the dependent variable can either be 1 (successful) or 0 (unsuccessful). Secondly, there has to be a linear relationship between the dependent and independent variables. This assumption is met, as it can be seen from the Graph 1.1 and 1.2 in the Appendix. Thirdly, there should be no extreme values or outliers. Only one outlier was found in for the variable Target, which was later replaced with a value closer to the range. Lastly, there should be no multicollinearity between the predictors. The control variable GDP_x had an overlap with the variables Location and Duration, causing the latter to be excluded from the regression due to multicollinearity. Likewise, Location also showed signs of multicollinearity. Consequently, the variables were excluded from the analysis. Moreover, a VIF test was implemented to check whether the independent variables are correlated. As all of the variables have a VIF value of slightly above 1, it can be concluded that there is a weak correlation between the variables, and hence no multicollinearity. Table 4 includes the values of the VIF test. As all the assumptions have been met, we can pursue to analyze the model.

Furthermore, three regression models have been developed to test the hypotheses. In the first model, two regressions were performed. Firstly, success was regressed with the variable Target, without making the distinction between the targets of USA or Switzerland. The results yielded an odds ratio of 1.03 and a robust standard error of 0.01. Likewise, the coefficient has a p-value of 0.016 and a Z value of 2.42, making the coefficient statistically significant at 5% critical level. Also, the model has a log-pseudolikelihood value of - 63.52, a probability of Chi-squared of 0.015 and a Pseudo R-squared value of 0.07. The second regression added the other independent variables (Twitter, Year, Duration) as well as the control variables experts_rating and teamsize. The model of fit improved, showing a pseudo R-squared value of 0.14. However, the probability of chi-squared increased to 0.09. Regarding the coefficients, only the variables Amount_raised and Experts_rating are significant. The first variable has a p value of 0.027 and a Z value of 2.22. The second variable has a p-value of 0.015 and a Z value of -2.44. The odds ratio of Target remained constant at 1 while Experts_rating has an odds ratio of 0.316

In the second model, the dependent variable success was regressed with the funding targets for each category (Target_USA, Target_Swiss and Target_Others). According to the model, only the variable Target_USA is significant, with a p-value of 0.049 and a Z value of -1.97. The odds ratio has a value of 0.9985, which translates to a success probability of 49.96%. Similarly, the value of the robust standard error is 0.0007. However, the model has a Pseudo R-squared of only 0.025 and a probability of chi-squared of 0.24. In order to improve the model, the rest of the independent and control variables have been included. The second regression yields significant results only for 3 variables: Target_USA, Experts_Rating and Target. While the first variable has a p-value of 0.05. the p value of the second variable is 0.014 and 0.01 for the last one. The probability of success for Target_USA remained constant at 49.96%, while its robust SE decreased to 0.0005. In the case of Experts_Rating, the probability of success decreased to 16.46% with a 0.13 robust SE. Additionally, likelihood of success for Target increased to 50% with 0.02 robust SE. Similarly, the model`s Chi-squared probability decreased to 0.005, while the pseudo R-squared increased to 0.24. Lastly, the value of Log pseudolikelihood decreased to -33.43. Table 5 provides a description of the odds ratio for each significant variable in the second model, and their equivalent to probability of success/failure. In order to convert the log-odds coefficients into probabilities, the odds ratio was divided by 1 + the odds ratio.

Variable Odds Ratio Probability of Success(P) Probability of Failure(1-P) Target 1.05 50% 50% Target_USA 0.9985 49.96% 50.04% Experts_rating 0.197 16.46% 83.54%

Table 5. Odds Ratios converted into probabilities.

The last model regressed the dependent variable success with the three independent variables who yielded significant results: Target_USA, Experts_rating and Target. The odds ratio of Target_USA and Target remained unchanged, while log-odds ratio for Experts_rating increased to 0.29 The values of the robust standard errors also remained constant. Moreover, the third model had a Chi-squared probability of 0.001, while the value of Pseudo R-squared decreased to 0.14. Similarly, the value of log-pseudolikelihood increased to -58.78. Table 6 provides the summary statistic of the three regression models, highlighting the statistically significant variables.

4.2 Discussion

4.2.1 Model Interpretation & Analysis

The second model has the highest Pseudo R-squared (0.24) meaning that 24% of the variation is explained by the model. Similarly, the model has the Highest Wald Chi-squared value of 23. A Wald test value close to 0 would indicate that certain variables can be removed, which is not the case. Also, the probability of Chi-squared is also the highest for the second model (0.0005) compared to the probabilities of 0.09 and 0.01 for the other models. Moreover, the odds ratio for experts rating decreased from 0.31 in the first model to 0.197 in the second model, yielding thus a better prediction of the odds ratio. For the other two significant variables, the odds ratio remains constant. Lastly, the model has the lowest value of log- pseudolikelihood (-33). Judging by all these factors, the second model has the best model of fit for the data, while also providing the most accurate results. Consequently, the values of the second model will serve as basis for interpreting the results.

We begin by analyzing the variable Target_USA. The probability of success is 49.96% while the probability of failure is 50.04%. This implies that, upon increasing the target, a company could maximize its likelihood of success by 49.96%. The variable is significant at 1% critical level, while also having a small robust standard error of 0.5%. One reason as to why US-based tokens may influence the probability of success could be that the former treats certain tokens as securities, while also offering the opportunity to trade futures and options contracts. Switzerland also has an ICO-friendly environment, however, as the former`s ICO regulation come merely in the form of guidelines. As such, the USA has more suitable regulations in place for conducting an ICO and developing a token, which might explain the result. Also, the findings come in accordance with the paper from Huang et al. (2019). The study discovered that countries with ICO-friendly regulations are able to attract more ICOs. Likewise, investors are more prone to fund a digital asset in countries when there is a clear regulatory framework in place, thus increasing the likelihood of a successful campaign.

Moving further, the variable Target has a probability of success equal to the probability of failure of 50%. The odds ratio is 1.05, translating into an increase in the probability of success of 5% at any value of the target, holding the other variables constant. Likewise, when the target increases, the probability of success increases by 50%. When a campaign sets a higher target, its chances of success will also increase upon meeting the target. The odds ratio is slightly higher than the ratio of Target_USA as more countries have been included. Yet, the odds ratio is primarily influenced by the targeted capital in the USA, while the other countries bring a small contribution to the increase in odds ratio. The finding also come in accordance with the study from Lyandres et al. (2018), who concluded that the funding targets are more frequently achieved when the amount of capital increases. The last significant variable of the model is Experts_Rating, who has an odds ratio of 0.197 and a probability of success of 16.46%. This implies that when the rating of experts increases, the likelihood of success increases by 16.46%. One potential explanation might be that higher ratings makes an ICO more attractive. Consequently, tokens with higher ratings might attract more funds and users compared to projects with lower ratings. The results complement the findings of Burns & Moro (2018) and Myalo & Gluckhov (2019), who also discovered that the experts` rating influences the success of an ICO. Yet, the funding target seems to have a higher influence on the probability of an ICO success than experts` rating.

Moreover, the study found that the duration of an ICO, the size of the developing team and the use of Twitter do not influence the likelihood of an ICO`s success. However, the results contradict several other papers. For instance, Myalo & Gluckhov (2019) found a positive relationship between the duration of an ICO and the increased funding, while Momatz (2018) concluded that an increased number of team member contribute to the success of an ICO. Similarly, Burns & Moro (2018) found a positive relationship between an increased amount of Twitter followers and the success of an ICO campaign. Likewise, Benedetti & Kostovetsky (2018) discovered that a higher number of Twitter followers leads to an increase in the market capitalization of a token. Also, Howell et al. (2018) provided empirical evidence for the correlation between abnormal returns achieved during an ICO and the number of followers on Twitter. The potential causes for not obtaining similar results will be discussed in section 4.3.

4.2.2 Implications for future policies

Based on the result obtained, the hypotheses can be accepted. The first sub-hypothesis stated that the US-based tokens can influence more the likelihood of success than Swiss tokens. While US-tokens do have a significant influence on the probability of an ICO`s success, Swiss based tokens did not yield significant results and hence do not have any effect on the latter. Likewise, the G18 countries also did have significant coefficients, meaning that the G18 countries also do not influence the probability of a successful outcome. Subsequently, the research question asked whether the geographical location of an ICO could influence the latter`s likelihood of success. The empirical results showed support for the effect of location on the probability of success. The findings complement the results of Huang et al. (2019), who discovered that ICOs occur more often in countries with developed financial systems, advanced digital technologies and ICO-friendly regulations.

Moreover, the study offers two recommendations for the policymakers based on the results obtained. Firstly, more governments should implement a regulatory framework targeted at Initial Coin Offerings. This can be done either through adapting current securities laws, or creating a new set of regulations for this type of funding campaigns. It is safe to assume that the ICO industry will continue to grow in the following years. Governments could contribute to an increase in ICO`s success simply by offering the required legal support for the latter`s implementation. Digital tokens create innovative services who can stimulate productivity and economic growth. Therefore, it is in the governments` best interest to create regulations that facilitate the implementation of ICO campaigns in their countries.

Secondly, the development of risk-assessment measures is likewise crucial for the funding capabilities of ICOs. A more complex metric system for ICO rating could be developed -similar to bonds rating- in order to determine the riskiness of a token, as well as the appropriate return on investment. By doing so, investors are more aware of their risk exposure, and would thus be more inclined to invest in project where the risk- return relationship is well defined. Similarly, appropriate risk metrics would encourage private-equity firms to diversify their investment portfolios and (partially) mitigate the idiosyncratic risk. Kaal and Dell’Erba (2017) provide an outline of the core risk factors related to an ICO. Likewise, the development of derivatives contracts in the crypto space might become powerful tools for hedging systematic risk, thereby decreasing the total risk. In spite of that, Misic, & Zernichow (2019) concluded that the volatility of Bitcoin did not decrease following the introduction of Bitcoin futures in the USA. To conclude, categorizing an ICO based on its level of risk might increase the latter`s sources of funding, thereby improving the chances of an ICO`s success

Last but not least, although the empirical results support the fact that the geographical location influences the likelihood of an ICO success, it cannot be determined to what extent the former has an effect on the latter. This is caused primarily by the limitations of the dataset and the general model, which will be covered in the next two sections.

4.3 Dataset limitations

This section will consider the internal validity of the models employed for answering the research question. Firstly, the size of the sample is relatively small, containing only 100 projects. The reason for such a small sample is because the data had to be manually selected within a limited timeframe. Upon including more data in the sample, certain variables might become statistically significant. For that, the data sampling criteria had to be less strict. More specifically, an equal number of projects originating in the USA, Switzerland and other countries might yield more accurate results. Moreover, the total amount raised by the tokens should be relatively similar in order to have a significant comparison between the different locations and avoid biasing the data.

Also, the variable Duration could not be a significant predictor as 94% of the data contained samples who lasted more than 6 months. In consequence, no significant distinction could have been made. Including more data on successful campaigns that lasted less than 6 months was not possible. The reason being is because of the initial two criteria a token has to meet before getting selected- the campaign had to start either in 2017 and 2018 and had to last a maximum of one year. In order to be able to include more projects that lasted less than 6 months, at least one of the above-mentioned criteria had to be dropped. Furthermore, the model assumed a one-way causality relationship between the independent and dependent variable. However, the causal relationship might be two way, meaning that certain independent variable could also influence other independent variables. For instance, the experts` rating might also have an impact on the duration of an ICO or the size of its developing team. Consequently, two-way causal relationships that are not accounted lead to the simultaneous causality bias. To avoid the latter a model accounting for both causal relationships should have been implemented.

Additionally, the highest Pseudo R-squared achieved in the second model had a value of 0.24. This implies that only a quarter of the model`s variation can be explained by the variables included. Even though the model had a Chi-squared probability of 0.005 more control variables could have been included for maximizing the value of the Pseudo R-squared. Yet, the values of Pseudo R-squared are not the same as for the R-squared, and perhaps the value of the latter would have been higher upon conducting an OLS Linear Regression.

Lastly, the model does not provide detailed explanations as to why increased funding in the United States influences the probability of success of an ICO, while for the other countries it does not. Although the coefficient was significant, the underlying factors who contribute to the success of an US ICO campaign cannot be determined from the statistical model employed-apart from higher ICO rating from the experts. Regardless, the model provides the empirical grounds for future research looking to study the relationship between an ICO`s location and its probability of success.

4.4 General Study Limitations & Questions for future research

The main limitation of the study comes from the way in which success was defined and interpreted. While the thesis considered success only during the ICO phase, the post-ICO phase also plays a crucial role in the survival and development of a coin. A token`s growth opportunities, investment decisions and network capabilities are crucial for the former`s post-ICO success, and could lead to the token`s failure upon making the wrong decisions. Consequently, future research could analyze the impact of post-ICO investment decisions on the development of a token.

Moreover, success was defined as a project reaching 100% of their targeted funds within one year. As such, projects who achieved 100% throughout a longer period were not included in the sample. This choice might have influenced the number of projects included in the sample and subsequently the results of the study. Also, reaching 100% of the targeted funds is not a guarantee that the company will continue to thrive. A project could continue to grow even if it only achieved 50% of their targeted capital during the campaign, depending on the investment strategies, growth opportunities, or the option to extend the ICO period. Furthermore, it would be plausible to assume that certain targets might have been set too high, due to either overly-optimistic views from the upper management or inaccurate predictions. Similarly, a funding target may have been set too low, which in turn caused the campaign to achieve 100% of their funds within a short period. While this thesis would categorize such a project as being successful, there is also a significant risk that the project might fail in the long term. Assuming however that the targets were correctly estimated, there is a chance that the tokens were underpriced, which could also contribute to the easiness with which a project obtains a certain amount of capital. To date, a limited number of articles cover ICO-underpricing. Consequently, future research could focus on developing methods in which the price of a token is correctly determined, as well as estimating an achievable target for obtaining funds.

Furthermore, tokens also vary in terms of funding requirements per industry. While some industries might be more prone to raising significant amounts of funds, others might struggle in that sense. This thesis did not consider the industry to which a project belongs to. Yet, the industry categorization is currently not explicitly defined, as one project might belong to multiple industries while other projects would not fit into any of the current ICO industries. Future research could also examine the differences for each type of industry and determine which one could be more prone to success.

Lastly, the behavior of the crypto community with regards to positive or negative news about ICOs would also have been worth considering. The study attempted to do so by assessing whether a token makes use of a Twitter account. As the results were not significant, the model simply assumed that the use of Twitter does not alter the probability of success for an ICO. In spite of that, more channels of communications could have been considered, as opposed to a single social media platform. Moreover, it is safe to assume that investors might also base their investment decisions upon the market sentiment. The latter is influenced by both the communication channels employed, as well as the development of the cryptocurrencies. A model including the above-mentioned factors might provide empirical supports regarding investor`s behavior in the crypto community.

5. Conclusion

The purpose of the thesis was to study the factors influencing the success of an ICO campaign. The study provided empirical support for the fact that an ICO`s location influences its likelihood of success. Subsequently, it was tested whether US-based tokens can have a higher contribution to the probability of an ICO`s success than other countries. The results showed that, the probability of an ICO`s success increases when a high funding target is set, a high target for US firms is established, as well as upon receiving a higher rating from industry experts. Subsequently, the hypothesis has been confirmed: US-based tokens are able to influence the probability of an ICO`s success, while tokens based in a different location do not.

Several countries have taken different approaches with regards to this type of funding campaigns. While some countries facilitated the development of ICOs under current regulations (USA, Switzerland), others discouraged investors to take part in these campaigns (UK, Japan), while others classified ICO as illegal (China, Korea). The future of the ICOs` industry is dependent upon the development of a regulatory framework and risk-assessment measures. As ICOs facilitate innovation and increase productivity, both developing and developed countries might benefit from the former`s development on their territories- especially after the economic shock of a global pandemic. Likewise, establishing several risk-assessment measures for ICOs could increase the support of institutional investors and private-equity firms. Both approaches could contribute to an increased number of successful campaigns, as well as prevent large projects from failing.

APPENDIX

Country Campaigns Included United States of America 31 Switzerland 15 United Kingdom 10

Russia 8

Singapore 6

Japan 5 Estonia 5 Hong Kong 4 Taiwan 4 India 2 Netherlands 2 Gibraltar 1

Peru 1

UAE 1

Thailand 1 Poland 1 Cayman Islands 1 British Virgin Islands 1 The Bahamas 1 Total 100 Table 1. Number of campaigns included for each country.

Dependent Variable Dummy variable, 1 if the project achieved 100% of the targeted funds and 0 Success otherwise Independent Variables Duration 1 if campaign finished in less than 6 months and 0 if it took up to 1 year. 1 if the project was set up in the USA and 0 otherwise. The variable was later Location dropped due to multicollinearity Year 1-ICO started in 2017 and 0 if it started in 2018 Target Targeted amount of funds to be raised (in millions) Twitter 1 if project has an official actively used Twitter account and 0 otherwise Target_USA Targeted amount to be raised by US companies (in millions) Target_Swiss The Targeted amount to be raised by Swiss companies (in millions) Target_Others Targeted amount to be raised by several other countries (in millions) Control Variables team_size The size of the team who started the ICO project experts_rating Rating of the project performed by experts. 1- If project received at least a 3.5/5 rating and 0 otherwise GDP_x The average GDP (in trillions) between 2017 and 2018, with x representing each country in the sample. The variable was later dropped due to multicollinearity.

Table 2. Summary of the variables included in the model.

Variable Obs Mean Std.Dev. Min Max location 100 .3 .461 0 1 teamsize 100 .5 .503 0 1 experts_ratig 100 .47 .502 0 1 year 100 .62 .488 0 1 duration 100 .94 .239 0 1 success 100 .58 .496 0 1 twitter 100 .44 .499 0 1 target 100 60.594 418.766 .05 420.66 target_usa 100 46.39 419.719 0 420.66 target_otherss 100 13.074 22.962 0 131.88 target_swiss 65 6.876 22.28 0 157.88

Table 3. Descriptive Statistics.

VIF 1/VIF experts_rating 1.406 .711

duration 1.226 .816 year 1.207 .828 target_others 1.197 .836

target_swiss 1.165 .858 target 1.157 .865

teamsize 1.142 .876 twitter 1.14 .877 target_usa 1.121 .892 Mean VIF 1.196 . Table 4. Variance inflation factor scores.

Graph 1.1- Linear relationship between success and targets.

Graph 1.2- Linear relationship between success and the other independent variables.

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