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Initial Coin Offerings:
Early Evidence on the Role of Disclosure in the Unregulated Crypto Market*
Thomas Bourveau
Emmanuel T. De George
Atif Ellahie
Daniele Macciocchi
July 2018
Abstract
We provide initial descriptive evidence on the emerging crypto capital market, and use this unique
unregulated setting to examine the role of disclosure for capital market outcomes. We analyze a
comprehensive global sample of more than 750 initial coin offerings (“ICOs”) with data from April
2014 to May 2018 and find that the ICO market has emerged as a significant financing channel
with over $13 billion raised by a diverse set of issuers from more than 50 countries. We also find
that the likelihood of successfully raising funds is positively associated with issuers’ disclosure,
the information environment, and hype. Further, weaker information environments are associated
with higher crash risk, illiquidity, and volatility. Finally, we present evidence on the capital market
benefits of new information intermediaries that have naturally evolved in this unregulated market
to monitor and assess the quality of tokens issued in an ICO. Overall, our study provides novel and
timely evidence on the role of disclosure in this completely unregulated capital market.
Keywords: initial coin offering, crypto-currency, crypto-token, crowdfunding, disclosure,
unregulated capital markets.
JEL Classifications: G1, G2, G3, M4
* Bourveau is at Columbia University. De George is at London Business School. Ellahie and Macciocchi are at the
David Eccles School of Business at the University of Utah. We acknowledge the financial support of Columbia
Business School, London Business School, and the University of Utah. We thank Gavin Cassar (discussant), Eric
Floyd, Henry Friedman, Joao Granja, Luzi Hail, Trevor Harris, DJ Nanda, Delphine Samuels, Doug Skinner, Irem
Tuna, and participants at the 2018 London Business School Accounting Symposium for valuable comments.
1
1. Introduction
Disclosure regulation and enforcement are considered the foundations of well-functioning
capital markets (e.g., Sutton [1997], Levitt [1998], La Porta et al. [1997], Christensen et al. [2016]).
Since 2014, the crypto market has emerged as a large and unregulated capital market that provides
an alternative to traditional more regulated sources of financing.1 According to coinschedule.com,
a major crypto-market data provider, over 650 issuers originating from more than 50 countries
have issued tokens through initial coin offerings (ICOs) and raised approximately $13 billion from
investors between April 2014 and May 2018.2 The crypto market offers virtually no investor
protection since issuers are not required to register with any securities market authority, nor are
they required to provide periodic financial and non-financial disclosures. Since the crypto market
uses decentralized global platforms, which keep a distributed ledger of transactions between
investors (i.e., a blockchain), issuers on this market do not fall under the jurisdiction of any
securities laws. However, securities regulators and governments are carefully observing this
nascent but fast-growing market and contemplating regulatory actions.
In creating a well-functioning market, regulators face a trade-off between imposing
disclosure requirements that are not too onerous for small firms and fulfilling their overarching
goal of ensuring investor protection (Leuz and Wysocki [2016]).3 This trade-off is particularly
relevant in the crypto market since it is comprised primarily of smaller entities in need of financing.
1 The crypto market includes both crypto-currencies and crypto-tokens (see Section 2 for more details). Tokens and
crypto-currencies are two forms of coins. The focus of this paper is on issuers that sell tokens through an initial coin
offering (ICO). We focus on tokens since they are more similar to equity securities, whereas crypto-currencies are
virtual currencies that serve as substitutes for fiat currencies. 2 We use the term “issuers” to refer to ICO teams, entities, virtual organizations, firms, and all forms of organizations
that sell tokens. These issuers are similar to startups that raise funds through angel or venture capital investors. 3 A major goal for regulators of securities markets is to ensure investor protection (Goshen and Parchomovsky [2006],
Mahoney [2009]). To achieve this goal, regulators often impose stringent disclosure requirements on firms, which can
be particularly costly for small firms to comply with. Such requirements have led to some deregistrations from public
markets (Leuz et al. [2008]).
2
Yet participants in the crypto market oppose any form of securities regulation and argue that costly
compliance would restrict access to available capital in this market. Instead, crypto-market
participants argue in favor of a self-regulated and decentralized market, where disclosure best
practices can emerge to ensure market integrity.
However, self-regulation raises potential concerns for investors about disclosure
credibility. In particular, it is unclear ex ante whether information disclosed by token issuers would
allow market participants to separate high-quality projects from sham projects that seek to
opportunistically raise capital in this unregulated market.4 Therefore, it is important to investigate
whether any disclosure norms and best practices have evolved in this market, and whether these
disclosure practices provide capital market benefits. Hence, the objective of this study is two-fold:
(1) to provide descriptive evidence on the ICO setting, focusing on the disclosure practices of
issuers and the broader information set available to investors in this large and fast-growing capital
market, and (2) to use this unregulated market as a unique setting to provide timely and topical
evidence on the relation between disclosure, the information environment, and market quality.
Companies seeking to raise capital on the crypto market perform an initial coin offering
(ICO). The term ICO is inspired by the term IPO (initial public offering), the name for the process
whereby a private firm lists its shares on a public stock exchange. However, unlike the IPO process
in which a firm mandatorily complies with strict and costly registration procedures prescribed by
securities regulators (Ritter [1987], Ibbotson et al. [1988]), the ICO process is currently
unregulated.5 Hence, available information about token issuers is limited, the level of detail and
the type of information provided vary significantly, and such information usually does not include
4 We discuss the potential role of information in assessing token and issuer quality in detail in section 2.3. 5 See Section 2 and Figure 1 for more details on the ICO process. Furthermore, Appendix B presents summary
information about the ten largest ICOs in each of the previous three years.
3
financial statements or accounting reports. Typically, the information released by token issuers
includes (i) the technical source code underlying the new technology, product, or service; (ii) the
whitepaper, which is an unaudited marketing document that might contain information such as the
business proposition for the crypto-token, addressable market opportunity, technology, proof-of-
concept case studies, expected progress timeline, identity and background of the team, ICO process
and platform, token distribution, vesting restrictions, use of proceeds, etc. Issuers release this
information to signal the quality and future prospects of their tokens in order to differentiate
themselves from lower-quality projects. However, these disclosures are not audited, which casts
doubt on their reliability and credibility. Thus, “pseudo” independent organizations that provide
“ratings” on the quality of the tokens have naturally emerged in this market and may contribute to
the development of the information environment.
During the ICO process, issuers exchange tokens against fiat currencies (e.g., USD) or
crypto-currencies (e.g., Bitcoin) to fund the development of their products or services. Tokens can
then be used at a later date to consume the products or services offered by the issuers, and their
value is determined by demand and supply. A key aspect of tokens is that, unlike equity securities
of a firm, tokens typically do not grant any control rights, claims to dividends, or liquidation value.
Hence, investors buy tokens for their utility value or for speculative reasons, such as a higher resale
price. 6 After the ICO is completed, all subsequent exchanges of tokens are transacted on the crypto
market for tokens (i.e., the crypto-tokens market), which operates on the blockchain and enables
6 In the absence of financial information, potential holders of tokens are likely to base their purchasing decisions on
the expected payoffs from crypto-tokens, which would depend on their intention to hold the tokens, either as customers
or as investors. Broadly speaking, the expected payoffs from crypto-tokens could be a combination of the value derived
from the ecosystem of the crypto-token (i.e., utility), the prospects of future distribution of profits, and the expected
future resale price; collectively, we refer to these as “future prospects.” We argue that more information allows a better
assessment of future prospects. We also note that while the utility payoff may be important for retail investors, more
institutional investors have emerged in the recent months (see, for example, https://www.cnbc.com/2018/06/22/circle-
sees-boom-in-institutional-demand-despite-bear-market.html).
4
verification, security, and data integrity. The main public crypto exchanges on which tokens are
listed include Binance, Poloniex, HitBTC, Bitfinex, and Bittrex.
In the first part of the paper, we provide descriptive evidence on the cross-section of entities
that have completed an ICO and are currently trading on the crypto-tokens market. We collect
detailed information pertaining to their disclosures, including attributes of the team and ICO, and
the broader information environment from a variety of sources. We examine the probability of
successfully completing the ICO and the amount of funds raised during the ICO process. To this
end, we compile and examine a comprehensive sample of 776 entities that have tried to access the
crypto-tokens market over the April 2014 to February 2018 period, out of which 659 have
successfully completed an ICO. In the second part of the paper, we analyze the consequences of
disclosure on market quality. We focus on three key aspects of market quality that securities
regulators are concerned about: crash risk, illiquidity, and volatility. Indeed, poor investor
protection could lead to lower investor participation and lower market liquidity (e.g., Guiso et al.
[2008]). Similarly, lack of corporate transparency, market manipulation, and fraud could increase
return volatility and crash risk (Jin and Myers [2006]), which would lower investor participation.
Beginning with a descriptive analysis of the cross-sectional distribution of first day ICO
returns, we find that the median first day return from the ICO offer price to the closing price on
the first day of trading is positive 49%. However, the first day price run-up dissipates quickly over
the following 30 days, and the median return is negative 30%. Interestingly, the mean return over
the following 30 days is positive 39%; the large dispersion in post-ICO returns points to the
influence of outsized returns from a few successful ICOs skewing the aggregate market statistics.
We then track the overall performance of the crypto-tokens market by developing a value-weighted
market index. Our findings corroborate the explosive growth in the value of this market since 2014,
5
with a $1 investment in the aggregate value-weighted crypto-tokens market index in August 2014
increasing in value to $4,621 by May 15, 2018.
We then examine the characteristics of successfully completed ICOs. Our univariate results
reveal that successfully completed ICOs tend to (1) disclose their sourcecode, (2) disclose more
informative whitepapers, (3) have more social media activity, (4) provide disclosure about vesting
periods for founders’ tokens, and (5) issue tokens that are rated higher by crypto-market
information intermediaries. We then undertake multivariate logit analysis, controlling for ICO
attributes, crypto-market sentiment, and quarter-year fixed effects, and we find that the likelihood
of successfully raising funds is positively associated with several measures of disclosure and social
media activity. We also find that an aggregate measure of token quality – i.e., a rating from an
external rating provider – is positively associated with the likelihood of completing an ICO. These
results indicate that the information intermediaries that have naturally evolved in the crypto market
appear to capture the underlying quality of a token and are viewed as credible by market
participants. In our analysis of a subset of ICOs with available information on the amount of funds
raised, we find similar results. Overall, the probability of completing an ICO and the amount of
funds raised seem to be explained by market hype, as captured by several proxies for social media
activity, and the amount and quality of information provided by issuers or other market information
intermediaries.
Next, we examine the association between ICO characteristics and subsequent price crash
risk. We employ two empirical measures of crash risk, an indicator for extreme negative returns,
and negative return skewness. In our analysis, we also include opening day ICO returns to provide
some insight into the recent criticism that the unregulated ICO crypto market is susceptible to
“pump and dump” strategies by issuers. In line with this view, we find evidence consistent with
6
some issuers strategically timing their capital raise during “hot” markets and engaging in “pump
and dump” schemes that harm investors. Second, we find that sourcecode disclosures are positively
related to crash risk. One possible interpretation of these results is that sourcecode disclosures
represent proprietary disclosures that subsequently erode the competitive advantage of the issuers
and allow others to mimic or build off their technologies. Third, we find that crash risk is positively
associated with whitepaper opacity and length. Lastly, we find that higher token quality, as
measured by assessments from external rating providers, is negatively associated with crash risk.
Overall, these findings suggest that information plays a role in the crypto-tokens market. In
particular, information intermediaries appear to play a vital role in monitoring issuers, which
speaks to the natural evolution of governance institutions within unregulated markets.
Finally, we investigate the capital market consequences of disclosure in the crypto-tokens
market. Issuers may have incentives to provide more information to market participants in order
to reduce uncertainty about the quality of their projects, which would plausibly reduce information
asymmetry in the crypto market. This could be of value to issuers’ insiders because it allows them
to benefit from greater market quality when they access the market to liquidate their token holdings
once they vest. To investigate this conjecture, we examine secondary market characteristics such
as liquidity and volatility. If disclosures help to reduce information asymmetry in the crypto
market, we would expect a negative relation with illiquidity and volatility. Consistent with these
expectations, we find that disclosure, social media activity, and an aggregate measure of the quality
of the token (i.e., rating), are associated with lower illiquidity and volatility.
This paper contributes to the nascent literature on the economics of the crypto market. A
few recent working papers have examined (i) factors underlying the completion of ICOs (Amsden
and Schweizer [2018], Adhami et al. [2018], Catalini and Gans [2018], Howell et al. [2018]), (ii)
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the market performance of Bitcoin and other crypto-currencies (Benedetti and Kostovetsky [2018],
Krueckeberg and Scholz [2018], Momtaz [2018]), (iii) the disruptive effects of blockchain
technology on equity crowdfunding (Catalini and Gans [2017, 2018]), and (iv) its potential
application in corporate governance (Yermack [2017]).7 Our paper contributes to this emerging
literature by empirically examining the most comprehensive sample of global ICOs available to
date, as well as by focusing on the capital market consequences of disclosure and the information
environment. We examine the likelihood of success and funds raised, as well as measures of
market quality that securities regulators are concerned about, such as crash risk, illiquidity, and
volatility. We provide evidence that in the crypto market, disclosure of information can play an
important role in signaling quality to potential investors and information intermediaries. Our
findings are relevant for current and potential investors in ICOs, and may inform regulators about
the emerging disclosure practices of issuers, the role of information intermediaries, and their
consequences for market quality.
We also contribute to the literature on the role of disclosure in capital markets (e.g.,
Diamond and Verrecchia [1991], Leuz and Verrecchia [2000], Healy and Palepu [2001], Botosan
and Plumlee [2002], Lambert et al. [2007], among others). Most of the studies in this literature
examine capital market settings with well-established regulations and enforcement. Stricter
regulations and enforcement are expected to enhance the credibility of firm disclosures, which in
7 Most relevant to our paper, Howell et al. [2018] is a contemporaneous working paper on ICOs that examines the
relation between issuer characteristics and measures of success, with a primary focus on liquidity. The objective of
our paper differs significantly from that of Howell et al. [2018] in that we focus primarily on the relation between
disclosure practices, the information environment, and a broad set of capital market outcomes. Hence, we collect a
more comprehensive set of measures to approximate the information set available to investors at the time of the ICO
and examine a broader set of capital market outcomes, such as entry, survival, and trading activity. Moreover, we
conduct detailed content analyses of whitepapers disclosed by issuers to assess informativeness, and we develop
measures designed to capture the broader information environment of the issuer and the ICO, such as social media
activity and whitepaper opacity. Finally and perhaps most importantly, by focusing on ratings providers, we shed light
on the role of the information intermediaries that have emerged in the unregulated crypto markets.
8
turn provides benefits to capital market participants (see Leuz and Wysocki [2016] for a review).
Other studies have investigated the value of providing voluntary information in equity markets in
periods with no mandated reporting regulation (e.g., Shivakumar and Waymire [1994]), or with
limited mandated reporting regulation (e.g., Barton and Waymire [2004], Bushee and Leuz [2005],
Brüggemann et al. [2018]). In a similar fashion, we document capital market benefits associated
with disclosure in the crypto market. In this fast-growing and unregulated market – where
disclosure is not mandated and the credibility of voluntary disclosure is unclear – we provide initial
evidence that information is important and can help in identifying firms with lower illiquidity,
lower volatility, and lower crash risk. Moreover, we find that in the absence of formal regulation,
external monitoring mechanisms have evolved in the form of ratings providers that assess the
overall quality and prospects of the issuers and tokens. Furthermore, our findings speak to the
value of information in other types of non-traditional capital markets, including peer-to-peer
lending (Michels [2012]) and crowdfunding (e.g., Cascino et al. [2018]).
The rest of the paper proceeds as follows. Section 2 provides institutional background on
blockchain technology, the crypto market, and the initial coin offering process, summarizes the
current debate about regulation, and discusses the role of information in crypto-tokens market.
Section 3 describes the data and research design. Section 4 presents the empirical findings, and
Section 5 concludes.
2. The Emerging Crypto Capital Market
2.1. Blockchain Technology, the Crypto Market, and Initial Coin Offerings
Blockchain technology is becoming prevalent around the world. This technology derives
its name from a virtual chain of blocks (i.e., digital containers), which can store all kinds of records
including financial transactions, real transactions, contracts, title deeds, etc. A software developer
9
who allegedly used the pseudonym Satoshi Nakamoto is credited with inventing the technology in
2008 to serve as the publicly distributed transaction ledger for the Bitcoin crypto-currency.8 The
idea behind the use of blockchain technology is to facilitate electronic exchange in a secure,
verifiable and immutable way that is independent of any central authority. Furthermore, the
original developers of this technology felt motivated to provide an alternative to the traditional
global financial system, given the perceived inability of governments and central banks to
guarantee the stability of the financial system during the recent financial crisis.
It is important to note that the term Bitcoin, as used in the popular press, may refer to two
related but distinct constructs. On the one hand, it corresponds to the coin that represents ownership
of a virtual currency, just like virtual dollars in a bank account. On the other hand, it may also refer
to the protocol itself that maintains a virtual ledger of coin balances.
The coins that are stored on the Bitcoin protocol (i.e., the blockchain) differ from regular
currencies on five major dimensions. First and most importantly, Bitcoin is decentralized. This
means that no single institution can claim control over the network. The network is run by a global
community of volunteer developers and is hosted on their dedicated computers. Such developers,
also called “miners,” provide computing power to the system to solve complex algorithms that
validate each transaction, and in exchange they receive a predetermined number of new Bitcoins.
For regular currencies this function is fulfilled by banks, but for crypto-currencies the integrity of
the transactions is maintained by a distributed and open (i.e., verifiable) network that is not owned
by anyone. Second, the total supply is limited to 21 million Bitcoins, whereas fiat currencies have
an unlimited supply since central banks can issue currencies with no limit. Conceptually, this
makes Bitcoins a more attractive asset as the currency cannot be manipulated through the
8 To date, the inventor’s true identity remains unknown.
10
modification of supply. Third, the identity of parties transacting using Bitcoins remains
anonymous, as there is no central monitor that validates or settles the transaction. Fourth, Bitcoin
transactions are immutable in the sense that they cannot be reversed, unlike traditional electronic
transactions. Indeed, once a transaction appears in the ledger stored in the blockchain, it cannot be
modified and is observable to anyone. Finally, Bitcoins offer a high level of divisibility, allowing
for potentially easier micro-transactions in the future.
Overall, Bitcoin supporters and developers (i.e., miners) claim that blockchain technology
offers three main advantages over the traditional financial system: security, disintermediation, and
autonomy. First, the decentralized framework and the blocks’ codes guarantee the absolute
security and immutability of the information. Furthermore, records (blocks) within the blockchain
cannot be counterfeited. Second, the blockchain is not regulated by any governmental or formal
institution but instead relies on market forces for governance. Thus, the concept of “consensus”
replaces the need for any sort of validation (i.e., the middleman) or centralized governance. Third,
the creation (i.e., mining) of coins provides economic incentives, thereby covering the costs of the
infrastructure involved.
It is crucial to note that Bitcoin was just the first in a series of coins based on blockchain
technology. Since the launch of Bitcoin in 2008, many additional coins have been developed, some
of which share some characteristics with traditional fiat currencies, while others are more similar
to securities. As of 2018, there are several hundred different digital coins. Each new coin operates
using either an independent blockchain protocol or is hosted by an existing blockchain protocol
(the largest are Ethereum, NEO, and NTX). As with Bitcoin, the number of coins in circulation
and the maximum supply are defined in advance and are publicly disclosed through a document
called the whitepaper.
11
Coins are not necessarily intended as a payment system between users. In fact, it is possible
to distinguish three separate types of coins that are adapted to the objectives of the communities
who use them. The first type is “crypto-currencies,” which are broadly inspired by Bitcoin.
Through technical innovation, crypto-currencies attempt to function either more easily, more
securely, or more rapidly (e.g., by reducing transaction verification times) than fiat currencies.
Examples include Bitcoin, Litecoin, Bitcoin Cash, Monero, and Bytecoin. The second type is
“infrastructure coins,” which aim to offer a platform for developing smart contracts (i.e., hosting
the development and launching of new crypto-currencies and tokens based on blockchain
technology). Examples are Ethereum, NEO, and NXT. Finally, the third type is “utility coins,”
more generally referred to as tokens, which allow the token holders to receive specific products or
services. Tokens provide a wide range of products and services, including real estate, insurance,
cloud storage, and media and entertainment, among others.
These three categories of coins help to partition the crypto market into two broad sub-
markets: (1) the crypto-currencies market, which includes crypto-currencies and infrastructure
coins, and (2) the crypto-tokens market, which includes utility coins, i.e., tokens. The main subject
of this study is the crypto-tokens market. We focus on this market because it is particularly relevant
to corporate finance and accounting given its similarities with corporate securities and the capital-
raising process. Tokens also represent a large and growing portion of the trading volume in the
crypto market.
A key aspect of the crypto-tokens market is that tokens can be obtained through two main
channels: (i) the initial coin offering (hereafter, ICO); and (ii) the secondary market. The ICO
process is inspired by the initial public offering process to list on traditional regulated exchanges.
Entities that decide to access the crypto-tokens market are usually smaller and in need of financing.
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Few have existing products, which makes them more likely to be ignored by venture capital funds.
As an alternative mean of external financing to the traditional channels (VCs, private equity, IPOs),
these companies may perform an ICO and thereby issue their tokens in exchange for traditional
fiat currencies or crypto-currencies such as Bitcoin.
The first step of an ICO is the public announcement, when an issuer publicly announces its
intention to perform an ICO on a variety of specialized websites. Contemporaneous with the ICO
announcement, issuers usually release a whitepaper that typically discloses information about the
technology, the product, the management team and any associated vesting restrictions on their
tokens, the expected progress of the project, and the use of proceeds. Further, they specify a
timeline for the offering process. Typically, potential participants signal their interest in the ICO
by registering on the website of the issuer during the ICO marketing period. Once the marketing
period has ended, interested participants are allocated tokens and are asked to send fiat currencies
or crypto-currencies to the issuer in exchange for tokens. See Figure 1 for an illustrative ICO
process.
In terms of the amount of money raised, ICOs can be capped or uncapped, which is
determined by different fundraising goals. In a capped ICO, the entity sets a limit on how much
funding it is willing to accept. Excess funds received over the limit are returned to investors. In an
uncapped ICO, the entity is willing to accept as much as it can raise, which can result in lower
token value than in a capped ICO. However, in both cases there is a “soft cap” that corresponds to
a lower acceptable funding level (i.e., a minimum threshold); failure to reach the soft cap amount
cancels the ICO, and the funds are returned to participants. Once the token allocation and
settlement process are completed, the tokens are eventually listed on various online platforms (e.g.,
Binance, Poloniex, HitBTC, Bitfinex, and Bittrex) and are available for trading on this secondary
13
market against regular fiat currencies or other crypto-currencies (which allows us to observe
prices). Typically, there is a lag between the completion of the ICO and the listing on an online
marketplace, and this lag varies across tokens (with a median of 17 days in our sample).
The ICO process presents both important similarities to, and differences from, the IPO
process for firms that list on traditional stock exchanges. In both cases, participants exchange
money for shares (tokens in the case of ICOs) that have some monetary value and are traded on
the secondary market, with prices presumably based on the market clearing mechanism. However,
there are important differences. First, the ICO market is unregulated, whereas the IPO market
follows a strict process defined by regulation where compliance is costly and mandatory, in line
with the goal of securities regulators to protect investors (Ritter [1987], Ibbotson et al. [1988]).
Further, there is usually little information available about the issuer performing an ICO, and none
of the documents voluntarily shared during the ICO process, such as the whitepaper, are audited
or independently verified.9 This explains why ICOs can be performed in a short period of time (a
few weeks). In contrast, IPOs can take months to complete due to the auditing process, the need
to put in place adequate internal controls and governance mechanisms, the lengthy process for
registration with the relevant securities regulator, and the extensive legal documentation
requirements.
Second and most importantly, ICOs correspond more to a crowdfunding model than to an
IPO model.10 Indeed, when firms issue shares they also relinquish some voting rights, whereas
9 A key differentiating aspect of this process is that some companies choose to make their protocol publicly available
so that their technology can be evaluated by anyone before the ICO takes place. An issuer’s technology is arguably
proprietary information; hence we exploit this disclosure policy in our empirical tests. 10 Note that crowdfunding can be debt-based or equity-based, whereas ICOs are more similar to equities. Also, while
the crypto market is unregulated, securities authorities around the world have already put in place some level of
regulation of crowdfunding platforms to protect investors. For example, US firms undergoing equity crowdfunding
can offer and sell their securities without registering with the SEC only if they qualify for exemptions from registration
under Regulation D of the Securities Act of 1933.
14
tokens do not allow any form of control over the issuer. In that sense, tokens are currently not
considered securities; instead, they are viewed as donations to a project that will be used as the
currency for receiving utility through the products or services developed by the entity.
Third, unlike IPOs that are typically conducted by firms with well-established technologies
and products, the vast majority of ICOs are for projects that are at a very early stage, and only a
few of the entities have pre-existing products. In that sense, the investment is much riskier and is
more comparable to pre-series A or series A equity investments in traditional early-stage start-up
companies. Furthermore, from a traditional governance perspective, ICOs fall short on several
dimensions, including the absence of voting rights, anti-dilution protections, specific information
on the use of proceeds, formal reporting and auditing mechanisms, and an elected board of
directors to oversee the entity.
Given the rapidly growing importance of the crypto-tokens market, the increasing
momentum of ICOs completed in recent months, and mounting concerns about investor protection,
various regulators around the world are currently debating the merits of classifying some tokens
as securities, which would bring them under the jurisdiction of existing securities regulation. To
date, however, the regulatory environment remains uncertain.
2.2. The Current State of Regulation in the Crypto Market
Since 2008, the crypto market has evolved through private initiatives of developers that
seek to establish a decentralized market that avoids any type of regulation. Although recent years
have seen a surge in the flow of funds invested in this market, the issuance process for new tokens
and their online trading remain largely unregulated despite a few preliminary regulatory decisions
around the world. For example, the Chinese government banned ICOs in September 2017.
However, commentators believe that the ban will eventually be lifted, since this regulatory action
15
has failed to prevent Chinese citizens from participating in ICOs.11 Similarly, the South Korean
government outlawed ICOs in late September 2017, but the Korean National Assembly recently
proposed regulation that will permit ICOs as long as investor protection mechanisms are
introduced.12 In Japan, the government issued soft ICO-friendly guidelines, while other countries
are currently developing other regulatory frameworks.13,14
In the United States, the SEC is carefully observing this nascent but fast-growing market
and is contemplating potential regulatory actions. For example, SEC Chairman Jay Clayton states
that “The SEC is studying the effects of distributed ledger and other innovative technologies and
encourages market participants to engage with us. We seek to foster innovative and beneficial
ways to raise capital, while ensuring – first and foremost – that investors and our markets are
protected (see Securities and Exchange Commission [2017]).” Last May, the SEC launched a mock
ICO called HoweyCoins (www.howeycoins.com). Investors who try to invest in this token sale
are promptly redirected to an SEC website where they are provided tips on identifying the signs
of fraudulent token sales. This SEC initiative is exploratory and is clearly aimed at protecting
investors through educational tools to avoid scams. The timing and severity of any formal
regulatory action by the US authorities remains uncertain.
So far, the primary issue regarding the regulation of the crypto-tokens market pertains to
the legal definition of tokens. At the moment, issuers are not considered to be offering “securities.”
As a result, they are not regulated under the US Securities Act of 1933 like traditional registered
issuers of equity securities, and therefore issuers do not need to comply with disclosure, liability,
11 https://smartereum.com/2427/chinese-initial-coin-offering-regulations-china-ico-ban-might-end-may-2018/ 12 https://www.coindesk.com/korean-national-assembly-makes-official-proposal-to-lift-ico-ban/ 13 https://www.newsbtc.com/2018/04/05/japan-authorities-legitimize-ico-market-increased-regulation/ 14 At the time of writing, Malta and Thailand recently issued a full regulatory framework for Distributed Ledger
Technology (DLT).
16
and registration requirements. However, recent local rulings in the United States have started to
rely on the Howey Test to classify some tokens as securities.15 The Howey Test has four prongs:
(i) an investment of money is made by the purchaser, (ii) the investment of money is in a common
enterprise, (iii) the success or failure of the enterprise depends entirely on the efforts of a promoter
or a third party, and (iv) there is an expectation of profits from the investment.16 While the first
two prongs of the Howey Test seem to apply to most token sales, there is an ongoing debate on
the applicability of the third and fourth prongs. Opponents of the Howey Test argue that since
many crypto-tokens are based on decentralized blockchain platforms, the success of the token may
or may not depend on the efforts of the members. Furthermore, if a token does not pay dividends
and the buyers claim that their intent is to acquire tokens strictly for their utility value, then they
may be considered customers rather than investors.
Adding to this debate, in a speech delivered on June 14, 2018, the SEC’s Director of
Corporate Finance William Hinman suggests that substance rather than form should determine
whether the sale is an investment contract or not.17 Although the speech was accompanied by a
disclaimer that it expressed the author’s views – and not necessarily the views of the SEC – it is
informative about the evolving regulatory environment for ICOs and the likelihood of future
regulatory actions in this market.
15 https://www.coindesk.com/us-judge-says-boxer-backed-ico-token-is-a-security/ 16 The rule originated from a Supreme Court case of 1946: SEC v. W. J. Howey Co., 328 U.S. 293 (1946). 17 In the speech, Hinman states that “Central to determining whether a security is being sold is how it is being sold
and the reasonable expectations of purchasers.” Hinman further states that “If the network on which the token or coin
is to function is sufficiently decentralized – where purchasers would no longer reasonably expect a person or group to
carry out essential managerial or entrepreneurial efforts – the assets may not represent an investment contract.” This
quotation would suggest that Bitcoin and Ethereum are not securities. Regarding ICOs, Hinman also acknowledged
that some digital assets could be structured more as consumer products than as securities. Thus, it is uncertain whether
all ICOs will be viewed as securities being offered under investment contracts. The full text of the speech is available
here: https://www.sec.gov/news/speech/speech-hinman-061418.
17
The second issue pertains to the question of whether any specific country can enforce its
securities laws on an issuer, since information about the country of origin for the issuers and their
team members is not known for most tokens (see Table 1 Panel B). Still, since the regulatory
environment is evolving and remains unpredictable, the fear of retroactive regulation has led some
token issuers to exclude US, Chinese, and South Korean citizens from their potential investor base.
Currently, crypto tokens are also outside the purview of regulations on crowdfunding.
Since the JOBS Act of 2012, in the United States companies can raise capital through
crowdfunding without necessarily registering with the SEC. Instead, they have to file a Form D
with the SEC and comply with federal laws and rules pertaining to the advertising of the
crowdfunding and the number of accredited and other investors they are allowed to solicit capital
from.
However, it is worth noting that some token issuers have used the Simple Agreement for
Future Tokens (SAFT) framework, which is essentially a forward contract between the issuer and
the investor for the future delivery of tokens, which gives the investor access to the issuer’s product
or service.18 Proponents of the SAFT framework argue that the tokens delivered are products, not
securities, and therefore should not be subject to any securities laws. However, tokens may be
subject to other regulations, such as consumer protection laws. For instance, SAFTs may fall under
Regulation D crowdfunding and be subject to restrictions on the investors they can approach.
Opponents of the SAFT interpret it as a private pre-sale of future public securities and argue that
it could be subject to SEC regulations. Hence, they see no future for the SAFT framework in the
18 The SAFT framework was developed in October 2017 by attorney Marco Santori and the team behind Filecoin
(Protocol Lab), a token issuer. SAFT is an agreement to issue coupons for tokens to be issued at a future date when
the platform they can be used on is complete.
18
crypto-tokens market.19 Moreover, if issuers consider their tokens as products rather than
securities, this could open further debate about whether the sale of tokens should generate taxable
income for the issuer. The desire to avoid income taxes on tokens could thus explain why some
issuers are located in tax haven countries (see Table 1 Panel B). Overall, the regulatory
environment for initial coin offerings remains uncertain.
2.3. The Role of Information in the Crypto-tokens Market
Prior research suggests that firms disclose information to signal their future prospects, and
this disclosure has positive capital market consequences through the reduction of information
asymmetry between the firm and investors (e.g., Spence [1973], Grossman [1981], Hughes [1986],
Verrecchia [2001]). Furthermore, information allows certain investors to develop expectations about
the future prospects of the firm, and these expectations influence trading activity between investors.
However, it is unclear whether traditional theories about disclosure and signaling from regulated
capital markets also apply to the unregulated crypto capital market. For example, the absence of
disclosure regulation and enforcement institutions may reduce the credibility and quality of disclosed
information, which may affect market quality (e.g., Brüggemann et al. [2018]).
Estimating the value of crypto-tokens is challenging since information about future cash
flows is seldom provided, and it is unclear whether traditional asset pricing theory applies in this
setting. Further, the expected payoff from crypto-tokens depends on the intention of token holders,
either as customers or as investors. As mentioned previously, the crypto-tokens market includes
utility coins that grant their holders access to the token’s ecosystem, product, or service, which
makes these token holders more similar to customers than investors. A small proportion of crypto-
19 https://www.crowdfundinsider.com/2018/03/131044-initial-coin-offerings-why-the-saft-is-dead/. Consistent with
this view, the SEC has issued subpoenas and information requests to SAFT issuers (https://www.wsj.com/articles/sec-
launches-cryptocurrency-probe-1519856266?mod=searchresults&page=1&pos=1).
19
tokens also offer holders the prospect of dividends or token repurchases at some future date, which
makes these token holders more similar to investors. Both types of holders can also sell their tokens
in the secondary market. Thus, the expected payoffs from crypto-tokens could be a combination
of the value derived from the ecosystem of the crypto-token (i.e., utility), the prospects of future
distribution of profits, and the expected resale price. We view these expected payoffs as the future
prospects of the issuer and the ICO. We conjecture that different token holders value these
expected payoffs differently, which could influence utility-driven or speculative trading activity.
Our empirical tests are motivated by the notion that stronger information environments may enable
investors to develop more precise expectations about future prospects.
Issuers wanting to raise capital through ICOs choose to provide two main forms of
disclosure to signal the quality of their tokens to potential investors: a whitepaper and the technical
sourcecode. Whitepapers are unaudited marketing documents released by issuers that may contain
information about the business proposition for the crypto-token, addressable market opportunity,
technology, proof-of-concept case studies, expected progress timeline, identity and background of
the team, ICO process, token distribution, vesting restrictions, and use of proceeds, among other
information. However, the level of detail and the type and quantity of information vary
significantly, with the length of the whitepaper ranging from 2 pages to 94 pages in our sample
(median of 24 pages), and some issuers not releasing whitepapers altogether. For example, some
issuers do not even disclose the identity of the team or founders.20 While the whitepaper is not
audited, the sourcecode can be verified by external industry participants since the issuer can choose
to release it on an online code repository, such as Github. However, releasing the sourcecode also
likely reveals proprietary information about the issuer’s technology. We believe that issuers release
20 Appendix C provides information, including the table of contents for three sample whitepapers.
20
the whitepaper and sourcecode to signal the quality and future prospects of their tokens with the
intent to differentiate themselves from lower-quality projects. Our empirical analyses seek to
exploit this cross-sectional variation in disclosure of information.
The information environment of ICOs is also characterized by other sources of information
that may enable investors to distinguish between low- and high-quality tokens. First, issuers
typically use several disclosure channels – such as their website and social media platforms – to
disseminate information about the ICO process and other attributes of the token (e.g., number of
tokens being offered, length of the ICO marketing period, vesting periods for founders and team
members, use and proceeds, etc.). Further, social media channels including Bitcointalk, Twitter,
Reddit, and Telegram serve as forums where potential investors acquire information about token
issuers and discuss the future prospects of the tokens being issued.
Second, and most important, this unregulated market has seen the rise of information
intermediaries that aim to reduce the information asymmetry among investors. Currently, we
observe two main types of information intermediaries: (i) information aggregators, and (ii) ICO
rating providers. Information aggregators disseminate crypto-market news and events; market
statistics; and information about past, ongoing, and future ICOs. Examples include coindesk.com,
coinschedule.com, tokendata.io, smithandcrown.com, and cointelegraph.com, among others. ICO
rating providers differ in that they analyze token issuers and various attributes of the ICO to
provide an overall assessment of the quality of the token and its future prospects. The business
model of ICO rating providers is based on “pseudo” independent crypto experts and algorithms
that provide ratings that should capture the overall quality of information provided by the token
21
issuer, as well as the overall quality of the token.21 Examples include icobench.com, icorating.com,
icodrops.com, and icoalert.com, among others. Based on data from Alexa, a website traffic
statistics provider, the combined monthly traffic on these websites was 5.4 million sessions as of
January 2018, with icobench.com being amongst the most frequently visited.22 These statistics
suggest that these rating providers are an important source of information and analysis for investors
in the crypto-tokens market.
The emergence of these rating providers presents an interesting setting, as their usefulness
in the information environment of the ICO market remains unclear. It is therefore important to
assess whether the rating providers are independent and free of conflicts of interest. Indeed, the
rating providers are aware of this concern and emphasize their independence on their website.
However, we observe that several of the rating providers accept payments from token issuers for
more prominent placement on their website, or for assistance in the marketing efforts for their
ICO. While rating providers highlight that these payments are not contingent on receiving a rating,
and that a paid listing does not affect the process of evaluation and does not improve ratings, it is
possible that the ratings are not neutral.23
21 While some rating providers conduct in-house analysis, we use ICObench.com, which bases ratings on (1) external
crypto experts who are invited to voluntarily contribute their views, and (2) an automated ratings engine (“Benchy”)
that evaluates over 20 ICO characteristics. Importantly, the external crypto-experts are not paid by ICObench.com.
Instead, these crypto-experts appear to be motivated by their desire to develop a positive reputation within the crypto
community. Our sample from ICObench covers 172 crypto-experts including ICO advisors, investors, entrepreneurs,
founders, and blockchain consultants, among others. On average, these crypto-experts provide ratings on 22 ICOs. 22 As of January 16, 2018, the monthly traffic on icobench.com was 1.67 million sessions, with an average session
duration of 2 minutes and 54 seconds. In terms of traffic sources, 63.4% of the site visitors came through search
engines, while 36.6% came directly by entering the website URL or through referrals. The highest percentage of
visitors originated from the USA (13.8%), followed by Russia (6.8%) and the United Kingdom (6.3%). Further, we
observe discussions on ICO messaging channels, which suggest that potential ICO investors use these external rating
providers as part of their due diligence and investment decision-making process. 23 To clarify this point, we contacted icorating.com and spoke directly to one of the lead managers on the team. The
manager emphasized the independence of the rating process, regardless of the payments received for a premium
listing.
22
Despite these concerns about the independence of ICO rating providers, their ratings could
still be informative. Indeed, past studies in more traditional capital markets have established that
despite the existence of (strategic) biases by analysts during IPOs or assessment by credit rating
agencies, their earnings forecasts and credit ratings are still, on average, informative in both equity
and debt markets (Hilary and Hsu [2013], Fracassi et al. [2016]). In sum, if ratings from the ICO
rating providers are able to capture cross-sectional variation in the quality of ICOs, they could be
informative about future capital market outcomes. Further, if these ratings are useful for investors,
this could be evidence in support of the monitoring role of these information intermediaries in the
unregulated crypto capital market.
3. Empirical Research Design and Data Description
3.1. Empirical Strategy
In order to assess the role of disclosure and information in the unregulated crypto capital
markets, we conduct a series of tests to examine whether the information set available to investors
is associated with cross-sectional variation in capital market outcomes, such as entry, survival, and
trading activity. First, we examine the likelihood of successful ICO completion and the amount of
funds raised during the ICO. Second, we examine the survival of ICOs by looking at post-ICO
crash risk, measured as extreme negative returns and skewness in returns. Third, we examine
secondary market measures that securities regulators typically focus on in order to assess market
quality – i.e., illiquidity and volatility. Finally, we study short-horizon returns for ICOs over the
first day of trading on crypto exchanges.
We use various measures to approximate the information set available to participants that
may assist them in developing their expectations about the future prospects of the issuer.
23
Specifically, our measures are designed to capture two dimensions of the potential information set
of investors. The first dimension is the overall information environment of the issuer and the ICO
based on public disclosure. Our measures of the information environment include (1) choice to
disclose business and technology information through whitepapers and released source code; (2)
disclosed information about various attributes of the issuer and the ICO such as team information
and past performance, funding requirements, and blockchain platform information; and (3) social
media presence and activity. In our view, social media presence and activity help to measure both
the dissemination of information by the issuer, and discussions about the ICO and its future
prospects amongst investors. The second dimension is the overall assessment of the quality of the
token based on ‘ratings’ provided by external information intermediaries that have emerged in the
crypto-tokens market. These external rating providers aggregate various elements of the token’s
quality and information environment, and they may be useful to investors in developing their
expectations about the future prospects of the token.
Our empirical strategy seeks to maximize the power of our tests for the different outcome
variables we examine. We begin by utilizing the full sample of 776 attempted ICOs between April
2014 and February 2018 – out of which 659 were successfully completed – to conduct univariate
analyses. Specifically, we test whether completed and failed ICOs are significantly different in
terms of our information environment measures and the overall ratings of token quality. We then
shift to a multivariate framework, utilizing a reduced sample based on data availability, and
estimate the following three specifications across several outcome variables.
Our first cross-sectional specification includes several explanatory variables that capture
the information environment of the token issuer and the ICO, as follows:
24
𝑦𝑖 = 𝛼0 + 𝛽1𝐵𝑇𝐶 𝑀𝑜𝑚𝑒𝑛𝑡𝑢𝑚𝑖 + 𝛽2𝑆𝑜𝑓𝑡 𝐶𝑎𝑝 𝑅𝑒𝑞𝑢𝑖𝑟𝑒𝑚𝑒𝑛𝑡𝑖 + 𝛽3𝑈𝑆𝐴 𝑅𝑒𝑠𝑡𝑟𝑖𝑐𝑡𝑒𝑑𝑖
+ 𝛽4𝑃𝑙𝑎𝑡𝑓𝑜𝑟𝑚 𝐼𝑛𝑓𝑜𝑟𝑚𝑎𝑡𝑖𝑜𝑛𝑖 + 𝛽5𝑆𝑜𝑢𝑟𝑐𝑒𝑐𝑜𝑑𝑒/𝐺𝑖𝑡ℎ𝑢𝑏 𝐷𝑖𝑠𝑐𝑙𝑜𝑠𝑢𝑟𝑒𝑖
+ 𝛽6𝐼𝐶𝑂 𝑇𝑒𝑎𝑚 𝑆𝑖𝑧𝑒𝑖 + 𝛽7𝑃𝑎𝑠𝑡 𝑆𝑢𝑐𝑐𝑒𝑠𝑠 𝑜𝑓 𝐼𝐶𝑂 𝑇𝑒𝑎𝑚𝑖
+ 𝛽8𝐼𝐶𝑂 𝑃𝑎𝑟𝑡𝑖𝑐𝑖𝑝𝑎𝑡𝑖𝑜𝑛 𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒𝑠𝑖 + 𝛽9𝑆𝑜𝑐𝑖𝑎𝑙 𝑀𝑒𝑑𝑖𝑎 𝐴𝑐𝑡𝑖𝑣𝑖𝑡𝑦𝑖
+ 𝛽10𝐼𝑛𝑓𝑜𝑟𝑚𝑎𝑡𝑖𝑣𝑒 𝑊ℎ𝑖𝑡𝑒𝑝𝑎𝑝𝑒𝑟𝑖 + 𝐶𝑜𝑢𝑛𝑡𝑟𝑦 𝐼𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟𝑠 + 𝑄𝑢𝑎𝑟𝑡𝑒𝑟 𝐹𝐸 + 𝜀𝑖
(1)
where 𝑦𝑖 refers to each of our outcome variables of interest (described in detail in sub-sections 3.2
through 3.5). We include BTC Momentum, which captures the prevailing market conditions at the
time of the ICO based on the past (three-month) return performance of the Bitcoin crypto-currency.
Since Bitcoin is the dominant and most visible crypto-currency, this variable captures market
sentiment, i.e., how ‘hot’ the crypto capital markets are, at the time of the ICO. Several crypto-
market commentators have argued that ICO teams may opportunistically take advantage of
overheated crypto markets when launching fraudulent or scam ICOs.
We then include the following attributes of the issuer and the ICO that are made available
to investors during the ICO process. We note that some of these variables also capture explicit
disclosure choices of ICO teams: (1) Soft Cap Required is an indicator variable that equals 1 if the
ICO has a stated minimum threshold for the amount of funds that need to be raised before the ICO
is deemed successfully complete, and 0 otherwise. (2) USA Restricted is an indicator variable that
equals 1 if the ICO team restricts US-based investors from participating in the capital raise, and 0
otherwise. (3) Platform Information is an indicator variable that equals 1 if potential ICO investors
are informed about the specific blockchain platform for the token (e.g., Ethereum, Waves, etc.),
and 0 otherwise. (4) ICO Team Size is measured as the natural logarithm of the number of disclosed
team members and serves as a proxy for the size of the issuer. (5) Past Success of Team is measured
as the proportion of team members that have previously been involved in a successful ICO. (6)
25
ICO Participation Incentives is an indicator variable that equals 1 if the issuer offers bonuses,
bounties, or other incentives to ICO investors to incentivize participation, and 0 otherwise.
To capture a key disclosure choice, we include an indicator variable that equals 1 if the
entity chooses to share its sourcecode on a publicly available code repository or through GitHub
disclosures (Sourcecode/Github Disclosure), and 0 otherwise.24 We also include a proxy for the
social media presence and activity of the issuer that is based on an aggregated measure of social
media disclosures (Social Media Activity) provided by ICObench.com. In the crypto market,
issuers communicate with crypto-market participants primarily through various social media
channels, such as Twitter releases, as well as through Medium, Telegram, and Reddit, among
others.25 In addition to serving as channels for dissemination of information by the issuer, social
media platforms serve as the primary forums for investors to discuss the future prospects of the
issuer. Thus, Social Media Activity is designed to capture news flow and hype about the ICO. In
addition, we capture variation in the quality of whitepaper disclosure by including an aggregated
indicator measure of whitepaper informativeness (i.e., Informative Whitepaper) based on an
assessment provided by ICObench.com.
Finally, we include country indicators for ICOs originating in the USA, Russia, China, and
Singapore, because these countries contribute a significant number to our sample. Moreover, the
uncertain and tightening regulatory environment within the USA and China, when contrasted with
24 Due to data limitations, the inclusion of ICO attributes results in a reduced sample where virtually all remaining
ICOs disclose a whitepaper; hence we are unable to include a simple indicator capturing the choice to disclose a
whitepaper (Whitepaper Disclosed). However, we examine this variable in univariate tests. 25 We note that some of the more recent, larger ICOs have started to formalize the investor communication process by
organizing roadshows and soliciting advice from consulting firms. However, we are not aware that any traditional
investment banks have sponsored or underwritten an ICO to date, perhaps due to more stringent compliance and
regulatory requirements for broker-dealers.
26
the laissez-faire approach of Russia and Singapore, may generate cross-sectional variation in our
variables of interest. We also include quarter fixed effects to control for unobservable time effects.
To gain further insight into information disclosed in whitepapers, we replace the indicator
variable Informative Whitepaper with detailed disclosure measures (the vector W in equation 3
below) that we develop by manually reading and coding the content of whitepapers and by
conducting textual analysis:
𝑦𝑖 = 𝛼0 + 𝛽1𝐵𝑇𝐶 𝑀𝑜𝑚𝑒𝑛𝑡𝑢𝑚𝑖 + 𝛽2𝑆𝑜𝑓𝑡 𝐶𝑎𝑝 𝑅𝑒𝑞𝑢𝑖𝑟𝑒𝑚𝑒𝑛𝑡𝑖 + 𝛽3𝑈𝑆𝐴 𝑅𝑒𝑠𝑡𝑟𝑖𝑐𝑡𝑒𝑑𝑖
+ 𝛽4𝑃𝑙𝑎𝑡𝑓𝑜𝑟𝑚 𝐼𝑛𝑓𝑜𝑟𝑚𝑎𝑡𝑖𝑜𝑛𝑖 + 𝛽5𝑆𝑜𝑢𝑟𝑐𝑒𝑐𝑜𝑑𝑒/𝐺𝑖𝑡ℎ𝑢𝑏 𝐷𝑖𝑠𝑐𝑙𝑜𝑠𝑢𝑟𝑒𝑖
+ 𝛽6𝐼𝐶𝑂 𝑇𝑒𝑎𝑚 𝑆𝑖𝑧𝑒𝑖 + 𝛽7𝑃𝑎𝑠𝑡 𝑆𝑢𝑐𝑐𝑒𝑠𝑠 𝑜𝑓 𝐼𝐶𝑂 𝑇𝑒𝑎𝑚𝑖
+ 𝛽8𝐼𝐶𝑂 𝑃𝑎𝑟𝑡𝑖𝑐𝑖𝑝𝑎𝑡𝑖𝑜𝑛 𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒𝑠𝑖 + 𝛽9𝑆𝑜𝑐𝑖𝑎𝑙 𝑀𝑒𝑑𝑖𝑎 𝐴𝑐𝑡𝑖𝑣𝑖𝑡𝑦𝑖 + ∑ 𝜑𝑾
+ 𝐶𝑜𝑢𝑛𝑡𝑟𝑦 𝐼𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟𝑠 + 𝑄𝑢𝑎𝑟𝑡𝑒𝑟 𝐹𝐸 + 𝜀𝑖
(2)
The detailed whitepaper disclosure variables include ICO Team Information, Token
Allocation Information, Founder Tokens Vesting Period, Use of Proceeds, Whitepaper Opacity,
and Whitepaper Length. We include an indicator variable for ICO Team Information that captures
whether issuers provide disclosure in their whitepapers of valid biographical information about
their team, including team members’ education, professional certifications, and prior work
experience. Token Allocation Information captures whether or not ICO entities provide detailed
disclosure about the allocation of tokens (e.g., percentage allocated to founders, reserves, public
ICOs, advisors, early adopters, angel investors, etc.). Founder Tokens Vesting Period captures
information about vesting restrictions attached to insider tokens, measured as the minimum
number of years over which founder tokens vest.26 Motivated by prior research that examines the
26 We note that some issuers refer to vesting restrictions and lock-up periods interchangeably. Where vesting
information is not provided or missing, we code this variable as zero.
27
consequences of disclosures in IPO prospectuses (e.g., Leone et al. [2007], Daily et al. [2005]), we
also code whether or not ICO entities provide information about the Use of Proceeds for the funds
raised through the ICO. This variable captures how issuers plan to spend the ICO proceeds and
conveys whether the ICO team has a defined vision/plan. We borrow from the computational
linguistics literature and other accounting studies (e.g., Li [2008]) and compute the readability of
the whitepaper (Whitepaper Opacity). In addition, we include a measure of the quantity of
disclosure based on Whitepaper Length.
In our final specification, we replace our disclosure and information environment variables
with an aggregate measure of issuer and token quality (i.e., Rating):
𝑦𝑖 = 𝛼0 + 𝛽1𝐵𝑇𝐶 𝑀𝑜𝑚𝑒𝑛𝑡𝑢𝑚𝑖 + 𝛽2𝑆𝑜𝑓𝑡 𝐶𝑎𝑝 𝑅𝑒𝑞𝑢𝑖𝑟𝑒𝑚𝑒𝑛𝑡𝑖 + 𝛽3𝑈𝑆𝐴 𝑅𝑒𝑠𝑡𝑟𝑖𝑐𝑡𝑒𝑑𝑖
+ 𝛽4𝑅𝑎𝑡𝑖𝑛𝑔𝑖 + 𝐶𝑜𝑢𝑛𝑡𝑟𝑦 𝐼𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟𝑠 + 𝑄𝑢𝑎𝑟𝑡𝑒𝑟 𝐹𝐸 + 𝜀𝑖
(3)
While there are no institutionalized governance mechanisms in place in the emerging
crypto markets, self-governance has emerged in the form of monitoring by external rating
providers who provide ratings on the quality of tokens based on several dimensions and also serve
as information intermediaries. A few of the most prominent platforms are ICObench.com,
ICOrating.com, ICOdrops.com, and ICOalert.com, all of which provide assessments of upcoming,
current, and past ICOs. While ratings from ICOrating, ICOdrops, and ICOalert are internally
developed based on in-house analysis, ICObench.com bases its ratings on (1) external crypto
experts that are invited to contribute their views voluntarily, and (2) an automated ratings engine
(“Benchy”) that evaluates over 20 ICO characteristics. Since the rating methodology of
ICObench.com is based on an algorithm that combines various ICO attributes in a relatively
transparent manner, and it relies on a community of crypto-experts rather than internal ratings, we
28
expect ICObench.com ratings to be less subject to bias; hence we employ this measure in our
analysis.27 We expect the ratings from ICObench.com to capture the overall quality of information
provided by the token issuer, as well as the overall quality of the token based on the combined
assessments of Benchy and the crypto experts.
3.2. Determinants of ICO Completion and Capital Raised
Our first set of analyses examines the likelihood of successfully completing an ICO. We
estimate equations (1) through (3), as described above, where our outcome variable, 𝑦𝑖, equals
Completed, an indicator variable that equals 1 if the ICO is successful in raising funds and the
raised funds exceed the minimum threshold stipulated by the token issuer (i.e., the soft cap), and
0 otherwise. From our final sample of 776 attempted ICOs between April 2014 and February 2018,
we estimate equation (1) using a reduced cross-section of 400 attempted ICOs (due to data
availability), out of which 365 were successfully completed.
We then focus on successfully completed ICOs and examine determinants of the amount
of funds raised by the token issuers. Specifically, our variable of interest is the natural logarithm
of the amount of funds in US dollars raised during the ICO. We expect our disclosure, information
environment, and aggregate rating variables to be positively associated with the amount of funds
raised during the ICO.
3.3. Crash Risk / Failure
Next, we examine the association between our measures of the information set available to
investors and ICO survival, as captured by price crash risk. Our outcome variable, 𝑦𝑖, is either
Extreme Negative Returns or Negative Return Skewness, measured over the three-month, six-
27 We describe the ratings data and our measures in detail in sub-section 3.6.
29
month, or twelve-month period after the completion of the ICO. Extreme Negative Returns is an
indicator variable that captures ICOs that experience extreme negative returns, i.e., less than –
75%, in any of the three windows, and it is adapted from Brüggemann et al. [2018]. Negative
Return Skewness is –1 multiplied by the coefficient of return skewness in the ICO period. We omit
Soft Cap Requirement from these specifications, as it is unlikely to be related to post-ICO failure.
Yet we include opening day ICO returns (i.e., Log open-to-close ICO Return) in order to provide
some insight into the recent criticism that unregulated crypto markets are susceptible to “pump
and dump” strategies by ICO teams. Following Brüggemann et al. [2018], we also control for ex
ante return volatility. Since we do not have trading data until after the ICO, we are unable to
compute lag return volatility. Hence, we include Return Volatility 1m computed using daily returns
over the one month after the ICO. Finally, we are unable to include quarter fixed effects in the
crash risk analysis, since we do not observe a crash in every quarter. However, we include BTC
Momentum, which helps capture common firm-invariant time effects since this variable does not
vary in the cross-section of ICOs that occurred at the same time.
3.4. Post-ICO Liquidity and Volatility
We then examine market quality in the form of post-ICO illiquidity and volatility over the
three months after the ICO. If disclosures help to reduce information asymmetry in the crypto
capital market, we would expect a negative relation with illiquidity and volatility.28 Our first
outcome variable, 𝑦𝑖, is the Amihud [2002] measure of Illiquidity computed over the three months
(90 days) after the completion of the ICO. We also control for contemporaneous return volatility
in order to isolate the effects of illiquidity that are unrelated to volatility. We then examine Return
28 Prior research suggests that information asymmetry is an important driver of the cost of capital (e.g., Easley and
O’Hara [2004], Lambert et al. [2007, 2011]).
30
Volatility separately as our outcome variable, 𝑦𝑖, which is measured over the three months (90
days) after the completion of the ICO using daily returns.
3.5. ICO Return Performance
Finally, we examine the first-day returns for the ICO as our outcome variable, 𝑦𝑖 . We
measure ICO returns in two ways. The first measure is the natural logarithm of the return from the
ICO offer price to the closing price on the first day of trading (Log Total ICO Return), consistent
with “first-day” returns studied in the IPO literature (e.g., Loughran and Ritter [2004]; Beatty and
Ritter [1986]). However, given the dynamics of the ICO process (i.e., pre-ICO offerings and
participation incentives such as discounts and bonus allocations), there are potential measurement
errors in estimating the price in US dollars at which initial investors are allocated tokens in the
ICO (i.e., the ICO offer price). Further, not all issuers publicly disclose the final offer price or the
amount of funds raised through the ICO. Therefore, ICO returns that rely on the ICO offer price
are available only for a subset of ICOs. Consequently, we employ a second measure that relies
only on token trading in the secondary market: the natural logarithm of the ICO’s first day return
from the opening price to the closing price (Log open-to-close ICO Return). To the extent that
more informative disclosures enable investors to better evaluate the prospects of the ICO, we
would expect our information environment and rating variables to be associated with ICO returns.
We do not have an ex ante prediction about the sign of the relation, since over- and underpricing
seem equally likely in the ICO market.
3.6. Data Description
We collect information on ICOs from several data sources and websites. Our final sample
includes 776 ICOs attempted between April 2014 and February 2018, which we identified using
various websites including coinmarketcap.com, coindesk.com, tokendata.io, coinschedule.com,
31
smithandcrown.com, ICOrating.com, and ICObench.com. Using multiple data sources enables us
to verify the accuracy of the ICO characteristics we capture and to combine the ICOs covered by
each website in order to maximize the observations we collect. We categorize 659 ICOs as being
successfully completed according to information from the above sources. An ICO is deemed
successful if it raises funds exceeding the minimum threshold stipulated by the token sellers
(referred to as the soft cap). Within our sample, approximately 25% of ICOs had a soft cap
requirement. See Table 1 for the sample composition over time and by country.
For the 659 successfully completed ICOs, we collect secondary market data through the
middle of May 2018, which gives us at least three months of post-ICO trading data needed for our
analysis of secondary market outcomes and crash risk. Coinmarketcap.com has emerged as the
leading data provider with the most comprehensive secondary market data about traded crypto-
currencies and crypto-tokens. However, the website is dynamically changing, with new ICOs
being added as trading commences and old ICOs that are no longer traded being dropped.29 The
mean (median) length of the ICO period during which the entity tries to raise capital is 29 (30)
days (see Figure 1, Panel A). Trading typically commences within 30 days after the completion of
the ICO token allocation process (i.e., the ICO closing), with a mean (median) of 29 (17) days. We
collect daily closing prices and trading volumes through May 15, 2018 for all active and inactive
tokens as of February 28, 2018. We use closing prices to compute post-ICO returns over the
horizon required for our total crash risk measure (i.e., three months, months months, or twelve
months after the ICO). Using daily returns and volume, we compute a measure of liquidity (i.e.,
29 ICOs that are no longer actively traded are dropped by coinmarketcap.com, although historical data for these inactive
ICOs are still made available.
32
Illiquidity). We also compute the volatility of daily returns over the three-month period after the
ICO.
We collect the amount of funds raised in US dollars from successful ICOs by combining
data from the various ICO data providers. While the various websites collectively classify 659
ICOs as “successful,” detailed information about funds raised is available for only a subset of 245
ICOs. However, this subset provides almost complete coverage of the larger ICOs that have better
availability of information. See Appendix B for a list of the ten largest ICOs in each of the previous
three years.
For our ICO returns analysis, we collect data on the ICO offer price. The ICO offer price
is typically quoted as the value per token in US dollars, Bitcoins, Ethereum, or some other fiat or
crypto-currency. We are able to collect the ICO offer price for a subset of 300 ICOs from the
various data providers. We manually convert all ICO prices into US dollars using the exchange
rate for the relevant fiat or crypto-currency on the date of the ICO. We measure the ICO returns
when the ICO price data become available on coinmarketcap.com, which can vary from 7 to 30
days after the completion of the ICO. Hence, ICO returns that rely on the ICO offer price will
inevitably contain some measurement error.
Finally, we collect information about ICO ratings from ICObench.com, which provides a
rating assessment of all upcoming, current, and past ICOs on various dimensions. The rating
methodology of ICObench.com is based on a combination of (1) an ICO profile rating generated
by an automated rating engine called Benchy that evaluates more than 20 different criteria (e.g.,
social media activity, team information, ICO information, whitepaper informativeness), and (2) a
rating of the ICO team, vision, and the proposed product, voluntarily provided by independent
crypto experts who follow a suggested rating methodology. The overall rating ranges from 0 to 5
33
and is a weighted average of the Benchy ratings and the average crypto-expert rating, with
individual weights on different crypto-experts being determined by the quality and activity of the
experts in the crypto community. Within the sample of 400 attempted ICOs that we use for the
multivariate analysis, all have a Benchy rating, and approximately 60% also have ratings provided
by crypto experts; amongst these, the mean (median) number of analysts contributing a rating is 5
(2).
4. Empirical Results
4.1. Summary Statistics
As Panel A of Figure 2 shows, the aggregate ICO market has experienced rapid growth,
with over 650 entities raising $13 billion over the February 2014 to May 2018 period. In
comparison, according to Renaissance Capital, 188 VC-backed IPOs in the USA raised total
proceeds of $24 billion over the 2015–2017 period. While the ICO market is still smaller than the
traditional IPO market, this comparison highlights its economic magnitude. Panel B of Figure 2
plots the cross-sectional distribution of first day ICO returns as well as post-ICO performance over
the 30 days after the ICO. The first day median (mean) return is positive 6% (14%), suggesting
that ICOs are slightly underpriced. However, over the 30 days after the ICO, the first day increase
dissipates quickly and the median return is negative 30%, while the mean return is positive 39%.
This large dispersion in post-ICO returns points to the influence of outsized returns from a few
successful ICOs pushing up the average, while the majority perform significantly poorly. We also
examine the overall performance of the crypto-tokens market by developing value-weighted and
equal-weighted market indices, which corroborate the explosive growth in the value of this market
since 2014. Figure 3, Panel A plots the value-weighted index performance on a logarithmic scale,
while Panel B plots the equal-weighted index performance. A $1 investment in the aggregate
34
(value-weighted) crypto-tokens market index in August 2014 would have increased in value to
$4,621 by May 15, 2018.
Panel A of Table 1 reports the frequency of completed ICOs over time. We observe a
dramatic uptick in the number of ICOs in October 2017 onwards, in line with the hype and
significant price increases of Bitcoin, which suggests that ICO teams may have been harnessing
Bitcoin momentum when trying to raise capital. Panel B reports the number of ICOs per country
of origin. We note that just over 50% of attempted ICOs are unable to be traced to a particular
country. While no single country dominates our full sample, we observe that a non-trivial portion
of ICOs within our sample are based in the USA (85), Russia (43), and Singapore (32). In terms
of sector focus, we find that the ICOs that reported sector information (just over 52%) are evenly
spread between Business Services, Financial, Media & Entertainment, Platform, and Technology.
Finally, Panel D shows that Ethereum is the overwhelming platform of choice for tokens, with
close to 70% of issuers choosing to utilize this blockchain platform.
Panel A of Table 2 reports the descriptive statistics for the primary outcome variables. To
mitigate the influence of outliers and data errors, we trim all variables at the 1% level. A few
observations are worth making. First, the median entity raised $6.8 million through the ICO. This
compares to the median offering size of $95 million for US-based IPOs in 2016. Second, the
median total ICO return (from ICO offer price to the close price on the first day of trading) is 40%
in log returns (49% in raw returns), which points to significant underpricing in the ICO market.
Panel B of Table 2 reports descriptive statistics for the ICO attributes, as well as the disclosure and
information environment variables for the entities in our sample. It is interesting to note that more
than 50% of the entities disclose sourcecode information, while more than 75% of the entities
35
release whitepapers. Panel C of Table 2 provides descriptive statistics on the timing and key events
of the ICO process.
Table 3 reports correlations between the various variables. Log USD Raised is negatively
correlated with Illiquidity and positively correlated with ICO Team Size, Social Media Activity,
Whitepaper Length, and Rating. The ICO return variables are not significantly correlated with
many of the other variables. Illiquidity and Return Volatility are positively correlated, consistent
with these two variables capturing similar effects on information asymmetry. Similarly, Extreme
Negative Returns and Negative Return Skewness are positively correlated. Finally, Rating is
significantly correlated with several of the other variables.
4.2. Univariate Results
As a first step, we report univariate relations between several measures of information
environment and overall token quality ratings, and the likelihood of successful ICO completion.
Panels A through H of Table 4 present mean values for information environment and ratings
measures for completed and failed ICOs, along with statistical tests of the differences in means.
We are able to utilize our full sample of 776 attempted ICOs, comprised of 659 completed and 117
failed ICOs. We find that 51% of issuers that successfully complete an ICO disclosed their
sourcecode on a publicly available code repository or through GitHub disclosures
(Sourcecode/Github Disclosures), as compared to only 15% of issuers who failed in their capital
raise. In contrast, we find that more failed issuers (88%) than successful issuers (78%) choose to
release a whitepaper. These findings suggest that the type of disclosure matters to market
participants, and capital providers tend to prefer verifiable technical information as opposed to the
unaudited marketing and “soft” information typically disclosed in whitepapers.
36
When we condition on ICOs that disclose whitepapers (Panel C), we find that successfully
completed ICOs release more informative whitepaper disclosures (Informative Whitepaper).
Therefore, while simply releasing a whitepaper in an unregulated environment is relatively
costless, issuers can distinguish themselves by providing more informative disclosures to help
participants assess the future prospects of the ICO. With respect to social media presence and
activity, Panel D reports that successful issuers tend to have a significantly higher Social Media
Activity than unsuccessful issuers, which indicates the importance of establishing a strong
information environment vis-à-vis dissemination through social media platforms, allowing
participants to share their views on future prospects and generate hype around the upcoming ICO.
Shifting to disclosed ICO attributes and potential governance mechanisms, in Panel E we
find that completed ICOs tend to have significantly higher Ratings than failed ICOs. This suggests
that even in unregulated markets, information intermediaries will naturally evolve, and ICO market
participants find their assessments credible. Conditional on disclosing token allocation to founders,
issuers that report longer vesting periods for insider tokens are much more likely to successfully
complete their capital raising (Panel F). This evidence is consistent with empirical research in the
IPO literature that finds a positive relation between lock-up periods and IPO performance (Bessler
and Kurth [2007], Ertimur et al. [2012]), suggesting that there is a role for traditional governance
mechanisms in the crypto capital market. Finally, ICO teams with greater experience in the ICO
markets are significantly more likely to successfully complete their ICO (Panel G), whereas
disclosing information regarding the use of proceeds does not seem to predict successful
completion (Panel H).
37
4.3. Determinants of ICO Capital Raise
Table 5 reports results from our multivariate logit analysis of the determinants of ICO
capital raise. We estimate equations (1) through (3) as described in section 3.1. In columns (1)
through (3), we examine the likelihood of successfully completing an ICO, and in columns (4)
through (6), we examine the amount of funds raised by the token issuers, conditional on successful
completion. In column (1), utilizing a reduced cross-section of 400 attempted ICOs with all
necessary data, we find a negative but insignificant coefficient on Sourcecode/Github Disclosure,
ICO Team Size, Past Success of ICO Team, and ICO Participation Incentives. In contrast, we find
that the likelihood of successfully completing an ICO is positively and reliably associated with
Platform Information, Informative Whitepaper, and Social Media Activity. This points to the
importance of providing informative disclosures and establishing a social media footprint to enable
news flow for a successful ICO capital raise. However, when replacing our aggregate Informative
Whitepaper variable with our manually coded whitepaper disclosure measures, we do not observe
any association with these measures (column 2), which again suggests that ICO success is likely
driven more by newsflow and hype than by whitepaper disclosures. Not surprisingly, we find a
reliably negative association with Soft Cap Required. This association is likely mechanical because
issuers with a minimum funding threshold may not clear this hurdle, whereas other issuers impose
no such constraint. Interestingly, we find that ICOs that restrict US investors from participating
are more likely to be successfully completed. One interpretation of this result is that prohibiting
US investors may reduce the risk of potential future SEC regulation and intervention, thereby
increasing participation in these ICOs.
In column (3) we replace our information environment proxies with Rating, which is an
aggregate measure that we expect to capture overall quality of the issuer and token. We find that
38
Rating is positively associated with the likelihood of completing an ICO, which indicates that
information intermediaries in the crypto market appear to capture quality, and that crypto-market
participants view them as being a credible input in the investment selection and due diligence
process.
Turning our attention to the subsample of successfully completed ICOs, we examine the
determinants of the amount of funds raised by issuers (Log USD Raised). Columns (4) through (6)
report these results for a reduced sample. Consistent with our analysis of the likelihood of success,
we find that prohibiting US investors from participating is strongly associated with raising more
capital, which indicates the willingness of ICO participants to fund projects that are less likely to
be subject to potential SEC regulatory intervention. With respect to disclosure, we find that
Sourcecode/Github Disclosure, while positive, is not reliably associated with the amount of funds
raised. However, in contrast to our analysis of success, we find that ICO Team Size is positively
related to the amount of funds raised, while ICO Participation Incentives is negatively associated
with the amount of funds raised. While offering incentives to participate may increase investor
interest, offering bonuses and bounties to ICO participants appears to reduce the average funds
raised relative to ICOs that do not offer such discounts and incentives. Also consistent with our
analysis of success, Social Media Activity yields a positive and significant coefficient, highlighting
the importance of hype and information dissemination among ICO participants. However, Social
Media Activity loses significance when we replace the Informative Whitepaper indicator with our
manually coded granular measures of whitepaper disclosure (Column 5). More specifically, we
find that disclosing token allocation information and whitepaper opacity are negatively associated
with the amount of funds raised. Also, longer vesting period restrictions are viewed positively by
ICO participants and are positively related to the amount of capital raised. Combined with our
39
results in columns (1) through (3), this suggests that while hype generated through social media
activity is important to ICO success, the quality (and quantity) of disclosure in the whitepaper
plays a more prominent role in determining the amount of capital that is raised.
Finally, in column (6) we replace our information environment measures with our
aggregate measure of issuer and token quality, Rating. We find a reliably positive association with
the amount of funds raised, which suggests that ICO participants view these information
intermediaries as credible when making investment decisions.
4.4. Post-ICO Performance
Next, we examine the association between post-ICO performance and our measures of
information environment and overall token and issuer quality. We examine crash risk, illiquidity
and volatility. Panel A of Table 6 reports the results for our two measures of crash risk: Extreme
Negative Returns and Negative Return Skewness. As discussed in section 3.3, we also include
controls for ex ante return volatility (Return Volatility 1m) and crypto-market sentiment (BTC
Momentum). In addition, we include opening day ICO returns (i.e., Log open-to-close ICO Return)
to provide some insight into the recent criticism that unregulated ICO crypto markets are
susceptible to “pump and dump” strategies by ICO entities.
As shown in Panel A of Table 6, across all specifications and both measures of crash risk,
we find a strong positive association with opening day ICO returns. This suggests that ICOs that
experience strong first day trading tend to crash in the following three-, six-, or twelve-month
period. Coupled with our observation that the coefficient on BTC Momentum is also positive and
significant for one of the measures (Extreme Negative Returns), this result suggests that issuers
may be strategically timing their capital raise during hot markets and engaging in ‘pump and dump’
strategies that could harm investors.
40
With respect to our measures of the information environment, in columns (1) and (2), we
observe that sourcecode disclosure is positively associated with the likelihood of Extreme Negative
Returns, while being positive but insignificantly associated with Negative Return Skewness. One
possible interpretation of the positive coefficient on Sourcecode/Github Disclosure is that these
disclosures represent proprietary information that subsequently erodes the competitive advantage
of issuers and allows others to mimic or build off their technologies. Alternatively, the disclosures
may have revealed issues with the underlying technology that were ignored due to hype during the
ICO process. Consistent with hard information being more important than hype in the post-ICO
period, we observe that the coefficient on Social Media Activity is only weakly negatively
associated with crash risk.
We also find that the track record of ICO teams (Past Success of ICO Team) is negatively
associated with crash risk, which indicates the importance of ability and previous experience in
the emerging crypto market for post-ICO success. Interestingly, offering participation incentives
is positively associated with extreme negative returns, which might indicate that lower-quality
ICOs tend to offer bonuses and bounties to drive up investor demand but subsequently fail,
consistent with the ‘pump and dump’ story.
In column (2), we find that several of our manually coded whitepaper disclosure measures
are strongly associated with future crash risk. Specifically, Whitepaper Opacity and Whitepaper
Length are positively associated with future price crashes, while ICO Team Information is
negatively associated with crash risk. Taken together, our results speak to the importance of
transparent disclosure in mitigating crash risk in unregulated markets.
Finally, when we replace our information environment proxies with our aggregated Rating
measure, we find that higher ratings are strongly negatively associated with both our measures of
41
crash risk. This finding suggests that information intermediaries in the crypto capital market can
play a vital role in monitoring issuers, and it speaks to the natural evolution of governance within
unregulated markets.
We then examine whether, consistent with more traditional theories of disclosure in
(regulated) capital markets, disclosures in the unregulated crypto capital market can also help to
reduce information asymmetry. Panel B of Table 6 reports our analysis of illiquidity (Illiquidity)
and volatility (Return Volatility), both measured over the three months following the ICO. Note
that we control for contemporaneous return volatility in our liquidity analysis.
We find that Past Success of ICO Team is negatively associated with illiquidity and return
volatility, which suggests that past crypto-market experience is valued by investors and may
reduce information asymmetry. Similarly, Social Media Activity is negatively associated with
illiquidity, as is ICO Team Size, which is consistent with observations in equity markets (e.g.,
Blankespoor et al. [2014], Peress [2014]). This finding suggests that larger issuers with stronger
information environments are more liquid. In addition, activity on social media channels is likely
contributing to more active trading in tokens.
We also find that Informative Whitepaper is not significantly associated with illiquidity but
is negatively associated with return volatility. In column (2) of Panel B, when we include our more
detailed whitepaper disclosure measures, we find that ICO disclosures related to the use of ICO
funds raised (Use of Proceeds) and Whitepaper Opacity appear to (modestly) reduce liquidity, i.e.,
the coefficient is positive and significant. In contrast, we find that disclosures pertaining to the
background of the ICO team (ICO Team Information) improve liquidity, i.e., the coefficient is
negative and significant (e.g., Gow et al. [2017]). Overall, these results suggest that disclosures
play a mixed role in explaining liquidity.
42
In our final specification, we replace the information environment variables with our
aggregated measure of quality, and we find that Rating is negatively associated with post-ICO
illiquidity and return volatility in columns (3) and (6), respectively. This suggests that higher-
quality ICOs, as rated by information intermediaries, tend to be more liquid and less volatile,
consistent with an expected negative relation between disclosure and information asymmetry.
4.5. ICO Return Performance
Having examined the determinants of ICO completion and amount of capital raised, we
examine ICO returns. Table 7 reports results for our two measures of ICO returns: Log open-to-
close ICO Return in columns (1) through (3), and Log Total ICO Return in columns (4) through
(6). Given the unique differences in institutional structure and microstructure between the ICO
market and the IPO market, we do not have a clear ex ante expectation about over- or underpricing.
We find some evidence that information on founder token allocation (Founder Token
Information) and the vesting conditions attached to these tokens (Founder Token Vesting) appear
to be negatively associated with our total first day ICO return and open-to-close return measures,
respectively. Interestingly, we find that ICO Rating is positive and significantly related to our
open-to-close ICO return measure, but not with our total first day ICO return measure.
We find that some of our information environment and rating variables are only weakly
associated with ICO returns. This is also evidenced by the relatively low adjusted R-squared for
these regressions. Overall, the lack of significant associations suggests that ICO returns and initial
trading on crypto capital markets is driven more by newsflow and hype than by fundamentals.
43
5. Conclusion
Disclosure regulations play a critical role in well-functioning capital markets by increasing
investor participation and improving market quality (e.g., Brüggemann et al. [2018], Guiso et al.
[2008]; Jin and Myers [2006]). Indeed, one of the main objectives of securities regulators such as
the SEC is to protect investors through regulations that facilitate credible disclosures. While prior
research examining the capital market consequences of disclosure has focused primarily on
regulated markets or exploited variations in disclosure regulation, we focus on a new unregulated
capital market that has grown exponentially since 2014: the crypto-tokens market.
The crypto-tokens market has evolved into a significant financing channel and has been
used by over 650 token issuers from more than 50 countries to raise $13 billion through initial coin
offerings (ICOs) from April 2014 to May 2018. We provide timely initial evidence on the role of
disclosure in this emerging capital market. In our first set of analyses, we find that disclosure and
the information environment of token issuers are positively associated with the likelihood of
successfully completing an ICO and with the amount of funds raised. We also find that social
media activity is an important channel through which information is disseminated in this new
market, which suggests that hype and investor attention play a significant role.
Furthermore, we examine several secondary market characteristics that securities
regulators are concerned about, including crash risk, illiquidity, and volatility. We find that issuers
with more opaque disclosures and lower-quality tokens are more likely to crash, and they have
higher illiquidity and volatility. Interestingly, our measure of token quality is based on ratings from
new information intermediaries that analyze the characteristics of the tokens, including the amount
and quality of the information disseminated by the issuers. These information intermediaries
appear to provide some monitoring of issuers, and are a result of the natural evolution of
44
governance institutions within this unregulated market. Overall, our findings are relevant for
current and potential investors in ICOs, and may inform regulators about the emerging disclosure
practices of issuers, the role of information intermediaries, and their consequences for market
quality.
45
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48
Appendix A: Description of Variables
Panel A: Outcome Variables
Variable Description and Calculation Methodology
Completed An indicator variable that equals 1 if the ICO is deemed successful, i.e.,
the amount raised exceeded the soft cap requirement (if applicable) and
token trading data are subsequently available on coinmarketcap.com,
and 0 otherwise.
Log USD Raised Natural logarithm of the amount of funding (in US dollars) raised
through token sales in a successfully completed ICO.
Extreme Negative Return An indicator variable that equals 1 if cumulative raw returns are less
than or equal to -75% at the end of either the three-month, six-month,
or twelve-month period after the ICO first begins trading on
coinmarketcap.com, and 0 otherwise. We adapt this measure from
Brüggemann et al. [2018].
Negative Return Skewness Negative skewness in return calculated using daily log returns over the
three months (90 days) after the ICO begins trading. Specifically,
following Hutton et al. [2009] and Chen et al. [2001], we calculate the
negative coefficient of skewness as the negative of the third moment
of daily log returns divided by the standard deviation of daily log
returns raised to the third power:
−𝑛(𝑛 − 1)
32 ∑ (𝑅𝑒𝑡𝑖,𝑡 − 𝑅𝑒𝑡𝑖
)390
𝑡=1
(𝑛 − 1)(𝑛 − 2) [∑ (𝑅𝑒𝑡𝑖,𝑡 − 𝑅𝑒𝑡𝑖 )
290𝑡=1 ]
32
Illiquidity Illiquidity measured as absolute daily returns divided by daily trading
volume averaged over the three months (90 days) after the ICO first
begins trading on coinmarketcap.com. Specifically, we calculate the
Amihud [2002] illiquidity measure as follows:
𝐼𝑙𝑙𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦𝑖 =1
90∑
|𝑅𝑒𝑡𝑖,𝑡|
𝑉𝑜𝑙𝑢𝑚𝑒𝑖,𝑡∗ 103
90
𝑡=1
Return Volatility Volatility of daily returns measured over the three months (90 days)
after the ICO first begins trading on coinmarketcap.com.
Log Total ICO Return Natural logarithm of total ICO return calculated using the close price
on day 1 of trading from coinmarketcap.com and the ICO offer price.
Log Open-to-Close ICO Return Natural logarithm of the first day’s ICO return calculated using the
close price and the open price on the first day of trading.
49
Panel B: Disclosure Variables and Other Characteristics
Variable Description and Calculation Methodology
Sourcecode/Github Disclosure An indicator variable that equals 1 if the entity selling tokens in the
ICO makes its technical source code and documentation publicly
available in an online repository such as Github, and 0 otherwise.
Whitepaper Disclosed An indicator variable that equals 1 if the entity selling tokens in the
ICO publicly discloses a business or technical whitepaper, and 0
otherwise.
ICO Team Size Natural logarithm of the total number of team members involved with
the entity selling tokens in the ICO.
Platform Information An indicator variable that equals 1 if information about the token
trading platform is available prior to the ICO, and 0 otherwise.
Soft Cap Requirement An indicator variable that equals 1 if the ICO has a soft cap
requirement that needs to be met before an ICO is deemed complete,
and 0 otherwise.
ICO Participation Incentives An indicator variable that equals 1 if the ICO offers bonuses, bounties,
or other incentives to ICO participants, and 0 otherwise.
Past Success of ICO Team Proportion of team members that have previously been involved in a
successful ICO, and 0 otherwise.
USA Restricted An indicator variable that equals 1 if US-based investors are restricted
from participating in the ICO, and 0 otherwise.
Social Media Activity A measure ranging from 0 to 1 based on the entity’s presence and
activity on various social media channels including Twitter, Medium,
Telegram, Reddit, and others, as assessed by ICObench.com.
Informative Whitepaper An indicator variable that equals 1 if whitepaper informativeness
rating assessed by ICObench.com is non-zero, and 0 otherwise.
Founder Tokens Vesting
Information
An indicator variable that equals 1 if information about a post-ICO
vesting period for founder tokens is disclosed in the whitepaper, and
0 otherwise.
Founder Tokens Vesting Period Number of years over which founder tokens vest. Where this
information is missing from the whitepaper, we assume a vesting
period of zero years.
Token Allocation Information An indicator variable that equals 1 if information about the token
allocation (e.g., to founders, reserves, public ICO, advisors, early
adopters, angel investors, etc.) is disclosed in the whitepaper, and 0
otherwise.
ICO Team Information An indicator variable that equals 1 if biographical information about
the founders is disclosed in the whitepaper, and 0 otherwise.
Use of Proceeds An indicator variable that equals 1 if the intended use of ICO proceeds
is disclosed in the whitepaper, and 0 otherwise.
50
Panel B: Disclosure Variables and Other Characteristics (continued)
Variable Description and Calculation Methodology
Whitepaper Opacity The Gunning Fog index for the whitepaper calculated as (words per
sentence + percent of complex words) × 0.4, following Li [2008].
Whitepaper Length Length of whitepaper measured as the natural logarithm of the number
of pages.
Rating The overall rating from 0–5 based on various characteristics of the
entity selling tokens during the ICO, as assessed by crypto experts
contributing to ICObench.com and/or ICOrating.com.
BTC Momentum A measure of past return performance in the Bitcoin (BTC) crypto-
currency market at the time of each token issuer’s ICO, measured
using daily BTC returns over the three months prior to the first day of
the ICO.
51
Appendix B: Largest ICOs by Funds Raised30
Panel A: Top 10 ICOs Completed in 2016
No. Name Raised – US$mm
(MV – US$mm)
Category
(Country)
Brief Description
1 Waves 16.4
(395.9)
Infrastructure
(Russia)
Platform offering blockchain-related tools and
infrastructure.
2 Iconomi 10.6
(86.9)
Trading & Investing
(Slovenia)
Financial technology firm offering a digital asset
management platform.
3 Golem 8.6
(534.2)
Infrastructure
(Poland)
Global, open source, decentralized supercomputer
for sharing computing power.
4 SingularDTV 7.5
(39.2)
Events & Entertainment
(USA)
Blockchain entertainment studio offering to
decentralize the entertainment industry.
5 Lisk 5.7
(899.2)
Infrastructure
(Switzerland)
Solution for developers to build and deploy
blockchain applications in JavaScript.
6 Digix DAO 5.5
(262.3)
Commodities
(Singapore)
Distributed ledger for tokenized gold on the
Ethereum platform.
7 FirstBlood 5.5
(19.7)
Gaming & VR
(USA)
Platform for electronic sports entertainment and
competition using in-platform tokens.
8 Synereo 4.7
(17.3)
Content Management
(Israel)
Developer of blockchain-enabled solutions for
digital content publishing and distribution.
9 Decent 4.2
(25.9)
Content Management
(Switzerland)
Provider of a decentralized digital content
management solution.
10 Antshares / NEO 3.6
(3,311.6)
Infrastructure
(China)
A non-profit, blockchain project that automates
digital asset management.
Total (43 ICOs) 95.2 Panel B: Top 10 ICOs Completed in 2017
No. Name Raised – US$mm
(MV – US$mm)
Category
(Country)
Brief Description
1 Hdac 258.0
(N.A.)
Communications
(Switzerland)
A blockchain-based transaction platform backed
by Hyundai BS&C.
2 Filecoin 257.0
(N.A.)
Data Storage
(USA)
Global, decentralized digital storage solution.
3 EOS 185.0
(10,852.8)
Infrastructure
(USA)
Solution for blockchain-based deployment of
decentralized applications.
4 Paragon 183.2
(9.9)
Drugs & Healthcare
(Russia)
Blockchain solution targeting transaction
facilitation in the cannabis industry.
5 Bancor 153.0
(193.1)
Infrastructure
(Israel)
Solution that facilitates transactions in various
crypto-currencies and tokens.
6 Status 90.0
(333.5)
Infrastructure
(Switzerland)
A free, open source mobile client for Android &
iOS built on Ethereum.
7 BANKEX 70.6
(20.2)
Finance
(USA)
Blockchain solution for the development of
decentralized capital markets.
8 TenX 64.0
(97.7)
Payments
(Singapore)
Provider of debit card and banking solutions for
the blockchain ecosystem.
9 Nebulas 60.0
(283.3)
Data Analytics
(USA)
Provider of data analytics related to blockchain
application usage.
10 MobileGO 53.1
(28.9)
Gaming & VR
(USA)
Crypto mobile gaming store with an in-game
payment system.
Total (210 ICOs) 3,880.0
30 Source: coinschedule.com. Market values are as of May 30, 2018.
52
Panel C: Top 10 ICOs Completed in 2018
No. Name Raised – US$mm
(MV – US$mm)
Category
(Country)
Brief Description
1 Telegram 1,700.0
(N.A.)
Communications
(Russia / UK)
A cloud-based instant messaging and voice over
IP service.
2 Petro 735.0
(N.A.)
Governance
(Venezuela)
Sovereign crypto asset backed by oil assets and
issued by Venezuela.
3 Dragon 320.0
(N.A.)
Gaming & VR
(British Virgin Islands)
An Ethereum utility token for participation in the
Dragon casino blockchain ecosystem.
4 Huobi token 300.0
(189.4)
Trading & Investing
(Singapore)
International multi-language digital currency
trading platform and exchange.
5 Bankera 150.9
(N.A.)
Finance
(Lithuania)
A digital bank offering payments, loans and
deposits, and investments.
6 Basis 133.0
(N.A.)
Finance
(USA)
An algorithmic central bank that aims to stabilize
crypto-currencies by managing supply.
7 Envion 100.0
(13.1)
Mining
(Switzerland)
Mobile mining unit that uses low-priced local
energy to mine crypto-currencies.
8 Elastos 94.1
(194.6)
Mining
(China)
Blockchain infrastructure that enables merged
mining of Bitcoin.
9 Flashmoni 72.0
(N.A.)
Payments
(UK)
Physical gold-backed crypto-currency and
payment solutions.
10 Neuromation 71.7
(22.2)
Machine Learning & AI
(Estonia)
Artificial intelligence development through
distributed computing.
Total (343 ICOs) 8,930.4
53
Appendix C: Tables of Contents of Selected Whitepapers
Name: Anryze
Team’s country of origin: Russia
Funds raised: US$1,759,130
ICO start date: September 12, 2017
ICO end date: October 12, 2017
ICO trading date: November 27, 2017
Whitepaper Length: 32 pages
Table of Contents of Whitepaper
54
Appendix C: Tables of Contents of Selected Whitepapers (continued)
Name: Kin
Team’s country of origin: Canada
Funds raised: US$98,500,326
ICO start date: September 12, 2017
ICO end date: September 26, 2017
ICO trading date: September 27, 2017
Whitepaper Length: 28 pages
Table of Contents of Whitepaper
55
Appendix C: Tables of Contents of Selected Whitepapers (continued)
Name: OpenZen
Team’s country of origin: Russia
Funds raised: FAILED
ICO start date: July 31, 2017
ICO end date: August 31, 2017
ICO trading date: N/A
Whitepaper Length: 11 pages
Table of Contents of Whitepaper
56
Figure 1: Illustrative Initial Coin Offering Process
This figure provides an illustrative outline of the ICO process. Panel A highlights the main process milestones and key events as well as the approximate time
elapsed in days based on the statistics for our sample of ICOs for which this information is available. Panel B provides further details on the specific tasks
required during each major phase of the ICO process.
Panel A: ICO Process – Key Milestones and Timeline
Planning
Announcement of ICO
Disclosure of Information (Whitepaper, Source Code, Other)
Optional Pre-ICO Process
ICO Start Date
Disclosure, Social Media Marketing, and Investor Communication
ICO End Date
Token Pricing and Allocation
Token Trading on Exchange
Total days of ICO process, including pre-ICO: 77 days (median)
Total days of ICO process, without pre-ICO: 47 days (median)
Time varies
30 days (median)
Up to 30 days
17 days (median)
57
Panel B: Typical ICO Process – Detailed Steps
Planning
• Develop project idea and put the team in place.
• Determine capital requirement, token supply, and other token features such as soft cap, hard cap, and bonuses.
•Determine whether a pre-ICO process is needed for "angel" investors.
• Determine proportion of tokens for founders/team and for public distribution.
• Determine use of proceeds.
• Determine token type (i.e., coins, loyalty points, certificates, in-game items, IOUs, other utility or benefit to token holder).
• Select blockchain platform on which token will be created (e.g., Ethereum, Waves).
Announcement of ICO and Disclosure of Information
• Publish website and disclose team and project information.
• Announce ICO launch date and process timeline on website and through ICO calendar information intermediaries, such as coinschedule.com.
• Release whitepaper describing business, technical, personnel, and token sale information that investors can use to assess future prospects of the project (optional).
• Release source code on online code repositories, such as Github (optional).
•Amount and quality of information provided by token issuers vary significantly and are used to signal prospects.
• Potential ICO participants signal their interest in the ICO by registering on the website of the token issuer before or during the ICO start and end date (i.e., the marketing period).
Disclosure, Social Media Marketing, and Investor
Communication
• Communicate with potential participants through social media channels, such as Twitter, Bitcointalk, and Reddit.
• Some project teams engage in investor roadshows during the marketing period.
• ICO rating providers such as ICObench and ICOrating release their ratings.
• Issuers can offer early investor bonuses, volume discounts, free tokens, and bounties for developers, marketers, and miners.
• Whitepapers may be revised during this period.
• Potential ICO participants use social media and messaging platforms, such as Telegram, to communicate with each other about the quality of the token and the price.
Token Pricing and Allocation
• Tokens are allocated to participants in exchange for fiat or crypto-currencies at the final ICO offer price (unless the price is lower due to some incentive such as bonuses, bounties, or through participation in the pre-ICO).
• If soft cap is not reached, the ICO is deemed unsuccessful and funds are returned to participants.
• If soft cap is reached, the ICO is deemed successful and funds are retained by the token issuer.
• If there is a hard cap, no tokens are sold once the hard cap is reached.
Token Trading on Exchange
• Tokens begin trading on crypto exchanges such as Kraken, Binance, Poloniex, and Bitfinex (among others), as a result of a dual agreement between ICO teams and the relevant exchanges.
•ICO participants are then able to sell their tokens on secondary market.
• Founders are able to sell their tokens subject to any lock-up conditions and vesting periods.
• ICO rating providers continually update ratings.
58
Figure 2: Aggregate ICO Market Statistics The following panels show selected statistics about the aggregate ICO market. Panel A plots (1) the number of new
ICOs, by six-month semesters from 2014 to 2016, and then monthly from January 2017 to February 2018; and (2) the
aggregate funds raised in millions of USD. Panel B plots the mean and median (as well as 25th and 75th percentile)
buy-and-hold returns over the 30 days after the ICO.
Panel A: Number of New ICOs and Aggregate Funding Raised
Panel B: Cross-Sectional Distribution of Aggregate Post-ICO Performance
-
500
1,000
1,500
2,000
2,500
3,000
0
10
20
30
40
50
60
70
80
90
Agg
rega
te F
un
ds
Rai
sed
($
mm
)
Nu
mb
er o
f n
ew IC
Os
Number of new ICOs Aggregate Funds Raised ($mm)Source: Coindesk ICO Tracker
-0.80
-0.60
-0.40
-0.20
0.00
0.20
0.40
0.60
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Ret
urn
s
Days relative to ICO date
mean p25 p50 p75
59
Figure 3: Aggregate ICO Market Index Performance The following figures plot the cumulative return on an investment of $1 (long only) in an aggregate ICO market index
from August 2014 through May 15, 2018. In Panel A, the index is value-weighted using daily market values of tokens
with available data on coinmarketcap.com. In Panel B, the index is equal-weighted. Our sample consists of all
completed ICOs from August 2014 through February 2018, and tokens that cease trading or delist simply drop out of
our index. Similarly, new tokens are added to the index as they commence trading on coinmarketcap.com. Note that
from February 2018 to May 2018, i.e., the shaded area, the index does not include any new ICOs and simply tracks
the cumulative return performance for the February 2018 portfolio for the following three months.
Panel A: Value-weighted ICO Market Index
Panel B: Equal-weighted ICO Market Index
1
10
100
1000
10000
Agg
rega
te I
CO
Mar
ket
Val
ue-
wei
ghte
d I
nd
ex
(lo
gari
thm
ic s
cale
)
A $1 investment in the aggregate ICO market value-weighted index in August 2014
would have increased in value to $3,714 by Feb 2018 and to $4,621 by May 15, 2018.
1
10
100
1000
10000
Agg
rega
te I
CO
Mar
ket
Equ
al-w
eigh
ted
In
dex
(l
oga
rith
mic
sca
le)
A $1 investment in the aggregate ICO market equal-weighted index in August 2014
would have increased in value to $4,260 by Feb 2018 and to $4,765 by May 15, 2018.
60
Table 1: Sample Composition This table summarizes the composition of the sample of ICOs used in this study. Panel A reports the frequency of
ICOs over our sample period. We report complete ICOs by six-month semester from 1 Jan 2014 through December
2016, and then monthly from Jan 2017 through February 2018. Panel B reports the frequency of ICOs by country of
origin, i.e., origin of the ICO entity/team members. Panel C reports the frequency of ICOs by sector. Panel D reports
the frequency of ICOs by platform.
Panel A: Frequency of Completed ICOs over Sample Period Observations
All ICOs (April 2014 to February 2018) 776
Less: Failed ICOs (117)
Completed ICOs in Sample (April 2014 to February 2018) 659
Completed ICOs Percent of ICOs
H1-14 1 0.2
H2-14 10 1.5
H1-15 7 1.1
H2-15 5 0.8
H1-16 11 1.7
H2-16 18 2.7
Jan-17 5 0.8
Feb-17 5 0.8
Mar-17 12 1.8
Apr-17 11 1.7
May-17 15 2.3
Jun-17 27 4.1
Jul-17 43 6.5
Aug-17 42 6.4
Sep-17 43 6.5
Oct-17 87 13.2
Nov-17 76 11.5
Dec-17 58 8.8
Jan-18 137 20.8
Feb-18 46 7.0
Total 659 100.0
61
Panel B: Frequency of ICOs, by Country of Origin
Country of Origin Freq. Percent Country of Origin Freq. Percent
Argentina 3 0.4 Luxembourg 1 0.1
Armenia 1 0.1 Malaysia 2 0.3
Australia 8 1.0 Malta 4 0.5
Austria 1 0.1 Marshall Islands 1 0.1
Belize 3 0.4 Mexico 1 0.1
Brazil 1 0.1 Moldova 1 0.1
British Virgin Islands 1 0.1 Netherlands 8 1.0
Bulgaria 3 0.4 Nigeria 1 0.1
Cambodia 1 0.1 Panama 1 0.1
Canada 9 1.2 Poland 2 0.3
Cayman Islands 5 0.6 Romania 1 0.1
China 12 1.6 Russia 43 5.5
Costa Rica 2 0.3 Saint Kitts and Nevis 1 0.1
Cyprus 2 0.3 Seychelles 1 0.1
Czech Republic 3 0.4 Singapore 32 4.1
Denmark 1 0.1 Slovenia 13 1.7
Estonia 11 1.4 South Africa 2 0.3
Finland 1 0.1 Spain 4 0.5
France 3 0.4 Sweden 1 0.1
Germany 6 0.8 Switzerland 26 3.4
Gibraltar 4 0.5 Taiwan 1 0.1
Hong Kong 6 0.8 Turkey 1 0.1
India 5 0.6 UK 19 2.5
Indonesia 1 0.1 USA 85 11.0
Israel 8 1.0 Ukraine 2 0.3
Italy 2 0.3 United Arab Emirates 6 0.8
Japan 11 1.4 Vanuatu 1 0.1
Kyrgyzstan 1 0.1 Virgin Islands 2 0.3
Latvia 1 0.1 Unknown 391 50.4
Liechtenstein 1 0.1
Lithuania 5 0.4 Total 776 100.0
62
Panel C: Frequency of ICOs, by Sector
Country of Origin Freq. Percent
Business Services 61 7.9
Financial 64 8.3
Media & Entertainment 58 7.5
Other 74 9.5
Platform 77 9.9
Technology 74 9.5
Unknown 368 47.4
Total 776 100.0
Panel D: Frequency of ICOs, by Platform
Platform Freq. Percent
Achain 1 0.1
Ardor 2 0.3
BitShares 19 2.5
Burst 5 0.6
Counterparty 9 1.2
Ethereum 541 69.7
Ethereum Classic 2 0.3
NEM 3 0.4
NEO 8 1.0
NuBits 1 0.1
Nxt 7 0.9
Omni 13 1.7
Qtum 11 1.4
Stellar 3 0.4
Ubiq 4 0.5
Waves 28 3.6
Unknown 119 15.3
Total 776 100.0
63
Table 2: Descriptive Statistics This table reports descriptive statistics for the main outcome and control variables, along with ICO process statistics. Panel A reports distribution statistics for the set of
primary outcome variables. We report descriptive statistics for the full sample and reduced sample sizes utilized in our empirical analysis. Panel B reports descriptive
statistics for our disclosure, information environment, and ICO attribute variables for the subset of completed ICOs, given that this forms the basis of the majority of our
empirical analysis. Panel C reports descriptive statistics relating to the timing and key events of the ICO process. All variables are described in Appendix A.
Panel A: Primary Outcome Variables
N Mean Std. dev. p1 p10 p25 p50 p75 p90 p99
Outcome variables:
Completed 776 0.85 0.36 0.00 0.00 1.00 1.00 1.00 1.00 1.00
Completed* 400 0.91 0.28 0.00 1.00 1.00 1.00 1.00 1.00 1.00
Log USD Raised 245 15.53 1.67 9.83 13.42 14.58 15.73 16.76 17.32 18.85
Log USD Raised* 225 15.59 1.60 10.58 13.64 14.73 15.76 16.79 17.32 18.85
Extreme Negative Return 569 0.26 0.44 0.00 0.00 0.00 0.00 1.00 1.00 1.00
Extreme Negative Return* 323 0.23 0.42 0.00 0.00 0.00 0.00 0.00 1.00 1.00
Negative Return Skewness 569 -0.26 1.38 -3.59 -1.57 -0.85 -0.30 0.18 0.75 6.54
Negative Return Skewness* 323 -0.22 1.58 -3.59 -1.53 -0.87 -0.35 0.17 0.83 7.53
Illiquidity 569 0.12 0.77 0.00 0.00 0.00 0.00 0.05 0.30 1.54
Illiquidity* 323 0.06 0.15 0.00 0.00 0.00 0.00 0.02 0.16 0.65
Return Volatility 569 0.23 0.23 0.05 0.10 0.12 0.17 0.26 0.43 1.28
Return Volatility* 323 0.22 0.23 0.07 0.11 0.13 0.16 0.23 0.35 1.16
Log Total ICO Return 300 0.39 1.15 -3.38 -0.68 -0.20 0.40 0.98 1.69 3.77
Log Total ICO Return* 289 0.39 1.16 -3.42 -0.68 -0.20 0.34 1.00 1.72 3.97
Log Open-to-Close ICO Return 659 0.14 0.39 -0.85 -0.12 -0.01 0.06 0.22 0.57 1.61
Log Open-to-Close ICO Return* 365 0.10 0.35 -1.10 -0.15 -0.02 0.07 0.22 0.46 1.18
* Reduced sample utilized for some specifications throughout the paper.
64
Table 2: Descriptive Statistics (continued)
Panel B: Measures of Disclosure, ICO Attributes, and Information Environment Variables
N Mean Std. dev. p1 p10 p25 p50 p75 p90 p99
Sourcecode/Github Disclosure 659 0.51 0.50 0.00 0.00 0.00 1.00 1.00 1.00 1.00
Whitepaper Disclosed 659 0.79 0.41 0.00 0.00 1.00 1.00 1.00 1.00 1.00
Soft Cap Requirement 365 0.24 0.42 0.00 0.00 0.00 0.00 0.00 1.00 1.00
USA Restricted 659 0.14 0.35 0.00 0.00 0.00 0.00 0.00 1.00 1.00
Platform Information 365 0.89 0.32 0.00 0.00 1.00 1.00 1.00 1.00 1.00
ICO Team Size 365 2.19 0.94 0.00 0.69 1.79 2.40 2.83 3.14 3.64
Past Success of ICO Team 365 0.27 0.36 0.00 0.00 0.00 0.13 0.36 1.00 1.00
ICO Participation Incentives 365 0.28 0.45 0.00 0.00 0.00 0.00 1.00 1.00 1.00
Social Media Activity 365 0.56 0.27 0.00 0.20 0.40 0.60 0.77 0.93 1.00
Informative Whitepaper 659 0.95 0.22 0.00 1.00 1.00 1.00 1.00 1.00 1.00
Token Allocation Information 505 0.60 0.49 0.00 0.00 0.00 1.00 1.00 1.00 1.00
Founder Tokens Distribution (percent) 497 0.09 0.11 0.00 0.00 0.00 0.06 0.15 0.22 0.50
Founder Tokens Vesting Information 488 0.28 0.45 0.00 0.00 0.00 0.00 1.00 1.00 1.00
Founder Tokens Vesting Period 488 0.54 1.12 0.00 0.00 0.00 0.00 0.50 2.00 5.00
ICO Team Information 489 0.47 0.50 0.00 0.00 0.00 0.00 1.00 1.00 1.00
Use of Proceeds 491 0.49 0.50 0.00 0.00 0.00 0.00 1.00 1.00 1.00
Whitepaper Opacity 479 17.37 1.76 13.24 15.29 16.24 17.40 18.54 19.44 21.48
Whitepaper Length 481 3.17 0.60 1.39 2.30 2.83 3.22 3.61 3.87 4.33
Rating 365 3.38 0.85 1.00 2.10 3.00 3.60 4.00 4.30 4.60
65
Table 2: Descriptive Statistics (continued)
Panel C: ICO Process Statistics
N Mean Std. dev. p1 p10 p25 p50 p75 p90 p99
Length of ICO Period in Days 416 26.85 16.90 0.00 3.00 16.00 30.00 31.00 44.00 91.00
Days for Token to Trade after ICO End 309 29.37 39.92 0.00 2.00 7.00 17.00 35.00 71.00 133.00
Token Trades within 1 Day after ICO End 309 0.07 0.26 0.00 0.00 0.00 0.00 0.00 0.00 1.00
Token Trades within 7 Days after ICO End 309 0.27 0.44 0.00 0.00 0.00 0.00 1.00 1.00 1.00
Pre-ICO Process 400 0.20 0.40 0.00 0.00 0.00 0.00 0.00 1.00 1.00
66
Table 3: Correlation Matrix This table reports correlations between the main variables. To compute correlation, we require all of these variables to be available. Pearson correlations are reported above
the diagonal, and Spearman correlations are reported below the diagonal. Correlations that cannot be computed because of lack of variation in indicator variables are marked
as not meaningful (n.m.). All variables are described in Appendix A. An asterisk (*) indicates statistical significance at the 10% (or lower) level.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
1 Log USD Raised -0.11 -0.07 -0.42* -0.20* 0.03 -0.02 0.10 0.00 -0.01 0.30* 0.15 0.23* 0.12 0.18* -0.16 0.28* 0.34*
2 Extreme Negative Return -0.10 0.18* -0.02 -0.01 0.21* 0.10 -0.01 0.02 0.05 -0.01 -0.17* -0.05 0.05 -0.06 0.05 0.07 -0.07
3 Negative Return Skewness -0.11 0.27* -0.01 -0.13* 0.18* 0.05 -0.03 -0.04 0.00 -0.15* -0.08 -0.13 -0.01 0.00 0.07 0.05 -0.16*
4 Illiquidity -0.63* 0.11* 0.22* 0.44* -0.21* 0.00 -0.11 -0.06 -0.13 -0.29* -0.17* -0.28* -0.17* -0.03 -0.02 -0.07 -0.38*
5 Return Volatility -0.32* 0.00 -0.24* 0.56* -0.24* 0.03 -0.12* 0.00 -0.10 -0.21* -0.10 -0.14* -0.25* -0.08 0.04 -0.32* -0.23*
6 Log Total ICO Return -0.03 0.21* 0.10 -0.27* -0.26* 0.08 -0.07 0.01 0.04 -0.10 0.05 -0.05 -0.04 -0.01 0.03 0.00 -0.06
7 Log Open-to-Close ICO Return -0.07 0.10 0.00 0.11* 0.12* 0.10 -0.01 -0.04 0.03 0.01 0.05 0.06 0.06 -0.06 0.01 -0.09 0.14*
8 Sourcecode/Github Disclosure 0.08 -0.01 -0.08 -0.23* -0.07 -0.04 0.01 0.30* 0.07 0.21* 0.11 0.35* 0.21* 0.03 -0.10 0.17* 0.28*
9 Whitepaper Disclosed 0.00 0.02 -0.10 -0.13* 0.08 0.02 0.03 0.30* 0.07 0.32* 0.13 0.21* 0.40* -0.10 n.m. n.m. 0.34*
10 Soft Cap Requirement 0.01 0.05 0.06 -0.08 -0.06 0.04 -0.02 0.07 0.07 0.11 0.10 0.11 0.10 0.13 -0.06 0.12 0.19*
11 ICO Team Size 0.29* 0.00 -0.10 -0.30* -0.18* -0.08 -0.03 0.23* 0.22* 0.12 0.29* 0.46* 0.29* 0.20* 0.02 0.34* 0.64*
12 Past Success of ICO Team 0.22* -0.14 -0.08 -0.29* -0.15* 0.05 0.01 0.15* 0.18* 0.12 0.43* 0.28* 0.13 0.11 -0.02 0.02 0.46*
13 Social Media Activity 0.19* -0.04 -0.10 -0.20* -0.15* -0.05 0.07 0.33* 0.20* 0.11 0.46* 0.37* 0.32* 0.16* -0.09 0.30* 0.69*
14 Informative Whitepaper Rating 0.06 0.00 0.01 0.01 -0.04 -0.07 0.01 0.12 0.31* 0.04 0.15* 0.17* 0.26* 0.03 -0.08 0.27* 0.50*
15 Founder Tokens Vesting Period 0.16 -0.08 -0.02 -0.19* -0.08 -0.08 -0.04 0.10 -0.09 0.16* 0.22* 0.07 0.16* 0.02 -0.02 0.24* 0.17*
16 Whitepaper Opacity -0.14 0.05 0.07 0.07 0.06 0.01 0.02 -0.13* n.m. -0.09 -0.07 -0.04 -0.12 -0.10 -0.03 -0.33* -0.06
17 Whitepaper Length 0.28* 0.09 0.03 -0.30* -0.27* -0.05 -0.05 0.18* n.m. 0.09 0.37* 0.09 0.34* 0.27* 0.31* -0.32* 0.32*
18 Rating 0.29* -0.07 -0.14 -0.27* -0.13 -0.06 0.09 0.24* 0.24* 0.19* 0.57* 0.59* 0.66* 0.31* 0.17* -0.11 0.33*
67
Table 4: Univariate Relations This table reports univariate results from differences-in-means tests for measures of disclosure, ICO attributes, and the
information environment for completed versus failed ICOs. We report number of observations, means, standard
deviation and the 95% confidence interval around our variables of interest, with t-statistics of the difference in means
reported under each panel. Note that t-statistics have been computed allowing for different variances across sub-samples
of completed versus failed ICOs. Panel A reports differences in virtual entities’ choice to disclose technical specs and
sourcecode via Github (Sourcecode/Github Disclosure); Panel B reports differences in the decision to disclose a
whitepaper; Panel C reports the average difference in Informative Whitepaper; Panel D reports average Social Media
Activity; Panel E reports differences in the average Rating; Panel F reports the average number of vesting years for
founder tokens, conditional on disclosure of founder token allocation; Panel G reports the average difference in Past
Success of ICO Team; finally, Panel H reports the average difference in the disclosure of Use of Proceeds. All variables
are described in Appendix A.
Panel A: Sourcecode/Github Disclosure Category N Mean Std. dev. 95% CI Low 95% CI High
Failed 117 0.15 0.36 0.09 0.22
Completed 659 0.51 0.50 0.47 0.55
Completed – Failed 0.35 t-statistic for difference (9.11)
Panel B: Whitepaper Disclosed Category N Mean Std. dev. 95% CI Low 95% CI High
Failed 117 0.86 0.35 0.80 0.93
Completed 659 0.79 0.41 0.76 0.82
Completed – Failed -0.08 t-statistic for difference (-2.12)
Panel C: Informative Whitepaper Category N Mean Std. dev. 95% CI Low 95% CI High
Failed 35 0.66 0.48 0.49 0.82
Completed 365 0.91 0.28 0.88 0.94
Completed – Failed 0.26 t-statistic for difference (3.08)
Panel D: Social Media Activity Category N Mean Std. dev. 95% CI Low 95% CI High
Failed 35 0.30 0.23 0.22 0.37
Completed 365 0.56 0.27 0.54 0.59
Completed – Failed 0.27 t-statistic for difference (6.50)
Panel E: Rating Category N Mean Std. dev. 95% CI Low 95% CI High
Failed 35 2.56 0.83 2.27 2.84
Completed 365 3.38 0.85 3.29 3.47
Completed – Failed 0.82 t-statistic for difference (5.57)
68
Table 4: Univariate Relations (continued)
Panel F: Founder Tokens Vesting Period
Category N Mean Std. dev. 95% CI Low 95% CI High
Failed 40 0.25 0.54 0.07 0.42
Completed 488 0.54 1.12 0.44 0.64
Completed – Failed 0.29 t-statistic for difference (2.93)
Panel G: Past Success of ICO Team
Category N Mean Std. dev. 95% CI Low 95% CI High
Failed 35 0.15 0.31 0.05 0.26
Completed 365 0.27 0.36 0.24 0.31
Completed – Failed 0.12 t-statistic for difference (2.15)
Panel H: Use of Proceeds Disclosure
Category N Mean Std. dev. 95% CI Low 95% CI High
Failed 42 0.45 0.50 0.30 0.61
Completed 491 0.49 0.50 0.44 0.53
Completed – Failed 0.03 t-statistic for difference (0.42)
69
Table 5: Determinants of ICO Capital Raise
This table reports coefficient estimates from logit estimation of equations (1) through (3) as described in sections 3.1 and 3.2. We
examine two outcome variables related to the success of an ICO capital raise: (1) Completed, an indicator variable that equals 1 if the
ICO is successful in raising funds and the raised funds exceed the minimum threshold stipulated by the token issuers, and 0 otherwise;
and (2) Log USD Raised, measured as the natural logarithm of the amount of funds in US dollars that are raised during the ICO. For
our Completed analysis, we use a reduced cross-section of 400 attempted ICOs over April 2014 to February 2018, out of which 365
ICOs were successfully completed. For the analysis of Log USD Raised, we focus on the sample of completed ICOs only, with
varying sample sizes based on available data. The reported t-statistics are based on robust standard errors. The asterisks (*, **, and
***) indicate statistical significance at the 10%, 5%, and 1% levels, respectively, for two-tailed tests. ^ indicates statistical
significance at the 10% level for a one-tailed test. See Appendix A for descriptions of variables
Successful Completion Log USD Raised
(1) (2) (3) (4) (5) (6)
BTC Momentum 0.637^ 0.411 0.493 -0.011 0.073 -0.001
(1.49) (0.85) (1.24) (-0.06) (0.38) (-0.00)
Soft Cap Requirement -1.187** -1.338** -1.233*** -0.395^ -0.207 -0.564**
(-2.34) (-2.44) (-2.64) (-1.42) (-0.78) (-2.06)
USA Restricted 1.237^ 1.856^ 1.209^ 0.880*** 0.676*** 0.873***
(1.60) (1.52) (1.48) (3.95) (2.95) (4.18)
Platform Information 1.091** 0.998^ 0.189 0.340
(2.12) (1.42) (0.70) (1.14)
Sourcecode/Github Disclosure -0.036 -0.223 0.072 0.053
(-0.08) (-0.40) (0.31) (0.24)
ICO Team Size -0.313 0.115 0.357*** 0.402***
(-1.18) (0.39) (2.72) (2.77)
Past Success of ICO Team 0.203 -0.060 0.233 0.256
(0.27) (-0.07) (0.90) (0.92)
ICO Participation Incentives -0.422 -0.235 -0.465* -0.339
(-0.87) (-0.43) (-1.84) (-1.40)
Social Media Activity 4.786*** 3.976*** 0.762* 0.344
(3.82) (2.83) (1.93) (0.84)
Informative Whitepaper 0.977* 0.061
(1.79) (0.14)
ICO Team Information -0.588 0.064
(-1.23) (0.27)
Token Allocation Information 0.689 -0.751**
(1.02) (-2.52)
Founder Tokens Vesting Period 0.405 0.172*
(1.10) (1.74)
Use of Proceeds 0.093 0.206
(0.12) (0.75)
Whitepaper Opacity -0.050 -0.119**
(-0.33) (-2.10)
Whitepaper Length 0.517 0.372^
(0.77) (1.33)
Rating 1.214*** 0.607***
(4.75) (3.83)
Ctry Ind. (USA, RUS, SGP, CHN)
CHN) Indicators Yes Yes Yes Yes Yes Yes
Quarter FE Yes Yes Yes Yes Yes Yes
Pseudo R-squared 0.339 0.319 0.281 0.232 0.275 0.247
Observations 400 341 400 225 200 225
70
Table 6: Post-ICO Performance
This table reports coefficient estimates from OLS estimation of equations (1) through (3) as described in sections 3.1 and 3.4. Panel
A reports results where the outcome variables capture crash risk. We use two proxies: (1) Extreme Negative Returns, and (2)
Negative Skewness. Panel B reports results for post-ICO attributes of (1) Illiquidity, based on Amihud [2002] computed over the
90 days following the completion of the ICO, and (2) return volatility, measured over the 90 days after the completion of the ICO
using daily returns. We include an additional control for contemporaneous return volatility in our liquidity analysis. Sample sizes
vary based on available data. The reported t-statistics are based on robust standard errors. The asterisks (*, **, and ***) indicate
statistical significance at the 10%, 5%, and 1% levels, respectively, while ^ indicates statistical significance at the 10% level for a
one-tailed test. See Appendix A for descriptions of variables.
Panel A: Crash Risk Extreme Negative Returns Negative Return Skewness
(1) (2) (3) (4) (5) (6)
Return Volatility 1m -0.386 -0.346 -0.505 -0.483 -1.294*** -0.515
(-0.59) (-0.53) (-0.86) (-0.70) (-3.59) (-0.69)
Log Open-to-Close ICO Return 1.386**
* 1.493*** 1.449*** 0.599** 0.764** 0.696**
(2.81) (2.74) (3.31) (2.07) (2.47) (2.25)
BTC Momentum 0.295* 0.314* 0.393*** -0.099 -0.051 -0.110
(1.78) (1.76) (2.66) (-1.09) (-0.57) (-1.28)
USA Restricted -0.085 0.105 0.160 0.034 0.040 0.067
(-0.23) (0.28) (0.45) (0.18) (0.21) (0.39)
Platform Information 0.004 -0.157 -0.417^ -0.482^
(0.01) (-0.31) (-1.32) (-1.44)
Sourcecode/Github Disclosure 0.658** 0.781** 0.003 0.041
(2.05) (2.22) (0.01) (0.19)
ICO Team Size 0.033 -0.033 -0.208* -0.214
(0.18) (-0.14) (-1.83) (-1.37)
Past Success of ICO Team -
1.438**
*
-1.369*** -0.180 -0.132
(-3.04) (-2.75) (-0.79) (-0.55)
ICO Participation Incentives 0.696** 0.678* -0.023 -0.056
(2.14) (1.96) (-0.15) (-0.32)
Social Media Activity -0.955^ -0.967^ -0.467 -0.451
(-1.45) (-1.38) (-1.22) (-1.18)
Informative Whitepaper 0.311 0.176
(0.52) (0.38)
ICO Team Information -0.713** -0.320*
(-2.05) (-1.91)
Token Allocation Information 0.331 0.238
(0.73) (1.33)
Founder Tokens Vesting Period -0.096 0.013
(-0.46) (0.11)
Use of Proceeds -0.216 0.152
(-0.50) (0.88)
Whitepaper Opacity 0.229** 0.095*
(2.14) (1.84)
Whitepaper Length 0.814** 0.318^
(2.14) (1.45)
Rating -0.385** -0.384***
(-2.32) (-2.73)
Ctry Ind. (USA, RUS, SGP, CHN)
CHN) Yes Yes Yes Yes Yes Yes
Pseudo / Adj. R-squared 0.109 0.146 0.062 0.025 0.053 0.041
Observations 323 287 323 323 287 323
71
Table 6: Post-ICO Performance (continued)
Panel B: Trading Attributes Illiquidity Return Volatility
(1) (2) (3) (4) (5) (6)
Return Volatility 3m 0.202** 0.153* 0.195*
(2.00) (1.76) (1.91)
USA Restricted 0.001 -0.000 0.004 -0.011 -0.026 -0.018
(0.05) (-0.02) (0.32) (-0.64) (-1.09) (-0.97)
Platform Information -0.050^ -0.016 -0.067 -0.016
(-1.46) (-0.62) (-1.18) (-0.35)
Sourcecode/Github Disclosure -0.013 0.010 -0.013 -0.004
(-0.75) (0.58) (-0.51) (-0.15)
ICO Team Size -0.017* -0.007 -0.036^ -0.032
(-1.72) (-0.68) (-1.46) (-1.26)
Past Success of ICO Team -0.022* -0.028** -0.046* -0.076**
(-1.83) (-2.20) (-1.95) (-2.40)
ICO Participation Incentives 0.015 0.015 0.002 -0.016
(0.93) (0.84) (0.10) (-0.75)
Social Media Activity -0.067** -0.069** 0.069 0.086
(-2.07) (-2.24) (0.96) (1.09)
Informative Whitepaper 0.032 -0.203*
(0.89) (-1.70)
ICO Team Information -0.028** 0.027
(-2.05) (0.88)
Token Allocation Information 0.015 0.007
(0.76) (0.20)
Founder Tokens Vesting Period -0.001 0.011
(-0.12) (0.90)
Use of Proceeds 0.028^ -0.009
(1.48) (-0.38)
Whitepaper Opacity 0.008* -0.010
(1.75) (-0.84)
Whitepaper Length 0.006 -0.136
(0.34) (-1.47)
Rating -0.041*** -0.052***
(-3.74) (-2.62)
Ctry Ind. (USA, RUS, SGP, CHN) Yes Yes Yes Yes Yes Yes
Quarter FE Yes Yes Yes Yes Yes Yes
Adjusted R-squared 0.322 0.216 0.325 0.096 0.073 0.046
Observations 323 287 323 323 287 323
72
Table 7: ICO Returns
This table reports coefficient estimates from OLS estimation of equations (1) through (3) as described in sections 3.1 and 3.3. The
outcome variable 𝑦𝑖 is the opening day ICO return, measured in two ways: (1) first day ICO return is measured as the natural log of
the open-to-close return on the first day of trading, using the first available trading price and closing price on day 1 of the ICO; and
(2) first day ICO return is measured as the total first day return inclusive of the ICO offer price. Sample sizes vary based on available
data. The reported t-statistics are based on robust standard errors. The asterisks (*, **, and ***) indicate statistical significance at the
10%, 5%, and 1% levels, respectively, for two-tailed tests. ^ indicates statistical significance at the 10% level for a one-tailed test.
See Appendix A for descriptions of variables
Log Open-to-Close ICO Return Log Total ICO Return
(1) (2) (3) (4) (5) (6)
USA Restricted 0.009 0.034 -0.006 0.112 0.095 0.088
(0.25) (0.85) (-0.18) (0.69) (0.56) (0.55)
Platform Information -0.049 -0.009 -0.119 -0.102
(-0.70) (-0.15) (-0.54) (-0.43)
Sourcecode/Github Disclosure -0.010 -0.039 -0.123 -0.157
(-0.21) (-0.83) (-0.86) (-1.07)
ICO Team Size -0.038 -0.030 -0.065 0.003
(-1.25) (-0.72) (-0.65) (0.03)
Past Success of ICO Team 0.084* 0.082^ 0.350** 0.314*
(1.67) (1.57) (2.12) (1.81)
ICO Participation Incentives 0.011 0.011 -0.126 -0.136
(0.27) (0.25) (-0.81) (-0.86)
Social Media Activity 0.018 0.039 -0.259 -0.253
(0.24) (0.44) (-0.96) (-0.86)
Informative Whitepaper 0.054 0.040
(0.61) (0.13)
ICO Team Information -0.034 -0.174
(-0.78) (-1.12)
Token Allocation Information 0.047 -0.404**
(0.79) (-2.01)
Founder Tokens Vesting Period -0.035* 0.064
(-1.74) (0.89)
Use of Proceeds -0.019 0.012
(-0.36) (0.06)
Whitepaper Opacity 0.007 0.016
(0.66) (0.39)
Whitepaper Length 0.017 0.126
(0.34) (0.72)
Rating 0.037^ -0.060
(1.38) (-0.67)
Ctry Ind. (USA, RUS, SGP, CHN) Yes Yes Yes Yes Yes Yes
Quarter FE Yes Yes Yes Yes Yes Yes
Pseudo / Adj. R-squared 0.011 -0.037 0.022 0.179 0.204 0.180
Observations 365 317 365 289 255 289
