�e Information Content of ICOWhite Papers∗

David Florysiak† Alexander Schandlbauer‡

September 20, 2019

Abstract

White papers are likely the most important source of information provided to potential Initial Coin O�er-

ing (ICO) investors in platform-based ventures. We use textual analysis to measure the information content of

white paper documents as a proxy for information asymmetry between ICO issuers and investors and test predic-

tions of adverse selection and signaling in several economic se�ings during the pre-ICO, ICO, and post-ICO phases.

Observed empirical associations between ratings, fraud probability, ICO success, funding volume, token exchange-

listing probability, underpricing, cumulative returns, and trading volume are jointly consistent with high-quality

ICO issuers signaling their type by providing more informative content, i.e. excess or new textual information not

contained in recent and peer white papers. Moreover, signaling is likely impaired during the ICO process as in-

vestors include potentially biased expert ratings in their investment decision-making. As a result, low-quality and

potential fraud ICO issuers successfully raise funds and exchange-list their tokens, leading to ine�cient funding

allocation, generating potentially large adverse selection costs, and increasing risk of market failure.

JEL classi�cations: G30, M13, O16, K29, K40.

Keywords: Initial coin o�erings, white paper, textual analysis, information content, signaling, platform economy.

∗We thank Hugo Benede�i, Ralf Elsas, Ioannis Floros, Bjarne Astrup Jensen, Evgeny Lyandres, Daniel Metzger, Ste�en Meyer,�iago de Oliveira Souza and Armin Schwienbacher for helpful comments, as well as seminar participants at the University ofWisconsin-Milwaukee, the Fourth SDU Finance Workshop, the 2019 Swiss Finance Annual meeting, the 2019 Midwest FinanceAssociation Annual meeting, and the Second Toronto FinTech conference. We also thank Andreas Kusk for excellent researchassistance.

†University of Southern Denmark and Danish Finance Institute, Campusvej 55, 5230 Odense, Denmark. Phone: (+45) 93507400.Email: �orysiak@sam.sdu.dk

‡University of Southern Denmark and Danish Finance Institute, Campusvej 55, 5230 Odense, Denmark. Phone: (+45) 93507001.Email: alsc@sam.sdu.dk

How do risky ventures obtain �nancing? Besides traditional channels like venture capital �nancing

(e.g., Gompers and Lerner, 1999) or fundraising through high growth stock market segments (e.g., Vismara

et al., 2012; Chang et al., 2017), initial coin o�erings (”ICOs”) are an alternative way to raise capital using

cryptocurrencies. Financing through ICOs has been rising tremendously over the past three years. �e

total amount raised accelerated from $0.9 billion in 2016 to $11.4 billion in 2018.1 Several reasons for the

popularity of ICOs exist: low costs, faster time to �nance, and regulation and costly �nancial intermediaries

(such as venture capitalists, banks, and stock exchanges) can be bypassed. Figure 1, however, suggests that

the ICO market is potentially subject to ”waves” with hot and cold market phases as the number of o�ered

ICOs decreased sharply in the second half of 2018.

ICOs are primarily used to �nance platform-based businesses, also called digital market places, ecosys-

tems, or simply networks. Entrepreneurs conduct an ICO and issue (utility) tokens (as opposed to shares

in an equity o�ering) to fund the creation of the platform. Users of the platform utilize tokens for con-

ducting transactions and those who maintain the platform are rewarded with tokens. Cong et al. (2019)

show that the value of a token can be interpreted as a form of convenience yield speci�c to the platform

and depends on the users’ transaction needs, the number of users of the platform, and the platform’s pro-

ductivity. Users decide on whether to participate in the platform and how many tokens to hold, which

depends on the magnitude of expected transactions (”transaction motive”) and the expected future token

price (”investment motive”). Users are therefore also investors and vice versa.

Traditional equity investors require information to reduce uncertainty regarding their investment de-

cisions. �is information, that relates to an ”investment motive”, can usually be found in IPO prospectuses

and contains disclosure on risk factors, use of proceeds, or MD&A (Management Discussion and Analysis).

For utility tokens, investors have additional uncertainty regarding their personal ”transaction motive” on

the platform, which requires additional information beyond an ”investment motive”. Information disclosed

to potential ICO investors should allow them to assess their expected transaction needs on the platform,

the expected number of users of the platform, and the expected platform’s productivity and thus their

valuations of the token and decisions to use the platform.

In this article, we ask the following question: do ICO issuers (successfully) signal their quality type1�e relevance of this new form of �nancing is further underlined by the fact that, according to Forbes Magazine, the ICO

market was 45% of the traditional initial public o�erings (IPO) market and 31% of the venture capital market during Q2 2018.Moreover, according to TechCrunch, the leading online publisher of technology industry news, ICOs delivered 3.5 times morecapital to blockchain startups than venture capital since 2017.

1

through information disclosure in their white papers? To answer this question, we, �rst, measure signaling

e�orts based on di�erences in the textual information content in white papers of 2,665 ICO issuers. We then

test and jointly evaluate a comprehensive set of predictions consistent with signaling in several economic

se�ings in the pre-ICO, ICO, and post-ICO phases. Speci�cally exploiting the di�erent phases an ICO may

go through, we are able to draw conclusions about the behavior of di�erent ICO market participants such

as the ICO issuer, investors participating in an ICO, rating experts, or investors in ICO issuers once they

are listed.

White papers are the most important source of information provided to potential ICO investors. A

white paper is voluntary disclosure addressing information needs regarding transaction and investment

motives and typically consists of a description of the platform-based business idea, a road-map including

key milestones for the development of the platform, the developer team, the rights a token provides, and

the token sale. �e white paper is the initial and most comprehensive disclosure channel that sets the path

and benchmark for any following developments of the business. ICO issuers use other disclosure channels

to communicate with investors, such as Twi�er, Telegram, or Slack, mainly to clarify information provided

in the white paper. As there is no standardized or mandatory white paper format, they di�er greatly in

their length, style, and content.

We use textual analysis to measure the information content of white paper documents as a proxy

for information asymmetry between ICO issuers and investors. Following the methodology of Hanley and

Hoberg (2010), we decompose a white paper’s information content into a standard and an informative part.

White papers that share many textual elements of both recently published white papers and industry peer

white papers have a high standard content measure. Any excess information not explained by standard

content factors is then captured by the measure for informative content. While standard content can

be interpreted as the degree to which related white papers borrow or even copy textual elements from

each other, informative content re�ects excess or new information not used in previous related white

papers. While standard content is positively related, informative content is negatively related to overall

information asymmetry, i.e. both content types can be seen as di�erent sources contributing to overall

information asymmetry.

Equipped with our proxy for information asymmetry, we test predictions of adverse selection and

signaling in the ICO market. �e information problem is likely severe: ICO issuers are mostly small and

opaque emerging growth businesses that have private information that is relevant to assess the platform’s

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quality, e.g. the expected platform’s productivity. Utility tokens will a�ract retail investors with limited

ability to conduct due diligence that intend to hold tokens for transaction and investment purposes. Self-

appointed rating experts with unknown monetary incentives produce unsolicited ratings with unknown

quality. Largely unregulated, this market se�ing is likely to a�ract a potentially large number of low-

quality or even fraudulent businesses and has a high likelihood of failure.

We argue that ICO issuers can signal whether they are a high-quality type business by providing

white papers with greater informative content. Providing excess or new information not used in previous

related white papers, as measured by our informative content measure, is costly for two reasons: revealing

strategic or proprietary information to competitors and the information production itself (acquisition and

due diligence). Low-quality ICO issuers are unable to easily imitate high-quality ICO issuers as they were

only able to produce informative content by disclosing additional (meaningless or meaningful) information

that would reveal their true type. Fraud ICO issuers have the incentive to pool with low-quality ICO

issuers or high-quality ICO issuers. Fraud ICO issuers have costs, such as expected criminal charges, to

imitate non-fraud low-quality or high-quality ICO issuers. �is cost is higher for pooling with high-quality

types as, for example, a more sophisticated fraud scheme that produces informative content that investors

will not easily recognize to be false or outright fraudulent is likely to scam a larger number of investors,

leading to higher criminal charges. In summary, providing informative white paper content may serve as

a signaling device (Spence, 1973) if not all ICO issuers in the market are fraudulent.

Our principal data source for pre-ICO information is from the ICO listing site www.ICOBench.com

and post-ICO secondary market data for exchange-listed tokens are from www.coinmarketcap.com. Our

full sample contains 2,665 ICOs for which all relevant variables and the white paper document is available.

In Figure 2, we motivate our conjecture that signaling through providing informative white paper content

correlates with ICO issuer type in our data. Going long an equally-weighted portfolio of all exchange-listed

ICOs with above median informative content and shorting correspondingly a below median informative

content portfolio produces an annualized Sharpe ratio of 0.85. �is indicates high-quality ICO issuers

outperform low-quality ICO issuers, potentially due to short-term reduction in mispricing and di�erences

in governance quality, which we discuss in more detail below.2

2ICO issuer returns are strongly correlated with Ethereum returns. �us, we report Ethereum-adjusted portfolio returns.In this motivational exercise, we ignore any liquidity or short-selling constraints. While the t-statistic for the Sharpe ratio is,however, slightly above the 10% signi�cance level, any interpretations as of now, both for or against any potential e�ects, shouldbe done with caution, given the short available time series of ICO returns.

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Pre-ICO, we �rst document that rating disagreement, measured as the standard deviation of ratings per

ICO, decreases by 11.6% standard deviations for a one standard deviation increase in informative content.

�is is consistent with the hypothesis that greater disclosure reduces information asymmetry and rating

experts process information contained in white papers. Investors require less informative content to accept

a high-quality type signal as credible in hot markets, as valuations of both high-quality and low-quality ICO

issuers increase, thus reducing signaling costs. We �nd that rating disagreement is unrelated to informative

content in a hot market environment, which is consistent with the provision of low informative content and

rating experts being less scrutinized by investors, thus exerting less e�ort in information production. In a

cold market, high-quality ICO issuers signal and experts exert e�ort, which decreases rating disagreement

to a large extent.3

For a small hand-collected sample of potential fraud ICO issuers, we �nd that fraudsters seem to imitate

high-quality type ICO issuers in cold markets and low-quality type ICO issuers in hot markets. �is might

indicate that expected gains from fraudulent activities, but also higher expected criminal charges, are

maximized in cold ICO markets by signaling a high-quality type, while the expected gain is still positive

by imitating low-quality types in cold markets.

In the ICO phase, we examine the likelihood of success, i.e. enough investors subscribe to the ICO and

tokens are eventually issued, and the corresponding funding volume of issued tokens. First, we expect

that greater informative content leads to higher funding success probability, thus high-quality type ICO

issuers are more likely to receive funding. If rating experts were able to separate high-quality and low-

quality ICO issuers and produced unbiased ratings, greater informative content would lead to a be�er

average rating.4 Our empirical results show that funding success is only related to informative content in

a hot ICO market. Investors likely rely on both their own assessment of white paper contents and on expert

ratings. If the weight on expert ratings becomes su�ciently large, the impact of informative white paper

content vanishes. A potential but worrisome explanation for our �ndings on funding success could be that3However, the relation of informative content and rating disagreement does not convey any evidence that experts are able to

separate high-quality and low-quality ICO issuers. �e opposite is likely true, in the appendix we provide evidence that greaterinformative content is not associated with expert rating levels, suggesting that experts are either unable or unwilling to produceratings with discriminatory power. �eir ratings highly rely on easy-to-extract publicly available information such as team sizeor the number of social media channels. Decreased rating disagreement might also be caused by a lower ”order e�ect”. �e nthrating is, on average, lower than the (n − 1)th rating, as observed for online product reviews (Godes and Silva (2012) and ourFigure 6).

4We do not �nd evidence that informative content and ratings are related (Table A3) and that more informative contentactually leads to a lower number of ratings per ICO (Table A2), suggesting that rating experts refrain from assigning ratings tohigh-quality type ICO issuers. In this case, a biased rating might be too obvious or experts may face the inability to processinformative content.

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rating experts excessively assign biased ratings in hot markets and high-quality ICOs try to desperately

signal to separate themselves from a high number of fraud ICO issuers that, according to our �nding above,

tend to pool with high-quality ICO issuers in hot markets. Second, we do not have a directional prediction

for the funding volume of issued tokens and our informative content measure. We �nd that ICO issuers

with greater disclosure through informative content, overall, tend to raise less funds, a result potentially

consistent with more e�cient capital allocation of high-quality ICO issuers.

Once funded, in the post-ICO phase, we examine the decision to list the token on an exchange and for

exchange-listed tokens: underpricing, performance, and trading volume. We argue that biased ratings are

likely driving investor’s decisions before tokens are traded in the secondary market, dismantling signal-

ing e�orts of high-quality ICO issuers and thus high-quality, low-quality, and fraud ICO issuers receive

funding. High-quality types may also signal their type by retaining tokens during the ICO (Chod and

Lyandres, 2018; Davydiuk et al., 2019). Similar to informative white paper content, we do not �nd an asso-

ciation between token retention and funding success. All ICO issuer types, however, have an incentive to

exchange-list if the expected valuation of retained tokens to cash out a�er a vesting period exceeds listing

costs. In our sample, 54% of ICO issuers that received funding seem to estimate a net bene�t to list their

tokens. We do not �nd an association between informative content and the probability to exchange-list,

indicating that all ICO issuer types are equally likely to list. However, we do �nd that be�er rated ICO

issuers are more likely to list their tokens, potentially due to ICO issuers with higher ratings estimating a

higher net bene�t of listing.

For ICO issuers that exchange-list, greater informative content is highly associated with higher under-

pricing, consistent with high-quality ICO issuers signaling their type through multiple channels. A one

standard deviation increase in informative content increases underpricing by 42.76% or 21.7% standard

deviations. ICO issuers use underpricing only in a cold market environment, which is in line with our

prediction of lower signaling costs in cold markets. Token retention, as alternative signaling device, is also

positively related to underpricing. We �nd that expert ratings are unrelated to underpricing, indicating

that ratings unlikely separate high-quality and low-quality ICO issuers.

Moreover, expert ratings are unrelated to token performance or trading volume once exchange-listed.

Token prices re�ect investors’ expectations of future productivity growth and user adoption (Cong et al.,

2019). Unlike �nancial assets that derive value from cash �ows, token values arise from a convenience yield

that is speci�c to the platform. Token values of low-quality issuers are likely overpriced while high-quality

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issuer tokens are likely underpriced. As the token market aggregates information about users’ transac-

tion needs, the number of users of the platform, and the platform’s productivity, we expect token prices

to reduce mispricing over time, resulting in higher expected returns for high-quality ICO issuer tokens.

Moreover, high-quality ICO issuers are likely be�er governed businesses outperforming low-quality ICO

issuers (Gompers et al., 2003). We �nd, however, that informative content and cumulative returns across

our 1 week, 1 month, and 3 month test periods are unrelated, indicating potentially stronger or persistent

mispricing in the ICO market. We expect higher demand (token trading volume) for high-quality type ICO

issuers’ tokens re�ecting a growing number of users in the platform’s network (Howell et al., 2019; Sockin

and Xiong, 2018). Consistent with this prediction, we �nd very strong and persistent positive associations

between informative content and volume as a one standard deviation increase in informative content leads

to increases of 20.3%, 21.7%, and 22.8% standard deviations of 1 week, 1 month, and 3 month trading

volume.

Summarizing our empirical results and contributions to the emerging literature on ICOs: �rst, we use

textual analysis to measure the information content of white paper documents as a proxy for information

asymmetry between ICO issuers and investors. Second, we provide comprehensive empirical evidence con-

sistent with high-quality type ICO issuers signaling through the provision of greater informative content

in their white papers. Signaling is likely impaired during the ICO process as investors include potentially

biased expert ratings in their investment decision-making. �is results in high-quality and low-quality ICO

issuers successfully raising funds and exchange-listing their tokens. If (forward-looking) expert ratings,

as measures for future performance, were unbiased and informative, we would expect to see a relation to

post-ICO outcome variables. However, once tokens trade in the secondary market, signaling (through the

provision of informative white paper content) is consistently related to underpricing, cumulative returns,

or trading volume while expert ratings are unrelated. �ird, we conduct detailed auxiliary analyses of the

role of expert ratings in the ICO market, in which we control for a potential lookahead bias for ratings

assigned a�er the ICO has ended. Our �ndings and interpretations that expert ratings are hindering in-

formation asymmetry reduction are in stark contrast to the existing literature (e.g. Lee et al., 2018; de Jong

et al., 2018).

Although we control for a comprehensive list of factors likely to a�ect our information content mea-

sures in our regressions and examine the robustness of di�erent representations of the document term

matrix underlying our textual analysis, our �ndings are subject to limitations. First, we are unable to ob-

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serve exogenous variation in our information content measures to establish causality in our tests. Second,

some ICO issuers have di�erent white paper versions. �e number of ICOs for which we have di�erent

versions of the white paper is very small and does not allow us to analyze the in�uence of changes in

white papers. We restrict our analysis to using the �rst white paper version available pre-ICO when infor-

mation is available to market participants for the �rst time. Finally, we acknowledge that we are unable

to test that white paper information content actually proxies for unobservable information asymmetry,

signaling through white paper contents is costly (enough), or investors and rating experts su�ciently un-

derstand white paper contents. We rely on economic consistency in the observed associations to back up

our conclusions.

�is article is structured as follows. Sections 1 and 2 discuss the related literature and the role of

white papers in ICOs. Section 3 describes our data sample and the construction of our information con-

tent measures. Moreover, we provide an intuitive explanation of our standard and informative content

measures and an analysis of which factors are associated with them. Section 4 describes the information

problem and outlines the mechanism underlying signaling through informative white paper content. We

then present the predictions and detailed results for our tests relating white paper information content to

outcome variables in several economic se�ings during the pre-ICO, ICO, and post-ICO phases. Section 5

concludes.

1 Related Literature

Despite ICOs being a very recent phenomenon, a widespread literature has emerged. Adhami et al.

(2018) provide one of the �rst comprehensive descriptions of the ICO phenomenon and of the determinants

of token o�erings documenting that the secondary market for ICO tokens is liquid on the �rst day of

trading, and an initial underpricing of 24%. Several studies have subsequently analyzed the determinants

of ICO success (e.g., Fenu et al., 2018; Boreiko and Vidusso, 2018). Howell et al. (2019) document that

liquidity and trading volume are higher when issuers o�er voluntary disclosure.5 Davydiuk et al. (2019)

show that the retention of tokens can serve as a quality signal and thereby reduce information asymmetry.5Moreover, while Momtaz (2019b) shows that, on average, ICOs create investor value in the short run, Benede�i and Kos-

tovetsky (2018) document positive returns to investing that persist even a�er the two months of the listing of exchange-tradedtokens. Amsden and Schweizer (2018) analyze whether the fact that a token is subsequently listed on an exchange and is tradedactively in�uences the initial success of an ICO. �e authors �nd that venture uncertainty is negatively related to ICO success,while venture quality has a positive in�uence.

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de Jong et al. (2018) document that favorable ratings positively in�uence the amount raised and Lee

et al. (2018) show that ratings a�ect investors’ subscriptions by large investors, that trigger an informa-

tion cascade and subsequently encourage token sales. In contrast, we �nd that expert ratings hinder the

reduction of information asymmetry.

Few other papers discuss the role and the information content of white papers. Typically, less ICOs are

analyzed and the textual analysis is restricted to the readability of white papers. Fisch (2019) examines the

dynamics of 238 ICOs in 2016 and 2017 and documents that several characteristics of the ICO campaign,

the underlying technology, and the language of the white paper determine the amount raised. Feng et al.

(2019) use 355 white papers and create an index based on three readability factors6 and Lyandres et al.

(2018) calculate the ratio of technical words to total words. Bourveau et al. (2019) examine the role of

disclosure for several capital market outcomes. Besides using a readability score to measure the white

paper opacity, an aggregated indicator measure of white paper informativeness, based on an assessment

provided by ICOBench, is included. Momtaz (2019a) uses a linguistic analysis on a sample of 495 ICOs and

shows that ICO issuers exaggerate information disclosed in white papers. We complement these papers

by decomposing the white papers’ information content into a standard and an informative part and by

di�erentiating between hot and cold market environments.

�ere also exists a growing theoretical literature studying the economics of cryptocurrencies and of

ICOs. Cong et al. (2019) show that the introduction of cryptocurrencies and crypto-tokens on blockchain

platforms speed up user adoption. Sockin and Xiong (2018) highlight the bene�ts of cryptocurrencies in

facilitating decentralized trading among participants of a platform, whereas Li and Mann (2018) present

a model that rationalizes the use of ICOs. �e la�er paper also provides several implications for policy

makers and argues against a universal ban that was recently adopted by China and Korea. Catalini and

Gans (2019) explore how entrepreneurs can use IPOs to fund venture start-up costs and to elicit demand

information through generating buyer competition for the tokens. Similarly, Chod and Lyandres (2018)

discuss the potential reasons for why an ICO can dominate traditional venture capital �nancing.

Last, our paper is related to the literature that analyzes the e�ect of disclosure on various �rm policies.7

Hanley and Hoberg (2012) document that disclosure and underpricing are substitute hedges against liability6�ese factors are the blockchain platform originality, whether the token is related to the company’s blockchain platform,

and the technicality of the writing.7See, e.g., Lang and Lundholm (2000); Healy and Palepu (2001) for seminal papers. Leuz et al. (2019) provide a comprehensive

review of this literature.

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risk when a �rm has a potential material omission, i.e., when it seriously faces the risk of liability. Hoberg

and Lewis (2017) use a textual analysis of the MD&A section of the annual reports to document that fraud

�rms disclose information that is similar to other fraud �rms. Additionally, several papers analyse the

recent JOBS Act, which eases many securities regulations on mandatory disclosures by �rms undertaking

IPOs.8

2 �e Role of White Papers in Initial Coin O�erings

Initial coin o�erings, also called token sales or crowd-sales, are a mechanism to raise external funding

through the emission of cryptocurrency tokens, which conceptually are entries on a blockchain. �e

blockchain publicly records all transactions made in the cryptocurrency, and the owner of the token has

a key that lets her re-assign the token ownership (Yermack, 2017). In an ICO, the token is o�ered to the

public for the �rst time. �is can be either a security token or a utility token. Security tokens, which are

conceptually similar to equity stock issued in conventional initial public o�erings, derive their value from

being a tradable asset.9 �ey are subject to (federal) regulations, yet o�en do not convey voting rights or

dividends (Momtaz, 2019b). Utility tokens are di�erent: they give access to products or services and are

used to pay for future services. Howell et al. (2019) phrases this as ”[…] buying the rights to a stadium

seat before the venue is built […]”. In the U.S., the SEC uses white papers as a primary document for the

Howey test to determine whether an issuance is a security or not (Oren, 2019).

A�er the initial development of an idea for a decentralized blockchain application, the coin o�ering

process typically begins with the publication of a document called a ”white paper”.10 A white paper is the

primary public tool to describe the cryptocurrency project.11 It is a detailed guide to the project, product or

service and typically includes information on the project’s objective, a road-map including key milestones,

the intended use of proceeds, the team, and a time schedule for the token sale. Moreover, as many technol-8�e 2012 JOBS (Jumpstart Our Business Startups) intends to stimulate funding of small businesses. While the act’s de-

burdening provisions increases information asymmetry (Barth et al., 2017), the e�ects remain unclear. Chaplinsky et al. (2017) donot �nd evidence that the costs to go public are reduced whereas Dambra and Gustafson (2018) �nd that subsequent to the act,newly public �rms invest more a�er going public.

9See, e.g., Ljungqvist (2007) for a detailed overview of the literature on IPOs.10Figure A1 in the Internet Appendix provides two examples of platform-based businesses’ time-lines and highlights that the

white papers are published early on, i.e., during the initial phase of the ICO.11Besides publishing a white paper, projects are commonly also advertised and discussed on various social media channels

such as Twi�er, Reddit, Medium, LinkedIn, and BitcoinTalk. �e discussions on these social media platforms serve the purposeof clarifying information provided in the white paper and of increasing investors’ interest in the token (i.e., marketing). See e.g.,Lyandres et al. (2018) for a more detailed analysis of the social media platforms.

9

ogy start-ups conduct ICOs, white papers may include a technical description of the underlying technology

and of the platform operation for which funding is sought. However, ICOs are not limited to technology

start-ups but could be used by any business to raise external funding. White papers are published on the

projects’ webpages and on ICO issuer listing sites, such as www.icobench.com or www.icodrops.com.

�e aim of a white paper is generally twofold: to provide information to potential investors and to

promote the token. Hence, on the one hand, white papers are similar to IPO prospectuses and include

information typically provided in �nancial statements or business plans. However, on the other hand,

both the content and the structure of white papers exhibit clear di�erences. As there is no underwriter

involved and no road show to potential investors is conducted in the ICO process, promoting the product

plays a bigger role. In fact, no standardized disclosure format exists and white papers o�en reveal li�le

about the issuing business or the founders, e.g., they frequently fail to give a postal or other contact address

(Zetzsche et al., 2018). Moreover, white papers are unaudited and published voluntarily without any legal,

regulatory, or exchange related requirements. Yet, while voluntary in nature, they are o�en regarded as

necessary (almost mandatory) from the perspective of the ICO issuer.12

�e information in white papers needs to be credible (Feng et al., 2019) and ICO issuers themselves

have an incentive to provide reliable white papers as these documents are the most important source of

information to potential investors. Moreover, national regulators such as the SEC in the U.S. play an active

role in ensuring the rightfulness of white papers published. A recent example from April 2018 is Centra

Tech, which was charged with orchestrating a fraudulent ICO that raised more than $32 million from

thousands of investors.13

3 Information Content Analysis

3.1 Sample

Our data consist of all ICOs that were listed on www.ICOBench.com (”ICOBench”) between August

2015 and September 2018. �is webpage provides a comprehensive overview of ICOs and is generally12�e website www.investinblockchain.com, which is one of the fastest growing websites in the cryptocurrency and

blockchain space (self-reported), recently highlighted in an article that ”the white paper is undoubtedly one of the most criti-cal aspects of a blockchain project”. Furthermore, ”it can’t be said enough: white papers ma�er”. (February 24, 2018)

13”We allege that Centra sold investors on the promise of new digital technologies by using a sophisticated marketing cam-paign to spin a web of lies about their supposed partnerships with legitimate businesses,” said Stephanie Avakian, Co-Director ofthe SEC’s Division of Enforcement. See www.sec.gov/news/press-release/2018-53 for more details. �e three co-founders wereindicted for fraud.

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regarded as one of the biggest ICO listing and rating sites. For each of the 4,053 ICOs, we download all

available data using the ICOBench API. �is includes variables such as the name, token ticker, initial price,

and money raised if the ICO has ended, and also textual descriptions of, e.g., milestones, team members, or

expert ratings. A link to the white paper is provided, from which we download the document. If a white

paper link is missing or no longer working, we search for it using several other webpages14 or via the

Internet Archive (www.archive.org). Our analysis is limited to those ICOs for which we are able to collect

the white paper. Moreover, we require a white paper to have at least 10 pages and 500 words.15 Our �nal

sample for which we are able to collect both white papers and all other relevant variables consists of 2,665

ICOs.

3.2 ICO Industry Classi�cation

One challenge is to determine in which industry an ICO issuer is active since no standardized industry

code, like a SIC or NAICS code, is available. Upon being listed on ICOBench, the issuer can choose from

a list of 29 di�erent industry categories. Two-thirds pick more than one industry category, on average 3.8

categories are then chosen, 10% pick more than six industries, and one ICO issuer picks all 29. Moreover,

certain categories such as ”platform” or ”cryptocurrency” are generic and occur very frequently (e.g., plat-

form 18%, or cryptocurrency, 14%) while others are more speci�c and are thus chosen more infrequently

(e.g., banking, 4% or health, 2%). Hence, we determine the appropriate peers by applying an approach

that is similar to the text-based network industry classi�cation of Hoberg and Phillips (2010). �at is, we

calculate a similarity score based on all chosen industry classi�cations. Moreover, to account for the gener-

ality of some of the industry categories, we down-weight the generic ones to 20% while more informative

industry classi�cations receive a weight of 100%, before we calculate the industry similarity scores.16,17

14Examples include www.cryptorating.eu/whitepapers, www.icosbull.com, and www.icorating.com.15We manually inspect white papers with low numbers of pages or words and �nd that using a word-to-page count ratio of at

least 50 selects white papers that can be considered to be a full description of the ICO. �e remaining ones are typical technicalwhite papers of (smaller) ICOs that focus technical logistics of how the platform operates. �at is, 298, out of a total of 2,963white papers, are disregarded.

16�e generic categories include: platform, cryptocurrency, so�ware, Internet, smart contract, big data, arti�cial intelligence,virtual reality, and others. �ese account for 54% of all industry classi�cations.

17We �rst sort issuers on their similarity measure and choose the 30 most similar issuers. In case the similarity measure is thesame, we sort ICO issuers chronologically and focus on the closest ones.

11

3.3 Information Content Decomposition

3.3.1 Methodology

We extract the text from the white paper’s pdf document and further pdf-speci�c characteristics, such

as the names of the authors, the producing so�ware, or the number of characters.18 For each document

in the corpus of all white paper documents, we transform the text into a sequence of unigrams, bigrams,

trigrams, and recognize named entities. We lemmatize terms and �lter stopwords, punctuation, determin-

ers, and numbers. We then transform the tokenized documents into a sparse document-term matrix of

shape (# white papers, # unique terms). Following Hanley and Hoberg (2010), we use relative term counts

in the document term matrix, i.e., the raw frequency of terms in a document is divided by the total num-

ber of terms in the document.19 Our document term matrix has 2,665 documents (rows) and 8,423 terms

(columns).

We closely follow Hanley and Hoberg (2010), who develop a methodology to determine the information

content of IPO prospectuses, to decompose the information contained in white paper documents into

standard or informative content components. �e standard content of a white paper is related to, �rst,

terms used in other concurrent white papers and, second, terms used in white papers in the same industry.

Hence, to estimate ICO issuer i’s exposure to the content of other recent ICO issuers, we calculate the

variable normrec,i, which is the average of the normalized document term vectors (normtot,k, where k is

one of the 2,665 rows in our document term matrix) for the K platform-based businesses that were �led

in the 30 days preceding issuer i′s ICO.20 �e formula for normrec,i is given by

normrec,i =1

K

K∑k=1

normtot,k.

�e second component of the standard information content, the variable normind,i, is calculated as18A very small number of white papers are not pdf documents. In these cases we convert the �le format like Word or HTML

to a pdf document.19Instead of using relative weights in our document term matrix following Hanley and Hoberg (2010), we also represent term

vectors using a t�df (term frequency–inverse document frequency) factor weighting. White paper-speci�c, local, term frequenciesare multiplied by their corpus-wide, global, inverse document frequencies. Terms appearing in many white papers have higherdocument frequencies and thus lower weights. We normalize the term vectors by the L2 norm. Our results are robust to using at�df weighted document term matrix.

20We limit K to at most 30 ICO issuers to account for the fact that the average founder of a platform-based business isunlikely to read/screen more white papers than that. �is results in the average ICO issuer being subject to 30 recent platform-based businesses. For each ICO issuer that has more potential recent peers, we randomly choose 30 peers. �is restriction reducesnoise and if it were relaxed, the average number of recent ICO issuers would increase to 248.

12

the average of the normalized term vectors of the white papers of the P ICOs that were �led within the

same industry as that of ICO issuer i. To be an eligible industry peer, entrepreneurs have to �le an ICO

within a two-month window; that is, at least 30 days and at most 90 days before ICO issuer i. �is time

choice ensures that both the recent information and the industry information content do not overlap. �e

formula for normind,i is given by

normind,i =1

P

P∑p=1

normtot,p.

One challenge is to determine the date on which the market �rst learned about the disclosure in the

ICO issuer’s white paper. ICOBench provides several dates for every platform-based business: a pre-ICO

start and end date and an ICO start and end date.21 We supplement this information with the date on

which the �rst expert rating was provided and with the creation and modi�cation dates of the white paper

(pdf) document.22 �e earliest of those dates is then chosen. 23

For each ICO issuer, we then run the following regression:

normtot,i = αrec,inormrec,i + αind,inormind,i + εi, (1)

in which the normalized term vector of the white paper of ICO issuer i (normtot,i) is regressed on the

average term usage in recent white papers (normrec,i) and in industry peer white papers (normind,i).

Standard content, which measures the variation explained by standardized terms in i’s white paper, is

then de�ned as the sum of the coe�cients αrec,i and αind,i,

αstandard,i = αrec,i + αind,i,

21In the pre-ICO period, investors are allowed to buy tokens before the commencement of the o�cial crowd sale (i.e. the ICOperiod). �e token price is discounted in the pre-ICO. 50% of all ICOs in our sample o�er a pre-ICO.

22In six rare cases, all dates are missing. In these instances, we approximate a date from those ICO issuers that have previouslybeen listed on ICOBench.

23In our sample, 1,007 ICOs occurred in 2017 and 1,649 occurred in the �rst 10 months of 2018. By contrast, there were only51 platform-based business in 2016 according to www.coinschedule.com; however, only for nine of these businesses detailed datais available in our sample.

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whereas informative content is de�ned as the sum of the absolute residuals of regression (1), i.e,

αinformative,i =∑i

|εi|

i.e., a function of the terms not explained by recent and industry white papers.

�e interpretation of αrec,i and αind,i is as follows: Given the corpus of all words, a normalized term

vector, normtot,i, contains the relative frequency of the words used in the document. For example, the

coe�cient on ”recent” is 0.32. If the relative usage of all words in the term vector αrec,i increases by a

marginal unit (say 1%), this translates into an increase of 0.32 times 1% of the average term usage in white

paper i. For instance, if the average white paper’s word usage is 0.1, the predicted usage from ”recent”

would be 0.1032 (0.10 + 0.32*1%) in white paper i.

�e methodology of Hanley and Hoberg (2010) has several advantages in our context of more diverse

language and word usage in ICO white papers compared to traditional �nancial texts. It can be applied to

any text as it relies on relative word usage. Another advantage, comparing an ICO issuer’s white paper

to other recent and industry white papers, which serve as reference points to calculate this relative term

usage, is similar to including time and industry �xed e�ects into a regression.

3.3.2 Intuitive Explanation of Standard and Informative Content

If one white paper has more standard content than another white paper, that does not mean that it has

automatically less informative content, or vice versa. �at is, there is no mechanistic negative correlation

between the two content measures by construction (Hanley and Hoberg, 2010). Figure 4 shows a sca�er

plot of informative against standard content for our sample. �e correlation between the two measures is

0.485 and indicates a positive relation. �e following example illustrates the intuition behind the standard

and informative content measures and why there is no mechanistic relationship between them.

Imagine a corpus that contains many white papers only using the words ”blockchain”, ”cryptocur-

rency”, ”app”, and ”platform”. Figure 5 shows the relative frequency distribution across these words for

two �ctitious white paper documents from this corpus. For example, the word distribution for the white

paper in Panel (a) shows that 30% of all words in the document is ”blockchain”, while the industry white

paper average usage of this word is also 30% and the average usage in recent white papers is 20%. Estimat-

14

ing Equation (1) for this simple white paper document term matrix24 yields a standard content measure of

1 and an informative content measure of 0.1. For the other �ctitious white paper in Panel (b), the words

”blockchain” and ”cryptocurrency” are used as in Panel (a). However, this white paper uses much less

the word ”app” and much more the word ”platform” compared to recent and industry usage. Its standard

content measure is still 1, i.e. the joint sensitivity to the recent and industry factors is the same as for the

white paper in Panel (b), the deviations from these factors are, however, larger than for the white paper

in Panel (a), and thus more informative, as indicated by an informative content measure of 0.2. One of

many interpretations could be that the white paper in Panel (b) discloses that the entrepreneur plans to

shi� from an app-based solution to a platform-based solution.

More informative content helps to reduce (relative) information asymmetry between investors and

ICO issuers. For example, the information disclosed by a platform-based business, that is not explained by

words used in industry and recent white papers, could be completely non-sense, for example, non-existing

fantasy words. �is information would be highly informative to investors and identify this ICO issuer to

potentially be a low-quality type. On the other hand, information disclosed could be highly value-relevant

such as the ICO issuer switching from an app-based to a platform-based solution as illustrated above. To

summarize, standard content measures the degree to which white papers borrow textual content from

recent and industry white papers and informative content measures to which word usage deviates from

recent and industry white paper contents. Both measures do not have a mechanistic relationship but are

moderately positively correlated in our sample.

3.4 Hot and Cold ICO Markets

�e IPO �nancing literature documents that IPOs occur in ”waves” in which ”hot” and ”cold” market

phases prevail i.e., the number of �rms that go public changes heavily over time (e.g., Pastor and Veronesi,

2005). Di�erent explanations for IPO waves have been proposed (Lowry et al., 2017). For example, an

increase in investor sentiment may in�ate equity prices and cause investors to overpay for newly public24�e document term matrix in the notation of the previous section is

normtot,i normind,i normrec,i

blockchain 0.30 0.30 0.20cryptocurrency 0.20 0.20 0.30app 0.25 0.20 0.30platform 0.25 0.30 0.20

.

15

companies during ”hot” periods. Sentiment thereby allows riskier or lower quality �rms to raise capital.

Alternatively, a �rm’s demand for capital can change as a result of variations in macroeconomic conditions.

We also observe a ”wave” pa�ern in the ICO market. Figure 1 shows the run-up in the number of newly

listed ICOs until early 2018 and a sharp decrease therea�er. Akin to that is the Ethereum price surge

in 2017 and the sharp decline therea�er; i.e. the price run-up and the subsequent sharp decrease of the

cryptocurrency that is (technically) underlying almost all platform-based businesses, which is possibly

caused by crypto-related investor sentiment (Drobetz et al., 2019).25,26

In our analysis, we split the overall sample period into ”hot” and ”cold” ICO market periods, as we

expect to �nd di�erences in the provision of information content in response to variations in ICO market

conditions (see Section 4.1 for details). To de�ne hot ICO market periods, we follow Helwege and Liang

(2004) and �rst calculate the three-month moving averages of the number of ICOs that were conducted.

We subsequently classify those periods that have a moving average ICO count of more than 202 ICOs (the

top quartile) as hot periods and the remaining periods as cold. Figure 1 visualizes the hot market phase

(gray shaded area).

3.5 Descriptive Statistics

Figure 1 highlights the well-known fact that the aggregate ICO market has experienced rapid growth

until the �rst half of 2018 and slowed down considerably therea�er. Both the number of ICOs and the

Ethereum prices show a similar pa�ern, which suggests that ICO issuers may have tried to bene�t from

the general market momentum when raising capital.

Panel A of Table 2 presents the summary statistics for the estimates of the standard and informative

content. �e average document in our sample has a standard content coe�cient of 0.97, which indicates

that the average white paper is relatively similar to the average past white paper. �e intuition behind this

is as follows: If the standard deviations of normtot,i, normrec,i, and normind,i are close to zero, αrec,i and

αind,i should be close to 0.5. However, a higher standard deviation in the term vectors causes the standard

information to be less than one. �e in�uence from recent ICO issuers is lower than that of past industry

ICO issuers (0.32 vs. 0.65). �e average of the informative content measure, i.e., the average of the sum25In untabulated results, we document that the Ethereum price Granger-causes the number of ICOs (i.e., the number of ICOs

is in�uenced by the lagged Ethereum price but not vice versa).26Ethereum is ”a blockchain with a built-in Turing-complete programming language, allowing anyone to write smart contracts

and decentralized applications”, according to Vitalik Buterin’s initial white paper. Similar price run ups have been observed forother cryptocurrencies, such as Bitcoin.

16

of the regression residuals, is 1.14. Moreover, the standard deviation of standard content is 0.15 and of

informative content is 0.14, indicating that a sizeable cross-sectional variation exists.

Panels B to F further report detailed market environment and ICO issuer-speci�c summary statistics.27

While 57% of the ICO issuers are successful in raising funds, the typical proceeds from conducting a suc-

cessful ICO are relatively small - the average (median) is $20.6 ($6.1) million. A fraction of 43% of the tokens

are distributed and around a half of all ICOs have a country or a Know Your Customer (KYC) restriction,

the la�er being a process where companies can verify the identity of their investors. �e average ICO team

consists of 13 members (10% have 23 or more) and, typically, the proposed future progress is measured via

8 milestones. ICO issuers also actively use 6.5 out of 9 di�erent social media channels.28

�e typical white paper is 34 pages long, though this number ranges from 16 to 55 when we look at

the 10th and 90th percentiles. It has over 42,000 characters or, stated di�erently, almost 8,300 words, which

corresponds to 245 words per page. As a rule of thumb, a text wri�en in Times New Roman, font size 12

points, has 500 words on a single spaced page and 250 words on a double spaced page. Hence, white papers

frequently use �gures, graphs, or pictures.

Readability measures the ability to decipher an intended message. It is, however, di�cult to de�ne pre-

cisely, and several popular measures exist.29 One of the very commonly applied measures is the Gunning-

Fog Index, which measures readability based on sentence and word length.30 �e average is 12.4, which

is signi�cantly lower than that of 10-K annual reports, which is 18.9 (Loughran and McDonald, 2014).

�is indicates that the text in white papers is wri�en in a simple language, whereas 10-K’s are deemed as

unreadable.31

To measure the sentiment of the white papers, we use a standard Naive Bayes classi�er trained on27An overview of the variable dentitions can be found in Table 128�e most common usage is Twi�er and Facebook (96% and 89%). While 85% of the ICO issuers have a telegram account, 73%

use YouTube. �e least popular social media outlets are Github and Reddit (56% and 61%).29While some authors have pointed out that �le size is a high-quality proxy for the readability of 10-K �lings (e.g., Loughran

and McDonald, 2014), the usage of �gures and pictures/graphs/�gures is likely to distort this (some may or may not require a lotof �le storage).

30It is de�ned as a combination of average sentence length and the proportion of complex words (i.e., words with three ormore syllables). Originally, it was developed to measure the years of formal education required to comprehend a narrative. Morerecently, it is also used in �nance applications. For instance, Li (2008) �nds that �rms with lower reported earnings tend tohave annual reports that are more di�cult to read. However, its usage to analyze �nancial documents has also been questioned.Loughran and McDonald (2014) point out that the usage of complex, multisyllable words can be misleading, as this decreases thereadability measure. Yet, many such words are frequently used in �nancial documents (e.g., the terms ”corporation”, ”manage-ment”, and ”operations” are not di�cult for an average investor to comprehend). Alternative readability measures include the �lesize (in megabytes) and the Flesch–Kincaid or the Flesch Reading Ease scores.

31Documents with a Gunning-Fog Index above 18 are generally considered unreadable since more than 18 years of schooling,i.e., a master’s degree, is needed to understand the text.

17

movie reviews to determine subjectivity and polarity. �e subjectivity measure di�erentiates between

objective and subjective words (0 to 1.0) and the polarity measure di�erentiates between negative and

positive words (-1.0 to 1.0). White papers are fairly subjective, the average subjectivity measure is 0.43,

while the polarity measure is 0.11, indicating a slightly positive tone.

Panel A of Figure 3 plots the document similarity measure for our sample period and shows that while

it remains relatively stable, substantial cross-sectional variation persists. Panel B plots both the standard

content and the informative content. While both measures are more volatile at the beginning of 2017,

which may be due to fewer observations, they remain stable therea�er.

3.6 Results

We start o� by examining what in�uences standard and informative content. Both variables are re-

gressed on several explanatory variables that are likely associated with information content.32 Columns

1 and 2 of Table 4 show that the market environment, as measured by the number of industry peers and

the industry success rate, positively in�uences standard content but not informative content. Hence, this

indicates that those who write a white paper take inspiration from their industry peers. Providing a larger

number of milestones only increases standard content but does not add to informative content. Shorter

white papers (i.e., with less characters) negatively in�uence both standard and informative content, though

this e�ect is mitigated for the informative content when controlling for the amount of pages wri�en. A

white paper that is more di�cult to read and which is wri�en in a more pessimistic tone, as measured by

the Gunning-Fog index and the sentiment polarity, has a lower informative content.

Moreover, several �nancing terms in�uence information content: while having a bounty program

positively in�uences standard content (i.e., having a bounty program is a way to increase the word-of-

mouth advertising, which is re�ected in white papers having more standard content), a bonus program,

which incentivizes investors to buy tokens, negatively in�uences the standard content. Hence, bounty and

bonus programs may serve as substitutes.

Columns 3-6 di�erentiate between hot and cold market phases and show similar relations between

explanatory variables and standard and informative content of white papers in the di�erent phases. Some

relations are, however, stronger under di�erent market conditions, e.g., sentiment polarity mostly in�u-32To account for time-invariant e�ects, quarter-year �xed e�ects are included in the regressions, and standard errors are

adjusted for clustering by quarter-years.

18

ences information content in cold, but not in hot, market phases.

4 Adverse Selection and Signaling through White Paper Information

Content

4.1 Information Problem and White Paper Disclosure

�e ICO market is characterized by high levels of information asymmetry between entrepreneurs and

investors, i.e. investors cannot easily separate high-quality and low-quality ICO issuers (Akerlof, 1970;

Chod and Lyandres, 2018; Sockin and Xiong, 2018). �ere are several factors adding to this information

problem. First, ICO issuers are mostly small and opaque emerging growth businesses. Second, predomi-

nantly retail investors and only very few institutional investors are active in the ICO market (Lee et al.,

2018). Compared to institutional investors, retail investors are unable to conduct the same level of due

diligence or to discipline managers to more disclosure in order to reduce information asymmetry (Boone

and White, 2015). �ird, rating experts, who engage in information production to uncover the type of an

ICO issuer, are potentially biased. Ratings in this market are unsolicited and thus not costly for the rated

ICO issuer. �is raises the question of both the quality of issued ratings and the monetary incentives of

self-appointed rating experts. Fourth, the ICO market is highly decentralized and unregulated. �ere is

no mandatory disclosure. In summary, small and opaque emerging growth businesses meet a very large

fraction of retail investors in a highly decentralized and unregulated market, in which self-appointed rat-

ing experts with unknown monetary incentives produce unsolicited ratings with unknown quality. �is

creates a severe information problem and a potentially high likelihood of market failure.33

Our information content measure of ICO white papers can be regarded as a proxy for information

asymmetry.34 Greater standard content is associated with a larger overlap in industry and recent white33�e ICO market and OTC stock markets share many characteristics. While most OTC markets have very low listing and

disclosure requirements (Vismara et al., 2012; Cumming and Johan, 2013), Floros et al. (2018) analyze an OTC stock marketsegment at Frankfurt Stock Exchange that required no mandatory disclosure by listed �rms. �is stock market is probably theone that is most comparable to the ICO market; it eventually collapsed due to an ever-increasing in�ux of fraud �rms until it wasshutdown by the regulator and the exchange.

34Hanley and Hoberg (2010) use a similar but di�erent interpretation within the analysis of information content of IPOprospectuses. �ey argue that if an underwriter and an issuer engage in more costly information production in the premar-ket, the larger will be the amount of informative content in the IPO prospectus. More informative content should lead to greaterpricing accuracy in the expected o�er price or range. �e alternative to disclosing more information in the prospectus is to letinvestors (costly) generate information during bookbuilding and set the price. �us, more informative content in the prospectusrequires less revelation of information from investors through bookbuilding and less underpricing to compensate investors forthis information production. �ey argue that the larger standard content, i.e. the larger the overlap of content with industry and

19

paper contents and thus lower disclosure leads to higher information asymmetry. Higher informative

content is associated with providing additional information beyond what is contained in industry and

recent white papers and thus greater disclosure leads to less information asymmetry between investors

and the ICO issuer.35 We argue that high-quality ICO issuers have an incentive to separate themselves

from low-quality ICO issuers by providing a white paper with higher informative content. Producing a

white paper with informative content, i.e. that contains additional information beyond what has been

disclosed by previous ICO issuers, is costly for two reasons: revealing strategic or proprietary information

to competitors and the information production itself (acquisition and due diligence).

Low-quality ICO issuers are unable to easily imitate high-quality ICO issuers. First, without any cost,

low-quality ICO issuers were only able to produce informative content captured by our measure by disclos-

ing additional (meaningless) information that would reveal their true type. �us, low-quality ICO issuers

have no incentive to disclose informative content. Second, a low-quality ICO issuer is either fraudulent or

non-fraudulent. High-quality ICO issuers have a higher valuation than low-quality ICO issuers, and non-

fraud ICO issuers have a higher valuation than fraud ICO issuers, which is zero. Hence, fraud ICO issuers

have the incentive to imitate low-quality ICO issuers or high-quality ICO issuers. To imitate high-quality

types is costlier: A sophisticated fraud scheme, which produces informative content that investors will not

easily recognize to be false or outright fraudulent, is likely to scam a larger number of investors leading

to higher criminal charges. In summary, providing informative white paper content may serve as a sig-

naling device (Spence, 1973). Low-quality ICO issuers are likely to provide no informative content while

high-quality ICO issuers are. Depending on the degree of crime, fraud ICO issuers imitate low-quality or

high-quality type ICO issuers.

IPO markets exhibit ”waves” in which ”hot” and ”cold” market phases prevail, i.e., the number of �rms

that go public changes heavily over time (e.g., Pastor and Veronesi, 2005). Waves might be caused by

investor sentiment in�ating prices and thereby allowing riskier or lower quality �rms to raise capital in

hot markets due to market timing. In hot ICO markets, the valuations of both high-quality and low-quality

ICO issuers increase, which leads investors to require less informative content provided by ICO issuers to

accept a signal as credible that the respective ICO issuer is a high-quality type. �us, the cost to signal

a high-quality type by providing informative content is lower in hot markets. In cold market phases, in

recent prospectuses, the more likely it is that information is generated during bookbuilding.35See Section 3.3.2 for a detailed intuitive explanation.

20

which more rational investors prevail, this cost is higher as such investors are less willing to invest in

low-quality types and are thus likely to require more informative content to accept a signal as credible.

Below we test predictions that are based on the notion of informative content being a proxy for in-

formation asymmetry and a signaling device in several economic se�ings during the pre-ICO, ICO, and

post-ICO phases. In our predictions, we focus on the e�ects of informative content on the respective eco-

nomic outcomes as both, standard and informative content, are related to overall information asymmetry.

While standard content is positively related, informative content is negatively related to overall informa-

tion asymmetry, i.e. both content types can be seen as di�erent sources contributing to overall information

asymmetry. Moreover, we also test our predictions in hot and cold market phases and provide alternative

explanations along with our predictions.

4.2 Empirical Design

In the pre-ICO phase, we examine how expert ratings react to variations in white paper information

content or how the probability of an ICO issuer being potentially fraudulent is related to information

content.36 During the ICO, we relate information content to the probability that an ICO issuer is successful

in raising funds and funding volume conditional on being successful. In the post-ICO phase, we examine

the relation between information content and probability of an ICO issuer’s token being subsequently

listed on a cryptocurrency exchange, ICO underpricing, and performance and trading volume.

For every speci�cation, we run the following cross-sectional regression

yi = α0 + β1 Standardi + β2 Informativei + β Xi + γq + εi, (2)

where yi refers to each of our outcome variables of interest. We include �ve comprehensive sets of control

variables (Xi) that capture the following characteristics of the ICO market and issuer: (1) market environ-

ment, (2) team and product idea, (3) white paper characteristics, (4) �nancing volume and terms, and (5)

social media. �e ICO market environment (1) is described via the number of ICOs per month, the number

of industry peers, the industry success rate (measured as the percent of ICOs that are successful in the

industry), the one month and one year Ethereum return, and dummy variables if the ICO has a country

restriction or a KYC. �e team and product idea (2) proxies for the size and quality of the project and36We recognize that fraud can also occur at later stages. However, we do not test for fraudulent activities in the post-ICO

phase due to limited data availability.

21

consists of the number of team members involved in the ICO, the number of milestones, and the expert

rating. White papers are described (3) by including the number of characters, the fraction of characters to

pages, the sentiment subjectivity and polarity measure, and the Gunning-Fog index. (4) includes a dummy

for having a hardcap and a so�cap, the number of tokens issued, the percent that is being distributed, the

days that an ICO takes, and a dummy if an ICO has a bonus, bounty program or a pre-ICO phase. �e last

control variable (5) is a proxy for the social media presence based on the number of active social media

channels. We include quarter-year �xed e�ects (γq) and allow for heteroskedastic error terms clustered at

the quarter-year level (εi).

4.3 Pre-ICO Phase

4.3.1 Rating Disagreement

Empirical Predictions

On ICO listing platforms, unsolicited quality ratings are frequently assigned to newly listed ICOs by

rating experts. Usually, that can be any person, as there is no formal accreditation. �is is similar to online

product reviews or also to a Morningstar fund rating. An expert is expected to rate an ICO on several

pre-de�ned rating categories that re�ect some measure of quality of the ICO issuer. Infrequently, experts

comment brie�y on various dimensions, such as their assessment of the quality of team members, the

vision, or the business idea. �us, an expert rating can be regarded as some prediction of success.

�e main goal of quality ratings is to help potential investors make more informed investment decisions

by reducing information asymmetries between investors and the ICO issuer. A rating expert uses her

ability to map the information provided by the ICO issuer into a quality assessment. It should be costly

for rating experts to produce a rating, and this raises the question of how rating experts are compensated.

�e incentives of rating experts are thus unclear, and market participants have pointed out that individual

expert ratings can actually be purchased by the ICO issuer.37

Beyond structured information provided on the ICO listing site, the white paper should be the most

important source of information for a rating expert. Since ICO white papers with higher informative

content potentially signal a high-quality type, we hypothesize that an increase in informative content is37See www.medium.com/alethena/this-is-how-easy-it-is-to-buy-ico-ratings-an-investigation-13d07e987394 for a discussion

of how easy it is to buy expert ratings.

22

associated with lower information asymmetry and therefore less rating disagreement (standard deviation

of ratings per ICO). Some of many alternative hypotheses are that rating experts are not able to produce

ratings with discriminatory power (i.e., they cannot separate ”high-quality” and ”low-quality” ICO issuers),

or that rating experts are able to produce ratings with discriminatory power, which, however, are not

independent and are illegally solicited.

Data, Descriptive Statistics, and Relevance of Ratings

Ratings on ICOBench range from 1 to 5 and a higher rating is associated with a be�er ICO assessment.38

Experts assign ratings in three categories: team, vision, and product. Moreover, ICOBench generates an

algorithmic rating for every ICO.39 According to ICOBench, sta� is screening the white paper for infor-

mation on the product presentation and availability of certain basic information but not its content. �e

overall ICO rating is then a weighted function of all expert ratings and the ICOBench rating.40

In total, there are 458 rating experts. While some ICO issuers are rated very infrequently, others are

rated by more than 100 experts. On average, an ICO is rated by 18.4 experts. If an ICO has just one rating

(39.2% of all observations), it is the algorithmic ICOBench rating only. �e average overall rating in our

sample, which is a weighted function of the algorithmic ICOBench and expert ratings, is 3.1441, which

is slightly lower than the overall rating that existed before the ICO has ended (3.25). Panel (a) of Figure

6 shows that the average overall rating is increasing over time and Panel (b) depicts that the number of

produced ratings follows the same trend as the number of newly listed ICOs. Moreover, the experts’ team,

vision, and product ratings are an increasing function over time - thus the more days that pass between

the ICO’s listing and the experts’ ratings, the higher are their ratings (Panel (c)). Panel (d) shows that

earlier expert ratings are higher than later ratings, i.e. the average rating per ICO is negatively related to

the order of the assigned rating. Hence, ratings exhibit temporal dynamics and one interpretation of this

is that raters are a�ected by the previous choice of others, which has also been documented for e.g., book

reviews (Godes and Silva, 2012).38Rating data is continuously updated on ICOBench and therefore we have to reverse engineer the history of the rating to

determine the ratings that occurred prior to a given ICO end date. See Appendix A for details.39A description of the rating methodology used by ICOBench is disclosed on can be found on their website: www.icobench.

com.40ICOBench discloses both the individual expert ratings and a weight that is assigned to each expert. �e weight is an undis-

closed function of several criteria, such as disclosure of personal information (e.g., having a LinkedIn pro�le), the number ofratings that were previously provided, tenure of being active on the platform, and others.

41Each individual expert’s weight is 6.6% on average. �e weight for the algorithmic ICOBench rating is 52.5% on average andvaries with the number of expert ratings, i.e., the weight decreases the more expert ratings are available.

23

�e �rst question should be whether expert ratings ma�er and the simple answer is yes. Table 7 shows

probit regressions that analyze factors in�uencing whether an ICO receives funding, i.e., the probability of

success. �e success probability is positively related to the average rating; the coe�cient of 0.55 is highly

signi�cant. A one standard deviation increase in the rating (0.70) translates to an increase in the probability

of success by 11.3%. Column 2 depicts the marginal e�ects and shows that the importance of ratings is

only surpassed by the market environment and the sentiment of the white paper.42 Moreover, not only do

ratings positively in�uence the funding success probability but also the amount raised (also documented

in e.g., de Jong et al., 2018). Table 8 shows what in�uences the amount raised of successful ICOs. A one

standard deviation increase in the average rating leads to a 28.7% increase in the amount raised.43,44

In the Internet Appendix, Section A.2, we analyze the relationship between the information content

in a white paper with both the rating level and the number of ratings that an ICO received. We show that

expert ratings are unrelated to standard and informative content and that in cold market phases fewer (but

less dispersed) ratings are produced.

Results

To test whether ICO issuers that provide more informative content in the white paper are subject to

less rating disagreement, Table 5 analyzes the standard deviation of the ICO issuers’ expert ratings. Col-

umn 1 shows a signi�cantly negative relationship between informative content and the experts’ ratings

disagreement. �e raw coe�cient estimate of -0.33 on informative content can be interpreted as follows:

A one standard deviation increase in informative content (0.13) leads to a 4.3% decrease in ratings disper-

sion. To be able to interpret the results between the di�erent regression speci�cations, Table 3 reports the

results in terms of a one standard deviation change in the dependent variable. A one standard deviation in-

crease in informative content leads to a 11.6% decrease of a rating deviation’s standard deviation.45 Hence,

more informative content, and thus less asymmetric information, leads to less disagreement between rat-

ings provided by experts for the same ICO. Other than that, shorter white papers also reduce the rating42�e market environment is measured by the number of newly listed ICOs per month, which is negatively related to ICO

success, indicating that ICO issuers may compete for funding. See Section 4.4.1 for the complete analysis of the factors of success.43Since the dependent variable is the log of the amount raised, we can calculate the e�ect of a one standard deviation change

in an independent variable as a exp(std. y ∗ coefficient) − 1 percentage change in the dependent variable. �e coe�cientestimate of rating is 0.39. Notice that the standard deviation of rating for this subsample is slightly lower than for the biggersample in Table 7 above, 0.65. �us exp(0.65 ∗ 0.39)− 1 = 0.2873.

44�e average rating is 3.32 and the average (median) amount raised is $19.23 (6.20) million. An increase from a 3.32 to 4.27increases the amount raised by, on average, approximately $5.5 million or $1.8 million for the median.

45�e standard deviation of rating disagreement is 0.37.

24

disagreement while having more social media channels leads to more disagreement.

Di�erentiating between hot and cold market phases, Columns 2 and 3 highlight that this e�ect is only

present, and reinforced, in a cold market environment. A one standard deviation increase in informative

content leads to a 12.3% decrease in ratings dispersion.46 In cold market phases, expert ratings are prob-

ably more monitored by market participants, which requires experts to exert more e�ort in the rating

production. Instead, in hot market phases, information content does not play a role which is consistent

with the provision of low informative content and rating experts being less scrutinized by investors and

thus exerting less e�ort in information production, i.e., to produce informative ratings.

4.3.2 Potential Fraud Probability

Empirical Predictions

Fraudulent activities in stock listed �rms can be observed across all �rm sizes and exchange segments.

Firms listed both on secondary exchanges and in OTC stock markets are especially vulnerable to fraud,

for example, they are prone to pump and dump schemes. Li et al. (2019) �nd that fraudulent pump and

dump schemes in the ICO market cause short-term bubbles with dramatic increases in prices, volume, and

volatility. According to the website www.news.bitcoin.com, 46% of the ICOs that were initiated in 2017

have already failed, i.e., the market valuation is close to zero, a potential result of fraud. For a number of

ICOs, the funds that were collected were subsequently stolen or, for example, the team has disappeared.

�is has sparked the a�ention of both investors and regulators. For instance, the SEC has issued a statement

saying that ”while these digital assets and the technology behind them may present a new and e�cient

means for carrying out �nancial transactions, they also bring increased risk of fraud and manipulation

because the markets for these assets are less regulated than traditional capital markets” (SEC, Aug. 21,

2018). A recent example of an ICO that was halted in January 2018 by a court order obtained by the SEC

on charges of fraud is AriseBank, which raised $600 million in just two months.47

Signaling a high-quality type by providing informative white paper content is assumed to be costly.

�is should be particularly costly for fraud ICO issuers that have to generate fake new and relevant infor-46Or, stated di�erently, a one standard deviation increase in informative content leads to a 31.1% decrease of a rating deviation’s

standard deviation (Table 3).47Shamoil T. Shipchandler, the director of the SEC’s Fort Worth Regional O�ce, recently stated that ”a�empting to conceal

what we allege to be fraudulent securities o�erings under the veneer of technological terms like ”ICO” or ”cryptocurrency” willnot escape the Commission’s oversight or its e�orts to protect investors”. For more details, see: www.sec.gov/news/press-release/2018-8.

25

mation that is not easily recognized by investors to be false (Section 4.1). Fraud ICO issuers can follow two

di�erent strategies. First, they can either costly try to imitate low-quality types or, second, with higher

cost, imitate high-quality type ICO issuers. We hypothesize that fraud ICO issuers tend to follow the �rst

strategy in cold markets, in which rational investors scrutinize ICO issuers more carefully, i.e. the risk to

be uncovered as fraudulent is high, and correspondingly the second strategy in a hot market environment.

�erefore, we predict no association between informative content and potential fraud in cold markets, in

which high-quality type and fraud ICO issuers would be di�cult to disentangle if both signal through

informative content provision, and a positive association between informative content and potential fraud

in hot markets.

Data

We hand-collect information on potentially fraudulent ICO issuers using three specialized webpages.48

On these webpages, users provide di�erent information, such as ”scam”, ”pump and dump scheme”, or

”ponzi and pyramid schemes” for various ICO issuers. By using this information, we identify 71 potential

fraud cases that are also covered in our ICOBench sample. We end up with 38 potential fraud ICO issuers,

for which we also have white papers and all relevant control variables. �is number is relatively small for

two potential reasons. First, ICOBench screens businesses before they get listed on the platform. �is is a

�rst hurdle, which more obviously fraudulent businesses are unlikely to take. Second, ICOBench deletes

potential or detected fraud businesses from their database. Even though we are able to back�ll part of

these deletions with the help of an earlier data retrieval, this leads to a selection of ICOs where fraud is

scarce and di�cult to detect. Our 38 fraud cases are potentially not yet detected fraud cases or classi�ed

as no potential fraud by ICOBench. Due to the small sample size, we match only industry peer ICOs to

the fraud case sample. �is leads to 685 observations in total, 38 fraud ICO issuers (23 in hot markets, 15

in cold markets), and up to 647 industry peer ICO issuers.49

48�e websites are www.icoscams.net, www.coinopsy.com, and www.deadcoins.com.49Potential fraud businesses are relatively evenly distributed across time and industry. Untabulated tests for di�erences in

means for several ICO characteristics and informative and standard content between the sample of potential fraud ICO issuersand non-fraud control ICO issuers reveal only di�erences in the amount raised by potential fraud ICO issuers, which is lower,and more potential fraud businesses use a bonus to a�ract investors. However, one potential caveat is the general di�culty inidentifying fraudulent ICO issuers which may bias our results.

26

Results

Table 6 shows probit regression results to predict potential fraud. Column 1 (”overall”) reveals a nega-

tive association between informative content and potential fraud for the overall sample, which is weakly

statistically signi�cant. �e marginal e�ect is −3.8%. Correspondingly, Table 3 shows that an increase in

one standard deviation of informative content leads to a 3.1% decrease as measured in standard deviations

of potential fraud probability. Providing more informative content is thus associated with a lower proba-

bility of being a fraud ICO. Table 6 also shows that informative content does not predict potential fraud in

hot markets but it is negatively associated with potential fraud probability in cold markets. �ese results

are in the opposite direction of our predictions for the behavior of fraud ICO issuers, i.e. fraudsters seem

to imitate high-quality type ICO issuers in cold markets and low-quality type ICO issuers in hot markets.

�is might indicate that expected gains from fraudulent activities in ICO markets or, alternatively, higher

expected criminal charges, are maximized in cold markets by signaling a high-quality type but still positive

by imitating low-quality types in cold markets.

4.4 ICO Phase

4.4.1 Success and Funding Volume

Empirical Predictions

In the ICO phase, we examine the likelihood of success, i.e. enough investors subscribe to the ICO and

tokens are eventually issued, and the corresponding funding volume of issued tokens. First, we expect

that greater informative content leads to higher funding success probability, thus high-quality type ICO

issuers are more likely to receive funding. Second, we do not have a directional prediction for the funding

volume of issued tokens and our informative content measure. �e required funding amount to establish

the platform business should be unrelated to the type of the issuer, for example, high-quality ICO issuers

do not necessarily require more funding.

Results

Table 7 shows the relationship between information content of white papers and the probability of

success. While insigni�cant for the entire sample, a higher informative content during hot market phases

27

increases the likelihood that an ICO is successful. Investors likely rely on both their own assessment of

white paper contents and on expert ratings. If the weight on expert ratings becomes su�ciently large,

the impact of informative white paper content vanishes. Increasing informative content by one standard

deviation increases the probability of success by 2.0%, which is equivalent to 4.0% of the standard deviation

in the dependent variable (Table 3). Column 4 reports marginal e�ects, and highlights that informative

content of white papers has the same relevance as the number of social media channels or the rating, both

of which increase the likelihood of success.

We next examine the relationship between information content of a white paper and the amount that

is raised in an ICO. Table 8 presents the results. �e coe�cient of -1.75 can be interpreted as follows: a

one standard deviation (13.7%) increase in informative content leads to a 24.0% decrease in amount raised,

which is a 13.5% decrease in amount raised standard deviations (see Table 3). �us, ICO issuers with

greater disclosure through informative content, overall, tend to raise (or require) less funds to establish

their platform-based businesses.

4.5 Post-ICO Phase

4.5.1 Exchange-Listing

Empirical Predictions

ICO issuers that successfully raised funds can subsequently list the issued tokens on a cryptocurrency

exchange. However, only 54% of the successful ICO issuers in our sample list their tokens. �e listing

decision is made by the ICO issuer and is associated with many factors surrounding the ICO including

(substantial and undisclosed) listing fees charged by cryptocurrency exchanges. We assume that low-

quality type ICO issuers have less incentives than high-quality types to list. Retained tokens of low-quality

(high-quality) types are more likely to be worth less (more) than the listing costs which reduces (increases)

the incentive to list tokens on an exchange. If providing informative content serves as a signaling device

for high-quality types, we thus predict a positive association between informative content and the listing

probability.

28

Results

Table 9 shows the results of a probit regression that relates the probability of an ICO being exchange-

listed to our information content measures and several controls. A lower industry success rate, a shorter

ICO length, a be�er rating, having no so�cap, and a lower fraction of distributed tokens (higher retained

share) lead to a higher probability of the ICO to list at a cryptocurrency exchange. We do not �nd a

signi�cant association between informative content and listing probability. However, higher standard

content, positively related to overall information asymmetry, leads to a lower likelihood of ge�ing listed.

In column 1 (”overall”), the coe�cient on standard content (−0.580) translates into a 3.0% decrease in the

probability of ge�ing listed if standard content increases by one standard deviation (0.15) or, as shown in

Table 3, to a decrease of 6.0% of one standard deviation of the probability of ge�ing listed.

4.5.2 Underpricing

Empirical Predictions

�ere are many potential explanations for why underpricing exists in IPO markets.50 �e set of models

that is based on asymmetric information between investors and the issuer typically assumes that only low-

quality issuers are willing to sell their shares at the average price (Akerlof, 1970, Welch, 1989). High-quality

issuers have an incentive to signal their type by selling shares at a price below their market value, thus

creating underpricing, which deters low-quality types from imitating. As there is no underwriting, the

initial token price in an ICO is set directly by the ICO issuer, which rules out any e�ects stemming from an

underwriting institution or bookbuilding (e.g., Benveniste and Spindt, 1989; Rock, 1986). Underpricing can

thus be a potential way for high-quality issuers to signal their type. �erefore, we predict that high-quality

issuers that signal their type through greater informative content also underprice their issue, leading to a

positive association between informative content and underpricing.

Moreover, according to Chod and Lyandres (2018), high-quality types may also signal their type by

retaining more tokens during the ICO. In Table 4, we document a negative association between the fraction

of distributed tokens (in percent) and informative content for the overall sample. �at is, a lower fraction

of distributed tokens (i.e., a higher retained share) leads to higher informative content. We expect all

high-quality type signals to be positively correlated.50See e.g., Lowry et al. (2017) for a recent review of the extensive literature.

29

Results

Table 10 analyzes the relationship between underpricing of ICO issuers and our measures of standard

and informative content. We follow Benede�i and Kostovetsky (2018) and use the natural logarithm of the

token’s �rst day’s opening price to its ICO price as our underpricing measure. As a robustness check, we

examine the market adjusted underpricing by subtracting the log of the fraction of the �rst day’s Ethereum

closing price (Ljungqvist, 2007).

While we do not �nd an association between retained tokens and underpricing, we document a very

strong relationship between informative content and underpricing for the overall sample. In column 1

(”overall”) the coe�cient on informative content of 2.618 translates into a 42.76% increase in underpricing

if informative content increases by one standard deviation (0.136) or as shown in Table 3 an increase of

21.7% measured in standard deviation of underpricing. We �nd similar results for adjusted underpricing

in column 2. When spli�ing the sample between hot and cold market phases, we �nd that this e�ect is

present in cold but not in hot market phases. In hot market phases, however, we �nd a negative association

between the fraction of distributed tokens (in percent) and underpricing, i.e. signaling through a higher

retained token share.

�e relationship between underpricing in ICOs and our measures of standard and informative content

is subject to one limitation: ICO issuers may have an incentive to sell tokens at a discount in order to be

listed, which may result in a sample selection problem. To overcome this, we apply a Heckman (1979) full

maximum likelihood estimator approach, where the selection variable is the log amount raised in the ICO.

Column 2 of Table 10 shows that this is unlikely to be driving our results.

4.5.3 Performance and Trading Volume

Empirical Predictions

Token values re�ect investors’ expectations of both future productivity growth of the platform-based

business and of user adoption (Cong et al., 2019). Due to the high asymmetric information in ICO markets,

token values of low-quality issuers also entering the market are likely overpriced while high-quality issuer

tokens are likely underpriced. As the token market aggregates information about users’ transaction needs,

the number of users of the platform, and the platform’s productivity, we expect token prices to reduce mis-

pricing over time, resulting in higher expected returns for high-quality ICO issuer tokens as measured by

30

white paper informative content. Moreover, high-quality ICO issuers are likely be�er governed businesses

outperforming low-quality ICO issuers (Gompers et al., 2003).51

Trading volume, an important driver of liquidity and particularly important in crypto asset markets,

contains important information, especially for consumers’ willingness to join token-based platforms (How-

ell et al., 2019; Sockin and Xiong, 2018). We predict a positive relation between informative content and

trading volume due to reduced information asymmetry and through higher demand for high-quality type

ICO issuers.

Data

For a subsample of 535 ICOs, we are able to match historical price and volume data from www.

coinmarketcap.com.52 �is sample selection only contains ICO issuers that successfully raised funds and

subsequently list their tokens on an exchange. We examine the e�ect of changes in information content

on both the cumulative return and trading volume a�er the listing for three time periods: one week, one

month, and three months.

Results

For performance, Column 1 of Table 11 shows that higher standard content is associated with higher

cumulative one week returns. �is relationship becomes insigni�cant for the one month return and it

reverses a�er three months. As standard content is positively related to overall information asymmetry,

this e�ect corresponds to higher information asymmetry leading to higher returns in the short run (1 week)

and this reversing to lower returns for longer periods (3 months). Other factors, such as a weaker market

environment (as measured by the one year Ethereum return) or having a bonus program, for example,

decrease cumulative returns, whereas having a hardcap increases returns.

We do not �nd a positive association between informative content and cumulative returns across our 1

week, 1 month, and 3 month test periods. An explanation for this result could be that potentially stronger

or persistent mispricing in the short-term. Due to sample size restrictions we are unable to meaningfully

conduct a separate hot and cold market analysis.53

51See also the motivational example of a trading strategy based on informative content in Figure 2 and its description above.52�e website www.coinmarketcap.com is the major provider of historical pricing information. Our sample is a snapshot that

contains information until October 2018; however, it does not cover all of the ICOs in our sample.53An additional test, which is beyond the scope of this paper, would be the analysis of the correlation of our informative

content measure with other factors, also found to be existent in OTC stock markets, such as illiquidity, size, value, volatility,

31

For trading volume, we �nd very strong and persistent positive associations between informative con-

tent and trading volume across our 1 week, 1 month, and 3 month test periods.54 �e estimated coe�cients

on informative content reported in Table 11, translate into 4.10%, 3.86%, and 3.67% increases in 1 week,

1 month, and 3 month trading volume if informative content increases by one standard deviation or as

shown in Table 3 increases of 20.3%, 21.7%, and 22.8% measured in standard deviations of 1 week, 1

month, and 3 month trading volume. Related similar �ndings are Howell et al. (2019) who �nd that the

existence of a white paper or disclosure of speci�c information such as vesting periods positively pre-

dicts both liquidity and volume, or Bourveau et al. (2019) who �nd that an opaque white paper increases

illiquidity. Also in related OTC stock markets, Bruggemann et al. (2018) �nd that OTC stocks subject to

stricter disclosure requirements have higher liquidity and lower crash risk. We control for trading volume

generated by potential high-quality type ICO issuers that could retain a higher token share during the ICO

to signal their type and dispose their shares in the secondary market. In Table 11, in all columns relating to

trading volume, a lower fraction of distributed tokens (i.e., a higher retained share) leads to higher trading

volume. Our �ndings for the relation between informative content and trading volume are thus above and

beyond this potential e�ect.

5 Conclusion

A market for small and opaque emerging growth businesses is naturally subject to high information

asymmetry between entrepreneurs and investors. But the ICO market has an additional problem, poten-

tially increasing information asymmetry instead of reducing it: Self-appointed rating experts with un-

known monetary incentives producing unsolicited ratings with unknown quality that enjoy the a�ention

of retail investors with limited ability to conduct due diligence. �e negative e�ects of fake or uninforma-

tive online product reviews or stock promotions in the related OTC stock markets, which we believe is a

good joint characterization of most expert ratings, are well understood and documented. In the ICO mar-

ket, we �nd evidence consistent with signaling being impaired during the ICO process as investors include

biased expert ratings in their investment decision-making. �is generates adverse selection costs for sev-

eral reasons: both high-quality and low-quality type ICO issuers successfully raise funds and exchange-list

or momentum (Ang et al., 2013), or recently discussed crypto asset factors as in Li and Yi (2019) or Liu et al. (2019), that couldin�uence our analysis of performance and trading volume.

54Appendix A.3 and Table A4 discuss and report corresponding similar results for the liquidity measure employed by Howellet al. (2019).

32

their tokens, inducing underpricing of high-quality and overpricing of low-quality ICO issuer tokens, and

not all high-quality ICO issuers receive funding or refrain from tapping the token market. Once tokens

trade in the secondary market, the information aggregation function of the market seems to work and

helps to separate high-quality and low-quality ICO issuers.

Some argue that ICOs are just a temporary phenomenon. �is is likely true for security token o�erings

(”STOs”) but unlikely for utility token o�erings. �e la�er is the subject of our analysis. Utility tokens

enable blockchain-based platform economies, potentially the cornerstone of the sharing economy. Since

utility tokens also comprise an investment motive, capital market regulators are concerned with assessing

whether a token is a security or not, which is undisputed for STOs. Irrespective of the discussion of

whether utility tokens can be regarded as securities, we argue that also utility token markets should be

subject to regulation. �is would facilitate functioning markets and protect all who are invested in and

transact on platform-based businesses from fraud and market failure. Such regulation will be di�erent

from standard capital markets regulation and account for the di�erences between securities and utility

tokens (e.g. Oren, 2019). White papers should be mandatory, clearly structured, and audited. �e cost

of mandatory disclosure in this market is likely lower than the costs of adverse selection. Informative

ratings need to be costly and rating experts need to be compensated. Costly rating expert certi�cation and

education could be a potential solution.

33

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A Rating Calculations

We reverse engineer the history of the rating by focusing on only those ratings that occurred prior

to a given ICO end date.55 Take for example the ICO called ”Rivetz”, a decentralized cyber security token

that raised $19.7 million by September 10th 2017, which currently has four ratings, two of which occurred

before the ICO and two a�erwards.56 To determine the historical rating, we focus on the two ratings that

existed before the ICO end date and we adjust their weights to account for the fact that with fewer ratings,

the weights still need to add up to 100%.57 We then calculate the back-dated overall rating as a weighted

average of the experts’ individual team, vision, and product ratings and of the ICOBench algorithmic

rating.58

55ICOs whose end date is missing are disregarded. �e algorithmic rating provided by ICOBench is assumed to occur as soonas an ICO is listed on ICOBench, since no exact date of this generic rating is available. 8% of the existing ratings are conductedsubsequent to an ICO.

56�ree individual experts rated it on Sept. 4th 2017, Feb. 7th 2018, and, on Aug. 24th 2018. Additionally, ICOBench itselfadded an algorithmic rating. �e weights are 14%, 11%, 14%, and 61%, respectively.

57�e adjusted weights are 26.5% and 73.5% (14% + 11%+14%2

and 61% + 11%+14%2

). We thus implicitly assume that once a newrating is added to an ICO, the existing weights are proportionally downward adjusted. �is approximation is necessary, as thereis unfortunately no further detailed information available of how rating weights are adjusted through time.

58�e original team, vision, and product ratings are 3, 4, and 3, while the ICOBench algorithmic rating is 3.4. �ese values aremultiplied by the adjusted weights of 0.265 and 0.735. �e overall rating is then the sum of the weighted averages of the team,vision, and product ratings (0.88) and of the ICOBench rating (2.50), which is 3.38.

39

B Tables and Figures

Table 1. Variable de�nitions

Variable De�nition

Panel A: Standard and informative (Hanley and Hoberg, 2010)Recent content Average of the normalized document term vectors for those ICOs that were �led in the 30 days

preceding the ICOPast industry content Average of the normalized document term vector of those ICOs that were �led within the same

industry (at least 30 days and at most 90 days before an ICO)Standard content �e sum of the coe�cients αrec,i and αind,i of regression 1Informative content Sum of the absolute residuals of regression 1

Panel B: ICO market environment and restrictionsNumber of industry peers Log number of peers in an industry whose similarity score is above 0.75, where the industry is

de�ned described in Section 3.3.1Industry success rate Amount of ICOs that successfully raised funds divided by the number of industry peersEthereum return One month Ethereum return, ln(Ethereum pricet/Ethereum pricet−30)Restriction Dummy variable that is 1 if the ICO has a country restrictionKYC Dummy variable that is 1 if the ICO has a Know Your Customer (KYC) process or is part of a

whitelistPanel C: Team and product idea

Rating Log of the rating, as described in Section ARating Dispersion Standard deviation of the mean of the experts’ team, vision and product ratings (if more than

one rating exists)Team size Log number of team membersNumber of milestones Log number of milestones

Panel D: White paper characteristicsNumber of pages Log number of pages of the white paperNumber of words Log number of words of the white paperNumber of terms Log number of terms of the white paperNumber of characters Log number of characters of the white paperSentiment subjectivity Subjectivity measure that di�erentiates between objective and subjective wordsSentiment polarity Polarity measure that di�erentiates between negative and positive wordsGunning-Fog index 0.4 [(words/sentences) + 100 (complex words/words)]

Panel E: Financing volume and termsToken Number of tokens issuedAmount raised Amount raised in the ICO (in million USD)Success Dummy variable that is 1 if an ICO raised a positive amount prior to Sept. 15, 2018 and if the

ICO end date is not missingHardcap Dummy variable that is 1 if a hardcap exitsSo�cap Dummy variable that is 1 if a so�cap existsDistributed Percentage of tokens distributedICO date Earliest day the ICO was known to the public (minimum of a pre-ICO start and end date and an

ICO start and end date, see Section 3.3.1 )Length Days between the start of the ICO and the ICO end datePre-ICO Dummy variable that is 1 if a pre-ico date existsBonus Dummy variable that is 1 if a bonus program existsBounty Dummy variable that is 1 if a bounty program existsCumulative return Cumulative log return of listed ICOsCumulative trading volume Log of the cumulative volume (in USD)Underpricing Log of the token’s �rst day’s opening price to its ICO price of listed ICOs (Benede�i and Kos-

tovetsky, 2018)Panel F: Social media and disclosure

Social media channels Log number of social media channels

40

Figure 1. �e number of newly listed ICOs and the Ethereum price over time�is �gure shows the three-week centered moving average of the Ethereum prices in USD (red dashed line) as well as of thenumbers of newly listed ICOs that are part of our sample and that are registered on ICOBench (blue solid line). �e hot ICOphase is de�ned analogously to Helwege and Liang (2004), i.e., we calculate the three-month moving averages of the number ofICOs and we de�ne the top quartile as hot. �is is depicted by the gray shaded area.

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Figure 2. Performance of Portfolios Sorted by Informative Content�is �gure shows the performance of ”above (below) median informative content ICOs” equally-weighted portfolios for all listedICOs for which information content measures are available and our informative content measure is above (below) the medianof the informative content measure for the time period 2017-07-01 to 2018-11-29 with daily rebalancing. Portfolio returns are inexcess of the Ethereum return. �e portfolio ”long/short informative ICOs” is long the “above median informative ICOs” portfolioand short the “below median informative ICOs” portfolio.

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Figure 3. Document similarity and standard/informative content over timePanel (a) plots the three week centered moving average of the average document similarity (solid line), as well as the 95% con�-dence interval (dashed lines). Panel (b) shows the three-week centered moving average of the average standard and informativecontent. Following Hanley and Hoberg (2010), the information content of the white papers based on the following �rst-stageregression for each ICO i: normtot,i = αrec,i normrec,i + αind,i normind,i + εi, in which the normalized term vector of thewhite paper of ICO i (normtot,i) is regressed on the average term usage in recent white papers (normrec,i) and in industrypeer white papers (normind,i). �e standard content is the sum of the coe�cients αrec,i and αind,i. �e informative content isthe sum of the absolute residuals. �e hot ICO phase is de�ned analogously to Helwege and Liang (2004), i.e., we calculate thethree-month moving averages of the number of ICOs and we de�ne the top quartile as hot. �is is depicted by the gray shadedarea.

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Figure 4. Standard and informative content�is �gure shows the relationship between the white papers’ standard content and informative content. Following Hanleyand Hoberg (2010), the information content of the white papers based on the following �rst-stage regression for each ICOi: normtot,i = αrec,i normrec,i + αind,i normind,i + εi, in which the normalized term vector of the white paper of ICOi (normtot,i) is regressed on the average term usage in recent white papers (normrec,i) and in industry peer white papers(normind,i). �e standard content is the sum of the coe�cients αrec,i and αind,i. �e informative content is the sum of theabsolute residuals. �e black line is the linear �t.

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Figure 5. Example of the information content calculation�is �gure plots two relative frequency distributions for an arbitrary corpus of white papers that only consists of the followingwords: ”blockchain”, ”cryptocurrency”, ”app”, and ”platform”. Panel (a) and Panel (b) di�er in how the words ”app” and ”platform”are used. �e �rst bars (in blue) show the overall average usage (normtot,i) of the four words, while the bars two and three (redand grey) show the relative industry and recent frequency usage (normind,i and normrec,i).

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45

Figure 6. Ratings over timePanel (a) plots the average ICOBench rating over the sample period (solid line) as well as the 95% con�dence interval (dashedlines). Panel (b) shows how many experts rate an ICO per week, normalized by the number of ICOs in that week. Panel (c)shows the average team, vision, and product expert ratings as over the �rst 40 days a�er an ICO was initiated. Panel (d) plots theevolution of ratings over the sequence of reviews. Each point represents the average rating across all ratings at the value order,which is depicted on the x-axis. �us, for example, the �rst point is the average rating that is the �rst in the sequence. To partiallycontrol for quality e�ects, we restricted the sample to those ICOs that have at least 40 ratings such that all points are averagesover the same set of ICOs.

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Table 2. Summary statistics�is table presents the summary statistics of the variables that are related to Initial Coin O�erings (ICOs). In total,2,665 ICOs were listed on ICOBench or before Sept. 15, 2018 for which all relevant variables are available. 1,016 ICOsoccurred before 2017, and 1,649 occurred in 2018. For each variable, the table contains the number of non-missingobservations, along with the cross-sectional mean, standard deviation, 10th, 50th, and 90th percentile values. Allvariables are de�ned in Table 1.

Count Mean Std. P10 Median P90

Panel A: Standard and informativeRecent content 2665 0.32 0.36 -0.16 0.35 0.73Past industry content 2665 0.65 0.37 0.22 0.60 1.15Standard content 2665 0.97 0.15 0.79 0.96 1.15Informative content 2665 1.14 0.14 0.97 1.13 1.33

Panel B: ICO market environment and restrictionsCount nr. of industry peers 2665 11.78 12.44 0.00 7.00 29.00Industry success rate 2665 0.25 0.35 0.00 0.00 0.861-month Ethereum return 2665 0.10 0.51 -0.39 -0.05 0.77Restriction (dummy) 2665 0.42 0.49 0.00 0.00 1.00KYC (dummy) 2665 0.50 0.50 0.00 0.00 1.00

Panel C: Team and product ideaRating 2665 3.19 0.74 2.20 3.20 4.20Rating dispersion 891 0.69 0.37 0.24 0.67 1.18Team size 2665 13.14 8.31 4.00 12.00 23.00Number of milestones 2665 7.93 4.84 2.00 7.00 14.00

Panel D: White paper characteristicsNumber of pages 2665 33.74 16.68 16.00 30.00 55.00Number of words 2665 8296 5628 3347 7157 14177Number of terms 2665 1143 409 644 1096 1708Number of characters 2665 42759 28978 17026 37070 72701Number of characters / page count 2665 1273 619 793 1207 1771Sentiment subjectivity 2665 0.43 0.05 0.38 0.43 0.47Sentiment polarity 2665 0.11 0.04 0.07 0.11 0.15Gunning-Fog index 2665 12.44 1.76 10.29 12.51 14.53

Panel E: Financing volume and termsToken 2079 1318 506930 0.00 0.15 1.58Amount raised (in mil. USD) 889 19.23 143.80 0.50 6.22 32.90Success (ICO end before Sept. 15, 2018) 1587 0.56 0.50 0.00 1.00 1.00Hardcap (dummy) 2665 0.74 0.44 0.00 1.00 1.00So�cap (dummy) 2665 0.54 0.50 0.00 1.00 1.00Distributed (in percent) 2665 0.43 0.30 0.00 0.50 0.80Length until start of ICO (in days) 2665 135.45 94.64 34.00 113.00 267.00Pre-ICO (dummy) 2665 0.53 0.50 0.00 1.00 1.00Bonus (dummy) 2665 0.48 0.50 0.00 0.00 1.00Bounty (dummy) 2665 0.35 0.48 0.00 0.00 1.001 month cum. return 528 0.44 3.41 -0.73 -0.29 1.741 month cum. volume 527 15.48 2.54 12.35 15.47 18.76Underpricing 523 9.55 106.86 0.07 0.79 3.52

Panel F: Social media and disclosureNumber of social media channels 2665 6.47 1.81 4.00 7.00 8.00

47

Table 3. Overview of results�is table presents the overview of the regression results in Section 4. Following Hanley and Hoberg (2010),the information content of white papers is based on the following �rst-stage regression for each ICO issuer i:normtot,i = αrec,i normrec,i + αind,i normind,i + εi, in which the normalized term vector of the white paperof ICO issuer i (normtot,i) is regressed on the average term usage in recent white papers (normrec,i) and in industrypeer white papers (normind,i). Standard content is the sum of the coe�cients αrec,i and αind,i. Informative contentis the sum of the absolute residuals from this regression. To facilitate a comparison between di�erent regressionspeci�cations, results are shown in standard deviation changes in the dependent variable for a one standard devia-tion increase in informative or standard content. �e hot ICO phase is de�ned analogously to Helwege and Liang(2004), i.e., we calculate the three-month moving averages of the number of ICOs and de�ne the top quartile as hot;all other ICOs are part of the cold ICO phase.

Panel A:

Pre-ICO ICO Post-ICO

Rating Fraud Success Funding Exchange-Listing UnderpricingDisagreement Probability Probability Volume Probability

Overall:

Standard 0 −11.4%* 0 0 −6.0%** −11.4%**Informative −11.6%*** −3.1%* 0 −13.5%** 0 +21.7%***

Hot:

Standard 0 0 0 0 −6.2%** 0Informative 0 0 +4.0%*** 0 0 0

Cold:

Standard 0 −7.2%*** +5.5%* 0 0 0Informative −31.1%*** −6.2%* 0 0 0 +20.1%**

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Post-ICO

Cum. Return Volume Cum. Return Volume Cum. Return Volume1 week 1 week 1 moth 1 month 3 months 3 months

Overall:

Standard +7.7%** −16.3%* 0 −16.6%* −12.4%* −16.1%*Informative 0 +20.3%*** 0 +21.7%*** 0 +22.8%***

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Table 4. Standard vs. informative content�is table presents the regressions for 2,665 ICOs, where the dependent variable is either the standard content or theinformative content of the white paper. Following Hanley and Hoberg (2010), the information content of the whitepapers based on the following �rst-stage regression for each ICO i: normtot,i = αrec,inormrec,i+αind,inormind,i+εi, in which the normalized term vector of the white paper of ICO i (normtot,i) is regressed on the average termusage in recent white papers (normrec,i) and in industry peer white papers (normind,i). �e standard content is thesum of the coe�cients αrec,i and αind,i. �e informative content is the sum of the absolute residuals. All regressionsinclude quarter-year �xed e�ects, and t-statistics are adjusted for clustering at the quarter-year level. �e hot ICOphase is de�ned analogously to Helwege and Liang (2004), i.e., we calculate the three-month moving averages of thenumber of ICOs and we de�ne the top quartile as hot; all other ICOs are part of the cold ICO phase.

Overall Hot Cold

Standard Informative Standard Informative Standard InformativeICO market environment

Number of ICOs per month (log) 0.011 −0.004 −0.034 −0.005 0.019** −0.005Number of industry peers (log) 0.009*** 0.000 0.009 −0.001 0.008* 0.002Industry success rate 0.005 0.002 0.004 0.004 0.006 −0.0011-month Ethereum return 0.014 0.010 −0.001 0.004 0.028 0.0141-year Ethereum return −0.000 −0.000 −0.000 −0.000 −0.001 −0.000Restriction (dummy) −0.007 −0.008 −0.006 −0.006 −0.008 −0.013*KYC (dummy) 0.004 −0.003 0.006** −0.006 −0.004 0.002

Team and product ideaTeam size (log) −0.002 −0.006 −0.004 −0.005 −0.000 −0.007***Number of milestones (log) 0.010*** −0.002 0.015 −0.003** 0.004 0.000

White paper characteristicsNumber of characters (log) −0.071*** −0.200*** −0.074*** −0.202*** −0.069*** −0.197***Number of characters / page count (log) −0.002 0.048*** 0.002 0.060*** −0.006 0.029***Sentiment subjectivity −0.003 0.060 0.005 0.036 −0.015 0.083Sentiment polarity 0.208*** −0.149*** 0.203* −0.114 0.220*** −0.186***Gunning-Fog index −0.003 −0.010*** −0.004 −0.011*** −0.003 −0.009***

Financing volume and termsHardcap (dummy) 0.018 −0.010*** 0.045** −0.010** −0.016 −0.009So�cap (dummy) −0.004 −0.006 −0.019 −0.004 0.016* −0.010*Number of tokens (log) 0.000 −0.000 0.001 0.000 −0.000 −0.000*Distributed (in percent) 0.025 −0.010** 0.018 −0.009 0.029 −0.012Length ICO (log) −0.005 −0.003 −0.002 −0.001 −0.008 −0.003Pre-ICO (dummy) 0.013*** 0.000 0.011* 0.003 0.020** −0.005Bonus (dummy) −0.010** −0.006 −0.010 −0.009 −0.010** −0.000Bounty (dummy) 0.018*** −0.003 0.018* −0.003 0.022** −0.006***

Social media and disclosureNumber of social media channels (log) 0.021** 0.005 0.005 0.006** 0.042*** 0.002

Observations 2665 2665 1574 1574 1091 1091Adj. R2 0.140 0.627 0.107 0.606 0.190 0.655

49

Table 5. Rating disagreement�is table presents three OLS regressions that analyze the dispersion of the average expert ratings (average of team,vision, and product rating) and their relation to standard and informative content. For each ICO, the dispersion ismeasured as the standard deviation of the average expert rating. Only ICOs with at least 2 expert ratings are included.We focus on expert ratings that occured prior to an ICO and only ICOs with a non-missing end date are included.All regressions include quarter-year �xed e�ects and the t-statistics are adjusted for clustering at the quarter level.�e hot ICO phase is de�ned analogously to Helwege and Liang (2004), i.e., we calculate the three-month movingaverages of the number of ICOs and we de�ne the top quartile as hot; all other ICOs are part of the cold ICO phase.

Expert Rating Dispersion

Overall Hot ColdStandard and informative

Standard content −0.027 −0.022 0.012Informative content −0.331*** −0.108 −0.977**

ICO market environmentNumber of ICOs per month (log) −0.120 −0.133** −0.062Number of industry peers (log) −0.007 −0.019 0.017Industry success rate 0.063 0.038 0.103**1-month Ethereum return 0.057*** 0.010 0.0631-year Ethereum return −0.001** −0.000 0.001Restriction (dummy) 0.013 0.012 −0.014KYC (dummy) −0.047 −0.025 −0.079*

Team and product ideaTeam size (log) 0.015 −0.006 0.056Number of milestones (log) 0.013 0.010 0.019

White paper characteristicsNumber of characters (log) −0.103*** −0.027 −0.290***Number of characters / page count (log) 0.039 −0.017 0.101Sentiment subjectivity −0.248 0.147 −0.949Sentiment polarity −0.483* −0.279 −0.894Gunning-Fog index −0.006 0.004 −0.020

Financing volume and termsHardcap (dummy) −0.074 −0.161 0.065**So�cap (dummy) 0.041 0.035 0.038Number of tokens (log) −0.001 −0.001 −0.002Distributed (in percent) −0.005 −0.000 −0.002Length ICO (log) −0.012 −0.025 0.017Pre-ICO (dummy) −0.057 −0.066 −0.043Bonus (dummy) 0.025 0.011 0.044Bounty (dummy) 0.046 0.052 0.033

Social media and disclosureNumber of of social media channels (log) 0.291** 0.299* 0.303*

Observations 891 568 323Adj. R2 0.030 0.009 0.064

50

Table 6. Potential Fraud probability�is table presents three probit regressions that relate potential fraud to informative and standard content and controlvariables. Both the β coe�cients and the marginal e�ects (Mfx) are depicted. All regressions include year �xed e�ectsand the t-statistics are adjusted for clustering at the quarter-year level. �e hot ICO phase is de�ned analogously toHelwege and Liang (2004), i.e., we calculate the three-month moving averages of the number of ICOs and we de�nethe top quartile as hot; all other ICOs are part of the cold ICO phase. �e variables restriction, KYC, and bounty aredummy variables and are excluded from the cold periods due to perfect multicollinearity.

Fraud

Overall Hot Coldβ Mfx β Mfx β Mfx

Standard and informativeStandard content −0.520* −0.038* 0.051 0.004 −1.505*** −0.008***Informative content −2.394* −0.173* −2.665 −0.191 −2.101* −0.011*

ICO market environmentNumber of ICOs per month −0.125 −0.009 −0.460 −0.033 −1.894*** −0.010***Number of industry peers (log) −0.067 −0.005 −0.196*** −0.014*** 0.330*** 0.002***Industry success rate 0.049 0.004 0.643*** 0.046*** −0.268*** −0.001***1-month Ethereum return −0.204 −0.015 −0.459 −0.033 −1.935** −0.010**1-year Ethereum return 0.005 0.000 0.005 0.000 0.223*** 0.001***Restriction (dummy) −0.287 −0.021 −0.239 −0.017 − −KYC (dummy) −0.430 −0.031 −0.335 −0.024 − −

Team and product ideaRating −0.596** −0.043** −0.449 −0.032 −1.835*** −0.009***Team size (log) −0.050 −0.004 −0.114 −0.008 0.452*** 0.002***Number of milestones (log) 0.029 0.002 −0.104*** −0.008*** 0.239*** 0.001***

White paper characteristicsNumber of characters (log) −0.079 −0.006 −0.118 −0.008 0.133 0.001Number of characters / page count −1.062*** −0.077*** −1.273*** −0.091*** −1.374*** −0.007***Sentiment subjectivity −1.122 −0.081 0.217 0.016 −4.252** −0.022**Sentiment polarity −0.207 −0.015 −0.242 −0.017 6.048* 0.031*Gunning-Fog index −0.008 −0.001 0.098*** 0.007*** −0.062 −0.000

Financing volume and termsHardcap (dummy) 0.022 0.002 −0.483*** −0.035*** 0.841*** 0.004***So�cap (dummy) −0.183 −0.013 −0.121 −0.009 −0.269*** −0.001***Number of tokens (log) 0.007 0.000 0.008 0.001 0.006 0.000Distributed (in percent) −0.077 −0.006 0.275 0.020 −1.306*** −0.007***Length ICO (in days) 0.088* 0.006* 0.016 0.001 0.319*** 0.002***Pre-ico (dummy) −0.158 −0.011 −0.241 −0.017 −0.374 −0.002Bonus (dummy) 0.149 0.011 0.165 0.012 −0.054 −0.000Bounty (dummy) 0.311 0.022 0.260 0.019 − −

Social media and disclosureNumber of of social media channels (log) 0.560 0.040 0.277 0.020 1.865*** 0.010***

Observations 685 351 306Pseudo R2 0.148 0.201 0.380

51

Table 7. Success�is table presents three probit regressions that examine whether an ICO is successful. Both the β coe�cients andthe marginal e�ects (Mfx) are depicted. An ICO is successful if it raised a positive amount. Only ICOs whose enddate is non-missing and is before Sept 16, 2018 (the day of the data retrieval) are included. �e variable rating iscalulated as the implied rating that existed prior to the end of an ICO (see Appendix A for details). �e columnsentitled ”Mfx” display the marginal e�ects evaluated at the means of the independent variables. All regressionsinclude quarter-year �xed e�ects, and the t-statistics are adjusted for clustering at the quarter-year level. �e hotICO phase is de�ned analogously to Helwege and Liang (2004), i.e., we calculate the three-month moving averagesof the number of ICOs and we de�ne the top quartile as hot; all other ICOs are part of the cold ICO phase.

Success

Overall Hot Coldβ Mfx β Mfx β Mfx

Standard and informativeStandard content 0.356 0.139 0.058 0.023 0.617* 0.212*Informative content −0.073 −0.028 0.458*** 0.183*** −0.853 −0.293

ICO market environmentNumber of ICOs per month (log) −1.060*** −0.413*** −1.634*** −0.652*** −0.892*** −0.306***Number of industry peers (log) 0.086** 0.034** 0.072* 0.029* 0.192*** 0.066***Industry success rate −0.097*** −0.038*** −0.009 −0.004 −0.279* −0.096*1-month Ethereum return 0.171*** 0.067*** 0.288 0.115 0.145 0.0501-year Ethereum return −0.001 −0.000 −0.004** −0.002** 0.007 0.002Restriction (dummy) 0.222*** 0.087*** 0.177*** 0.071*** 0.431** 0.148**KYC (dummy) −0.039 −0.015 0.027 0.011 −0.256* −0.088*

Team and product ideaRating 0.554*** 0.216*** 0.468*** 0.187*** 0.763*** 0.262***Team size (log) 0.249*** 0.097*** 0.302*** 0.120*** 0.163* 0.056*Number of milestones (log) −0.013 −0.005 0.061 0.024 −0.097** −0.033**

White paper characteristicsNumber of characters (log) 0.335** 0.131** 0.408*** 0.163*** 0.191 0.065Number of characters / page count (log) −0.168* −0.065* −0.160** −0.064** −0.159 −0.055Sentiment subjectivity 0.062 0.024 0.605 0.241 −0.066 −0.023Sentiment polarity 0.967 0.377 0.718 0.286 1.236 0.424Gunning-Fog index −0.023 −0.009 0.001 0.000 −0.065** −0.022**

Financing volume and termsHardcap (dummy) 0.116 0.045 0.354*** 0.141*** −0.252* −0.087*So�cap (dummy) −0.034 −0.013 −0.070*** −0.028*** −0.026 −0.009Number of tokens (log) −0.002 −0.001 0.001 0.001 −0.005 −0.002Distributed (in percent) −0.102 −0.040 −0.123 −0.049 −0.068 −0.023Length ICO (log) −0.283*** −0.110*** −0.386*** −0.154*** −0.182** −0.063**Pre-ICO (dummy) −0.027 −0.011 0.031 0.012 −0.108** −0.037**Bonus (dummy) −0.065* −0.025* −0.020 −0.008 −0.133 −0.046Bounty (dummy) −0.139* −0.054* −0.182** −0.073** 0.123 0.042

Social media and disclosureNumber of of social media channels (log) 0.323* 0.126* 0.471*** 0.188*** 0.097 0.033

Observations 1583 988 595Pseudo R2 0.222 0.216 0.243

52

Table 8. Funding volume�is table presents three regressions that relate the amount raised to the standard and informative content. �edependent variable is (log) amount raised. Only successful ICOs, whose end date is non-missing and is before Sept16, 2018 (the day of the data retrieval), are included. �e variable rating is calulated as the implied rating that existedprior to the end of an ICO (see Appendix A for details). All regressions include quarter-year �xed e�ects, and thet-statistics are adjusted for clustering at the quarter-year level. �e hot ICO phase is de�ned analogously to Helwegeand Liang (2004), i.e., we calculate the three-month moving averages of the number of ICOs and we de�ne the topquartile as hot; all other ICOs are part of the cold ICO phase.

Amount raised

Overall Hot ColdStandard and informative

Standard content −0.245 −0.493 −0.037Informative content −1.752** −2.673 −0.562

ICO market environmentNumber of ICOs per month (log) −0.435* −1.498** −0.685**Number of industry peers (log) −0.014 −0.011 0.031Industry success rate −0.055 0.064 −0.1641-month Ethereum return 0.281** 0.122 0.245**1-year Ethereum return −0.004* −0.006** 0.025Restriction (dummy) 0.091 0.130 0.110*KYC (dummy) −0.081 −0.059 0.069

Team and product ideaRating 0.391*** 0.505* 0.382**Team size (log) 0.318*** 0.194 0.336**Number of milestones (log) −0.114 0.046 −0.266**

White paper characteristicsNumber of characters (log) 0.158 −0.100 0.531Number of characters / page count (log) 0.237* 0.366 0.079Sentiment subjectivity −2.460* −3.578** −1.850Sentiment polarity −3.013** −2.423 −3.300Gunning-Fog index −0.007 −0.016 −0.009

Financing volume and termsHardcap (dummy) 0.173 0.041 0.246So�cap (dummy) −0.076 −0.123 0.057Number of tokens (log) 0.004 0.015 −0.002Distributed (in percent) −0.588** −0.532** −0.445Length ICO (log) −0.388*** −0.622** −0.287***Pre-ICO (dummy) 0.059 0.112 0.024Bonus (dummy) −0.132 −0.122 −0.176Bounty (dummy) −0.414** −0.458 0.093

Social media and disclosureNumber of of social media channels (log) −0.096 −0.771 0.196

Observations 889 498 391Adj. R2 0.182 0.219 0.155

53

Table 9. Listing�is table presents three regressions that relate the probability of an ICO being listed to the standard and informativecontent. �e dependent variable is one if an ICO is listed (i.e., if its token is traded on an exchange) and zero if anICO does not have tokens listed despite having raised a positive amount. �e variable rating is either the impliedrating that existed when the ICO got listed or the general rating if an ICO is not listed (See Appendix A for details).�e columns entitled ”Mfx” display the marginal e�ects evaluated at the means of the independent variables. Allregressions include quarter-year �xed e�ects and the t-statistics are adjusted for clustering at the quarter-year level.�e hot ICO phase is de�ned analogously to Helwege and Liang (2004), i.e., we calculate the three-month movingaverages of the number of ICOs and we de�ne the top quartile as hot; all other ICOs are part of the cold ICO phase.

Listing probability

Overall Hot Coldβ Mfx β Mfx β Mfx

Standard and informativeStandard content −0.580** −0.230** −0.600** −0.239** −0.746 −0.279Informative content 0.173 0.069 0.431 0.172 0.025 0.010

ICO market environmentNumber of ICOs per month (log) −0.204 −0.081 −0.096 −0.038 −0.439*** −0.164***Number of industry peers (log) −0.039 −0.015 −0.014 −0.006 −0.054 −0.020Industry success rate −0.240*** −0.095*** −0.294*** −0.117*** −0.224 −0.0841-month Ethereum return −0.035 −0.014 −0.096 −0.038 0.039 0.0151-year Ethereum return −0.000 −0.000 0.003 0.001 0.020* 0.007*Restriction (dummy) 0.079 0.031 0.172 0.068 −0.249 −0.093KYC (dummy) −0.127 −0.050 −0.044 −0.018 −0.293 −0.110Length ICO (log) −0.181*** −0.072*** −0.151 −0.060 −0.245*** −0.092***

Team and product ideaRating 0.413*** 0.164*** 0.499** 0.199** 0.309** 0.116**Team size (log) 0.094 0.037 0.083 0.033 0.107 0.040Number of milestones (log) −0.043 −0.017 −0.081 −0.032 0.002 0.001

White paper characteristicsNumber of characters (log) 0.164 0.065 0.190 0.076 0.191 0.072Number of characters / page count (log) 0.214** 0.085** 0.137*** 0.054*** 0.274 0.103Sentiment subjectivity 1.476** 0.585** 1.330 0.529 1.231 0.461Sentiment polarity 1.404 0.557 1.020 0.406 1.836*** 0.688***Gunning-Fog index 0.036 0.014 0.042 0.017 0.033 0.012

Financing volume and termsHardcap (dummy) 0.011 0.004 −0.142 −0.056 0.019 0.007So�cap (dummy) −0.207*** −0.082*** −0.284*** −0.113*** −0.046 −0.017Number of tokens (log) −0.003 −0.001 0.003 0.001 −0.008 −0.003Distributed (in percent) −0.535*** −0.212*** −0.510** −0.203** −0.592*** −0.222***Pre-ICO (dummy) −0.086 −0.034 −0.217** −0.086** 0.210* 0.079*Bonus (dummy) −0.196** −0.078** −0.045 −0.018 −0.422*** −0.158***Bounty (dummy) −0.230* −0.091* −0.331** −0.132** 0.118 0.044

Social media and disclosureNumber of of social media channels (log) −0.058 −0.023 −0.062 −0.025 0.030 0.011

Observations 997 566 431Pseudo R2 0.119 0.105 0.158

54

Table 10. Underpricing�is table presents four cross-sectional regressions that relates the underpricing to the standard and informativecontent (columns 1,2,4,5). Column 3 reports the maximum likelihood estimation estimates from the Heckman (1979)procedure. �e dependent variable is the natural logarithm of the ratio of the token’s �rst day’s opening price to itsICO price (’underpricing’) (Benede�i and Kostovetsky, 2018). �e variable rating is the implied rating that existedwhen the ICO got listed (see Appendix A for details). Column 1 further adjusts the underpricing by the openingday’s Ethereum return. �e hot ICO phase is de�ned analogously to Helwege and Liang (2004), i.e., we calculate thethree-month moving averages of the number of ICOs and we de�ne the top quartile as hot; all other ICOs are partof the cold ICO phase. Standard errors are adjusted for heteroskedasticity.

Underpricing

OverallOveralladjusted

OverallHeckman

Hot Cold

Standard and informativeStandard content −1.267** −1.267** −0.543 −0.880 −0.875Informative content 2.579*** 2.618*** 2.099*** 1.240 2.510**

ICO market environmentNumber of ICOs per month (log) 0.137 0.139 −0.012 0.604 −0.031Number of industry peers (log) −0.019 −0.018 −0.012 −0.008 −0.102Industry success rate 0.087 0.085 0.091 −0.347 0.560**1-month Ethereum return 0.089 0.089 0.066 0.402 0.0631-year Ethereum return −0.004 −0.004 −0.005* −0.008* −0.006Restriction (dummy) −0.367 −0.363 −0.203 −0.016 −0.271KYC (dummy) −0.050 −0.046 −0.145 0.072 0.406

Team and product ideaRating 0.235 0.232 0.499*** 0.300 0.231Team size (log) −0.052 −0.047 −0.273** −0.293 −0.072Number of milestones (log) −0.276*** −0.279*** −0.179* −0.339*** −0.371**

White paper characteristicsNumber of characters (log) 0.660** 0.669** 0.344 0.180 0.661Number of characters / page count (log) −0.363 −0.368 −0.289 −0.324 −0.168Sentiment subjectivity 1.481 1.481 1.162 3.732 2.228Sentiment polarity −0.976 −1.015 −1.303 −4.241 0.476Gunning-Fog index 0.058 0.056 0.047 −0.013 0.090

Financing volume and termsHardcap (dummy) −0.161 −0.164 0.065 −0.266 −0.228So�cap (dummy) −0.109 −0.112 −0.123 0.186 −0.202Number of tokens (log) 0.005 0.005 0.002 0.011 0.005Distributed (in percent) −0.123 −0.127 0.032 −0.893** 0.336Length ICO (log) −0.172*** −0.169*** −0.254*** −0.161** −0.137Pre-ICO (dummy) −0.286 −0.286 −0.135 −0.037 −0.395Bonus (dummy) −0.131 −0.128 −0.097 −0.150 −0.097Bounty (dummy) −0.338* −0.332* −0.366* −0.445** −0.202

Social media and disclosureNumber of of social media channels (log) −0.522** −0.522** −0.847*** −0.181 −0.703**

Observations 499 499 1567 198 301R2 0.201 0.200 0.191 0.284Rho −0.318**Wald test for Rho=0, p-value 0.013

55

Table 11. Performance and trading volume�is table presents six OLS regressions that relate the one week, one month, and three months cumulative returnand cumulative trading volume to the standard and informative content. �e variable rating is the implied ratingthat existed when the ICO got listed (see Appendix A for details). All regressions include quarter-year �xed e�ectsand the t-statistics are adjusted for clustering at the quarter-year level. �e hot ICO phase is de�ned analogously toHelwege and Liang (2004), i.e., we calculate the three-month moving averages of the number of ICOs and we de�nethe top quartile as hot; all other ICOs are part of the cold ICO phase.

1 week 1 month 3 months

return volume return volume return volumeStandard and informative

Standard content 0.847** −0.215* 0.380 −0.193* −3.417* −0.166*Informative content −1.394 0.291*** −0.465 0.274*** 2.846 0.257***

ICO market environmentNumber of ICOs per month (log) −0.117 0.030 −0.492* 0.010 0.195 0.043Number of industry peers (log) 0.002 0.001 0.228** 0.003 0.045 0.007*Industry success rate 0.201 −0.022 −0.005 −0.023 0.976 −0.0331-month Ethereum return 0.150 −0.005 −0.164 0.005 0.203 0.0001-year Ethereum return −0.005** −0.000 −0.002 −0.000 0.010 −0.000Restriction (dummy) −0.126 −0.025** 0.221 −0.013 0.173 −0.022KYC (dummy) 0.067 0.023 0.105 0.021* −0.721 0.045***

Team and product ideaRating −0.010 −0.023 −0.057 −0.030 0.075 −0.023Team size (log) −0.019 0.039 0.227 0.038* 0.282 0.034**Number of milestones (log) 0.078 −0.015 0.146** −0.019* 0.107 −0.019*

White paper characteristicsNumber of characters (log) −0.355 0.054** −0.527 0.065* −0.487 0.057*Number of characters / page count (log) −0.156 −0.026 −0.394 −0.041 0.862* −0.022Sentiment subjectivity −0.426 0.015 −2.856** −0.103 5.783** −0.206Sentiment polarity −1.012 −0.037 −2.950 0.046 9.843* 0.193Gunning-Fog index 0.035 0.006 −0.078 0.003 0.102 0.002

Financing volume and termsHardcap (dummy) 0.399** −0.010 0.435* −0.008 0.224 −0.019So�cap (dummy) −0.105 0.019 −0.537 0.018 −0.329 0.004Number of tokens (log) 0.011 0.001 0.009 0.001 0.002 −0.000Distributed (in percent) 0.519 −0.036*** 0.415** −0.051*** 0.376 −0.052***Length ICO (log) 0.124 −0.029** 0.135 −0.027*** 0.191 −0.027***Pre-ICO (dummy) −0.281 −0.005 −1.033*** −0.004 −0.725* 0.006Bonus (dummy) −0.172* −0.011 0.491* −0.010 −0.056 −0.004Bounty (dummy) −0.013 −0.051 0.288 −0.046** 0.159 −0.060**

Social media and disclosureNumber of of social media channels (log) −0.681 0.024 −0.306** 0.032 −0.255 0.047*

Observations 535 533 527 527 475 475R2 0.066 0.128 0.052 0.153 0.086 0.194

56

Internet Appendix for:

�e Information Content of ICOWhite Papers(not for publication)

September 20, 2019

1

A Additional Tables and Results

A.1 Topic analysis

For our sample of 2,665 ICO white papers, Table A1 contains the most frequently used terms for a

non-negative matrix factorization (NMF) topic modeling algorithm for 10 topics.

A.2 Ratings

Table A2 contains results for regressions of the level of average ratings on informative and standard

content and further control variables. Column 1 focuses on the overall rating that existed at the ICO

and shows that the standard content of a white paper is positively related to the rating while informative

content plays no signi�cant role. �us more standard content leads to a be�er ICOBench rating on average.

�is result is consistent with the production process of the ICOBench rating and our measurement of

standard content. When ICOBench sta� screen white papers to extract basic information, such as team

members, milestones, token sales, or a basic product presentation, the corresponding textual elements

should be well captured by terms used in peer and recent white papers, i.e. our standard content measure.

Columns 2 and 3 then show that the e�ect of standard content on ratings is not statistically signi�cant any

more when spli�ing between the ICOBench rating and the expert rating. Instead the ICOBench rating is

positively related to the informative content.

Table A3 shows that the informative content is negatively related to the number of ratings produced,

mostly in a cold market phase. In a cold market environment and consistent with the �ndings for rating

disagreement, rating production is probably more costly due to enhanced scrutiny of market participants

and as a result the number ratings per ICO decreases. As for the levels of ratings, easy-to-extract infor-

mation, such as team size or the number of social media channels, determines the number of ratings. If

ICOs are seemingly easy to evaluate, experts produce some rating at a very low cost that is unable to

discriminate between good and bad ICOs.

A.3 Liquidity measure

We follow Howell et al. (2019) and calculate the average �ve day liquidity measure, which can be

interpreted as the volume needed to move the price by one percent, as:

2

liquidity = −log

(1

T

T∑t=1

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Table A4 regresses the liquidity measure, measured 1 week, 1 month, and 3 month a�er the start of

trading, on our standard content and informative content measures and on the set of control variables.

Consistent with the main results, a higher measure of informative content increases liquidity.

A.4 Tables and Figures

3

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Table A2. Determinants of ratings�is table presents �ve OLS regressions that relate expert ratings to informative and standard content and controlvariables. We focus on ratings that occured prior to an ICO and only ICOs with a non-missing end date are included.All regressions include quarter-year �xed e�ects and the t-statistics are adjusted for clustering at the quarter-yearlevel. �e hot ICO phase is de�ned analogously to Helwege and Liang (2004), i.e., we calculate the three-monthmoving averages of the number of ICOs and we de�ne the top quartile as hot; all other ICOs are part of the cold ICOphase.

OverallRating

ICOBenchRating

ExpertRating

ExpertRating

Hot

ExpertRatingCold

Standard and informativeStandard content 0.153** 0.066 −0.180 −0.245 0.015Informative content 0.043 0.335** 0.311 0.564 −0.119

ICO market environmentNumber of ICOs per month −0.052 −0.136 0.180** −0.035 0.186*Number of industry peers (log) 0.017** −0.003 0.026 0.005 0.075*Industry sucess rate 0.023 −0.032 0.106 0.130 0.0461-month ethereum return 0.064*** 0.075 0.006 −0.095 −0.0101-year ethereum return −0.001** −0.006*** 0.004*** 0.004 0.024*Restriction (dummy) 0.049 0.010 0.036 −0.071 0.315**KYC (dummy) 0.116*** −0.050 0.086 0.054 0.146

Team and product ideaTeam size (log) 0.325*** −0.015 0.440*** 0.447*** 0.401**Number of milestones (log) 0.101*** −0.025 0.095*** 0.116 0.077

White paper characteristicsNumber of characters (log) 0.167*** −0.124*** 0.370*** 0.471 0.189Number of characters / page count −0.035 0.111 −0.200 −0.197 −0.169**Sentiment subjectivity −0.070 −0.462 1.526** 1.654** 1.932Sentiment polarity 0.259 −0.051 0.427 0.940 −0.523Gunning-Fog index 0.010 0.008 0.012 0.004 0.025*

Financing volume and termsHardcap (dummy) 0.080*** −0.132* 0.183* 0.225 0.109*So�cap (dummy) −0.009 −0.014 0.020 −0.024 0.058Number of token (log) 0.002** −0.001 0.004 0.008 −0.000Distributed (in percent) −0.065 −0.094** 0.070 0.176** −0.133Length ICO (in days) 0.014 −0.182*** 0.171** 0.058 0.317**Pre-ico (dummy) 0.010 −0.086** −0.023 −0.031 −0.005Bonus (dummy) 0.020 −0.142*** 0.076** 0.092*** 0.019Bounty (dummy) 0.136*** −0.119** 0.259*** 0.245** 0.314**

Social media and disclosureNumber of social media channels 0.894*** 0.153* 0.854*** 0.966** 0.804***

Observations 2101 2101 1202 752 450Adj. R2 0.614 0.173 0.312 0.300 0.332

5

Table A3. Number of ratings�is table presents three OLS regressions that relate the number of ratings (log) to standard and informative content.We focus on ratings that occured prior to an ICO and only ICOs with a non-missing end date are included. Allregressions include quarter-year �xed e�ects and the t-statistics are adjusted for clustering at the quarter-year level.�e hot ICO phase is de�ned analogously to Helwege and Liang (2004), i.e., we calculate the three-month movingaverages of the number of ICOs and we de�ne the top quartile as hot; all other ICOs are part of the cold ICO phase.

Number of ratings

Overall Hot ColdStandard and informative

Standard content 0.155 0.053 0.343*Informative content −0.387*** −0.071 −0.964**

ICO market environmentNumber of ICOs per month (log) 0.088 −0.263 0.162**Number of industry peers (log) 0.004 −0.002 0.027**Industry success rate 0.031 0.054 −0.0571-month Ethereum return −0.005 −0.043 0.0361-year Ethereum return 0.005*** 0.004 0.002Restriction (dummy) 0.021 −0.025 0.138KYC (dummy) 0.111** 0.043 0.334***

Team and product ideaTeam size (log) 0.272*** 0.328** 0.177***Number of milestones (log) 0.131*** 0.154* 0.119***

White paper characteristicsNumber of characters (log) 0.241*** 0.282 0.157*Number of characters / page count (log) −0.166 −0.105 −0.262***Sentiment subjectivity 0.204 0.369 0.126Sentiment polarity 0.273 0.439 −0.019Gunning-Fog index −0.009 −0.023 0.003

Financing volume and termsHardcap (dummy) 0.186** 0.206 0.176***So�cap (dummy) 0.020 0.021 0.005Number of tokens (log) 0.001 0.005 −0.003Distributed (in percent) 0.048 0.042 0.056Length ICO (log) 0.202*** 0.206*** 0.198***Pre-ICO (dummy) 0.085 0.041 0.156Bonus (dummy) 0.166*** 0.202** 0.082***Bounty (dummy) 0.195*** 0.177 0.214**

Social media and disclosureNumber of of social media channels (log) 0.645*** 0.741** 0.510***

Observations 2101 1211 890R2 0.387 0.371 0.417

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Table A4. Liquidity�is table presents the regressions that relate the one week, one month, and three months liquidity measure of Howellet al. (2019) to the standard and informative content. �e variable rating is the implied rating that existed when theICO got listed (see Section A for details). All regressions include quarter-year �xed e�ects and the t-statistics areadjusted for clustering at the quarter-year level. �e hot ICO phase is de�ned analogously to Helwege and Liang(2004), i.e., we calculate the three-month moving averages of the number of ICOs and we de�ne the top quartile ashot; all other ICOs are part of the cold ICO phase.

1 week 1 month 3 months

Liquidity Liquidity LiquidityStandard and informative

Standard content −4.087** −2.941 −3.123Informative content 7.317* 5.699*** 7.439**

ICO market environmentNumber of ICOs per month (log) 0.256 −0.714 −0.368Number of industry peers (log) 0.060 0.003 0.095Industry success rate −0.618* −0.918* −0.8871-month Ethereum return 0.009 0.105 −0.1291-year Ethereum return −0.018 −0.008 −0.009Restriction (dummy) −0.605 −0.430 −0.233KYC (dummy) 0.209 0.545** 0.095

Team and product ideaRating −0.277 −0.613 −0.727Team size (log) 0.793 1.095*** 1.311***Number of milestones (log) −0.520** −0.301 −0.304

White paper characteristicsNumber of characters (log) 1.857** 1.544* 1.311*Number of characters / page count (log) −0.930 −0.470 0.593Sentiment subjectivity −2.258 −4.519 −0.554Sentiment polarity −6.667 −1.115 −1.213Gunning-Fog index 0.140 0.071 0.091

Financing volume and termsHardcap (dummy) −0.628 −0.654 −0.066So�cap (dummy) 0.423 0.061 −0.482**Number of tokens (log) 0.007 −0.018 −0.026Distributed (in percent) −0.014 −0.647 −0.725Length ICO (log) −0.706*** −0.734*** −0.866***Pre-ICO (dummy) −0.254 −0.588 −0.544Bonus (dummy) −0.484 −0.131 −0.009Bounty (dummy) −0.986* −1.646*** −1.070**

Social media and disclosureNumber of of social media channels (log) 0.313 1.158 0.916

Observations 532 522 467R2 0.194 0.228 0.230

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Figure A1. Examples of ICO time-lines

(a) Notary

(b) Haladinar

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