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Try out PMC Labs and tell us what you think. Learn More. Section 3 of my updated paper explains in detail how all data can be accessed and how the variables were constructed, providing exact mathematical formulas, where applicable. Researchers may access ICObench data using their API. The same applies to Coinmarketcap. This paper examines the market for initial coin offerings ICOs. ICOs are smart contracts based on blockchain technology that are designed for entrepreneurs to raise external finance by issuing tokens without an intermediary. Unlike existing mechanisms for early-stage finance, tokens potentially provide investors with rapid opportunities thanks to liquid trading platforms. The marketability of tokens offers novel insights into entrepreneurial finance, which I explore in this paper. First, I document that investors earn on average 8. Second, I explore the determinants of market outcomes and find that management quality and the ICO profile are positively correlated with the funding amount and returns, whereas highly visionary projects have a negative effect. I find that the ICO market is highly susceptible to such environmental shocks, resulting in substantial welfare losses for investors. Initial Coin Offerings ICOs or token sales are smart contracts based on distributed ledger technology DLT or blockchain designed to raise external finance by issuing coins or tokens. Smart contracts are computer protocols that automatize value-exchange transactions between the entrepreneur and investors, potentially creating perfect disintermediation.
However, there is a regulatory distinction between utility, security, and cryptocurrency tokens see, for a detailed discussion, Momtaz [ 1 ] and Section I in this article. While the latter two token types fall under securities or asset laws, utility tokens operate in a legal grey zone. But investors in utility tokens currently do not hold enforceable claims in many jurisdictions, which seems to be in conflict with the corporate governance and law and finance literature [ 2 , 3 ]. Therefore, the purpose of this study is to provide an empirical characterization of the ICO market. This study contributes to an emerging body of contemporaneous research on ICOs. Theoretical work is diverse and presents a dynamic asset-pricing model for tokens [ 4 ], a model of token value from a consumer demand perspective [ 5 ], a model of tokens as membership in peer-to-peer platforms and compensation for miners [ 6 ], an agency theory comparing the optimality of ICOs to more traditional venture capital [ 7 ], a model rationalizing ICOs for building peer-to-peer platforms [ 8 ], and a theory of optimal token contract design [ 9 ]. Empirical work examines general ICO success along various dimensions [ 10 , 11 , 12 ] as well as the determinants such as an agency-related explanation [ 13 ], the price difference between the ICO and the first trading price [ 14 ], the long-run performance of ICOs and token volatility [ 15 , 16 ], token liquidity [ 17 ], investor sentiment and the timing of ICOs [ 18 ], the role of information disclosure and signaling for ICO success [ 19 , 20 , 21 , 22 ], a moral hazard-based explanation of ICO market outcomes [ 23 ], a wisdom of the crowd-related test of ICO success [ 24 ], the role of large and institutional investors [ 25 , 26 , 27 ] and aggregator platforms [ 28 ], as well as the geographic determinants of ICOs [ 29 ].
Although the empirical work is rapidly evolving and has produced important insights into the functioning of the ICO market, a comprehensive empirical characterization of ICOs is still missing. I acknowledge, however, that there are concurrent efforts toward a comprehensive characterization of the ICO market see, for example, Lyandres, Palazzo, and Rabetti [ 16 ]. Block et al. Liu [ 31 ] provide a broader review of the blockchain, cryptocurrency, and ICO literature. This paper aims to fill this gap. My paper is closely related to concurrent work by Kostovetsky and Benedetti [ 14 ] and Howell, Niessner, and Yermack [ 17 ].
Kostovetsky and Benedetti [ 14 ] examine the determinants of ICO underpricing. In keeping with the IPO literature, they define underpricing as the relative difference between issuance and opening prices. In contrast, I examine first-day returns, defined as the relative difference between opening and closing prices. The definition is also used in the IPO context see, e. Both measures reflect the financial incentives provided to potential investors by the ICO firm, but at different points in time. Both measures are important as they reflect different aspects of the ICO market. My work sheds light on similar aspects of operating success, namely, the time it takes ICO firms to successfully complete their fundraising campaign. The paper is structured in four parts. First, it gives a comprehensive conceptual overview over the ICO phenomenon, covering token types, the life cycle of a typical ICO, a discussion of key advantages and challenges, and a detailed comparison between ICOs and more conventional financing methods.
Third, it explores potential determinants of these ICO market characteristics. Fourth, it sheds light on the market effects of adverse industry events such as regulatory bans and technical vulnerabilities. My empirical results indicate that ICOs create, on average, investor value in the short run. The first-day mean returns, measured as raw and as equally- and value-weighted abnormal returns, range from 6. The range is significantly higher than that for median first-day returns, which lies between 2. In fact, between The average magnitude of first-day returns does not significantly change over the sample period — This reflects the fact that most funding is concentrated around a small number of ICOs. The amount of ICO gross proceeds is significantly increasing over time.
These findings add to Catalini and Gans [ 5 ], who show that ICO funding is higher when the amount of token supply is limited. Turning to time-to-market indicators, the average median time from project initiation, as reported by the firms themselves, to the ICO start is days. After the ICO, it takes the average median firm another 93 42 days to get listed on a token exchange platform. Note that I focus on the 26 major platforms that were tracked on Coinmarketcap, although about exchanges existed during the sample period.
This does not seem to be an issue because a delisting from all major exchanges usually causes token prices to fall to zero. Next, a regression framework is presented to shed more light on the the determinants of these ICO market characteristics. In line with existing research in entrepreneurial finance [ 34 , 35 ], I assume that investment decisions are heavily based on the anticipated project quality as a reference point and derive a number of testable hypotheses related to the following three proxies for project quality: quality of the management team, platform vision, and ICO profile. The regression results of first-day returns on the three proxies for project quality and a vector of other explanatory variables confirm my empirical predictions. In particular, the quality of the management team is significantly positively related to first-day returns as is the ICO profile, albeit insignificantly. A subsequent analysis shows that this can be explained by the fact that highly visionary projects are more likely to fail. Furthermore, the results suggest that general crypto-market sentiment and whether the project uses a standardized technical process to conduct its ICO ERC20 , see section I also positively affects first-day returns.
Moreover, these results hold when first-day returns are replaced as the dependent variable by an indicator variable of positive first-day returns, suggesting that extreme outliers are not driving the results. However, only the coefficient on ICO profile is highly significant in the gross-proceeds regressions. Moreover, ICO gross proceeds are lower when a project conducts a Pre-ICO and decrease with the duration of the actual ICO, while they are increasing in market sentiment and when projects accept legal tender as means of exchange for tokens. Nominal first-day returns decrease also when an ICO involves a know-your-customer KYC process, in which the project team gathers information from investors to be compliant with anti-money laundering laws. Finally, ICO size and country restrictions increase nominal first-day returns, with the latter implying that projects have to create stronger incentives to attract investors if they restrict the pool of potential investors.
In addition, this study provides evidence on the determinants of time-to-market indicators and project failure. Time-to-market is reduced by a professional ICO profile, but delayed if the project uses a KYC process and accepts legal tender in exchange for its tokens. Project failure can be predicted fairly accurately using the three proxies for project quality. A one standard deviation increase in the quality of the management team reduces the probability of project failure by This finding gives an important explanation for why investors are reserved when facing promising project visions.
Further, ICO profile has an economically weak but statistically significant effect on project failure. The final section of the paper sheds some light on the sensitivity of the ICO market to adverse industry events. In particular, a regression framework is employed to analyze the largest hacks of cryptocurrency projects, the most severe regulatory bans by the Chinese and the South Korean governments, and the recent Facebook announcement to ban ICO ads. These drastic events had a dramatic market impact and spurred much debate. The events are explained in detail in section VII. I construct an aggregate index for ICOs taking place within one month after the focal event. First-day returns are regressed on the index and on the events separately. The results are statistically and economically significant. On average, the first-day returns diminish after the events, using the aggregate-index model.
The coefficient is For example, the hack of Parity Wallet , a leading digital wallet service provider that is linked to the Ethereum blockchain , resulted in a decline in first-day mean returns of This suggests that the hack reversed the positive average first-day returns into wealth losses for investors. In contrast, the Chinese ban of ICOs together with declaring ICOs an illegal activity lead to an average decrease of first-day mean returns of 6.
Similarly, the South Korean ICO ban is associated with an average decrease of 5. This study makes at least two contributions to the emerging literature on ICOs. First, it provides comprehensive empirical evidence of ICO market characteristics and determinants, complementing concurrent papers such as Howell, Niessner, and Yermack [ 17 ], Kostovetsky and Benedetti [ 14 ], and Lyandres, Palazzo, and Rabetti [ 16 ] as well as an accessible conceptual overview over the life cycle of ICOs, token types, advantages and challenges, and features distinguishing ICOs from other forms of external finance. The descriptive statistics show there is considerable skewness in all dimensions in the ICO market, an important feature which has to be accounted for in theoretical work. First-day returns, gross proceeds, and time-to-market are all positively skewed.
Average first-day returns are positive for the mean and the median firm. There are competing explanations for the observed level of first-day returns. One explanation is that token issuers have an incentive to set the opening price below the expected equilibrium price in order to generate market liquidity as a knock-on effect for platform growth, which, in turn, may increase the inherent token value [ 36 ]. It is left to future research to disentangle the possible competing explanations. Either way, both explanations suggest positive first-day returns, which is consistent with the empirical evidence. Moreover, examining a comprehensive set of ICO market characteristics, the paper is able to distinguish determinants that are consistent across all characteristics from those that only predict certain market characteristics.
All three measures are determined by a large number of industry experts, suggesting that the wisdom of the crowd works effectively in the ICO market. A second contribution relates to the study of regulatory events, technical vulnerabilities, and the FaceBook ban. The ICO market reacted highly sensitively to all three event types, although the magnitude of how the event types affected tokenholders differ. Regulatory bans of ICOs in China and South Korea wiped out initial gains to investors worldwide, whereas technical hacks even imposed significant losses onto holders of unrelated tokens. In fact, the findings suggest that more than twice as much market uncertainty stems from technical issues compared to regulatory actions. The results help explain the high observed volatility in token prices [ 16 ], [ 15 ], [ 39 ]. The analysis has implications for theoretical work guiding policy-making e. The remainder is organized as follows: Section I provides some background on ICOs, testable hypotheses are developed in section II, and section III presents the data and initial results. The regression results are discussed in sections IV first-day returns , V gross proceeds and nominal first-day returns , VI time-to-market and project failure , and VII sensitivity analysis of industry events.
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