2018
DOI: 10.1098/rsos.180381
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Classification of cryptocurrency coins and tokens by the dynamics of their market capitalizations

Abstract: We empirically verify that the market capitalizations of coins and tokens in the cryptocurrency universe follow power-law distributions with significantly different values for the tail exponent falling between 0.5 and 0.7 for coins, and between 1.0 and 1.3 for tokens. We provide a rationale for this, based on a simple proportional growth with birth and death model previously employed to describe the size distribution of firms, cities, webpages, etc. We empirically validate the model and its main predictions, i… Show more

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Cited by 48 publications
(36 citation statements)
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“…14 With standard errors already above 10% induced by estimated parameters, excluding additional prediction uncertainty due to persistent fluctuations of active users about the mean. 15 Although the parameters vary depending on the fitting window, even allowing for fitting windows starting in 2016, where one obtains a high exponent β (above 2.5), an overvaluation of about a factor of two is still indicated.…”
Section: Fundamental Value Of Bitcoin: Active Users and A Generalized Mmentioning
confidence: 99%
“…14 With standard errors already above 10% induced by estimated parameters, excluding additional prediction uncertainty due to persistent fluctuations of active users about the mean. 15 Although the parameters vary depending on the fitting window, even allowing for fitting windows starting in 2016, where one obtains a high exponent β (above 2.5), an overvaluation of about a factor of two is still indicated.…”
Section: Fundamental Value Of Bitcoin: Active Users and A Generalized Mmentioning
confidence: 99%
“…As a response to investor's growing demand for alternative investments in the cryptocurrency market, the emergence of a multitude of new digital coins ensued. Throughout 2017, the cryptocurrency market changed its structure from being dominated by Bitcoin to a more diversified market offering numerous technologies and variants of cryptocurrencies [79]. Figure 12 shows the dominance of Bitcoin at the beginning of 2017 over the complete cryptocurrency market with its market share, measured with respect to the total market capitalization of the top 1000 cryptocurrencies, being as high as 90%.…”
Section: Third Long Bubble: January 2016 -December 2017mentioning
confidence: 99%
“…A prevalent and common choice is the Geometric Brownian Motion (GBM) which has been used for modeling stocks (Merton 1973;Black and Scholes 1973;Wilmott et al 1995;Reddy and Clinton 2016;Øksendal 2003) and more recently also for cryptocurrencies Tarnopolski 2017;Kreuser and Sornette 2018;Wu et al 2018). There is, however, debate on the extent these assets follow GBM dynamics.…”
Section: Introductionmentioning
confidence: 99%
“…While GBM operates using Gaussian noise, there is evidence that the noise distribution of stocks such as the S&P 500 has curvature different than a normal distribution (Mantegna and Stanly 2000). Implication of this difference is most prominent in the frequency of rare events as a result of heavy-tailed noise, also possibly present for cryptocurrencies (Kreuser and Sornette 2018;Wu et al 2018;Fry 2018). For small sample sizes, this difference is small enough that many continue to use GBM and other variants for modeling.…”
Section: Introductionmentioning
confidence: 99%