ArticleRoyal Society open science2018
Classification of cryptocurrency coins and tokens by the dynamics of their market capitalizations.
Article in Royal Society open science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Statistical patterns in the Ethereum blockchain: Analysis of EOAs and smart contracts in ERC20 token network.PloS one · 2026Article
- Stocks and cryptocurrencies: Antifragile or robust? A novel antifragility measure of the stock and cryptocurrency markets.PloS one · 2023Article
- Modeling innovation in the cryptocurrency ecosystem.Scientific reports · 2022Article
- Testing macroecological theories in cryptocurrency market: neutral models cannot describe diversity patterns and their variation.Royal Society open science · 2022Article
- Complexity in Economic and Social Systems: Cryptocurrency Market at around COVID-19.Entropy (Basel, Switzerland) · 2020Article
- A model of the optimal selection of crypto assets.Royal Society open science · 2020Article
- Critical slowing down associated with critical transition and risk of collapse in crypto-currency.Royal Society open science · 2020Article
- Dissection of Bitcoin's multiscale bubble history from January 2012 to February 2018.Royal Society open science · 2019Article
- Are Bitcoin bubbles predictable? Combining a generalized Metcalfe's Law and the Log-Periodic Power Law Singularity model.Royal Society open science · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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, in terms of proportional growth (Gibrat's Law) of the coins and tokens. Estimating the main parameters of the model, the theoretical predictions for the power-law exponents of coin and token distributions are in remarkable agreement with the empirical estimations, given the simplicity of the model. Our results clearly characterize coins as being 'entrenched incumbents' and tokens as an 'explosive immature ecosystem', largely due to massive and exuberant Initial Coin Offering activity in the token space. The theory predicts that the exponent for tokens should converge to 1 in the future, reflecting a more reasonable rate of new entrants associated with genuine technological innovations.
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