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Evolutionary dynamics of the cryptocurrency market
Abeer ElBahrawy, Laura Alessandretti, Anne Kandler, Romualdo Pastor-Satorras, Andrea Baronchelli
TL;DR
Comprehensive market-wide evidence has been limited because prior studies mainly examined Bitcoin or small cryptocurrency subsets. The paper analyzes 1,469 cryptocurrencies across the market’s history, finding stable statistical properties alongside declining Bitcoin share and showing that a simple neutral evolutionary model captures several observations. These findings provide a first formal link between ecological modeling and cryptocurrency-market analysis.
Problem
A comprehensive analysis of cryptocurrency-market dynamics was lacking because existing studies focused on Bitcoin or restricted groups of cryptocurrencies.
Method
The paper analyzes the evolution and market shares of the entire cryptocurrency market from April 2013 to June 2017 using an ecological perspective and neutral evolutionary modeling.
Results
The neutral model captures several observed market properties, including the steadily decreasing market share of Bitcoin and stable system-level observables.
Takeaways & Limitations
Simple hypotheses can account for some long-term properties of the cryptocurrency market, establishing a first formal link between ecological modeling and this growing system.
Takeaways & Limitations
The model is simple and does not capture the full complexity of the cryptocurrency ecology.
Abstract
from arXiv · showhide
The cryptocurrency market surpassed the barrier of \$100 billion market capitalization in June 2017, after months of steady growth. Despite its increasing relevance in the financial world, however, a comprehensive analysis of the whole system is still lacking, as most studies have focused exclusively on the behaviour of one (Bitcoin) or few cryptocurrencies. Here, we consider the history of the entire market and analyse the behaviour of 1,469 cryptocurrencies introduced between April 2013 and June 2017. We reveal that, while new cryptocurrencies appear and disappear continuously and their market capitalization is increasing (super-)exponentially, several statistical properties of the market have been stable for years. These include the number of active cryptocurrencies, the market share distribution and the turnover of cryptocurrencies. Adopting an ecological perspective, we show that the so-called neutral model of evolution is able to reproduce a number of key empirical observations, despite its simplicity and the assumption of no selective advantage of one cryptocurrency over another. Our results shed light on the properties of the cryptocurrency market and establish a first formal link between ecological modelling and the study of this growing system. We anticipate they will spark further research in this direction.
1 Introduction
The cryptocurrency market is a growing, volatile ecosystem whose comprehensive dynamics remain understudied. This paper analyzes the market’s evolution and finds declining Bitcoin share, stable system-level properties, and agreement between several observations and an ecological neutral model.
- Market background: Cryptocurrencies share blockchain-based transaction technology and reward mechanisms but typically operate on isolated networks, with uses spanning payments, speculation, and non-monetary applications.Many are Bitcoin clones with altered parameters, while others reflect larger blockchain innovations.
- Market background: The market is economically significant and highly volatile, with millions of users exchanging tokens and active cryptocurrencies exceeding $91 billion in capitalization by May 2017.Bitcoin remained dominant, although technical concerns and improvements in other cryptocurrencies challenged its position.
- 1 Introduction: Existing research lacked a comprehensive market-wide analysis, focusing mainly on Bitcoin or small groups of selected cryptocurrencies.Studies also disagreed about whether Bitcoin’s dominant position was threatened.
- 1 Introduction: The paper analyzes cryptocurrency market evolution from April 2013 to June 2017, focusing on the market shares of different cryptocurrencies.It presents the first complete analysis of the market over this period.
- 1 Introduction: Bitcoin steadily lost market share while the number of active cryptocurrencies, market-share distribution, ranking stability, and birth and death rates remained stable for years.The study also reports an exponential guide for the evolution of total market capitalization.
- 1 Introduction: An ecological neutral model reproduced several observed market distributions and captured the decrease in Bitcoin’s market share despite assuming no selective advantage among cryptocurrencies.The authors present these findings as a first step toward better understanding and modeling of the market.
2 Results
Across April 2013–May 2017, cryptocurrency capitalization grew exponentially while several market-structure statistics remained stable. Bitcoin’s share declined as runners-up gained ground, and a neutral evolutionary model reproduced four observed patterns.
- Market growth and concentration: 1,469 cryptocurrencies were analyzed, with around 600 active by May 2017, while total capitalization increased more than fourfold in one year.Capitalization followed C ∼exp(λt), with λ = 0.30 ± 0.02 in 15-week time units.
- Market growth and concentration: Bitcoin’s market share steadily decreased, with an annual change of b = −0.035 ± 0.002, while the top five runners-up gained share.The Bitcoin trend was observed across four years despite short-term oscillations; the top five excluding Bitcoin showed a positive annual fit of b = 0.021 ± 0.002.
- Stable market properties: The number of active cryptocurrencies remained stable because birth and death rates were similar from the end of 2014 onward.Average monthly birth and death rates were 1.16% and 1.04%, respectively, corresponding to approximately seven cryptocurrencies appearing and being abandoned each week.
- Stable market properties: The market-share distribution remained stable across years and aggregation windows, exhibiting a broad power-law tail with exponent α = 1.58 ± 0.12.The frequency-rank distribution was also consistent with a power law: the theoretical β = 1.72 agreed with the empirical β = 1.93 ± 0.23, including in individual years.
- Stable market properties: Rank dynamics were stable over time: top-rank occupation times declined rapidly with rank, low-rank occupation approached one week, and ranking turnover was substantially stable.Ranks 2–6 contained 33 cryptocurrencies averaging 12.6 weeks, whereas ranks 7–12 contained 70 cryptocurrencies averaging 3.6 weeks.
3 Discussion and Outlook
The paper identifies stable long-term market properties alongside exponential capitalization growth and declining Bitcoin share. A neutral evolutionary model captures several observed patterns, while the authors note important complexity and future modelling needs.
- The total market capitalization entered a phase of exponential growth one year before the paper’s conclusion, while Bitcoin’s market share steadily decreased.
- The number of active cryptocurrencies, market-share distribution, and rank turnover remained stable since the beginning of the time series.
- The neutral model of evolution captured several observed cryptocurrency-market properties despite assuming no selective advantage among currencies.
- The model’s fit suggests that some long-term market properties can be accounted for using simple hypotheses, including independent allocation of money packets.
- The authors identify future needs to model expanding capitalization and incorporate single-transaction information where available.
- Legislative, technical, and social developments may seriously affect the market, while speculative use may promote diversification and payment use may promote winner-take-all dynamics.
4 Material and methods
The study uses weekly cryptocurrency-market data collected from Coin Market Cap and defines its principal market variables from circulating supply, prices, and capitalization. The dataset and website inclusion rules constrain which cryptocurrencies enter the analysis.
- Weekly data from 157 exchange platforms covered April 28, 2013 to May 13, 2017, with trading-volume data beginning December 29, 2013.
- Coin Market Cap provides market capitalization, U.S.-dollar price, and preceding-24-hour trading volume for living cryptocurrencies.
- The website lists cryptocurrencies older than 30 days with an API and public mined-supply URL, excluding currencies lacking recent trading activity.
- The circulating supply is the number of coins available to users, price is the exchange rate, and market capitalization equals supply multiplied by price.
- Market share is a cryptocurrency’s market capitalization normalized by the total market capitalization.
- Analyses based on market capitalization and market share neglect destroyed or dormant coins, including a reported 51% of mined Bitcoins in an earlier period.
Authors’ contributions
The paper assigns conception, design, data acquisition and preprocessing, analysis, interpretation, and manuscript drafting across the listed contributors.
- Study conception was assigned to AB; design involved AE, LA, AK, RPS, and AB; data acquisition and preprocessing were assigned to AE.
- Analysis, interpretation, and manuscript drafting were assigned across AE, LA, AK, RPS, and AB.
Funding
R.P.-S. acknowledges financial support from Spanish MINECO projects and additional support from ICREA Academia funded by the Generalitat de Catalunya.
- R.P.-S. acknowledges Spanish MINECO support under projects FIS2013-47282-C2-2 and FIS2016-76830-C2-1-P.
- Additional financial support came from ICREA Academia, funded by the Generalitat de Catalunya.
A.1 some relevant cryptocurrencies
Table 1 presents selected cryptocurrencies that either occupied high ranks or were introduced early in the market, using data collected in May 2017.
- Table 1 covers cryptocurrencies selected for high-rank positions or early market introduction.The table uses data collected in May 2017.
- Bitcoin is listed as a 2009 proof-of-work cryptocurrency with a $35B market capitalization and rank 1.
- Ethereum is listed as a 2015 proof-of-work cryptocurrency with a $15B market capitalization, rank 2, and smart-contract functionality.
- Ripple is listed with an open-source consensus ledger, $8B market capitalization, rank 3, and adoption by companies and banks.
- Other listed cryptocurrencies include NEM, Ethereum Classic, Litecoin, Dash, Monero, and NameCoin, with differing technologies, ranks, and market roles.Dash and Monero are described as privacy-focused, while Dash and Monero gained momentum or market share later.
A.2 Simulations
The simulations calibrate the neutral model to cryptocurrency entry rates and initial dominance, then test whether its distributions and rank dynamics are robust across conditions.
- The model uses µ = 7/N so one generation corresponds to one week, matching approximately seven new cryptocurrencies entering weekly.N is the model population size.
- Simulations start with one species and use N = 105, representing Bitcoin’s initial dominance and mapping one model individual to approximately $100,000.Results do not depend on N when it is sufficiently large.
- New cryptocurrencies are assigned an average modeled size of m = 15, corresponding to approximately $1.5 million, with sizes sampled from [10, 20].
- The simulated species-size distribution exhibits a power-law phase with exponent α = 1.5, and this exponent is robust to broad changes in µ and other conditions.
- Rank measures aggregated over 52 generations are stable across starting generations except for high mobility during the earliest generations.The turnover profile and average rank lifetime are compared between cryptocurrency data and simulations.
A.3 technologies, same distribution
The paper compares cryptocurrencies using proof-of-work and proof-of-stake or hybrid technologies and finds that their market-share distributions follow the same behavior.
- The analysis tests whether technical differences leave detectable fingerprints in statistical distributions by comparing two main blockchain consensus algorithms.
- Proof-of-work generates blocks through computational and electrical work, with miners rewarded in coins and difficulty adjusted every 2016 blocks.
- Proof-of-work raises security and sustainability concerns, including potential manipulation by dominant mining pools and substantial energy consumption.The text notes that one pool controlled 42% of Bitcoin mining power in 2014 and estimates Bitcoin’s annual consumption at 12.76 TWh.
- Proof-of-stake assigns mining power according to the proportion of coins held rather than computational resources.
- Proof-of-work and proof-of-stake or hybrid cryptocurrencies exhibit the same market-share distribution behavior.Figure A3 displays the two groups separately and includes a power-law curve with exponent α = 1.5.
A.4 share and frequency-rank distributions for individual years
The paper evaluates market-share and frequency-rank distributions separately by year and finds consistency with neutral-model predictions.
- Power-law fits for market-share distributions and frequency-rank distributions are consistent with neutral-model theoretical predictions in individual years.
- Market-share fit coefficients are computed using the cited methodology with errors obtained by 1,000 bootstrap repetitions.
- Table 2 reports power-law fit coefficients for market-share distributions.
- Table 3 reports power-law fit coefficients for frequency-rank distributions.