Evolutionary dynamics of the cryptocurrency market
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Evolutionary dynamics of the cryptocurrency market
- 1. Evolutionary dynamics of the cryptocurrency market Abeer ElBahrawy Laura Alessandretti, Anne Kandler, Romualdo Pastor-Satorras, Andrea Baronchelli
- 3. Bitcoin Invented by Satoshi Nakamoto in 2008 Digital currency
- 4. Bitcoin Invented by Satoshi Nakamoto in 2008 Digital currency Public ledger
- 5. Bitcoin Invented by Satoshi Nakamoto in 2008 Digital currency Cryptography Public ledger
- 10. Growing market
- 11. Growing market
- 12. Growing market
- 13. Growing market
- 14. Growing market
- 16. Our focus Global view of the market dynamics
- 17. Our focus The evolution of the market Global view of the market dynamics
- 18. Our focus The evolution of the market Competition between cryptocurrencies Global view of the market dynamics
- 19. Data
- 20. Data Price collected from 10328 exchange platforms.
- 21. Data Circulating number of coins.
- 22. Data Trading volume across the platforms.
- 23. Data The product of the price and the supply,
- 24. Data Market share: normalised market cap,
- 25. Results
- 26. Expanding market
- 33. Stable properties Probability distribution of the market share
- 34. Stable properties Probability distribution of the market share
- 37. Stable properties Average life time per rank
- 38. Stable properties Average life time per rank
- 39. Stable properties Turnover for the top ranked
- 40. Stable properties Turnover for the top ranked
- 41. Stable properties can we model these dynamics?
- 42. Stable properties progeny distribution can we model these dynamics?
- 44. The progeny distribution • Progeny distribution: the progeny distribution logs the abundances of species which produce new individuals over a fixed period of time k
- 45. The neutral model • Progeny distribution: the progeny distribution logs the abundances of species which produce new individuals over a fixed period of time • For the neutral model, it’s well established that it follows a power law distribution1,2,3 k 1. RA Bentley, MW Hahn, and SJ Shennan. Random drift and culture change. Proceedings of the Royal Society of London B: Biological Sciences, 271 (1547):1443–1450, 2004. 2. MW Hahn and RA Bentley. Drift as a mechanism for cultural change: an example from baby names. Proceedings of the Royal Society of London B: Biological Sciences, 270(Suppl 1):S120–S123, 2003. 3. LS Premo. Cultural transmission and diversity in time-averaged assemblages. Current Anthropology, 55(1):105–114, 2014. P(x) ⇠ x ↵
- 46. The neutral model • The exponent is shown to be equal to 1. James P O’Dwyer and Anne Kandler. Inferring processes of cultural transmission: the critical role of rare variants in distinguishing neutrality from novelty biases P(x) ⇠ x 3/2
- 47. The neutral model Interactions between species are equivalent on an individual ‘per capita’ basis
- 48. The neutral model Interactions between species are equivalent on an individual ‘per capita’ basis Sampling theory
- 49. The neutral model Interactions between species are equivalent on an individual ‘per capita’ basis Sampling theory Genetics, English words, technology patents citation … 1. Motoo Kimura.The neutral theory of molecular evolution. Cambridge University Press, 1983. 2. Damian Ruck, R Alexander Bentley,Alberto Acerbi, Philip Garnett, and Daniel J Hruschka. Neutral evolution and turnover over centuries of english word popularity. arXiv preprint arXiv:1703.10698, 2017. 3. R Alexander Bentley, Matthew W Hahn, and Stephen J Shennan. Random drift and culture change. Proceedings of the Royal Society of London B: Biological Sciences, 271(1547):1443–1450, 2004.
- 50. t = 0 The neutral model
- 51. 5 2 7 t = 0 The neutral model
- 52. 5 2 7 t = 0 t = 1 The neutral model
- 53. 5 2 7 t = 0 t = 1 The neutral model
- 54. 5 2 7 1 8 3 1 1 t = 0 t = 1 The neutral model
- 55. 5 2 7 1 8 3 1 1 t = 0 t = 1 3 15 8 1 1 Progeny distribution The neutral model
- 56. 1 7 3 1 2 5 2 7 1 8 3 1 1 t = 0 t = 1 t = 2 3 15 8 1 1 3 22 11 3 2 1 Progeny distribution The neutral model
- 57. How is it relevant?
- 58. The mapping
- 60. The mapping CryptocurrenciesSpecies ( )m Individuals ( ) DollarsN
- 61. The mapping CryptocurrenciesSpecies ( )m Individuals ( ) DollarsN Generation ( ) One weekt
- 62. The mapping CryptocurrenciesSpecies ( )m Individuals ( ) DollarsN Mutation ( ) A new cryptocurrency enters the market µ Generation ( ) One weekt
- 64. Initial conditions Bitcoin One species m = 1 Population size N = 105 One individual maps to $100,000
- 65. Initial conditions Bitcoin One species m = 1 Population size N = 105 One individual maps to $100,000 µ = 7/N Mutation rate 7 new cryptocurrencies every week
- 66. model informing new species enter with average number of individuals = 15 average market cap introduced by new cryptocurrencies 1500,0000
- 67. Neutral model and data
- 68. What does it mean?
- 69. What does it mean? Technological differences may not be a steering factor in the performance
- 70. What does it mean? market share
- 71. What does it mean? Technological differences may not be a steering factor in the performance Bitcoin domination
- 72. What does it mean? Bitcoin domination
- 73. Our focus The evolution of the market Competition between cryptocurrencies Global view of the market dynamics
- 74. Our focus Looking at the entire market around 1500 cryptocurrencies
- 75. Our focus Looking at the entire market around 1500 cryptocurrencies Market dynamics and properties from April 2013 till May 2017
- 76. Our focus Looking at the entire market around 1500 cryptocurrencies Market dynamics and properties from April 2013 till May 2017 Modelling the competition dynamics using the neutral model
- 78. Next steps Market inefficiency What are the drivers?