The document discusses the evolutionary dynamics and competition between cryptocurrencies in the market from 2013 to 2018. It analyzes the market share and rankings of over 1500 cryptocurrencies over time. The dynamics are modeled using the neutral model, which assumes equal chance of reproduction and death for all species. The model suggests technological differences may not influence performance and Bitcoin's market share may decrease. Future work includes predicting price fluctuations using machine learning.

1. Evolutionary dynamics of the
cryptocurrency market
Abeer Yehia ElBahrawy
PhD student at
City, University of London
The Alan Turing Institute

2. Growing volume (relevance)

3. Growing relevance

4. Growing relevance

5. Growing relevance

6. Growing relevance

7. Growing relevance

8. Growing relevance
>1500

9. The evolution of the market
Competition between cryptocurrencies
Global view of the market dynamics
Our focus

10. The evolution of the market
Competition between cryptocurrencies
Global view of the market dynamics
Our focus

11. The evolution of the market
Competition between cryptocurrencies
Global view of the market dynamics
Our focus

12. Data
Coinmarketcap.com

13. Data

14. Data

15. Data

16. Data

17. Market share: normalised market cap,
Data

18. Results

19. Expanding market

20. Bitcoin is losing

21. Bitcoin is losing

22. Stable properties

23. Stable properties

24. Stable properties

25. Stable properties

26. Market share
distribution
Stable properties

27. Ranking
distribution
Stable properties

28. Average life
time per rank
Stable properties

29. Turnover
for the top
ranked
Stable properties

30. Can we model
these dynamics?
Stable properties

31. Can we model
these dynamics?
Progeny
distribution
Stable properties

32. The neutral model

33. Interactions between species are equivalent on
an individual ‘per capita’ basis
Sampling theory
Genetics, English words, technology patents citation …
1. MotooKimura. Theneutral theoryof molecularevolution. CambridgeUniversityPress, 1983.
2. DamianRuck, RAlexanderBentley, AlbertoAcerbi, PhilipGarnett, andDaniel J Hruschka. Neutral evolutionandturnoverovercenturiesof englishwordpopularity. arXivpreprintarXiv:1703.10698, 2017.
3. RAlexanderBentley, MatthewWHahn, andStephenJ Shennan. Randomdriftandculturechange. ProceedingsoftheRoyal SocietyofLondonB: Biological Sciences, 271(1547):1443–1450, 2004.
The neutral model

34. The neutral model

35. 5
2
7
The neutral model

36. 5
2
7
The neutral model

37. 5
2
7
The neutral model

38. The neutral model
5
2
7
1
8
3
1
1

39. 5
2
7
1
8
3
1
1
3
15
8
1
1Progeny
distribution
The neutral model

40. 3
22
11
3
2
1
3
15
8
1
1
1
7
3
1
2
5
2
7
1
8
3
1
1
Progeny
distribution
The neutral model

41. How is it relevant?

42. CryptocurrenciesSpecies ( )
The mapping

43. CryptocurrenciesSpecies ( )
Individuals ( ) Dollars
The mapping

44. CryptocurrenciesSpecies ( )
Individuals ( ) Dollars
Generation ( ) One week
The mapping

45. CryptocurrenciesSpecies ( )
Individuals ( ) Dollars
Mutation ( )
A new
cryptocurrency
enters the market
Generation ( ) One week
The mapping

46. BitcoinOne species
Model informing

47. BitcoinOne species
Population size
One individual maps to
$100,000
Model informing

48. BitcoinOne species
Population size
One individual maps to
$100,000
Mutation rate
7 new cryptocurrencies
every week
Model informing

49. new species enter with
average number of
individuals = 15
average market cap
introduced by new
cryptocurrencies
1500,0000
Model informing

50. Neutral model and crypto data

51. Now!

52. What does it mean?

53. Technological differences may not be a
steering factor in the performance
What does it mean?

54. What does it mean?

55. Technological differences may not be a
steering factor in the performance
Bitcoin domination
What does it mean?

56. What does it mean?

57. Looking at the entire market around 1500
cryptocurrencies
Our focus

58. Looking at the entire market around 1500
cryptocurrencies
Market dynamics and properties from
April 2013 till October 2018
Our focus

59. Looking at the entire market around 1500
cryptocurrencies
Market dynamics and properties from
April 2013 till October 2018
Modelling the competition dynamics using
the neutral model
Our focus

61. Market inefficiency
What are the drivers?
The evolution of the blockchain
transactions
Next steps

62. Machine learning
Which cryptocurrencies should I buy
today?
Which one there prices are going to
increase?
?
$
Can we predict cryptocurrencies prices?

63. What is the price of Ethereum
tomorrow?
Can we predict cryptocurrencies prices?

64. 0
25
50
75
100
125
April May June July
vo lu me
price
Etherum time
series
What is Ethereum price tomorrow?

65. 0
25
50
75
100
125
April May June July
vo lu me
price
Etherum time
series
What is Ethereum price tomorrow?

66. 0
25
50
75
100
125
April May June July
vo lu me
price
Etherum time
series
What is Ethereum price tomorrow?
0.0352 BTC

67. 0
25
50
75
100
125
April June
vo lu me
Etherum time
series
What is Ethereum price tomorrow?
0.0352 BTC
0
20
40
60
80
100
April June
vo lu me
DASH

68. 0
25
50
75
100
125
April May June July
vo lu me
price
Etherum time
series
What is Ethereum price tomorrow?
0.0352 BTC

69. Can we predict cryptocurrencies prices?

70. https://arxiv.org/abs/1805.08550

71. Romualdo Pastor-
Satorras
Universitat
Politecnica de
Catalunya, Spain
Andrea Baronchelli
City, University of London
UCL Centre of Blockchain
technology
Laura Alessandretti
Technical University of
Denmark
Anne Kandler
Max Planck Institute
Luca maria aiello
Nokie Bell labs

72. Thanks!
abeeryehia19.wixsite.com /abeer
abeer.yehia19@gmail.com