Title: Exploring the Interconnectedness of Cryptocurrencies using Correlation Networks,
Abstract: Correlation networks were used to detect characteristics which, although
fixed over time, have an important influence on the evolution of prices over
time. Potentially important features were identified using the websites and
whitepapers of cryptocurrencies with the largest userbases. These were assessed
using two datasets to enhance robustness: one with fourteen cryptocurrencies
beginning from 9 November 2017, and a subset with nine cryptocurrencies
starting 9 September 2016, both ending 6 March 2018. Separately analysing the
subset of cryptocurrencies raised the number of data points from 115 to 537,
and improved robustness to changes in relationships over time. Excluding USD
Tether, the results showed a positive association between different
cryptocurrencies that was statistically significant. Robust, strong positive
associations were observed for six cryptocurrencies where one was a fork of the
other; Bitcoin / Bitcoin Cash was an exception. There was evidence for the
existence of a group of cryptocurrencies particularly associated with Cardano,
and a separate group correlated with Ethereum. The data was not consistent with
a token's functionality or creation mechanism being the dominant determinants
of the evolution of prices over time but did suggest that factors other than
speculation contributed to the price.