Abstract
Cryptocurrencies have experienced recent surges in interest and price. It has been discovered that there are time intervals where cryptocurrency prices and certain online and social media factors appear related. In addition it has been noted that cryptocurrencies are prone to experience intervals of bubble-like price growth. The hypothesis investigated here is that relationships between online factors and price are dependent on market regime. In this paper, wavelet coherence is used to study co-movement between a cryptocurrency price and its related factors, for a number of examples. This is used alongside a well-known test for financial asset bubbles to explore whether relationships change dependent on regime. The primary finding of this work is that medium-term positive correlations between online factors and price strengthen significantly during bubble-like regimes of the price series; this explains why these relationships have previously been seen to appear and disappear over time. A secondary finding is that short-term relationships between the chosen factors and price appear to be caused by particular market events (such as hacks / security breaches), and are not consistent from one time interval to another in the effect of the factor upon the price. In addition, for the first time, wavelet coherence is used to explore the relationships between different cryptocurrencies.
Highlights
Cryptocurrencies are receiving a new wave of media attention
Numerous studies have attempted to provide understanding of how cryptocurrency prices can be predicted, many of these focussing on monitoring online factors, especially those derived from social media activity
It should be noted that the dark blue areas between 2010 and 2012 for Bitcoin subscriber growth, Google Trends, and Wikipedia views are due to a lack of data for these metrics prior to 2012
Summary
Cryptocurrencies are receiving a new wave of media attention. Numerous studies have attempted to provide understanding of how cryptocurrency prices can be predicted, many of these focussing on monitoring online factors, especially those derived from social media activity (given social media’s success in predicting stock prices [2]).
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