Cryptocurrency price drivers: Wavelet coherence analysis revisited

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.

Mutual-Excitation of Cryptocurrency Market Returns and Social Media Topics

“…Activity on Reddit was used to detect the epidemic-like spread of investment ideas beneficial in the prediction of cryptocurrency price bubbles [8]. The application of wavelet coherence further validated these findings; identifying that correlations between factors derived from Reddit and cryptocurrency prices strengthen in bubble-like regimes [9].…”

“…This allows for comparison between different community characteristics and their different interactions with the price. The previous work by the current authors [8,9] has considered quantitative metrics to monitor user involvement within a particular subreddit such as the number of posts and new authors per day. To our knowledge, the current literature has not explored which topics are being discussed on cryptocurrency subreddits, which this work addresses by looking at the content of each submission.…”

Cryptocurrency Price Prediction based on Multiple Market Sentiment

“…Besides, more and more countries have recognized that cryptocurrency is a legitimate payment method. Bitcoin has been created in 2009 by Nakamoto, which ranks first in the cryptocurrency market, following by Ethereum and Ripple [2].…”

“…However, some scholars believe that online factors do not necessarily maintain a stable relationship with prices [2]. It is believed that the relationship between online factors and prices has been previously seen to appear and disappear overtime due to the existence of bubble-like regimes in the price of cryptocurrencies.…”

“…Vector Autoregressive model and multivariate vector autoregressive model [2,16,17,18,19,20]. With the development of big data technology and artificial intelligence, a growing body of empirical studies has applied machine learning models to classification and prediction.…”