Mutual-Excitation of Cryptocurrency Market Returns and Social Media Topics

Abstract

Cryptocurrencies have recently experienced a new wave of price volatility and interest; activity within social media communities relating to cryptocurrencies has increased significantly.There is currently limited documented knowledge of factors which could indicate future price movements. This paper aims to decipher relationships between cryptocurrency price changes and topic discussion on social media to provide, among other things, an understanding of which topics are indicative of future price movements. To achieve this a well-known dynamic topic modelling approach is applied to social media communication to retrieve information about the temporal occurrence of various topics. A Hawkes model is then applied to find interactions between topics and cryptocurrency prices. The results show particular topics tend to precede certain types of price movements, for example the discussion of 'risk and investment vs trading' being indicative of price falls, the discussion of 'substantial price movements' being indicative of volatility, and the discussion of 'fundamental cryptocurrency value' by technical communities being indicative of price rises. The knowledge of topic relationships gained here could be built into a real-time system, providing trading or alerting signals.

The Butterfly “Affect”: impact of development practices on cryptocurrency prices

“…multiple tokens issued on the Ethereum blockchain). In a related study on topic analysis of tech forums on Reddit [22], authors also find that topics on "fundamental cryptocurrency value" are very frequent in the Ethereum community threads and are correlated with increase in prices.…”

“…More broadly, Google search data related to cryptocurrencies can be relevant to characterise the set of Bitcoin users [21]. Temporal topic analysis of Bitcoin and Ethereum discussions on Reddit also show correlations with variations of cryptocurrency prices [22].…”

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

“…The popular modern techniques from Artificial Intelligence and general softcomputing paradigms have been utilized in recent work [89], where price prediction based on historical price values was compared over machine learning with different types of neural networks, and further, in [72], where the sentiment-based prediction of alternative cryptocurrency price fluctuations using a gradient boosting tree model is given. In [105], a mutual-excitation of cryptocurrency market returns and social media topics is investigated, whereas the paper [84] reports a recent study considering the impact of social media on Bitcoin value. Finally, a study measuring the interaction between media sentiment, based on news articles as well as blog posts and the Bitcoin price, is given in [136].…”

Cryptocurrency Price Prediction based on Multiple Market Sentiment

“…Cryptocurrencies such as Bitcoin assume the role of digital currency. It is not only used worldwide for online payments, but also traded as an investment tool on the market [3]. Therefore, the ability to predict the price volatility of Bitcoin will facilitate payment and investment decisions.…”

“…For example, Kim et al adopted machine learning models to mine user reviews and found that the number of responses to user reviews and online comments affects the number of transactions between users [12]. Peng et al constructed the predictive model by combining SVR and GARCH model [3]. Finally, the performance of SVR-GARCH model is better than GARCH, EGARCH and GJR-GARCH models.…”