The dynamics of cryptocurrency market behavior: sentiment analysis using Markov chains

Abstract

PurposeThe authors examine cryptocurrency market behavior using a hidden Markov model (HMM). Under the assumption that the cryptocurrency market has unobserved heterogeneity, an HMM allows us to study (1) the extent to which cryptocurrency markets shift due to interactions with social sentiment during a bull or bear market and (2) the heterogeneous pattern of cryptocurrency market behavior under these two market conditions.Design/methodology/approachThe authors advance the HMM model based on two six-month datasets (from November 2017 to April 2018 for a bull market and from December 2018 to May 2019 for a bear market) collected from Google, Twitter, the stock market and cryptocurrency trading platforms in South Korea. Social sentiment data were collected by crawling Bitcoin-related posts on Twitter.FindingsThe authors highlight the reaction of the cryptocurrency market to social sentiment under a bull and a bear market and in two hidden states (an upward and a downward trend). They find: (1) social sentiment is relatively relevant during a bull compared to a bear market. (2) The cryptocurrency market in a downward state, that is, with a local decreasing trend, tends to be more responsive to positive social sentiment. (3) The market in an upward state, that is, with a local increasing trend, tends to better interact with negative social sentiment.Originality/valueThe proposed HMM model contributes to a theoretically grounded understanding of how cryptocurrency markets respond to social sentiment in bull and bear markets through varied sequences adjusted for cryptocurrency market heterogeneity.

Modelling Determinants of Cryptocurrency Prices: A Bayesian Network Approach

“…In particular, possibly due to human psychology, both inference and diagnosis analyses show more significant changes in the number of tweets in the occurrence of an upward trend market. This result is in line with other studies such as (Kim et al, 2021) where an upward trend market is highlighted as more influential in comparison to a downward trend market in investigating the social sentiments of people's tweets. The fluctuation in the number of tweets is observed more in Binance…”

“…In a similar study, Rouhani & Abedin (2019) show that Ripple has the highest percentage of positive tweets (52%), and Bitcoin receives the highest percentage of negative tweets (27%) among all the cryptocurrencies by analysing around five million tweets. The predictive power of social media sentiment analysis is studied in Kraaijeveld & De Smedt (2020) and Kim et al (2021), and it is shown that Twitter sentiment has predictive power for price returns of Bitcoin, Bitcoin Cash, and Litecoin, and cryptocurrency markets are more responsive to positive social sentiment when it is in a downward trend.…”

Emergence of Blockchain Applications with the 6G-Enabled IoT-Based Smart City

“…2) Transition matrix P : We model the evolution of the total computing power in the blockchain as a time-homogeneous Markov chain with transition matrix P . This Markov assumption is widely used [14]. We will justify the Markov assumption in Sec.IV using a real Bitcoin dataset.…”

“…Related to the modeling of the blockchain system, [12] and [13] employ a Markov process model to study performance and network security in a distributed ledger. The evolution of cryptocurrency as a Hidden Markov Model is explored in [12], [14].…”

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“…However, sentiment analysis alone cannot capture the emotions associated with a particular message, and these emotions are clearly important for understanding the underlying meaning. Recent research has examined the relationship between sentiment, emotions and price fluctuations in cryptocurrency-related tweets (Ahn and Kim, 2021;Sapkota, 2022;Bourghelle et al, 2022). However, the impact of an opinion leader's tweet is likely to be much greater than that of an average user.…”