Cryptocurrency trading: a comprehensive survey

Fang, Fan; Ventre, Carmine; Basios, Michail; Kanthan, Leslie; Martínez-Rego, David; Wu, Fan; Li, Lingbo

doi:10.1186/s40854-021-00321-6

Cited by 256 publications

(139 citation statements)

References 204 publications

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“…In the past decade, a new class of financial assets, cryptocurrencies, emerged into the asset management industry in risky portfolios, due to their low correlations with other major financial assets, and the benefits blockchain technologies bring (Fang et al , 2020). As per Coinmarketcap [1], the cryptocurrency market has evolved dramatically over the past few years, with market capitalization surpassing US$2tn.…”

Section: Introductionmentioning

confidence: 99%

Predicting bitcoin price movements using sentiment analysis: a machine learning approach

Gurrib

Kamalov

2021

SEF

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Purpose Cryptocurrencies such as Bitcoin (BTC) attracted a lot of attention in recent months due to their unprecedented price fluctuations. This paper aims to propose a new method for predicting the direction of BTC price using linear discriminant analysis (LDA) together with sentiment analysis. Design/methodology/approach Concretely, the authors train an LDA-based classifier that uses the current BTC price information and BTC news announcements headlines to forecast the next-day direction of BTC prices. The authors compare the results with a Support Vector Machine (SVM) model and random guess approach. The use of BTC price information and news announcements related to crypto enables us to value the importance of these different sources and types of information. Findings Relative to the LDA results, the SVM model was more accurate in predicting BTC next day’s price movement. All models yielded better forecasts of an increase in tomorrow’s BTC price compared to forecasting a decrease in the crypto price. The inclusion of news sentiment resulted in the highest forecast accuracy of 0.585 on the test data, which is superior to a random guess. The LDA (SVM) model with asset specific (news sentiment and asset specific) input features ranked first within their respective model classifiers, suggesting both BTC news sentiment and asset specific are prized factors in predicting tomorrow’s price direction. Originality/value To the best of the authors’ knowledge, this is the first study to analyze the potential effect of crypto-related sentiment and BTC specific news on BTC’s price using LDA and sentiment analysis.

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Section: Introductionmentioning

confidence: 99%

Predicting bitcoin price movements using sentiment analysis: a machine learning approach

Gurrib

Kamalov

2021

SEF

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“…In addition to this, there is a growing number of services providing out of the box, unified APIs which support many exchanges and merchant APIs. The work by [8] reports a comprehensive and detailed overview of the services currently available for data retrieving.…”

Section: Datamentioning

confidence: 99%

“…A cryptocurrency is defined as a medium of exchange which exploits, on the one hand, the blockchain technology to gain decentralization, transparency and immutability, and, on the other hand, cryptographic protocols to guarantee the security of transactions [6,7]. As currencies, they have properties similar to fiat currencies [8]. The main differences being the exclusion of financial institutions as intermediaries [9] and not being controlled and regulated by any central authority [10].…”

Section: Introductionmentioning

confidence: 99%

“…The main differences being the exclusion of financial institutions as intermediaries [9] and not being controlled and regulated by any central authority [10]. Thanks to the above mentioned characteristics, cryptocurrency market is available 24h a day, 7 days a week and transactions take place between individuals with different physical locations across the globe [8]. Standard features of financial systems joined with peculiarities listed above, make cryptocurrencies highly volatile instruments.…”

Section: Introductionmentioning

confidence: 99%

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Dependency structures in cryptocurrency market from high to low frequency

Briola¹,

Aste²

2022

Preprint

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We investigate logarithmic price returns cross-correlations at different time horizons for a set of 25 liquid cryptocurrencies traded on the FTX digital currency exchange. We study how the structure of the Minimum Spanning Tree (MST) and the Triangulated Maximally Filtered Graph (TMFG) evolve across time horizons from high (15 seconds) to low (1 day) frequency time resolutions. For each horizon, we test the stability, statistical significance and economic meaningfulness of the networks. Results give a deep insight into the evolutionary process of the time dependent hierarchical organization of the system under analysis. A decrease in correlation between pairs of cryptocurrencies is observed for finer time sampling resolutions. A growing structure emerges for coarser ones, highlighting multiple changes in the hierarchical reference role played by mainstream cryptocurrencies. This effect is studied both in its inter-and intrasector realizations.

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“…Recently, some researchers focused their attention on possible use of BTC as a hedging instrument for Forex [44], for gold and other commodities [45], as well as for the stock markets [46,47]. There is also a few review papers devoted to the cryptocurrency markets: [11,48,49].…”

Section: Cryptocurrency Marketmentioning

confidence: 99%

Complexity in economic and social systems: cryptocurrency market at around COVID-19

Drożdż,

Kwapień,

Oświęcimka

et al. 2020

Preprint

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Social systems are characterized by an enormous network of connections and factors that can influence the structure and dynamics of these systems. Among them the whole economical sphere of human activity seems to be the most interrelated and complex. All financial markets, including the youngest one, the cryptocurrency market, belong to this sphere. The complexity of the cryptocurrency market can be studied from different perspectives. First, the dynamics of the cryptocurrency exchange rates to other cryptocurrencies and fiat currencies can be studied and quantified by means of multifractal formalism. Second, coupling and decoupling of the cryptocurrencies and the conventional assets can be investigated with the advanced cross-correlation analyses based on fractal analysis. Third, an internal structure of the cryptocurrency market can also be a subject of analysis that exploits, for example, a network representation of the market. In this work, we approach the subject from all three perspectives based on data from a recent time interval between January 2019 and June 2020. This period includes the peculiar time of the Covid-19 pandemic; therefore, we pay particular attention to this event and investigate how strong its impact on the structure and dynamics of the market was. Besides, the studied data covers a few other significant events like double bull and bear phases in 2019. We show that, throughout the considered interval, the exchange rate returns were multifractal with intermittent signatures of bifractality that can be associated with the most volatile periods of the market dynamics like a bull market onset in April 2019 and the Covid-19 outburst in March 2020. The topology of a minimal spanning tree representation of the market also used to alter during these events from a distributed type without any dominant node to a highly centralized type with a dominating hub of USDT. However, the MST topology during the pandemic differs in some details from other volatile periods.

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Cryptocurrency trading: a comprehensive survey

Cited by 256 publications

References 204 publications

Predicting bitcoin price movements using sentiment analysis: a machine learning approach

Predicting bitcoin price movements using sentiment analysis: a machine learning approach

Dependency structures in cryptocurrency market from high to low frequency

Complexity in economic and social systems: cryptocurrency market at around COVID-19

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