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EuroMed Journal of Business

ISSN : 1450-2194

Article publication date: 17 May 2022

Issue publication date: 16 August 2022

Cryptocurrencies put consumers at the heart of a potential revolution by shifting central authority to a distributed peer-to-peer monetary system. This study aims to perform a systematic literature review and bibliometric analysis within the topic of cryptocurrencies and consumer trust.

Design/methodology/approach

The Web of Science database has been selected, and the analyses performed allowed us to identify five research trends obtained from the bibliographic coupling analysis: (1) Understanding consumer's (non)acceptance of cryptocurrencies, (2) Ethical aspects and trust in cryptocurrencies, (3) Blockchain technology as a trust-free technology, (4) The blockchain/trust economy, and (5) Blockchain technology: challenging trust.

Findings uncover the intellectual structure in the field of cryptocurrencies and consumers' trust and offer insights on the pros and cons of consumers' willingness to trust the digital currency.

Originality/value

The study proved a great gap in the current literature in linking cryptocurrencies and trust theories in a consumer context. The authors also outline several gaps that allowed us to propose future research guidelines.

• Cryptocurrency
• Consumer trust
• Systematic literature review
• Bibliometric analysis

Acknowledgements

This work is supported by national funding’s of FCT - Fundação para a Ciência e a Tecnologia, I.P., in the project UIDB/04005/2020.

Sousa, A. , Calçada, E. , Rodrigues, P. and Pinto Borges, A. (2022), "Cryptocurrency adoption: a systematic literature review and bibliometric analysis", EuroMed Journal of Business , Vol. 17 No. 3, pp. 374-390. https://doi.org/10.1108/EMJB-01-2022-0003

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Article Contents

1. data and basic characteristics, 2. cryptocurrency-specific factors, 3. exposures to other assets, 4. additional results, 5. conclusion, acknowledgement, risks and returns of cryptocurrency.

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Yukun Liu, Aleh Tsyvinski, Risks and Returns of Cryptocurrency, The Review of Financial Studies , Volume 34, Issue 6, June 2021, Pages 2689–2727, https://doi.org/10.1093/rfs/hhaa113

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We establish that cryptocurrency returns are driven and can be predicted by factors that are specific to cryptocurrency markets. Cryptocurrency returns are exposed to cryptocurrency network factors but not cryptocurrency production factors. We construct the network factors to capture the user adoption of cryptocurrencies and the production factors to proxy for the costs of cryptocurrency production. Moreover, there is a strong time-series momentum effect, and proxies for investor attention strongly forecast future cryptocurrency returns.

Cryptocurrency is a recent phenomenon that is receiving significant attention. On the one hand, it is based on a fundamentally new technology, the potential of which is not fully understood. On the other hand, at least in the current form, it fulfills similar functions as other, more traditional assets. Extensive academic attention has focused on developing theoretical models of cryptocurrencies. The theoretical literature on cryptocurrencies has suggested a number of factors that are potentially important in the valuation of cryptocurrencies. The first group of papers builds models stressing the network effect of cryptocurrency adoption (e.g., Pagnotta and Buraschi 2018 ; Biais et al. 2018 ; Cong, Li, and Wang 2019 ) and emphasizes the price dynamics induced by the positive externality of the network effect. The second group of papers focuses on the production side of the coins—the miners’ problem (e.g., Cong, He, and Li 2018 ; Sockin and Xiong 2019 )—and shows that the evolution of cryptocurrency prices is linked to the marginal cost of production. The third group of papers ties the movements of cryptocurrency prices to those of traditional asset classes such as fiat money (e.g., Athey et al. 2016 ; Schilling and Uhlig 2019 ; Jermann 2018 ). There is also a growing literature on the empirical regularities of cryptocurrencies. Borri (2019) shows that individual cryptocurrencies are exposed to cryptomarket tail-risks. Makarov and Schoar (2020) find that cryptocurrency markets exhibit periods of potential arbitrage opportunites across exchanges. Griffin and Shams (2020) study Bitcoin price manipulation. Our paper is the first comprehensive analysis of cryptocurrencies through the lens of empirical asset pricing. Its contribution is twofold. First, it tests the mechanisms and predictions of the existing theoretical models. Second, it establishes a set of basic asset pricing facts for this asset class, which provides a common benchmark that the current and future models of cryptocurrencies should take into consideration.

We start by constructing an index of cryptocurrency (or coin) market returns. This index is the value-weighted returns of all the coins with capitalizations of more than 1 million USD (1,707 coins in total) and covers the period of January 1, 2011, to December 31, 2018. We now describe some basic statistical properties of this index. During the sample period, the averages of the coin market returns at the daily, weekly, and monthly frequencies are 0.46%, 3.44%, and 20.44%, respectively. The daily, weekly, and monthly standard deviations of the coin market returns are 5.46%, 16.50%, and 70.80%, respectively. The coin market returns have positive skewness and kurtosis. We observe that the mean and standard deviation of the coin market returns are an order of magnitude higher than those of the stock returns during the same period. The Sharpe ratios at the daily and weekly levels are about 60% and 90% higher, and the Sharpe ratio at the monthly level is comparable to those of stocks. The returns have positive skewness increasing with the frequencies from daily to monthly. The returns experience high probabilities of extreme losses and gains. For example, an extreme loss of the daily 20% negative return on the coin market happens with a probability of 0.48%, while an extreme gain of the same size occurs with a probability of 0.89%.

We next turn to examine the relationship between the coin market returns and the main cryptocurrency-specific factors that are proposed in the theoretical literature. We formulate and investigate potential drivers and predictors for cryptocurrency returns. Specifically, we construct cryptocurrency network factors, cryptocurrency production factors, cryptocurrency momentum, proxies for average and negative investor attention, and proxies for cryptocurrency valuation ratios. For each of these factors, we aim to provide a number of possible empirical measures, as there are no canonical ways to define them in the cryptocurrency market.

We consider five measures to capture the cryptocurrency network effect. Consistent with the cryptocurrency models based on the network effect, 1 we find that the coin market returns are positively and significantly exposed to cryptocurrency network growth. Furthermore, we show that the evolution of cryptocurrency prices not only reflects current cryptocurrency adoption but also contains information about expected future network growth.

We then study the implications of the cryptocurrency models based on the miners’ production problem. 2 We construct production factors of cryptocurrency to proxy for the cost of mining and test the relationship between these production factors and cryptocurrency prices. To the first approximation, mining a cryptocurrency requires two inputs: electricity and computer power. We separately construct eight proxies for electricity costs and six proxies for computing costs. For electricity, we use time-varying and location-specific measures of the price, consumption, and generation of electricity in the United States and China (including Sichuan province, which hosts the largest mining farm in the world). For proxies of computing costs, we use the prices of Bitmain Antminer, one of the common Bitcoin mining equipments, as our primary measure. We also consider indirect measures—the stock returns of the companies that are major manufacturers of mining chips. Overall, we find that the coin market returns are not significantly exposed to the cryptocurrency production factors.

The existing theoretical models of cryptocurrencies have a number of implications for the predictability of cryptocurrency returns. Some papers argue that the evolution of cryptocurrency prices should follow a martingale, and thus cryptocurrency returns are not predictable (e.g., Schilling and Uhlig 2019 ). Other papers argue that, in dynamic cryptocurrency valuation models, cryptocurrency returns could potentially be predicted by momentum, investor attention, and cryptocurrency valuation ratios (e.g., Cong, Li, and Wang 2019 ; Sockin and Xiong 2019 ). We show that momentum and investor attention strongly predict future cryptocurrency cumulative returns, but cryptocurrency valuation ratios do not.

First, we show that there is a significant time-series momentum phenomenon in the cryptocurrency market. We find that the current coin market returns predict cumulative future coin market returns from one week to eight weeks ahead. For example, a one-standard-deviation increase in the current coin market returns predicts a 3.30% increase in the weekly returns over the next week. Grouping weekly returns by terciles, we find that the top terciles outperform the bottom terciles over the one- to four-week horizons. For example, at the one-week horizon, the average return of the top tercile is 8.01% per week with a t -statistic of 4.30, while the average return of the bottom tercile is only 1.10% per week with a t -statistic of 0.92. The time-series momentum results are valid both in sample and out of sample.

Second, we construct proxies for investor attention with Google searches and show that high investor attention predicts high future returns over the one- to six-week horizons. For example, a one-standard-deviation increase in the investor attention measure yields a 3.0% increase in the 1-week-ahead future coin market returns. At the one-week horizon, the average return of the investor attention tercile is 6.53% per week with a t -statistic of 3.82, while the average return of the bottom tercile is only 0.43% per week with a t -statistic of 0.42. Another proxy for investor attention we construct is Twitter post counts, and we reach similar results with the Twitter measure. Additionally, we construct a proxy for negative investor attention and show that relatively high negative investor attention negatively predicts future cumulative coin market returns.

Research on the equity market (e.g., Hong, Lim, and Stein 2000 ; Hou, Xiong, and Peng 2009 ) shows that there is a strong interaction between momentum and investor attention. Sockin and Xiong (2019) also show that investor attention can generate momentum in the cryptocurrency market, and in their model, the momentum effect disappears controlling for investor attention. We investigate whether there is a similar interaction between momentum and investor attention in the cryptocurrency market. We find that investor attention is high during and after periods of high coin market returns. However, in a bivariate coin market predictability regression with both variables, we show that the two effects do not subsume each other. Finally, we test whether the magnitude of the momentum effect is different during periods of high investor attention and vice versa. In contrast to the equity market, we show that there is limited interaction between cryptocurrency momentum and investor attention.

Moreover, we test whether the cryptocurrency valuation ratios similar to those in the financial markets can predict future coin market returns. In the equity market, the fundamental-to-market ratios are commonly referred to as valuation ratios and are measured as the ratio of the book value of equity to the market value of equity or some other ratio of fundamental value to market value. It is more difficult to define a similar measure of the fundamental value for cryptocurrency. In their dynamic cryptocurrency asset pricing model, Cong, Li, and Wang (2019) show that the cryptocurrency fundamental-to-value ratio, defined as the number of user adoptions over market capitalization, negatively predicts future cryptocurrency returns. Motivated by the theoretical model and studies of other financial markets, we construct six cryptocurrency valuation ratios and test the return predictability of these valuation ratios. Although the coefficient estimates are consistently negative, none of the six cryptocurrency valuation ratios predict future cumulative coin market returns significantly.

Another approach to study what cryptocurrencies represent is to examine the exposures of cryptocurrency returns to other asset classes. In other words, we assess how investors and markets value current and future prospects of cryptocurrencies. The theoretical literature and the community of cryptocurrency have proposed various narratives for what cryptocurrencies represent. Schilling and Uhlig (2019) argue that, in an endowment economy where fiat money and cryptocurrency coexist and compete, the cryptocurrency returns comove with the price evolution of the fiat money. Athey et al. (2016) emphasize the importance of currency exchange rates on cryptocurrency prices. Another popular narrative is that cryptocurrency is “digital gold” and represents a new way to store value. Specifically, we study whether major cryptocurrencies comove with currencies, commodities, stocks, and macroeconomic factors. In contrast to these popular explanations, we find that the exposures of cryptocurrencies to these traditional assets are low. Overall, there is little evidence, in the view of the markets, behind the narrative that there are similarities between cryptocurrencies and these traditional assets.

We note several additional results. First, we acknowledge that we have a short time series and that there is much uncertainty and learning about cryptocurrencies during the sample period. We show that our main results are similar for the first half and the second half of the sample. Second, we discuss the relationship between the cryptocurrency time-series momentum and cross-sectional momentum. Third, we investigate the importance of regulative events in affecting cryptocurrency prices, and show that negative regulative events but not positive regulative events significantly affect cryptocurrency prices. Fourth, we examine the importance of speculative interests in driving cryptocurrency prices. We show that cryptocurrency returns are higher when speculative interests increase, but the coefficient estimates are only marginally significant. Fifth, we construct a direct measure of cryptocurrency investor sentiment and show that the expected coin market return is higher when investor sentiment is high. In the multivariate regressions with the sentiment, investor attention, and momentum measures, all three variables are statistically significant in predicting future cryptocurrency returns. Sixth, we test the role of beauty contests in the cryptocurrency market. Motivated by Biais and Bossaerts (1998) , we use the volume-volatility ratio to capture the degree of disagreement in the cryptocurrency market and show that cryptocurrency return is high when the current volume-volatility ratio is high. Seventh, we conduct a VAR analysis with the coin market returns and the different measures of coin network growth measures. Eighth, we test the effect of production factors with an alternative specification. Lastly, we examine the subsample results based on cryptocurrency characteristics.

Our paper uses standard textbook empirical asset pricing tools and methods, the discussion of which we mostly omit for conciseness. Our findings on momentum are related to a series of papers such as Jegadeesh and Titman (1993) , Moskowitz and Grinblatt (1999) , Moskowitz, Ooi, and Pedersen (2012) , Asness, Moskowitz, and Pedersen (2013) , and Daniel and Moskowitz (2016) . Da, Engelberg, and Gao (2011) use Google searches to proxy for investor attention.

Yermack (2015) is one of the first papers that brought academic attention to the field of cryptocurrency. Several recent articles document individual facts related to cryptocurrency investment. Stoffels (2017) studies cross-sectional cryptocurrency momentum. Hu, Parlour, and Rajan (2018) show that individual cryptocurrency returns correlate with Bitcoin returns. Borri (2019) shows that individual cryptocurrencies are exposed to cryptomarket tail-risks. Makarov and Schoar (2020) and Borri and Shakhnov (2018) find that cryptocurrency markets exhibit periods of potential arbitrage opportunites across exchanges. Griffin and Shams (2020) study Bitcoin price manipulation. Corbet et al. (2019) studies cryptocurrencies as a financial asset. Moreover, a number of recent papers develop models of cryptocurrencies (see, e.g., Weber 2016 ; Huberman, Leshno, and Moallemi 2017 ; Biais et al. 2018 ; Chiu and Koeppl 2017 ; Cong and He 2019 ; Cong, Li, and Wang 2019 ; Cong, He, and Li 2018 ; Sockin and Xiong 2019 ; Saleh 2018 ; Schilling and Uhlig 2019 ; Jermann 2018 ; Abadi and Brunnermeier 2018 ; Routledge and Zetlin-Jones 2018 ).

We collect trading data of all cryptocurrencies available from Coinmarketcap.com. Coinmarketcap.com is a leading source of cryptocurrency price and volume data. It aggregates information from over 200 major exchanges and provides daily data on opening, closing, high, and low prices, as well as volume and market capitalization (in dollars) for most of the cryptocurrencies. 3 For each cryptocurrency on the website, Coinmarketcap.com calculates its price by taking the volume-weighted average of all prices reported at each market. A cryptocurrency needs to meet a list of criteria to be listed, such as being traded on a public exchange with an application programming interface (API) that reports the last traded price and the last 24-hour trading volume, and having a nonzero trading volume on at least one supported exchange so that a price can be determined. Coinmarketcap.com lists both active and defunct cryptocurrencies, thus alleviating concerns about survivorship bias.

We first construct a coin market return as the value-weighted return of all the underlying coins. We use daily close prices to construct daily coin market returns. The weekly and monthly coin market returns are calculated from the daily coin market returns. We require the coins to have information on price, volume, and market capitalization. We further exclude coins with market capitalizations of less than 1,000,000 USD. For earlier years that are not covered by Coinmarketcap.com, we splice the coin market returns with Bitcoin returns from earlier years. The data of the earlier year Bitcoin returns are from CoinDeck and span from January 1, 2011, to April 29, 2013. We start from January 1, 2011, because there was not much liquidity and trading before that date. Altogether, the index of the coin market return covers the period from January 1, 2011, to December 31, 2018.

We use four primary measures to proxy for the network effect of user adoption: the number of wallet users, the number of active addresses, the number of transaction count, and the number of payment count. The data of wallet users are from Blockchain.info. We obtain data on active addresses, transaction count, and payment count from Coinmetrics.io. We use seven primary production factors to proxy for the cost of mining: the average price of electricity in the United States, the net generation of electricity of all sectors in the United States, the total electricity consumption of all sectors in the United States, the average price of electricity in China, and the average price of electricity in Sichuan province. We obtain data on the average price of electricity in the United States, the net generation of electricity of all sectors in the United States, and the total electricity consumption of all sectors in the United States from the U.S. Energy Information Administration. We obtain data on the average price of electricity in China and the average price of electricity in Sichuan province from the National Bureau of Statistics of China and the Price Monitoring Center, NDRC. Our primary computing cost data are the prices of Bitmain Antminer. We extract the Bitmain Antminer data from Keepa.com. The data for Bitmain Antminer start from September 2015.

Google search data series are downloaded from Google. Twitter post counts for the word “Bitcoin” are downloaded from Crimson Hexagon. 4 The spot exchange rates in units of U.S. dollars per foreign currency are from the Federal Reserve Bank of St. Louis. We focus on five major currencies: Australian dollar, Canadian dollar, euro, Singaporean dollar, and U.K. pound. The spot prices of precious metals are from several sources. The gold and silver prices are from the London Bullion Market Association (LBMA). Platinum prices are from the London Platinum and Palladium Market (LPPM).

Aggregate and individual stock returns are from CRSP. Detailed SIC three-digit industry return data series are constructed using individual stock returns. Chinese stock return data are from CSMAR. We build the value-weighted aggregate Chinese stock returns and detailed CIC (China Industry Classification) industry return data series from the individual stocks. The data series of Chinese stock returns last until December 2016. The return series of the 155 anomalies are downloaded from Andrew Chen’s website. 5

We obtain data on the Fama-French three-factor, Carhart four-factor, Fama-French five-factor, and Fama-French six-factor models from Kenneth French’s website. We also collect the return series of Fama-French 30 industries, Europe, Japan, AsiaExJapan, and North America from Kenneth French’s website.

The macroeconomic data series are from the website of the Federal Reserve Bank of St. Louis. Nondurable consumption is defined as the sum of personal consumption expenditures: nondurable goods, and personal consumption expenditures: services.

Stock market prices, dividends, and earnings, as well as the three-month Treasury bill rates, are from Robert Shiller’s website. Using these data series, we construct the stock market price-to-dividend ratio (pd), price-to-earnings ratio (pe), and the relative bill rate (tbill). The relative bill rate is defined as the three-month Treasury bill rate minus its 12 month backward moving average. Credit spread (credit) is defined as the yield spread between BAA corporate bonds and AAA corporate bonds. Term spread (term) is defined as the yield spread between the 10-year Treasury and 3-month Treasury. Data series on the BAA corporate yield, AAA corporate yield, 10-year Treasury yield, and 3-month Treasury yield are from the Federal Reserve Bank of St. Louis’s website.

We now document the main statistical properties of the time series for the coin market returns. Figure 1 shows the return distributions of coin market returns and coin market log returns at daily, weekly, and monthly frequencies. Figure 2 plots the price movements of the coin market compared with those of the three major cryptocurrencies. There are strong comovements across the three major cryptocurrencies. Table 1 compares the properties of the coin market returns with those of Bitcoin returns, Ethereum returns, Ripple returns, and stock market returns.

Coin market return distributions

This figure plots the distributions of daily, weekly, and monthly cryptocurrency returns and log returns.

Cryptocurrency market returns and major coins

This figure plots the cryptocurrency market returns against Bitcoin, Ethereum, and Ripple. The figures show the value of investment over time for one dollar of investment at the starting point of the graphs. The Bitcoin graph starts at April 29, 2013. The Ethereum graph starts at August 8, 2015. The Ripple graph starts at August 5, 2013.

Summary statistics

This table documents the summary statistics of the coin market returns (CMKT). Panel A reports the daily, weekly, and monthly summary statistics of the coin market index and compares them with returns for Bitcoin, Ethereum, Ripple, and the stock market. The mean, standard deviation, t -statistics, Sharpe ratio, skewness, kurtosis, and the percentage of obervations that are positive are reported. Panel B reports the percentage of extreme events based on the daily coin market index returns. The coin market returns, the Bitcoin returns, and the stock market returns are from January 1, 2011, to December 31, 2018. The Ethereum returns are from August 8, 2015, to December 31, 2018. The Ripple returns are from August 5, 2013 to December 31, 2018.

Table 1 shows the statistics of the coin market returns at the daily, weekly, and monthly frequencies compared with those of the stock market returns. Both the average and the standard deviation of the coin market returns are very high. At the daily frequency, the mean return is 0.46% and the standard deviation is 5.46%; at the weekly frequency, the mean return is 3.44% and the standard deviation is 16.50%; at the monthly frequency, the mean return is 20.44% and the standard deviation is 70.80%. Both the means and the standard deviations are an order of magnitude higher than those for the stock market returns. These facts are broadly known.

The Sharpe ratios of the coin market returns are 0.08 at the daily frequency, 0.21 at the weekly frequency, and 0.29 at the monthly frequency. At the daily and weekly frequencies, the Sharpe ratios of the coin market are about 60% and 90% higher than those of the stock market for the comparable time period. At the monthly frequency, the Sharpe ratio is similar to that of the stock market for the comparable time period.

We compare the characteristics of the coin market returns to those of the Bitcoin, Ripple, and Ethereum returns. Note that the Ripple return series starts on August 4, 2013, and the Ethereum return series starts on August 7, 2015. For the Bitcoin returns, the Sharpe ratios are 0.08 at the daily frequency, 0.21 at the weekly frequency, and 0.29 at the monthly frequency. For Ethereum, the Sharpe ratios are 0.08 at the daily frequency, 0.20 at the weekly frequency, and 0.36 at the monthly frequency. The Ethereum returns have a higher mean and standard deviation than the coin market returns. For the Ripple returns, the Sharpe ratios are 0.07 at the daily frequency, 0.13 at the weekly frequency, and 0.24 at the monthly frequency. The Ripple returns have a markedly higher mean and standard deviation compared with those of the coin market returns. The Sharpe ratios of Ripple returns are lower than those of the coin market returns at all three frequencies.

The coin market returns are positively skewed at all frequencies, in contrast to the stock returns, which are negatively skewed. The skewness increases from 0.74 at the daily frequency to 1.74 at the weekly frequency, and to 4.37 at the monthly frequency. The corresponding kurtosis is 15.52 at the daily frequency, 10.22 at the weekly frequency, and 26.54 at the monthly frequency. All three of the major cryptocurrencies have positive skewness and high kurtosis. The coin market returns have high probabilities of exceptional negative and positive daily returns. For example, the probability of a –20% daily return is almost 0.5%, and the probability of a 20% daily return is almost 0.9%.

In the Internet Appendix , we also show the mean, standard deviation, and Sharpe ratios of the returns on different days of the week. In contrast to the stocks, there is no pronounced Monday effect. However, the returns are lower on Saturdays: the average Sunday coin market return is 0.28% with a Sharpe ratio of 0.05, compared with a 0.46% daily average with a Sharpe ratio of 0.08; the average Sunday Bitcoin is 0.29% with a Sharpe ratio of 0.06, compared with a 0.46% daily average with a Sharpe ratio of 0.08; the average Sunday Ethereum is 0.25% with a Sharpe ratio of 0.03, compared with a 0.60% daily average with a Sharpe ratio of 0.08; and the average Sunday Ethereum is –0.15% with a Sharpe ratio of –0.02, compared with a 0.53% daily average with a Sharpe ratio of 0.07. While the coin market and Bitcoin returns are somewhat lower on Sundays, the returns on Saturday are consistently lower.

The theoretical literature has proposed a number of cryptocurrency-specific factors as drivers of cryptocurrency prices and as predictors of cryptocurrency returns. In this section, we develop and investigate the implications of cryptocurrency-specific factors. We first construct cryptocurrency network and production factors. We find that the coin market returns are strongly exposed to the network factors but not the production factors. Then, we test if cryptocurrency returns are predictable by studying whether different cryptocurrency-specific factors can predict future coin market returns. We consider momentum, proxies for investor attention, and proxies for cryptocurrency valuation ratios. All of these variables are specific to the cryptocurrency markets. We find that momentum and proxies for investor attention can account for future coin market returns, and thus strongly reject the notion that cryptocurrency prices are a martingale.

2.1 Network factors

The theoretical literature on cryptocurrency has emphasized the importance of network factors in the valuation of cryptocurrencies (e.g., Cong, Li, and Wang 2019 ; Sockin and Xiong 2019 ; Pagnotta and Buraschi 2018 ; Biais et al. 2018 ). In particular, the network effect of user adoption can potentially play a central role in the valuation of cryptocurrencies. Because users’ adoption of cryptocurrencies generates positive network externality, cryptocurrency prices respond to user adoptions. Hence, variations in user adoptions of the cryptocurrency network could contribute to movements in cryptocurrency prices.

We construct network factors of cryptocurrency and test whether these factors can account for variations in cryptocurrency prices. We use four measures to proxy for the network effect: the number of wallet users, the number of active addresses, the number of transaction count, and the number of payment count. 6 Thus, we measure cryptocurrency network growth using the wallet user growth, active address growth, transaction count growth, and payment count growth. We also construct a composite measure by taking the first principal component of the four primary measures, which we denote as |$PC^{network}$|⁠ . Panel A of Table 2 reports the correlation across the network factors we consider. The four primary measures correlate with each other positively, with correlations ranging from 0.17 to 0.77. The first principal component of the four demand factors strongly correlates with all four of the primary measures. The first principal component has correlations of 0.45, 0.88, 0.88, and 0.90 with the wallet user growth measure, the active address growth measure, the transaction count growth measure, and the payment count growth measure, respectively.

Cryptocurrency return loadings to network factors

This table reports the factor loadings of the coin market returns on the network factors. The network factors include wallet user growth, active address growth, transaction count growth, payment count growth, and the first principal component of the four primary measures. Panel A shows the correlation matrix of the variables. Panel B reports the loadings of the coin market returns on the network factors. The standard t -statistic is reported in parentheses. *, **, and *** denote significance levels at the 10%, 5%, and 1% levels based on the standard t -statistics. The data frequency is monthly.

We regress the coin market returns on each of the four measures of changes in the cryptocurrency network and the composite measure. Panel B of Table 2 presents the results using the network factors. The coin market returns positively correlate with all four of the individual cryptocurrency network factors and the composite measure. The coefficient on the wallet user growth measure is significant at the 10% level, and the three other coefficients are significant at the 1% level. The |$R^2$| s range from 5% for the wallet user growth measure to 30% for the active address growth measure. The |$R^2$| s using the composite measure is 19%. Consistent with the theoretical models, these results suggest that the network factors that measure the network effect of user adoptions are important drivers of cryptocurrency prices.

Moreover, in a dynamic cryptocurrency pricing model with the network effect, cryptocurrency prices not only reflect current cryptocurrency adoption but also contain information about expected future network growth—a key mechanism of Cong, Li, and Wang (2019) . We test this model implication by examining whether current coin market returns contain information about future cryptocurrency network growth. In particular, we predict cumulative future cryptocurrency adoption growth over different horizons using current coin market returns. We investigate cumulative future cryptocurrency adoption growth from one-month to eight-month horizons. We use cumulative wallet user growth, active address growth, transaction count growth, and payment count growth to capture cryptocurrency adoption growth.

Consistent with the prediction that cryptocurrency returns reflect expected future cryptocurrency adoptions, we find that coin market returns positively predict future cryptocurrency adoption growth as shown in Table 3 . Specifically, coin market returns positively and statistically significantly predict cumulative wallet user growth at all the horizons. Coin market returns positively and statistically significantly predict cumulative active address growth and cumulative payment count growth for the first three periods and two periods, respectively, and cease to be significant afterward. The coin market returns positively predict cumulative transaction count growth for the first five periods, but the predictability is not statistically significant. The only exception is the transaction growth measure: there is an insignificant, negative effect on transaction growth over the long horizons. A possible explanation for the negative effect is congestion, as it becomes very expensive to transact in Bitcoin when there is congestion, which deters many of the smaller transactions that would have occurred otherwise (e.g., Easley, O’Hara, and Basu 2019 ).

Predicting future network growth

This table reports the results of predicting cumulative future coin network growth with coin market returns. The network factors include wallet user growth, active address growth, transaction count growth, and payment count growth. Data are monthly. The t -statistics are reported in parentheses and are Newey-West adjusted with |$n-1$| lags. *, **, and *** denote significance levels at the 10%, 5%, and 1% levels. The data frequency is weekly.

2.2 Production factors

Several papers have argued that the costs of mining are essential for the infrastructure and security of cryptocurrencies (e.g., Sockin and Xiong 2019 ; Abadi and Brunnermeier 2018 ; Cong, He, and Li 2018 ). Notably, Sockin and Xiong (2019) show that, in a general equilibrium model with cryptocurrency production, the prices of the cryptocurrency are intimately linked to the marginal cost of mining.

We construct production factors of cryptocurrency to proxy for the cost of mining and test the relationship between these production factors and cryptocurrency prices. To the first approximation, mining a cryptocurrency requires two inputs: electricity and computer power. We separately construct proxies for electricity costs and computing costs. We first discuss our proxies for electricity costs. For electricity, we use seven primary measures. Three of the seven primary measures are U.S.-related: (i) average price of electricity in the United States, (ii) net generation of electricity of all sectors in the United States, and (iii) total electricity consumption of all sectors in the United States. The other four measures are China-related: (i) average price of electricity in China, (ii) electricity generation in China, (iii) average price of electricity in Sichuan province, and (iv) electricity generation in Sichuan province. We include the China proxies, because electricity supply is location specific and because China is considered to have the largest coin-mining operation among all countries. 7 We include Sichuan province proxies because Sichuan province hosts the largest mining farm in the world. Similarly, we also construct a composite measure as the first principal component of these seven primary measures. We denote the composite measure as |$PC^{elec}$|⁠ .

Panel A of Table 4 presents the correlation matrix of the electricity factors. Except for the two electricity price measures in China, the other five primary measures positively and strongly correlate with one another. Electricity prices in China are under strict government control. Unsurprisingly, they have low correlations with other electricity measures. The first principal component of the seven electricity factors strongly and positively correlates with most of the seven primary factors. The correlations are 0.76, 0.93, 0.88, 0.71, and 0.77 with the U.S. electricity price growth measure, the net U.S. generation growth measure, the U.S. electricity consumption growth measure, the China generation growth measure, and the Sichuan generation growth measure, respectively. The correlation between the first principal component and the China electricity price growth measure is –0.15, and the correlation between the first principal component and the Sichuan electricity price growth measure is 0.18. Panel B of Table 4 presents the electricity factor results for the coin market returns. Somewhat surprisingly, the coin market returns are not statistically significantly exposed to any of these production factor proxies. The |$R^2$| s of these regressions are low.

Cryptocurrency return loadings to electricity factors

This table reports the factor loadings of the coin market returns on the production factors that relate to electricity costs. Panel A shows the correlation matrix of the production factors. Panel B reports the factor loadings of the coin market returns on the production factors. Standard t -statistics are reported in parentheses. *, **, and *** denote significance levels at the 10%, 5%, and 1% levels based on the standard t -statistics. The data frequency is monthly.

For proxies of computing costs, we use as our primary measure the prices of Bitmain Antminer, a major piece of Bitcoin mining equipment. We also consider the excess stock returns of the companies that are major manufacturers of either GPU mining chips (Nvidia Corporation and Advanced Micro Devices, Inc.) or ASIC mining chips (Taiwan Semiconductor Manufacturing Company, Limited, and Advanced Semiconductor Engineering, Inc.). 8 We construct a composite measure as the first principal component of these five primary computing factors. We denote the composite measure as |$PC^{comp}$|⁠ .

Panel A of Table 5 presents the correlation matrix of the computing factors. Most of the pairs are positively correlated. The correlation between Antminer price growth and Nvidia return is –0.03, and the correlation between Antminer price growth and AMD return is –0.15. The first principal component is positively correlated with the four return measures and has a low correlation with the Antminer price growth measure. Panel B of Table 5 presents the computing factor results for the coin market returns. The coin market returns have insignificant loadings on the four excess return measures. The coin market returns have some loadings on the Antminer price growth measure, but they are only significant at the 10% level. The coin market returns are not significantly exposed to the first principal component.

Cryptocurrency return loadings to computing factors

This table reports the factor loadings of the coin market returns on the production factors that relate to computing costs. Panel A shows the correlation matrix of the production factors. Panel B reports the factor loadings of the coin market returns on the production factors. Standard t -statistics are reported in parentheses. *, **, and *** denote significance levels at the 10%, 5%, and 1% levels based on the standard t -statistics. The data frequency is monthly.

The model of Sockin and Xiong (2019) primarily concerns utility tokens. Therefore, we conduct our analyses on production factors on Bitcoin, Ethereum, and Ripple, respectively. Because Ethereum and Ripple are utility tokens, while Bitcoin is not, we expect to find that Ethereum and Ripple load significantly on the production factors. We show the results in the Internet Appendix . There is some evidence that Bitcoin returns are exposed to the Bitmain Antminer price growth, but Bitcoin and Ripple returns do not load significantly on these production factors. Overall, there is limited evidence that the computing factors are important drivers of cryptocurrency returns.

Lastly, we test the lead-lag effects between the changes in production factors and cryptocurrency returns to account for possible anticipation effects. We document the results in the Internet Appendix . We show that the one-month-ahead coin market returns are not significantly exposed to most of the production factors. The only exception is the changes in the average price of electricity in the United States, but the significant level is negative and only at the 10% level. However, we find that the current coin market returns positively predict some of the future production factors. In particular, the coin market returns positively and statistically significantly predict future changes in the average price of electricity in the United States, net generation of electricity of all sectors in the United States, total electricity consumption of all sectors in the United States, electricity generation in Sichuan province, and the first principal component of the production factors. Interestingly, we find that the results are stronger for the U.S.-based measures relative to the China-based measures. This is consistent with the fact that electricity prices and generation are heavily regulated in China. These results are consistent with a potential anticipation effect of production costs in the cryptocurrency market.

2.3 Are cryptocurrency returns predictable?

In this section, we test whether the coin market returns are predictable. The existing theoretical models of cryptocurrencies provide various predictions on the predictability of cryptocurrency returns. Schilling and Uhlig (2019) argue that the evolution of cryptocurrency prices should follow a martingale, and thus cryptocurrency returns are not predictable. Other papers predict that, in dynamic cryptocurrency valuation models, cryptocurrency returns could potentially be predicted by momentum, investor attention, and cryptocurrency valuation ratios (e.g., Cong, Li, and Wang 2019 ; Sockin and Xiong 2019 ). Motivated by the existing theoretical development and empirical findings in the financial markets, we test whether the cryptocurrency returns are predictable by momentum, investor attention, and proxies for cryptocurrency valuation ratios.

2.3.1 Cryptocurrency momentum

One of the most studied asset pricing regularities is momentum (e.g., Jegadeesh and Titman 1993 ; Moskowitz and Grinblatt 1999 ). As discussed in Cong, Li, and Wang (2019) , the network effect of user adoption generates a positive externality that is not immediately incorporated into cryptocurrency prices. This channel can potentially lead to a momentum effect in cryptocurrency returns. In their model, Sockin and Xiong (2019) generate momentum in the cryptocurrency market through investor attention—a mechanism similar to De Long et al. (1990) .

In this section, we start by establishing that there is strong evidence of time-series momentum at various time horizons. Panel A in Table 6 documents the time-series momentum results in the regression setting. Specifically, we regress cumulative future coin market returns on current coin market returns from the one-week to eight-week horizons. The current coin market returns positively and statistically significantly predict cumulative future coin market returns at all eight horizons. The results are significant at the 5% level for the one-week to five-week horizons and are significant at the 10% level from the six-week to eight-week horizons. For example, a one-standard-deviation increase in the current coin market return leads to increases in cumulative future coin market returns of 3.30%, 9 8.09%, 13.37%, and 17.66% increases at the one-week, two-week, three-week, and four-week horizons, respectively. Specifically, the one-week-ahead weekly return is that of buying the underlying coin market index at 11:59:59 UTD Sunday and selling the underlying coin market index at 11:59:59 UTD one week later. In the Internet Appendix , we also report the results based on noncumulative returns. The current coin market returns positively and significantly predict one-week- to five-week-ahead returns. The current coin market returns positively but insignificantly predict six-week- and seven-week-ahead returns. The current coin market returns negatively but insignificantly predict eight-week-ahead returns, suggesting some potential long-term reversal effect.

Time-series momentum

This table reports the time-series momentum results. Panel A shows the regression results, and panel B shows the results based on grouping weekly coin market returns into terciles. The first part of panel B reports results for the whole sample. The second part of panel B uses the first two years of data to determine the tercile cutoffs and examine the out-of-sample time-series momentum performance. The t -statistics are reported in parentheses and are Newey-West adjusted with |$n-1$| lags. *, **, and *** denote significance levels at the 10%, 5%, and 1% levels. The data frequency is weekly.

In the first part of panel B in Table 6 , we estimate the magnitude of the time-series momentum by grouping weekly returns into terciles and evaluating their performance going forward. We find that the top terciles outperform the bottom terciles at the one- to four-week horizons, consistent with the time-series regression results presented earlier. For example, at the one-week horizon, the average return of the top tercile is 8.01% per week with a t -statistic of 4.30, while the average return of the bottom tercile is 1.10% per week with a t -statistic of 0.92. The difference between the top and bottom terciles is 6.91% at the one-week horizon. At the two-week horizon, the average of the cumulative coin market returns of the top tercile is 16.22%, and that of the bottom tercile is only 3.59%. The difference between the top and bottom terciles is 12.63%. In the additional results section, we restrict our sample to 2014 onward. Again, we find a strong and significant momentum effect of somewhat smaller magnitude. 10

In the second part of panel B in Table 6 , we use the first two years of data to determine the tercile cutoffs and study the out-of-sample time-series momentum performance. We find a strong and significant momentum effect for the out-of-sample tests. For example, at the one-week horizon, the average return of the top tercile is 6.42%, and that of the bottom tercile is 0.80%. The difference between the top and bottom terciles is 5.62%, which is economically large and slightly smaller than the in-sample result of 6.91%.

Additionally, we test whether the time-series momentum effect is linked to network externalities, as suggested in Cong, Li, and Wang (2019) . In their dynamic cryptocurrency valuation model, the momentum effect is generated by the positive externality of the network effect that is not incorporated into cryptocurrency prices immediately. That is, their model implies that controlling for cryptocurrency adoption growth would subsume the time-series momentum effect. In Table 7 , we show that there is evidence that cryptocurrency adoption growth positively predicts future coin market returns. However, controlling for cryptocurrency adoption growth does not subsume the time-series momentum effect documented presented earlier.

Momentum and network effect

This table reports the results that compare coin market return predictability of momentum and network effect. The table reports the results of predicting cumulative future coin market returns with current coin market returns and each of the network factors. The Newey-West adjusted t -statistics with |$n-1$| lags are reported in parentheses. *, **, and *** denote significance levels at the 10%, 5%, and 1% levels. The data frequency is weekly.

2.3.2 Cryptocurrency investor attention

The theoretical literature of cryptocurrencies has also suggested that investor attention could potentially be linked to future cryptocurrency returns (e.g., Sockin and Xiong 2019 ). In this section, we investigate the role of investor attention in predicting cryptocurrency returns. Specifically, we construct the deviation of Google searches for the word “Bitcoin” in a given week compared with the average of those in the preceding four weeks. We standardize the Google search measure to have a mean of zero and a standard deviation of one. We use Google searches for the word “Bitcoin” to proxy for investor attention of the cryptocurrency market because Bitcoin is by far the largest and most visible cryptocurrency available. In panel A of Table 8 , we report the results of regressing cumulative future coin market returns from one-week to eight-week horizons on the Google search measure. The Google search measure statistically significantly predicts the one-week to six-week ahead cumulative coin market returns at the 5% level. The coefficient estimates of the seven-week and eight-week horizons are positive but are no longer statistically significant. A one-standard-deviation increase in searches leads to increases in weekly returns of about 3% for the one-week ahead cumulative coin market returns and about 5% for the two-week-ahead cumulative coin market returns. 11 In the Internet Appendix , we also report results based on noncumulative returns. The current coin market returns positively and significantly predict one-week- to four-week-ahead returns. The current coin market returns positively but insignificantly predict five-week-ahead returns. The current coin market returns negatively but insignificantly predict six-, seven-, and eight-week-ahead returns.

Google searches

This table reports the time-series Google search results. Panel A shows the regression results, and panel B shows the results based on grouping weekly coin market returns into terciles. The first part of panel B reports results for the whole sample. The second part of panel B uses the first two years of data to determine the tercile cutoffs and examine the out-of-sample time-series performance. The Google search measure is constructed as the Google search data for the word “Bitcoin” minus its average of the previous four weeks, and then normalized to have a mean of zero and a standard deviation of one. The t -statistics are reported in parentheses and are Newey-West adjusted with |$n-1$| lags. *, **, and *** denote significance levels at the 10%, 5%, and 1% levels. The data frequency is weekly.

In the first part of panel B in Table 8 , we investigate the return predictability of the Google search measures by grouping them into terciles and evaluating their performance going forward. Consistent with the regression results, we find that the top tercile outperforms the bottom tercile in terms of cumulative coin market returns at the one- to four-week-ahead horizons. For example, at the one-week horizon, the average return of the top tercile is 6.53% per week with a t -statistic of 3.82, while the average return of the bottom tercile is 0.43% per week with a t -statistic of 0.42. The difference between the top and bottom terciles is 6.09% at the one-week horizon. At the two-week horizon, the average of the cumulative coin market returns of the top tercile is 13.95% with a t -statistic of 4.89, and that of the bottom tercile is only 0.02% with a t -statistic of 0.01. The difference between the top and bottom terciles is 13.93%. In the additional results section, we restrict our sample to 2014 onward and find similar return predictive power of the Google search measures.

In the second part of panel B in Table 8 , we use the first two years of data to determine the tercile cutoffs and study the out-of-sample effect of investor attention, and we find a strong positive investor attention effect as well. For example, at the one-week horizon, the average return of the top tercile is 6.12%, and that of the bottom tercile is 0.70%. The difference between the top and the bottom terciles is 5.42%, which is economically large and slightly smaller than the in-sample estimate of 6.09%.

2.3.3 Negative investor attention

We have shown that unconditionally investor attention positively predicts cryptocurrency returns. However, not all investor attention is positive. For example, in their model, Sockin and Xiong (2019) differentiate positive investor attention and negative investor attention, and show that negative investor attention is followed by cryptocurrency price depreciation in the future.

In this section, we investigate whether negative investor attention predicts cryptocurrency returns. We construct a ratio between Google searches for the phrase “Bitcoin hack” and searches for the word “Bitcoin” to proxy for negative investor attention. We standardize the measure to have a mean of zero and a standard deviation of one. Panel A of Table 9 shows the results of the predictive regressions. The ratio negatively and significantly predicts one- to six-week-ahead cumulative coin market returns. For example, a one-standard-deviation increase in the ratio leads to a 2% decrease of coin market returns in the next week. Panel B of Table 9 reports the in-sample and out-of-sample return predictability of the negative investor attention measures by grouping them into terciles and evaluating their performance going forward. Consistent with the regression results, we find strong negative return predictability results of the negative investor attention measures.

Bitcoin hack

This table reports the time-series Bitcoin hack results. Panel A reports the regression results, and panel B reports the sorting results. The Bitcoin hack measure is constructed as the ratio between Google searches for the phrase “Bitcoin hack” and searches for the word “Bitcoin,” and then normalized to have a mean of zero and a standard deviation of one. Results are based on weekly data. The t -statistics are reported in parentheses and are Newey-West adjusted with |$n-1$| lags. *, **, and *** denote significance levels at the 10%, 5%, and 1% levels. The data frequency is weekly.

Another way to see the results on the investor attention is that our measures of investor attentions proxy for speculative interest and sentiment in cryptocurrencies. Positive investor sentiment is followed by cryptocurrency price appreciation, and negative investor sentiment is followed by depreciation. We further investigate these issues in Section 4 .

2.3.4 Interaction between momentum and attention

We have shown that there are strong effects of time-series momentum and investor attention in the cryptocurrency market. The equity market research (e.g., Hong, Lim, and Stein 2000 ; Hou, Xiong, and Peng 2009 ) shows that there is a strong relationship between momentum and investor attention. It is possible that these two results capture the same underlying phenomenon. For example, Sockin and Xiong (2019) propose a potential channel to generate momentum. In their model, momentum arises because users have incorrect expectations about future prices—a mechanism similar to De Long et al. (1990) . Their model suggests that cryptocurrency momentum and investor attention could potentially arise from the same underlying mechanism. The cryptocurrency momentum and investor attention results could also interact with each other. For example, the cryptocurrency time-series momentum effect may be weaker at times of high investor attention, because there is little information leakage at times of high investor attention.

First, we show that the current investor attention of cryptocurrencies is indeed associated with current and past coin market performance. We regress the current deviation in the Google searches on the contemporaneous and the coin market returns of the previous four weeks. Table 10 documents the results. We find that the deviations in Google searches are positively and significantly associated with contemporaneous and the previous week’s coin market returns. The Google search measures do not significantly correlate with past coin market returns beyond one week. Intuitively, these results suggest that investor attention is elevated after superior cryptocurrency market performance.

Google searches and past returns

This table reports the relationships between the Google search measure and past coin market returns. The Google search measure is constructed as the Google search data for the word “Bitcoin” minus its average of the previous four weeks, and then normalized to have a mean of zero and a standard deviation of one. The standard t -statistic is reported in parentheses, and the bootstrapped t -statistic is reported in brackets. *, **, and *** denote significance levels at the 10%, 5%, and 1% levels based on the standard t -statistics. The data frequency is weekly.

We further test the interaction between the time-series momentum and the investor attention phenomena. The results are reported in Table 11 . In the first test of Table 11 , we regress cumulative future coin market returns on current coin market returns and Google search measures. We find that the coefficients to the current coin market returns are significant for all the horizons, and the coefficients to the Google search measures are significant from the one-week to the five-week horizons. The magnitudes of the coefficients are similar to the standalone estimates. For example, the one-week-ahead coefficients under the univariate regressions are 0.20 and 0.03 for the current coin market returns and the Google search measures, respectively, while they are 0.18 and 0.03 under the bivariate regressions. These results show that the time-series momentum and the investor attention results do not subsume each other.

Interaction between momentum and attention

This table reports the predictive regressions of future cumulative coin market returns on momentum, attention, and the interaction of the two. The indicator variable |$1_{\{Google>0\}}$| equals one if the current Google search measure is above the sample mean and zero otherwise. The indicator variable |$1_{\{R>0\}}$| equals one if the current coin market return is positive and zero otherwise. Results are based on weekly returns. The Newey-West adjusted t -statistics with |$n-1$| lags are reported in parentheses. *, **, and *** denote significance levels at the 10%, 5%, and 1% levels. The data frequency is weekly.

In the second test of Table 11 , we test the performance of the time-series momentum result when investor attention is high. We construct an indicator variable, |$1_{\{Google>0\}}$|⁠ , that equals one if the current Google search measure is above the sample mean and zero otherwise. We regress the cumulative future coin market returns from one-week to eight-week horizons to the current coin market return, the indicator variable, and the interaction term. The interaction term is not significant at any of the eight horizons, suggesting that the magnitude of the time-series momentum effect is similar for high and low investor attention periods. In the third test of Table 11 , we test the performance of the investor attention result when the current coin market return is high. We construct an indicator variable, |$1_{\{R>0\}}$|⁠ , that equals one if the current coin market return is positive and zero otherwise. We regress the cumulative future coin market returns from one-week to eight-week horizons to current Google search measure, the indicator variable, and the interaction term. The interaction term is not significant at any of the eight horizons, suggesting that the magnitude of the investor attention effect is similar for high and low coin market return periods.

Furthermore, we study the cross-section of time-series momentum for high- and low-attention coins. We collect Google attention data for the 10 largest cryptocurrencies from the beginning of 2014 to the end of 2018. The sample period is shorter for this analysis, because before 2014, there are very few cryptocurrencies and the data for alternative coins are hard to get. The list of coins is Bitcoin, Ethereum, Ripple, Litecoin, Tether, Bitcoin-Cash, Tezos, Binance-coin, Monero, and Cardano. At a given point in time, we group the existing coins into two subsamples based on the Google attention data—a group of high-attention coins and a group of low-attention coins. We construct the value-weighted returns of the high-attention group and the low-attention group, separately, and test the time-series momentum strategy effect in each subgroup.

We regress the future cumulative returns on current returns for each of the subsamples and report the results in the Internet Appendix . We find that in this sample, the time-series momentum effect is stronger for the relatively low-attention coins. In particular, the coefficient estimates for both the high-attention and the low-attention subgroups are positive, suggesting that there are time-series momentum effects for both groups. However, the coefficient estimates for the high-attention subgroup is not statistically significant, while the coefficient estimates for the low-attention subgroup is statistically significant up to six weeks out. The magnitudes of the coefficient estimates are also much larger for the low-attention subgroup relative to the high-attention subgroup. The results are consistent with the “underreaction” mechanism of momentum.

2.3.5 Cryptocurrency valuation ratio

Additionally, we test whether the cryptocurrency valuation ratios similar to those in the financial markets can predict future coin market returns. In the equity market, the fundamental-to-market ratios are commonly referred to as valuation ratios and are measured as the ratio of the book value to the market value of equity or some other fundamental value to market value (e.g., dividend-to-price; earnings-to-price). Another value measure used in the literature that has been shown to correlate highly with fundamental-to-market value is the negative of the long-term cumulative past returns (e.g., De Bondt and Thaler 1985 , 1987 ; Fama and French 1996 ; and Moskowitz 2015 ). It is more difficult to define a similar measure of fundamental value for cryptocurrency. However, in their dynamic cryptocurrency asset pricing model, Cong, Li, and Wang (2019) argue that the cryptocurrency fundamental-to-value ratio can be defined as the number of user adoptions over market capitalization, which negatively predicts future cryptocurrency returns.

The market value of cryptocurrency is readily available. However, there is no direct measure of fundamental value for the cryptocurrencies. In its essence, value is a measure of the gap between the market value and the fundamental value of an asset. Because of the lack of a standard “book” value measure of the cryptocurrency market, we use an array of different proxies to capture the idea of fundamental value. We proxy the fundamental-to-market ratio by a number of value measures motivated by the finance literature. The first one is the long-term past performance measure: the negative of the past 100-week cumulative coin market return. The other four measures aim to proxy the cryptocurrency fundamental-to-market value directly: the user-to-market ratio, the address-to-market ratio, the transaction-to-market ratio, and the payment-to-market ratio. The idea of these four measures is to use some measures of the “book” value of the underlying cryptocurrency market and scale by the current market capitalization. The user base of the cryptocurrency market seems to capture the concept of “book” value in the financial markets. This is consistent with the theoretical literature of the cryptocurrency market that emphasizes the notion of the network effect, which can be proxied by the current user base of the cryptocurrencies. On the other hand, the market value of the cryptocurrency provides a market assessment of the current value of the complete cryptocurrency infrastructure. Therefore, the user base-to-market value measure can capture the notion of fundamental-to-market ratio in the financial markets. In panel A of Table 12 , we report the correlations across the different valuation ratios in the cryptocurrency market. The five primary measures are highly correlated with one another, with correlations ranging from 0.73 to 0.91. The first principal component measure for the five fundamental-to-market ratios has correlations of 0.91, 0.91, 0.96, 0.93, and 0.93 with the long-term past returns, the wallet user-to-market ratio, the active address-to-market ratio, the transaction-to-market ratio, and the payment-to-market ratio, respectively.

Cryptocurrency valuation ratio

This table reports the predictive regressions of coin market returns on proxies for cryptocurrency market fundamental-to-market ratio. The proxies for cryptocurrency valuation ratio include the (negative) past 100-week cumulative coin market returns, the user-to-market ratio, the address-to-market ratio, the transaction-to-market ratio, payment-to-market ratio, and the first principal component of the previous five proxies. The ratios are estimated using the cointegration method. Results are based on weekly returns. The Newey-West adjusted t -statistics with |$n-1$| lags are reported in parentheses. *, **, and *** denote significance levels at the 10%, 5%, and 1% levels. The data frequency is weekly.

We regress the coin market returns on the lagged cryptocurrency fundamental-to-market ratios, and the results are reported in panel B of Table 12 . We document the regression results from one-week to eight-week horizons. Although the coefficient estimates are consistently negative, none of the five standalone fundamental-to-market ratios predict future coin market returns significantly over any horizon. The principal component measure also fails to predict future coin market returns over these horizons. Overall, there is a very weak relationship between the future coin market returns and the current cryptocurrency fundamental-to-value ratio.

Both the cryptocurrency literature and the community have debated the nature of cryptocurrencies. For example, Schilling and Uhlig (2019) show that, in an endowment economy with both fiat money and cryptocurrency, the evolution of cryptocurrency prices is linked to that of the fiat money. Athey et al. (2016) emphasize the importance of fiat money risks of cryptocurrencies. The cryptocurrency community has proposed that cryptocurrencies are “digital gold” and serve the purpose of the traditional precious metal commodity. Moreover, Schilling and Uhlig (2019) argue that cryptocurrency returns can have exposure to macroeconomic risks such as monetary policies. In this section, we evaluate these claims by examining the relationship between cryptocurrency returns and traditional asset returns such as currency, commodity, and equity.

3.1 Currency and commodity factor loadings

In an endowment economy where fiat money and cryptocurrency coexist and compete with each other, Schilling and Uhlig (2019) show that the evolution of cryptocurrency prices is correlated with the that of the fiat money prices. Athey et al. (2016) also emphasize the importance of fiat money risks of cryptocurrencies. We test this prediction by investigating the cryptocurrency exposures to traditional currencies. Columns (1) to (6) of Table 13 show the coin market returns’ exposures to the traditional currency returns. For currency returns, we consider five major currencies: Australian dollar, Canadian dollar, euro, Singaporean dollar, and U.K. pound. The exposures of the coin market returns to these major currencies are not statistically significant, and the alpha estimates barely change. We further test cryptocurrency exposures on currency factors as in Lustig, Roussanov, and Verdelhan (2011) instead of individual major currency returns. 12 Columns (7) to (9) of Table 13 report the coin market returns’ exposures to these currency factors. Consistent with the results on individual currency returns, we find that the coin market returns do not have significant exposures to the currency factors. We conclude that there is no consistent evidence of systematic currency exposures in cryptocurrencies.

Currency loadings of coin market returns

This table reports the factor loadings of the coin market returns on returns of different currencies and currency factors. The currencies include Australian dollar, Canadian dollar, euro, Singapore dollar, and U.K. pound. The currency factors are based on Lustig, Roussanov, and Verdelhan (2011) . The returns are in percentage. The results are based on monthly returns. The standard t -statistic is reported in parentheses, and the bootstrapped t -statistic is reproted in brackets. *, **, and *** denote significance levels at the 10%, 5%, and 1% levels based on the standard t -statistics. The data frequency is monthly.

Another popular narrative around cryptocurrencies is that cryptocurrencies serve the same purpose as traditional precious metal commodities. That is, cryptocurrencies are “digital gold.” If the investors of cryptocurrencies hold this belief, we would expect to find that the returns of cryptocurrencies comove with the returns of the traditional precious metal commodities. We test the precious metal commodity exposures of the coin market returns and report the results in Table 14 . For precious metal commodities, we consider gold, platinum, and silver. The exposures of the coin market return to these three major commodities are not statistically significant. Overall, we conclude that there is no consistent evidence of systematic precious metal commodity exposures in cryptocurrencies.

Commodity loadings of coin market returns

This table reports the factor loadings of the coin market returns on returns of different precious metal commodities. The commodities include gold, platinum, and silver. Returns are in percentage. The standard t -statistic is reported in parentheses and the bootstrapped t -statistic is reproted in brackets. *, **, and *** denote significance levels at the 10%, 5%, and 1% levels based on the standard t -statistics. The data frequency is monthly.

3.2 Equity factor loadings

We document the common stock factor exposures of the coin market returns in the Internet Appendix . For the equity risk factors, we choose the Capital Asset Pricing Model (CAPM), Fama-French three-factor, Carhart four-factor, Fama-French five-factor, and Fama-French six-factor models. 13 The alphas for all of the considered models are statistically significant. The average return of the period is 20.44% per month. The CAPM-adjusted alpha decreases to 17.53% per month—a reduction of about 14%. The CAPM beta is large at 3.15 but not statistically significant. The betas are statistically significant at the 10% level only for the five-factor and six-factor models. The corresponding alphas are 15.32% and 15.00% per month for the five-factor model and six-factor model, respectively. The exposures to the other factors are not statistically significant. The exposures to the SMB (small-minus-big) factor are negative but not stable across the specifications: the magnitude of the coefficient decreases when five-factor and six-factor models are considered. The exposures to the HML (high-minus-low) factor are negative and have consistent magnitudes and signs; this suggests that the coin market returns may comove more with growth rather than with value firms. The exposures to the RMW (robust-minus-weak) factor are positive and are estimated slightly more accurately than other statistically not significant factors; this suggests that the coin market returns comove more with high-profit rather than low-profit firms. The point estimates on the MOM (momentum) and CMA (conservative-minus-aggressive) factors are very inaccurate. 14

3.2.1 Exploring the factor zoo

The finance literature has documented more than a hundred factors for predicting the cross-section of stock returns (see e.g., summarizes in Feng, Giglio, and Xiu 2017 and Chen and Velikov 2017 ). To investigate whether any of those factors may be important in pricing cryptocurrencies, we estimate the loadings of the 155 common factors from Andrew Chen’s website. One caveat is that this data set ends at the end of 2016 and thus does not cover the most recent return experiences. We report the results in the Internet Appendix due to the large number of factors. We find that only four out of the 155 factors are significant, but those four factors do not form any discernible patterns.

3.3 Macroeconomic factors

We further examine the macroeconomic factor exposures of the coin market returns. For macroeconomic factors, we consider the nondurable consumption growth, durable consumption growth, industrial production growth, and personal income growth. We document the results in the Internet Appendix . We find that the coin market returns do not significantly load on these macroeconomic factors. We further investigate the three major cryptocurrencies individually. For Bitcoin and Ripple, all of the exposures are not statistically significant. For Ethereum, notably, the durable consumption growth factor has a significant loading.

4.1 Short sample

We have eight years of coin market return data spanning from the beginning of 2011 to the end of 2018. The short sample is a potential barrier to study cryptocurrency that we cannot avoid. Moreover, there is a great deal of uncertainty and learning about cryptocurrencies during the period. As argued by Pástor and Veronesi (2003) , it takes time for investors to fully learn and understand emerging technologies, which can lead to price bubbles.

One approach we take to partially address these concerns is to break the sample into two halves and check whether our results are stable for these subsamples. During the first half of the sample, there are considerably more uncertainty and learning about cryptocurrency as an asset class. We document these results in the Internet Appendix . We find that the directions of all of the results are the same for the first and second halves of the sample. The magnitudes of the results are also comparable between the two subsamples. There is potentially still a lot of uncertainty and learning about cryptocurrencies today, but the assumption we need for the subsample tests is relatively mild: the uncertainty has decreased from the first half of the sample to the second half of the sample. The analysis on the volatility of the coin market returns also supports this assumption. We find that the standard deviation of coin market returns decreased significantly from the first half to the second half of the sample period. The figure in the Internet Appendix shows a significant decrease in the volatility of the coin market returns over time.

4.2 Time-series momentum and cross-sectional momentum

We study the relationship between time-series momentum and cross-sectional momentum. It is difficult to directly compare the time-series momentum and cross-section momentum results. The time-series momentum is a phenomenon on the aggregate coin market returns, while the cross-sectional momentum results are neutral in terms of the aggregate performance of the coin market. We use two different methods to test the relationships between the time-series momentum and the cross-sectional momentum results. In the first method, we use coin market returns to predict the cross-sectional cryptocurrency momentum. This approach gives us a sense about whether the cross-sectional momentum effect is stronger when the time-series momentum is on a positive trajectory. We report the results in the Internet Appendix . The coin market returns do not significantly predict future cumulative cross-sectional momentum returns. This result suggests that the profitable periods of the cryptocurrency time-series momentum and cross-sectional momentum are different.

In the second method, we follow the approach similar to Moskowitz, Ooi, and Pedersen (2012) and construct a portfolio version of the time-series momentum. For our set of instruments, we use one of the following: largest three coins, largest five coins, and largest ten coins. For each instrument and month, we consider whether the excess return over the past three weeks is positive or negative and go long the instrument if positive and short if negative. We hold the position for one week, so there is no overlapping sample. The unadjusted excess returns are positive and significant at the 1% level for all three of the specifications. The economic magnitudes of the excess returns are large, ranging from 3.17% for the top three coins to 4.62% for the top ten coins. Controlling for the coin market returns, the economic magnitudes of the excess returns barely change. Controlling for the cryptocurrency cross-sectional momentum as constructed in Liu, Tsyvinski, and Wu (2019) , the magnitudes of the spreads decrease but remain highly statistically significant. It is not surprising that the magnitudes of the spreads decrease after controlling for the cross-sectional momentum because the excess returns of the strategy contain information about the cross-sectional momentum by construction. However, there is additional information coming from the construction similar to Moskowitz, Ooi, and Pedersen (2012) , which is evidenced by the fact that the magnitudes of the spreads remain positive and statistically significant after controlling for the cross-sectional momentum. Finally, we also test whether the strategies contain information above and beyond the cryptocurrency three-factor model in Liu, Tsyvinski, and Wu (2019) . After controlling for the three-factor model, the magnitudes of the spreads further decrease but still remain positive and significant at the 5% level for the top five and top ten coins and at the 10% level for the top three coins.

4.3 Regulations

A potentially important determinant of cryptocurrency valuation is regulations. To test whether cryptocurrency regulations are important determinants of cryptocurrency valuations, we follow the method of Auer and Claessens (2018) and Shanaev et al. (2019) and determine 120 regulative events. We further categorize these regulative events into positive and negative events based on Auer and Claessens (2018) . We document the list of regulative events and the results in the Internet Appendix . We find that the contemporaneous cryptocurrency returns are lower during the days of regulative events. However, we find that the cryptocurrency returns respond to negative regulative events but not to positive regulative events.

4.4 Speculative interest and sentiment

In this section, we test whether speculation and investor sentiment may be important drivers of cryptocurrency prices. We extract the speculative shares of cryptocurrency usage from Coindesk.com. We test whether the cryptocurrency returns strongly respond to the contemporaneous and expectations of future speculative share growth. We further control for the network growth rates as discussed earlier to examine whether the results of network effects are driven by variations in speculative interests. We document the results on speculative interests in the Internet Appendix . We find some evidence that the cryptocurrency returns positively load on the contemporaneous speculative share growth, but the coefficient estimates are not significant. In the bivariate regressions, we show that the loadings of the cryptocurrency returns to the contemporaneous network growth remain positive and statistically significant. Furthermore, we show that the coin market returns positively forecast future speculative share growth. The coefficients are significant at the three-month and eight-month horizons. These results show that current coin market returns also contain information about expectations of future speculative share growth.

To test the effect of sentiment, we construct a measure that is directly aimed to capture investor sentiment. 15 The measure of cryptocurrency sentiment is defined as the log ratio between the count of positive and the count of negative phrases of cryptocurrencies in Google searches. The positive and negative phrases are described in the corresponding table in the Internet Appendix . Therefore, when the measure is high, investor sentiment is more positive and vice versa. We test whether the sentiment measure predicts future cryptocurrency returns, and compare that to the measures of investor attention and momentum. We find that the sentiment measure positively and significantly predicts future cryptocurrency returns. However, this result is distinct from the investor attention and cryptocurrency momentum results. All three variables are statistically significant in predicting future cryptocurrency returns in the multivariate regressions.

4.5 Beauty contests

One potential theoretical explanation of high volatility in financial markets may be that they are represented by the Keynesian beauty contest model. In this section, we aim to test the role of beauty contests in the cryptocurrency markets. We need a time-varying measure of the degrees of disagreement among the cryptocurrency investors. Ideally, we would like to have the expectations of individual cryptocurrency investors. Practically, this is not feasible due to data limitation at this time. We take a different route and measure the dispersion of investor expectations using the ratio between cryptocurrency volume and return volatility. This choice is motivated by Biais and Bossaerts (1998) , who show theoretically that the volume-volatility ratio summarizes the degree of disagreement among the investors and discriminates between genuine disagreement and mere Bayesian learning with agreeing agents. We test empirically whether the coin market returns respond to the volume-volatility ratio contemporaneously and whether the volume-volatility ratio predicts future coin market returns. We summarize these results in the Internet Appendix . We find that the coin market return is higher when the current volume-volatility ratio is higher. This result is consistent with the idea that investors tend to bid the price up when there is a lot of disagreement in the cryptocurrency market. The flip side is that the volume-volatility ratio does not predict future cumulative coin market returns over any horizon.

4.6 VAR analysis

One concern about the network factor analysis is that the contemporaneous correlations between the network size/activity factors and coin market returns might be mechanical and not truly capture the value of network externalities. To address the concern, we conduct a bivariate VAR analysis with the coin market returns and different coin network growth measures. The results are documented in the Internet Appendix .

To differentiate the network effects from the potential mechanical effects, we examine how changes in the network factors affect valuations in the future—that is, what is the cumulative permanent return associated with a shock to network size/activity factors. The bottom-left graph of each panel shows the associated impulse response function. We find that a shock to the wallet user growth, active address growth, and transaction count growth factors positively predict the coin market returns in the future and that there is not any reversal effect. The effects tend to concentrate on the first couple of weeks. In particular, the wallet user growth and active address growth factors positively and statistically significantly predict coin market returns in the future. The payment count growth measure is the only exception, but the point estimate is not significant. In terms of the cumulative effect, the cumulative return responses to one standard deviation of network factor shock are about 4% based on the changes in wallet user growth, about 2% based on the active address growth, and about 0.45% based on the transaction count growth.

Moreover, there is a bidirectional relationship between the network factors and the cryptocurrency returns. Consistent with the results in Table 3 in the paper, the VAR shows that the coin market returns positively and significantly predict future network growth based on all four specifications. The top-right graph of each panel shows the associated impulse response function. In the bivariate VAR framework, the approach accounts for this bidirectional effect, that returns may affect trading decisions and therefore affect the network size. Additionally, the bivariate VAR again reveals a coin market momentum effect in all four specifications. The top-left graph of each panel shows the associated impulse response function. The VAR approach also helps account for the momentum effect in the cumulative effect of network size/activity factors on the valuation of cryptocurrencies. Overall, the VAR suggests some evidence that a positive shock to the network size/activity factors leads to a permanent increase in the valuations of the coin market in the future. In terms of the timing of the effects, the impulse response functions suggest that it can take a few weeks for the impulse responses to decay to zero.

4.7 Additional production factor test

When the electricity price increases, the return to mining should decrease. However, the reduction in the return to mining would force some miners to exit, which leads to a higher probability of any given miner receiving the reward plus fees and a reduction of the difficulty of the cryptographic puzzles. These two effects would endogenously restore the profitability of the mining. Therefore, the shocks to electricity prices or computing power do not necessarily affect the marginal cost of mining because a change in these costs would affect the profitability of miners, causing an adjustment in the number of miners and therefore an adjustment in the required computing effort to restore profitability (e.g., Easley, O’Hara, and Basu 2019 ).

To address this effect, we include another set of tests. We regress the coin market returns on the number of Bitcoins given as a block reward, controlling for the price of Bitcoins and the fees paid. The rationale of the test is the following: the price of Bitcoin is endogenous, the fees paid could be endogenous as they are driven by network usage, but the number of Bitcoins given as a block reward is exogenous and deterministically changes through time according to the Bitcoin protocol. Therefore, the exogenous variation, controlling for the price of Bitcoin and the fees, could be used to identify the effects of mining costs.

Column (1) of the table documents the baseline specification. The coefficient of interest is in the changes of the number of Bitcoins given as a block reward, or |$\Delta Gen\ Coin$|⁠ . We find that, although the coefficient estimate is positive, it is not statistically significant. Column (2) uses next-month changes of the number of Bitcoins given as a block reward to test any anticipation effect. The point estimate is again positive but not statistically significant. Column (3) includes both the current and next-month changes in the number of Bitcoins given as a block reward as the independent variables, and both coefficient estimates are not significant. Columns (4) to (6) repeat the exercises of columns (1) to (3) but include the first principal component of the production factor, and the results are similar. Columns (7) to (12) repeat the exercises but control for the level of fees instead of changes in fees. We find that the coefficient estimates of |$\Delta Gen\ Coin$| and |$\Delta Gen\ Coin_{+1}$| are not statistically significant, consistent with results in columns (1) to (6).

Easley, O’Hara, and Basu (2019) argue that fee is not the only endogenous variable in the mining process, and that the confirmation time is also endogenously determined as a function of miner competition. Therefore, we also control for the confirmation time in our analysis and examine the results. Columns (13) to (24) report the results controlling for the confirmation time. In particular, columns (13) to (18) control for the changes of confirmation time, and columns (19) to (24) control for the level of confirmation time. We find that the coefficient estimates of |$\Delta Gen\ Coin$| and |$\Delta Gen\ Coin_{+1}$| remain statistically insignificant. Overall, we do not find a significant effect of the exogenous variation in the number of Bitcoins given as a block reward, controlling for the price of Bitcoin and the fees, on the cryptocurrency valuations.

4.8 Subsample by cryptocurrency characteristics

In this section, we consider a number of cryptocurrency characteristics: (i) whether the cryptocurrency is based on Proof-of-Work (PoW) or Proof-of-Stake (PoS), (ii) whether the cryptocurrency is minable, (iii) whether the cryptocurrency is built on an Ethereum blockchain, (iv) whether the cryptocurrency is a stable coin, and (v) whether the cryptocurrency is a smart contract. Based on each characteristic, we form a value-weighted portfolio of all the underlying cryptocurrencies. In the untabulated results, we look at the loadings of returns for each subgroup on the network factors, production factors, currency factors, commodity factors, equity factors, and macroeconomic factors.

First, we examine the loadings on the network factors. We find that the returns for the subgroups generally load positively on the network factors. Based on the first principal component of the four primary measures, we show that the returns of all six subgroups load positively on the network factors. In particular, the returns of PoW, minable coins, Ethereum blockchain coins, and stable coins positively and statistically significantly expose to the first principal component. On the other hand, the returns of PoS and smart contract coins do not statistically significantly expose to the first principal component. Then, we turn to the loadings on the production factors. We find that the returns for most of the subgroups do not significantly load on the production factors. We conclude that the factor exposures of the subgroup returns are largely consistent with the aggregate coin market returns. Lastly, we turn to the loadings of the returns for subgroups on the currency, commodity, equity, and macroeconomic factors. In general, we find that the returns of the subgroups have low exposures to these factor models. We conclude that the factor exposures of the subgroup returns are largely consistent with the aggregate coin market returns.

We find that cryptocurrency returns strongly respond to cryptocurrency network factors, as suggested by the theoretical literature. However, our empirical results do not support the notion that the evolution of cryptocurrency prices is linked to cryptocurrency production factors. At the same time, the returns of cryptocurrency can be predicted by two factors specific to its markets: momentum and investor attention. In contrast to the equity market, we show that the momentum result and the investor attention result are distinct phenomena and that there is only limited interaction between them. Moreover, cryptocurrency returns have low exposures to traditional asset classes such as currencies, commodities, and stocks, and to macroeconomic factors.

Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

We thank Andrew Atkeson, Nicola Borri, Eduardo Davila, Stefano Giglio, William Goetzmann, Andrew Karolyi, Ye Li, Stephen Roach, Robert Shiller, Michael Sockin, and Jessica Wachter for their comments. Colton Conley provided outstanding research assistance. Supplementary data can be found on The Review of Financial Studies web site.

1 See, e.g., Cong, Li, and Wang (2019) , Pagnotta and Buraschi (2018) , and Biais et al. (2018) .

2 See, e.g., Cong, He, and Li (2018) and Sockin and Xiong (2019) .

3 Some coins are not tracked by the website because the coins’ exchanges do not provide accessible APIs.

4 We thank William Goetzmann for kindly sharing the Twitter post count data with us.

5 One of the 156 anomalies does not exist during the sample period. The database ends at December 2016.

6 Because Bitcoin is by far the largest and well-known cryptocurrency available, we use Bitcoin network data. The tests in the paper use coin market returns; in the Internet Appendix , we show that our results are qualitatively similar using Bitcoin returns.

7 See Jasper Pickering and Fraser Moore, “How China Become a Haven for People Looking to Cash in on the Bitcoin Gold Rush,” Business Insider, December 12, 2017.

8 See Shanthi Rexaline, “The Companies Behind the Chips That Power Cryptocurrency Minning,” Benzinga, February 2, 2018.

9 The 3.30% weekly return is calculated by multiplying a one-standard-deviation increase of coin market returns (16.50%) and the coefficient estimate (0.20).

10 Stoffels (2017) documents that a cross-sectional momentum strategy based on 15 cryptocurrencies generates abnormal returns during the period between 2016 and 2017.

11 Wang and Vergne (2017) use the level of newspaper mentions of Bitcoin to proxy for the “buzz” of Bitcoin. They document that high “buzz” predicts low Bitcoin returns in the future. Mai et al. (2016) use the level of Twitter post counts to predict Bitcoin returns.

12 We thank Nicola Borri for providing us with the up-to-date currency factors.

13 The Fama-French three-factor model is based on Fama and French (1993) and Fama and French (1996) . The Carhart four-factor model is based on Carhart (1997) . The Fama-French five-factor model is based on Fama and French (2016) . The Fama-French six-factor model is based on Fama and French (2017) .

14 Stoffels (2017) and Gilbert and Loi (2018) examine cryptocurrency loadings on the CAPM and Fama-French three-factor models.

15 Chen et al. (2019) classify cryptocurrency-related positive and negative words of StockTwits and Reddit.

Abadi, J. , and Brunnermeier M. . 2018 . Blockchain economics . Working Paper , Princeton University .

Google Scholar

Google Preview

Asness, C. , Moskowitz T. J. , and Pedersen L. H. . 2013 . Value and momentum everywhere . Journal of Finance 68 : 929 – 985 .

Athey, S. , Parashkevov I. , Sarukkai V. , and Xia J. . 2016 . Bitcoin pricing, adoption, and usage: Theory and evidence . Working Paper , Stanford University .

Auer, R. , and Claessens S. . 2018 . Regulating cryptocurrencies: Assessing market reactions . BIS Quarterly Review 51 – 65 .

Biais, B. , Bisiere C. , Bouvard M. , Casamatta C. , Menkveld A. J. . 2018 . Equilibrium bitcoin pricing . Working Paper , Toulouse School of Economics .

Biais, B. , and Bossaerts P. . 1998 . Asset prices and trading volume in a beauty contest . Review of Economic Studies 65 : 307 – 340 .

Bianchi, D. 2017 . Cryptocurrencies as an asset class: An empirical assessment . Working Paper , Warwick University .

Borri, N. 2019 . Conditional tail-risk in cryptocurrency markets . Journal of Empirical Finance 50 : 1 – 19 .

Borri, N. , and Shakhnov K. . 2018 . Cryptomarket discounts . Working Paper , LUISS University .

Carhart, M. M. 1997 . On persistence in mutual fund performance . Journal of Finance 52 : 57 – 87 .

Chen, A. , and Velikov M. . 2017 . Accounting for the anomaly zoo: A trading cost perspective . Working Paper , Penn State University .

Chen, C. Y. , Despres R. , Guo L. , and Renault T. . 2019 . What makes cryptocurrencies special? Investor sentiment and return predictability during the bubble . Working Paper .

Chiu, J. , and Koeppl T. V. . 2017 . The economics of cryptocurrencies–bitcoin and beyond . Working Paper , Queen’s University .

Cong, L. W. , and He Z. . 2019 . Blockchain disruption and smart contracts . Review of Financial Studies 32 : 1754 – 1797 .

Cong, L. W. , He Z. , and Li J. . 2018 . Decentralized mining in centralized pools . Working Paper , University of Chicago .

Cong, L. W. , Li Y. , and Wang N. . 2019 . Tokenomics: Dynamic adoption and valuation . Working Paper , Columbia University .

Corbet, S. , Lucey B. M. , Urquhart A. , and Yarovaya L. . 2019 . Cryptocurrencies as a financial asset: A systematic analysis . International Review of Financial Analysis 62 : 182 – 199 .

Da, Z. , Engelberg J. , and Gao P. . 2011 . In search of attention . Journal of Finance 66 : 1461 – 1499 .

Daniel, K. , and Moskowitz T. J. . 2016 . Momentum crashes . Journal of Financial Economics 122 : 221 – 247 .

De Long, J. B. , Shleifer A. , Summers L. H. , and Waldmann R. J. . 1990 . Noise trader risk in financial markets . Journal of Political Economy 98 : 703 – 738 .

De Bondt, W. F. , and Thaler R. . 1985 . Does the stock market overreact? Journal of Finance 40 : 793 – 805 .

De Bondt, W. F. , and Thaler R. . 1987 . Further evidence on investor overreaction and stock market seasonality . The Journal of Finance 42 : 557 – 581 .

Easley, D. , O’Hara M. , and Basu S. . 2019 . From mining to markets: The evolution of bitcoin transaction fees . Journal of Financial Economics 134 : 91 – 109 .

Fama, E. F. , and French K. R. . 1993 . Common risk factors in the returns on stocks and bonds . Journal of Financial Economics 33 : 3 – 56 .

Fama, E. F. , and French K. R. . 1996 . Multifactor explanations of asset pricing anomalies . Journal of Finance 51 : 55 – 84 .

Fama, E. F. , and French K. R. . 2016 . Dissecting anomalies with a five-factor model . Review of Financial Studies 29 : 69 – 103 .

Fama, E. F. , and French K. R. . 2017 . Choosing factors . Working Paper , University of Chicago .

Feng, G. , Giglio S. , and Xiu D. . 2017 . Taming the factor zoo . Working Paper , University of Chicago .

Gilbert, S. , and Loi H. . 2018 . Digital currency risk . International Journal of Economics and Finance 10 : 108 .

Griffin, J. M. , and Shams A. . 2020 . Is bitcoin really un-tethered? Journal of Finance 52 : 57 – 87 .

Hong, H. , Lim T. , and Stein J. C. . 2000 . Bad news travels slowly: Size, analyst coverage, and the profitability of momentum strategies . Journal of Finance 55 : 265 – 295 .

Hou, K. , Xiong W. , and Peng L. . 2009 . A tale of two anomalies: The implications of investor attention for price and earnings momentum . Working Paper , Princeton University .

Hu, A. , Parlour C. A. , and Rajan U. . 2018 . Cryptocurrencies: Stylized facts on a new investible instrument . Working Paper , University of California , Berkeley .

Huberman, G. , Leshno J. D. , and Moallemi C. C. . 2017 . Monopoly without a monopolist: An economic analysis of the bitcoin payment system . Working Paper , University of Chicago .

Jegadeesh, N. , and Titman S. . 1993 . Returns to buying winners and selling losers: Implications for stock market efficiency . Journal of Finance 48 : 65 – 91 .

Jermann, U. J. 2018 . Bitcoin and Cagan’s model of hyperinflation . Working Paper , University of Pennsylvania .

Liu, Y. , Tsyvinski A. , and Wu X. . 2019 . Common Risk Factors in Cryptocurrency . Working Paper , Yale University .

Lustig, H. , Roussanov N. , and Verdelhan A. . 2011 . Common risk factors in currency markets . Review of Financial Studies 24 : 3731 – 3777 .

Mai, F. , Bai Q. , Shan Z. , Wang X. S. , and Chiang R. . 2016 . The impacts of social media on Bitcoin performance . Working Paper , University of Cincinnati .

Makarov, I. , and Schoar A. . 2020 . Trading and arbitrage in cryptocurrency markets . Journal of Financial Economics 135 : 293 – 319 .

Moskowitz, T. J. 2015 . Asset pricing and sports betting . Working Paper , Yale University .

Moskowitz, T. J. , and Grinblatt M. . 1999 . Do industries explain momentum? Journal of Finance 54 : 1249 – 1290 .

Moskowitz, T. J. , Ooi Y. H. , and Pedersen L. H. . 2012 . Time series momentum . Journal of Financial Economics 104 : 228 – 250 .

Pagnotta, E. , and Buraschi A. . 2018 . An equilibrium valuation of bitcoin and decentralized network assets . Working Paper , Imperial College Business School .

Pástor, L. and P. Veronesi. 2003 . Stock valuation and learning about profitability . Journal of Finance 58 : 1749 – 1789 .

Routledge, B. , and Zetlin-Jones A. . 2018 . Currency stability using blockchain technology . Working Paper , Carnegie Mellon University .

Saleh, F. 2018 . Blockchain without waste: Proof-of-stake . Working Paper , McGill University .

Schilling, L. , and Uhlig H. . 2019 . Some simple bitcoin economics . Journal of Monetary Economics 106 : 16 – 26 .

Shanaev, S. , Sharma S. , Shuraeva A. , and Ghimire B. . 2019 . Taming the blockchain beast? Regulatory implications for the cryptocurrency market . Working Paper , University of London .

Sockin, M. , and Xiong W. . 2019 . A model of cryptocurrencies . Working Paper , Princeton University .

Stoffels, J. 2017 . Asset pricing of cryptocurrencies and momentum based patterns . Working Paper , Erasmus School of Economics .

Wang, S. , and Vergne J. . 2017 . Buzz factor or innovation potential: What explains cryptocurrencies’ returns? PloS One 12 .

Weber, W. E. 2016 . A Bitcoin standard: Lessons from the gold standard . Working Paper , Bank of Canada .

Yermack, D. 2015 . Is Bitcoin a real currency? An economic appraisal. In Handbook of Digital Currency , ed. Chuen D. L. K. , 31 – 43 . Amsterdam : Elsevier .

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Cryptocurrency trading: a comprehensive survey

• Fan Fang 1 , 2 ,
• Carmine Ventre 1 ,
• Michail Basios 2 ,
• Leslie Kanthan 2 ,
• David Martinez-Rego 2 ,
• Fan Wu 2 &
• Lingbo Li   ORCID: orcid.org/0000-0002-3073-1352 2  

Financial Innovation volume  8 , Article number:  13 ( 2022 ) Cite this article

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In recent years, the tendency of the number of financial institutions to include cryptocurrencies in their portfolios has accelerated. Cryptocurrencies are the first pure digital assets to be included by asset managers. Although they have some commonalities with more traditional assets, they have their own separate nature and their behaviour as an asset is still in the process of being understood. It is therefore important to summarise existing research papers and results on cryptocurrency trading, including available trading platforms, trading signals, trading strategy research and risk management. This paper provides a comprehensive survey of cryptocurrency trading research, by covering 146 research papers on various aspects of cryptocurrency trading ( e . g ., cryptocurrency trading systems, bubble and extreme condition, prediction of volatility and return, crypto-assets portfolio construction and crypto-assets, technical trading and others). This paper also analyses datasets, research trends and distribution among research objects (contents/properties) and technologies, concluding with some promising opportunities that remain open in cryptocurrency trading.

Introduction

Cryptocurrencies have experienced broad market acceptance and fast development despite their recent conception. Many hedge funds and asset managers have begun to include cryptocurrency-related assets into their portfolios and trading strategies. The academic community has similarly spent considerable efforts in researching cryptocurrency trading. This paper seeks to provide a comprehensive survey of the research on cryptocurrency trading, by which we mean any study aimed at facilitating and building strategies to trade cryptocurrencies.

As an emerging market and research direction, cryptocurrencies and cryptocurrency trading have seen considerable progress and a notable upturn in interest and activity (Farell 2015 ). From Fig. 1 , we observe over 85% of papers have appeared since 2018, demonstrating the emergence of cryptocurrency trading as a new research area in financial trading. The sampling interval of this survey is from 2013 to June 2021.

The literature is organised according to six distinct aspects of cryptocurrency trading:

Cryptocurrency trading software systems (i.e., real-time trading systems, turtle trading systems, arbitrage trading systems);

Systematic trading including technical analysis, pairs trading and other systematic trading methods;

Emergent trading technologies including econometric methods, machine learning technology and other emergent trading methods;

Portfolio and cryptocurrency assets including research among cryptocurrency co-movements and crypto-asset portfolio research;

Market condition research including bubbles (Flood et al. 1986 ) or crash analysis and extreme conditions;

Other Miscellaneous cryptocurrency trading research.

In this survey we aim at compiling the most relevant research in these areas and extract a set of descriptive indicators that can give an idea of the level of maturity research in this area has achieved.

Cryptocurrency Trading Publications (cumulative) during 2013–2021(June 2021)

We also summarise research distribution (among research properties and categories/research technologies). The distribution among properties defines the classification of research objectives and content. The distribution among technologies defines the classification of methods or technological approaches to the study of cryptocurrency trading. Specifically, we subdivide research distribution among categories/technologies into statistical methods and machine learning technologies. Moreover, We identify datasets and opportunities (potential research directions) that have appeared in the cryptocurrency trading area. To ensure that our survey is self-contained, we aim to provide sufficient material to adequately guide financial trading researchers who are interested in cryptocurrency trading.

There has been related work that discussed or partially surveyed the literature related to cryptocurrency trading. Kyriazis ( 2019 ) investigated the efficiency and profitable trading opportunities in the cryptocurrency market. Ahamad et al. ( 2013 ) and Sharma et al. ( 2017 ) gave a brief survey on cryptocurrencies, merits of cryptocurrencies compared to fiat currencies and compared different cryptocurrencies that are proposed in the literature. Mukhopadhyay et al. ( 2016 ) gave a brief survey of cryptocurrency systems. Merediz-Solà and Bariviera ( 2019 ) performed a bibliometric analysis of bitcoin literature. The outcomes of this related work focused on specific area in cryptocurrency, including cryptocurrencies and cryptocurrency market introduction, cryptocurrency systems / platforms, bitcoin literature review, etc. To the best of our knowledge, no previous work has provided a comprehensive survey particularly focused on cryptocurrency trading.

In summary, the paper makes the following contributions:

Definition This paper defines cryptocurrency trading and categorises it into: cryptocurrency markets, cryptocurrency trading models, and cryptocurrency trading strategies. The core content of this survey is trading strategies for cryptocurrencies while we cover all aspects of it.

Multidisciplinary survey The paper provides a comprehensive survey of 146 cryptocurrency trading papers, across different academic disciplines such as finance and economics, artificial intelligence and computer science. Some papers may cover multiple aspects and will be surveyed for each category.

Analysis The paper analyses the research distribution, datasets and trends that characterise the cryptocurrency trading literature.

Horizons The paper identifies challenges, promising research directions in cryptocurrency trading, aimed to promote and facilitate further research.

Figure 2 depicts the paper structure, which is informed by the review schema adopted. More details about this can be found in " Paper collection and review schema " section.

Tree structure of the contents in this paper

Cryptocurrency trading

This section provides an introduction to cryptocurrency trading. We will discuss Blockchain , as the enabling technology, cryptocurrency markets and cryptocurrency trading strategies .

Blockchain technology introduction

Blockchain is a digital ledger of economic transactions that can be used to record not just financial transactions, but any object with an intrinsic value (Tapscott and Tapscott 2016 ). In its simplest form, a Blockchain is a series of immutable data records with timestamps, which are managed by a cluster of machines that do not belong to any single entity. Each of these data block s is protected by cryptographic principle and bound to each other in a chain (cf. Fig.  3 for the workflow).

Cryptocurrencies like Bitcoin are conducted on a peer-to-peer network structure. Each peer has a complete history of all transactions, thus recording the balance of each account. For example, a transaction is a file that says “A pays X Bitcoins to B” that is signed by A using its private key. This is basic public-key cryptography, but also the building block on which cryptocurrencies are based. After being signed, the transaction is broadcast on the network. When a peer discovers a new transaction, it checks to make sure that the signature is valid (this is equivalent to using the signer’s public key, denoted as the algorithm in Fig.  3 ). If the verification is valid then the block is added to the chain; all other blocks added after it will “confirm” that transaction. For example, if a transaction is contained in block 502 and the length of the blockchain is 507 blocks, it means that the transaction has 5 confirmations (507–502) (Johar 2018 ).

Workflow of Blockchain transaction

From Blockchain to cryptocurrencies

Confirmation is a critical concept in cryptocurrencies; only miners can confirm transactions. Miners add blocks to the Blockchain; they retrieve transactions in the previous block and combine it with the hash of the preceding block to obtain its hash, and then store the derived hash into the current block. Miners in Blockchain accept transactions, mark them as legitimate and broadcast them across the network. After the miner confirms the transaction, each node must add it to its database. In layman terms, it has become part of the Blockchain and miners undertake this work to obtain cryptocurrency tokens, such as Bitcoin. In contrast to Blockchain, cryptocurrencies are related to the use of tokens based on distributed ledger technology. Any transaction involving purchase, sale, investment, etc. involves a Blockchain native token or sub-token. Blockchain is a platform that drives cryptocurrency and is a technology that acts as a distributed ledger for the network. The network creates a means of transaction and enables the transfer of value and information. Cryptocurrencies are the tokens used in these networks to send value and pay for these transactions. They can be thought of as tools on the Blockchain, and in some cases can also function as resources or utilities. In other instances, they are used to digitise the value of assets. In summary, cryptocurrencies are part of an ecosystem based on Blockchain technology.

Introduction of cryptocurrency market

What is cryptocurrency.

Cryptocurrency is a decentralised medium of exchange which uses cryptographic functions to conduct financial transactions (Doran 2014 ). Cryptocurrencies leverage the Blockchain technology to gain decentralisation, transparency, and immutability (Meunier 2018 ). In the above, we have discussed how Blockchain technology is implemented for cryptocurrencies.

In general, the security of cryptocurrencies is built on cryptography, neither by people nor on trust (Narayanan et al. 2016 ). For example, Bitcoin uses a method called “Elliptic Curve Cryptography” to ensure that transactions involving Bitcoin are secure (Wang et al. 2017 ). Elliptic curve cryptography is a type of public-key cryptography that relies on mathematics to ensure the security of transactions. When someone attempts to circumvent the aforesaid encryption scheme by brute force, it takes them one-tenth the age of the universe to find a value match when trying 250 billion possibilities every second (Grayblock 2018 ). Regarding its use as a currency, cryptocurrency has properties similar to fiat currencies. It has a controlled supply. Most cryptocurrencies limit the availability of their currency volumes. E.g. for Bitcoin, the supply will decrease over time and will reach its final quantity sometime around 2140. All cryptocurrencies control the supply of tokens through a timetable encoded in the Blockchain.

One of the most important features of cryptocurrencies is the exclusion of financial institution intermediaries (Harwick 2016 ). The absence of a “middleman” lowers transaction costs for traders. For comparison, if a bank’s database is hacked or damaged, the bank will rely entirely on its backup to recover any information that is lost or compromised. With cryptocurrencies, even if part of the network is compromised, the rest will continue to be able to verify transactions correctly. Cryptocurrencies also have the important feature of not being controlled by any central authority (Rose 2015 ): the decentralised nature of the Blockchain ensures cryptocurrencies are theoretically immune to government control and interference.

The pure digital asset is anything that exists in a digital format and carries with it the right to use it. Currently, digital assets include digital documents, motion picture and so on; the market for digital assets has in fact evolved since its inception in 2009, with the first digital asset “Bitcoin” (Kaal 2020 ). For this reason, we call the cryptocurrency the “first pure digital asset”.

As of December 20, 2019, there exist 4950 cryptocurrencies and 20,325 cryptocurrency markets; the market cap is around 190 billion dollars (CoinMaketCap 2019 ). Figure  4 shows historical data on global market capitalisation and 24-h trading volume (TradingView 2021 ). The blue line is the total cryptocurrency market capitalization and green/red histogram is the total cryptocurrency market volume. The total market cap is calculated by aggregating the dollar market cap of all cryptocurrencies. From the figure, we can observe how cryptocurrencies experience exponential growth in 2017 and a large bubble burst in early 2018. In the wake of the pandemic, cryptocurrencies raised dramatically in value in 2020. In 2021, the market value of cryptocurrencies has been very volatile but consistently at historically high levels.

Total market capitalization and volume of cryptocurrency market, USD (TradingView 2021 )

There are three mainstream cryptocurrencies (Council 2021 ): Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC). Bitcoin was created in 2009 and garnered massive popularity. On October 31, 2008, an individual or group of individuals operating under the pseudonym Satoshi Nakamoto released the Bitcoin white paper and described it as: ”A pure peer-to-peer version of electronic cash that can be sent online for payment from one party to another without going through a counterparty, ie. a financial institution.” (Nakano et al. 2018 ) Launched by Vitalik Buterin in 2015, Ethereum is a special Blockchain with a special token called Ether (ETH symbol in exchanges). A very important feature of Ethereum is the ability to create new tokens on the Ethereum Blockchain. The Ethereum network went live on July 30, 2015, and pre-mined 72 million Ethereum. Litecoin is a peer-to-peer cryptocurrency created by Charlie Lee. It was created according to the Bitcoin protocol, but it uses a different hashing algorithm. Litecoin uses a memory-intensive proof-of-work algorithm, Scrypt.

Figure  5 shows percentages of total cryptocurrency market capitalisation; Bitcoin and Ethereum account for the majority of the total market capitalisation (data collected on 14 September 2021).

Percentage of Total Market Capitalisation (Coinmarketcap 2020 )

Cryptocurrency exchanges

A cryptocurrency exchange or digital currency exchange (DCE) is a business that allows customers to trade cryptocurrencies. Cryptocurrency exchanges can be market makers, usually using the bid-ask spread as a commission for services, or as a matching platform, by simply charging fees. A cryptocurrency exchange or digital currency exchange (DCE) is a place that allows customers to trade cryptocurrencies. Cryptocurrency exchanges can be market makers (usually using the bid-ask spread as a commission for services) or a matching platform (simply charging fees).

Table  1 shows the top or classical cryptocurrency exchanges according to the rank list, by volume, compiled on “nomics” website (Nomics 2020 ). Chicago Mercantile Exchange (CME), Chicago Board Options Exchange (CBOE) as well as BAKKT (backed by New York Stock Exchange) are regulated cryptocurrency exchanges. Fiat currency data also comes from “nomics” website (Nomics 2020 ). Regulatory authority and supported currencies of listed exchanges are collected from official websites or blogs.

First we give a definition of cryptocurrency trading .

Definition 1

Cryptocurrency trading is the act of buying and selling of cryptocurrencies with the intention of making a profit.

The definition of cryptocurrency trading can be broken down into three aspects: object, operation mode and trading strategy. The object of cryptocurrency trading is the asset being traded, which is “cryptocurrency”. The operation mode of cryptocurrency trading depends on the means of transaction in the cryptocurrency market, which can be classified into “trading of cryptocurrency Contract for Differences (CFD)” (The contract between the two parties, often referred to as the “buyer” and “seller”, stipulates that the buyer will pay the seller the difference between themselves when the position closes (Authority 2019 )) and “buying and selling cryptocurrencies via an exchange”. A trading strategy in cryptocurrency trading, formulated by an investor, is an algorithm that defines a set of predefined rules to buy and sell on cryptocurrency markets.

Advantages of trading cryptocurrency

The benefits of cryptocurrency trading include:

Drastic fluctuations The volatility of cryptocurrencies are often likely to attract speculative interest and investors. The rapid fluctuations of intraday prices can provide traders with great money-earning opportunities, but it also includes more risk.

24-h market The cryptocurrency market is available 24 h a day, 7 days a week because it is a decentralised market. Unlike buying and selling stocks and commodities, the cryptocurrency market is not traded physically from a single location. Cryptocurrency transactions can take place between individuals, in different venues across the world.

Near anonymity Buying goods and services using cryptocurrencies is done online and does not require to make one’s own identity public. With increasing concerns over identity theft and privacy, cryptocurrencies can thus provide users with some advantages regarding privacy.

Different exchanges have specific Know-Your-Customer (KYC) measures for identifying users or customers (Adeyanju 2019 ). The KYC undertook in the exchanges allows financial institutions to reduce the financial risk while maximising the wallet owner’s anonymity.

Peer-to-peer transactions One of the biggest benefits of cryptocurrencies is that they do not involve financial institution intermediaries. As mentioned above, this can reduce transaction costs. Moreover, this feature might appeal to users who distrust traditional systems.

Over-the-counter (OTC) cryptocurrency markets offer, in this context, peer-to-peer transactions on the Blockchain. The most famous cryptocurrency OTC market is “LocalBitcoin (Localbtc 2020 )”.

Programmable “smart” capabilities Some cryptocurrencies can bring other benefits to holders, including limited ownership and voting rights. Cryptocurrencies may also include a partial ownership interest in physical assets such as artwork or real estate.

Disadvantages of trading cryptocurrency

The disadvantages of cryptocurrency trading include:

Scalability problem Before the massive expansion of the technology infrastructure, the number of transactions and the speed of transactions cannot compete with traditional currency trading. Scalability issues led to a multi-day trading backlog in March 2020, affecting traders looking to move cryptocurrencies from their personal wallets to exchanges (Forbes 2021 ).

Cybersecurity issues As a digital technology, cryptocurrencies are subject to cyber security breaches and can fall into the hands of hackers. Recently, over $600 million of ethereum and other cryptocurrencies were stolen in August 2021 in blockchain-based platform Poly Network (Forbes 2021 ). Mitigating this situation requires ongoing maintenance of the security infrastructure and the use of enhanced cyber security measures that go beyond those used in traditional banking (Kou et al. 2021 ).

Regulations Authorities around the world face challenging questions about the nature and regulation of cryptocurrency as some parts of the system and its associated risks are largely unknown. There are currently three types of regulatory systems used to control digital currencies, they include: closed system for the Chinese market, open and liberal for the Swiss market,and open and strict system for the US market (UKTN 2021 ). At the same time, we notice that some countries such as India is not at par in using the cryptocurrency. As Buffett said, “It doesn’t make sense. This thing is not regulated. It’s not under control. It’s not under the supervision of \([\ldots ]\) United States Federal Reserve or any other central bank (Forbes 2017 ).”

Cryptocurrency trading strategy

Cryptocurrency trading strategy is the main focus of this survey. There are many trading strategies, which can be broadly divided into two main categories: technical and fundamental. Technical and fundamental trading are two main trading analysis thoughts when it comes to analyzing the financial markets. Most traders use these two analysis methods or both (Oberlechner 2001 ). From a survey on stock prediction, we in fact know that 66% of the relevant research work was based on technical analysis; while 23% and 11% were based on fundamental analysis and general analysis, respectively (Nti et al. 2020 ). Cryptocurrency trading can draw on the experience of stock market trading in most scenarios. So we divide trading strategies into two main categories: technical and fundamental trading.

They are similar in the sense that they both rely on quantifiable information that can be backtested against historical data to verify their performance. In recent years, a third kind of trading strategy, which we call programmatic trading, has received increasing attention. Such a trading strategy is similar to a technical trading strategy because it uses trading activity information on the exchange to make buying or selling decisions. programmatic traders build trading strategies with quantitative data, which is mainly derived from price, volume, technical indicators or ratios to take advantage of inefficiencies in the market and are executed automatically by trading software. Cryptocurrency market is different from traditional markets as there are more arbitrage opportunities, higher fluctuation and transparency. Due to these characteristics, most traders and analysts prefer using programmatic trading in cryptocurrency markets.

Cryptocurrency trading software system

Software trading systems allow international transactions, process customer accounts and information, and accept and execute transaction orders (Calo and Johnson 2002 ). A cryptocurrency trading system is a set of principles and procedures that are pre-programmed to allow trade between cryptocurrencies and between fiat currencies and cryptocurrencies. Cryptocurrency trading systems are built to overcome price manipulation, cybercriminal activities and transaction delays (Bauriya et al. 2019 ). When developing a cryptocurrency trading system, we must consider the capital market, base asset, investment plan and strategies (Molina 2019 ). Strategies are the most important part of an effective cryptocurrency trading system and they will be introduced below. There exist several cryptocurrency trading systems that are available commercially, for example, Capfolio, 3Commas, CCXT, Freqtrade and Ctubio. From these cryptocurrency trading systems, investors can obtain professional trading strategy support, fairness and transparency from the professional third-party consulting companies and fast customer services.

Systematic trading

Systematic trading is a way to define trading goals, risk controls and rules. In general, systematic trading includes high frequency trading and slower investment types like systematic trend tracking. In this survey, we divide systematic cryptocurrency trading into technical analysis, pairs trading and others. Technical analysis in cryptocurrency trading is the act of using historical patterns of transaction data to assist a trader in assessing current and projecting future market conditions for the purpose of making profitable trades. Price and volume charts summarise all trading activity made by market participants in an exchange and affect their decisions. Some experiments showed that the use of specific technical trading rules allows generating excess returns, which is useful to cryptocurrency traders and investors in making optimal trading and investment decisions (Gerritsen et al. 2019 ). Pairs trading is a systematic trading strategy that considers two similar assets with slightly different spreads. If the spread widens, short the high cryptocurrencies and buy the low cryptocurrencies. When the spread narrows again to a certain equilibrium value, a profit is generated (Elliott et al. 2005 ). Papers shown in this section involve the analysis and comparison of technical indicators, pairs and informed trading, amongst other strategies.

Tools for building automated trading systems

Tools for building automated trading systems in cryptocurrency market are those emergent trading strategies for cryptocurrency. These include strategies that are based on econometrics and machine learning technologies.

Econometrics on cryptocurrency

Econometric methods apply a combination of statistical and economic theories to estimate economic variables and predict their values (Vogelvang 2005 ). Statistical models use mathematical equations to encode information extracted from the data (Kaufman 2013 ). In some cases, statistical modeling techniques can quickly provide sufficiently accurate models (Ben-Akiva et al. 2002 ). Other methods might be used, such as sentiment-based prediction and long-and-short-term volatility classification based prediction (Chang et al. 2015 ). The prediction of volatility can be used to judge the price fluctuation of cryptocurrencies, which is also valuable for the pricing of cryptocurrency-related derivatives (Kat and Heynen 1994 ).

When studying cryptocurrency trading using econometrics, researchers apply statistical models on time-series data like generalised autoregressive conditional heteroskedasticity (GARCH) and BEKK (named after Baba, Engle, Kraft and Kroner, 1995 (Engle and Kroner 1995 )) models to evaluate the fluctuation of cryptocurrencies (Caporin and McAleer 2012 ). A linear statistical model is a method to evaluate the linear relationship between prices and an explanatory variable (Neter et al. 1996 ). When there exists more than one explanatory variable, we can model the linear relationship between explanatory (independent) and response (dependent) variables with multiple linear models. The common linear statistical model used in the time-series analysis is the autoregressive moving average (ARMA) model (Choi 2012 ).

Machine learning technology

Machine learning is an efficient tool for developing Bitcoin and other cryptocurrency trading strategies  (McNally et al. 2018 ) because it can infer data relationships that are often not directly observable by humans. From the most basic perspective, Machine Learning relies on the definition of two main components: input features and objective function. The definition of Input Features (data sources) is where knowledge of fundamental and technical analysis comes into play. We may divide the input into several groups of features, for example, those based on Economic indicators (such as, gross domestic product indicator, interest rates, etc.), Social indicators (Google Trends, Twitter, etc.), Technical indicators (price, volume, etc.) and other Seasonal indicators (time of day, day of the week, etc.). The objective function defines the fitness criteria one uses to judge if the Machine Learning model has learnt the task at hand. Typical predictive models try to anticipate numeric (e.g., price) or categorical (e.g., trend) unseen outcomes. The machine learning model is trained by using historic input data (sometimes called in-sample) to generalise patterns therein to unseen (out-of-sample) data to (approximately) achieve the goal defined by the objective function. Clearly, in the case of trading, the goal is to infer trading signals from market indicators which help to anticipate asset future returns.

Generalisation error is a pervasive concern in the application of Machine Learning to real applications, and of utmost importance in Financial applications. We need to use statistical approaches, such as cross validation, to validate the model before we actually use it to make predictions. In machine learning, this is typically called “validation”. The process of using machine learning technology to predict cryptocurrency is shown in Fig.  6 .

Process of machine learning in predicting cryptocurrency

Depending on the formulation of the main learning loop, we can classify Machine Learning approaches into three categories: Supervised learning, Unsupervised learning and Reinforcement learning. We list a general comparison (IntelliPaat 2021 ) among these three machine learning methods in Table  2 . Supervised learning is used to derive a predictive function from labeled training data. Labeled training data means that each training instance includes inputs and expected outputs. Usually, these expected outputs are produced by a supervisor and represent the expected behaviour of the model. The most used labels in trading are derived from in sample future returns of assets. Unsupervised learning tries to infer structure from unlabeled training data and it can be used during exploratory data analysis to discover hidden patterns or to group data according to any pre-defined similarity metrics. Reinforcement learning utilises software agents trained to maximise a utility function, which defines their objective; this is flexible enough to allow agents to exchange short term returns for future ones. In the financial sector, some trading challenges can be expressed as a game in which an agent aims at maximising the return at the end of the period.

The use of machine learning in cryptocurrency trading research encompasses the connection between data sources’ understanding and machine learning model research. Further concrete examples are shown in a later section.

Portfolio research

Portfolio theory advocates diversification of investments to maximize returns for a given level of risk by allocating assets strategically. The celebrated mean-variance optimisation is a prominent example of this approach (Markowitz 1952 ). Generally, crypto asset denotes a digital asset (i.e., cryptocurrencies and derivatives). There are some common ways to build a diversified portfolio in crypto assets. The first method is to diversify across markets, which is to mix a wide variety of investments within a portfolio of the cryptocurrency market. The second method is to consider the industry sector, which is to avoid investing too much money in any one category. Diversified investment of portfolio in the cryptocurrency market includes portfolio across cryptocurrencies (Liu 2019 ) and portfolio across the global market including stocks and futures (Kajtazi and Moro 2019 ).

Market condition research

Market condition research appears especially important for cryptocurrencies. A financial bubble is a significant increase in the price of an asset without changes in its intrinsic value (Brunnermeier and Oehmke 2013 ; Kou et al. 2021 ). Many experts pinpoint a cryptocurrency bubble in 2017 when the prices of cryptocurrencies grew by 900 \(\%\) . In 2018, Bitcoin faced a collapse in its value. This significant fluctuation inspired researchers to study bubbles and extreme conditions in cryptocurrency trading. The cryptocurrency market has experienced a near continuous bull market since the fall of 2020, with the value of Bitcoin soaring from $10,645 on October 7, 2020 to an all-time high of $63,346 on April 15, 2021. This represents a gain of approximately +600% in just six months (Forbes 2021 ). Some experts believe that the extreme volatility of exchange rates means that cryptocurrency exposure should be kept at a low percentage of your portfolio. “I understand if you want to buy it because you believe the price will rise, but make sure it’s only a small part of your portfolio, maybe 1 or 2%!” says Thanos Papasavvas, founder of research group ABP Invest, who has a 20-year background in asset management (FT 2021 ). In any case, bubbles and crash analysis is an important researching area in cryptocurrency trading.

Paper collection and review schema

The section introduces the scope and approach of our paper collection, a basic analysis, and the structure of our survey.

Survey scope

We adopt a bottom-up approach to the research in cryptocurrency trading, starting from the systems up to risk management techniques. For the underlying trading system, the focus is on the optimisation of trading platforms structure and improvements of computer science technologies.

At a higher level, researchers focus on the design of models to predict return or volatility in cryptocurrency markets. These techniques become useful to the generation of trading signals. on the next level above predictive models, researchers discuss technical trading methods to trade in real cryptocurrency markets. Bubbles and extreme conditions are hot topics in cryptocurrency trading because, as discussed above, these markets have shown to be highly volatile (whilst volatility went down after crashes). Portfolio and cryptocurrency asset management are effective methods to control risk. We group these two areas in risk management research. Other papers included in this survey include topics like pricing rules, dynamic market analysis, regulatory implications, and so on. Table  3 shows the general scope of cryptocurrency trading included in this survey.

Since many trading strategies and methods in cryptocurrency trading are closely related to stock trading, some researchers migrate or use the research results for the latter to the former. When conducting this research, we only consider those papers whose research focuses on cryptocurrency markets or a comparison of trading in those and other financial markets.

Specifically, we apply the following criteria when collecting papers related to cryptocurrency trading:

The paper introduces or discusses the general idea of cryptocurrency trading or one of the related aspects of cryptocurrency trading.

The paper proposes an approach, study or framework that targets optimised efficiency or accuracy of cryptocurrency trading.

The paper compares different approaches or perspectives in trading cryptocurrency.

By “cryptocurrency trading” here, we mean one of the terms listed in Table 3 and discussed above.

Some researchers gave a brief survey of cryptocurrency (Ahamad et al. 2013 ; Sharma et al. 2017 ), cryptocurrency systems (Mukhopadhyay et al. 2016 ) and cryptocurrency trading opportunities (Kyriazis 2019 ). These surveys are rather limited in scope as compared to ours, which also includes a discussion on the latest papers in the area; we want to remark that this is a fast-moving research field.

Paper collection methodology

To collect the papers in different areas or platforms, we used keyword searches on Google Scholar and arXiv, two of the most popular scientific databases. We also choose other public repositories like SSRN but we find that almost all academic papers in these platforms can also be retrieved via Google Scholar; consequently, in our statistical analysis, we count those as Google Scholar hits. We choose arXiv as another source since it allows this survey to be contemporary with all the most recent findings in the area. The interested reader is warned that these papers have not undergone formal peer review. The keywords used for searching and collecting are listed below. [Crypto] means the cryptocurrency market, which is our research interest because methods might be different among different markets. We conducted 6 searches across the two repositories until July 1, 2021.

[Crypto] + Trading

[Crypto] + Trading system

[Crypto] + Prediction

[Crypto] + Trading strategy

[Crypto] + Risk Management

[Crypto] + Portfolio

To ensure high coverage, we adopted the so-called snowballing  (Wohlin 2014 ) method on each paper found through these keywords. We checked papers added from snowballing methods that satisfy the criteria introduced above until we reached closure.

Collection results

Table  4 shows the details of the results from our paper collection. Keyword searches and snowballing resulted in 146 papers across the six research areas of interest in " Survey scope " section.

Figure  7 shows the distribution of papers published at different research sites. Among all the papers, 48.63% papers are published in Finance and Economics venues such as Journal of Financial Economics (JFE), Cambridge Centre for Alternative Finance (CCAF), Finance Research Letters, Centre for Economic Policy Research (CEPR), Finance Research Letters (FRL), Journal of Risk and Financial Management (JRFM) and some other high impact financial journals; 4.79% papers are published in Science venues such as Public Library Of Science one (PLOS one), Royal Society open science and SAGE; 14.38% papers are published in Intelligent Engineering and Data Mining venues such as Symposium Series on Computational Intelligence (SSCI), Intelligent Systems Conference (IntelliSys), Intelligent Data Engineering and Automated Learning (IDEAL) and International Conference on Data Mining (ICDM); 4.79% papers are published in Physics / Physicians venues (mostly in Physics venue) such as Physica A and Maths venue like Journal of Mathematics; 10.96% papers are published in AI and complex system venues such as Complexity and International Federation for Information Processing (IFIP); 15.07% papers are published in Others venues which contains independently published papers and dissertations; 1.37% papers are published on arXiv. The distribution of different venues shows that cryptocurrency trading is mostly published in Finance and Economics venues, but with a wide diversity otherwise.

Publication venue distribution

Survey organisation

We discuss the contributions of the collected papers and a statistical analysis of these papers in the remainder of the paper, according to Table  5 .

The papers in our collection are organised and presented from six angles. We introduce the work about several different cryptocurrency trading software systems in " Cryptocurrency trading software systems " section. " Systematic trading " section introduces systematic trading applied to cryptocurrency trading. In " Emergent trading technologies " section, we introduce some emergent trading technologies including econometrics on cryptocurrencies, machine learning technologies and other emergent trading technologies in the cryptocurrency market. Section 8 introduces research on cryptocurrency pairs and related factors and crypto-asset portfolios research. In " Bubbles and crash analysis " and " Extreme condition " sections we discuss cryptocurrency market condition research, including bubbles, crash analysis, and extreme conditions. " Others related to cryptocurrency trading " section introduces other research included in cryptocurrency trading not covered above.

We would like to emphasize that the six headings above focus on a particular aspect of cryptocurrency trading; we give a complete organisation of the papers collected under each heading. This implies that those papers covering more than one aspect will be discussed in different sections, once from each angle.

We analyse and compare the number of research papers on different cryptocurrency trading properties and technologies in " Summary analysis of literature review " section, where we also summarise the datasets and the timeline of research in cryptocurrency trading.

We build upon this review to conclude in " Opportunities in cryptocurrency trading " section with some opportunities for future research.

Cryptocurrency trading Software Systems

Trading infrastructure systems.

Following the development of computer science and cryptocurrency trading, many cryptocurrency trading systems/bots have been developed. Table  6 compares the cryptocurrency trading systems existing in the market. The table is sorted based on URL types (GitHub or Official website) and GitHub stars (if appropriate).

Capfolio is a proprietary payable cryptocurrency trading system which is a professional analysis platform and has an advanced backtesting engine (Capfolio 2020 ). It supports five different cryptocurrency exchanges.

3 Commas is a proprietary payable cryptocurrency trading system platform that can take profit and stop-loss orders at the same time (3commas 2020 ). Twelve different cryptocurrency exchanges are compatible with this system.

CCXT is a cryptocurrency trading system with a unified API out of the box and optional normalized data and supports many Bitcoin / Ether / Altcoin exchange markets and merchant APIs. Any trader or developer can create a trading strategy based on this data and access public transactions through the APIs (Ccxt 2020 ). The CCXT library is used to connect and trade with cryptocurrency exchanges and payment processing services worldwide. It provides quick access to market data for storage, analysis, visualisation, indicator development, algorithmic trading, strategy backtesting, automated code generation and related software engineering. It is designed for coders, skilled traders, data scientists and financial analysts to build trading algorithms. Current CCXT features include:

Support for many cryptocurrency exchanges;

Fully implemented public and private APIs;

Optional normalized data for cross-exchange analysis and arbitrage;

Out-of-the-box unified API, very easy to integrate.

Blackbird Bitcoin Arbitrage is a C++ trading system that automatically executes long / short arbitrage between Bitcoin exchanges. It can generate market-neutral strategies that do not transfer funds between exchanges (Blackbird 2020 ). The motivation behind Blackbird is to naturally profit from these temporary price differences between different exchanges while being market neutral. Unlike other Bitcoin arbitrage systems, Blackbird does not sell but actually short sells Bitcoin on the short exchange. This feature offers two important advantages. Firstly, the strategy is always market agnostic: fluctuations (rising or falling) in the Bitcoin market will not affect the strategy returns. This eliminates the huge risks of this strategy. Secondly, this strategy does not require transferring funds (USD or BTC) between Bitcoin exchanges. Buy and sell transactions are conducted in parallel on two different exchanges. There is no need to deal with transmission delays.

StockSharp is an open-source trading platform for trading at any market of the world including 48 cryptocurrency exchanges (Stocksharp 2020 ). It has a free C# library and free trading charting application. Manual or automatic trading (algorithmic trading robot, regular or HFT) can be run on this platform. StockSharp consists of five components that offer different features:

S#.Designer - Free universal algorithm strategy app, easy to create strategies;

S#.Data - free software that can automatically load and store market data;

S#.Terminal - free trading chart application (trading terminal);

S#.Shell - ready-made graphics framework that can be changed according to needs and has a fully open source in C#;

S#.API - a free C# library for programmers using Visual Studio. Any trading strategies can be created in S#.API.

Freqtrade is a free and open-source cryptocurrency trading robot system written in Python. It is designed to support all major exchanges and is controlled by telegram. It contains backtesting, mapping and money management tools, and strategy optimization through machine learning (Fretrade 2020 ). Freqtrade has the following features:

Persistence: Persistence is achieved through SQLite technology;

Strategy optimization through machine learning: Use machine learning to optimize your trading strategy parameters with real trading data;

Marginal Position Size: Calculates winning rate, risk-return ratio, optimal stop loss and adjusts position size, and then trades positions for each specific market;

Telegram management: use telegram to manage the robot.

Dry run: Run the robot without spending money;

CryptoSignal is a professional technical analysis cryptocurrency trading system (Cryptosignal 2020 ). Investors can track over 500 coins of Bittrex, Bitfinex, GDAX, Gemini and more. Automated technical analysis includes momentum, RSI, Ichimoku Cloud, MACD, etc. The system gives alerts including Email, Slack, Telegram, etc. CryptoSignal has two primary features. First of all, it offers modular code for easy implementation of trading strategies; Secondly, it is easy to install with Docker.

Ctubio is a C++ based low latency (high frequency) cryptocurrency trading system (Ctubio 2020 ). This trading system can place or cancel orders through supported cryptocurrency exchanges in less than a few milliseconds. Moreover, it provides a charting system that can visualise the trading account status including trades completed, target position for fiat currency, etc.

Catalyst is an analysis and visualization of the cryptocurrency trading system (Catalyst 2020 ). It makes trading strategies easy to express and backtest them on historical data (daily and minute resolution), providing analysis and insights into the performance of specific strategies. Catalyst allows users to share and organise data and build profitable, data-driven investment strategies. Catalyst not only supports the trading execution but also offers historical price data of all crypto assets (from minute to daily resolution). Catalyst also has backtesting and real-time trading capabilities, which enables users to seamlessly transit between the two different trading modes. Lastly, Catalyst integrates statistics and machine learning libraries (such as matplotlib, scipy, statsmodels and sklearn) to support the development, analysis and visualization of the latest trading systems.

Golang Crypto Trading Bot is a Go based cryptocurrency trading system (Golang 2020 ). Users can test the strategy in sandbox environment simulation. If simulation mode is enabled, a fake balance for each coin must be specified for each exchange.

Real-time cryptocurrency trading systems

Bauriya et al. ( 2019 ) developed a real-time Cryptocurrency Trading System. A real-time cryptocurrency trading system is composed of clients, servers and databases. Traders use a web-application to login to the server to buy/sell crypto assets. The server collects cryptocurrency market data by creating a script that uses the Coinmarket API. Finally, the database collects balances, trades and order book information from the server. The authors tested the system with an experiment that demonstrates user-friendly and secure experiences for traders in the cryptocurrency exchange platform.

Turtle trading system in Cryptocurrency market

The original Turtle Trading system is a trend following trading system developed in the 1970s. The idea is to generate buy and sell signals on stock for short-term and long-term breakouts and its cut-loss condition which is measured by Average true range (ATR) (Kamrat et al. 2018 ). The trading system will adjust the size of assets based on their volatility. Essentially, if a turtle accumulates a position in a highly volatile market, it will be offset by a low volatility position. Extended Turtle Trading system is improved with smaller time interval spans and introduces a new rule by using exponential moving average (EMA). Three EMA values are used to trigger the “buy” signal: 30EMA (Fast), 60EMA (Slow), 100EMA (Long). The author of Kamrat et al. ( 2018 ) performed backtesting and comparing both trading systems (Original Turtle and Extended Turtle) on 8 prominent cryptocurrencies. Through the experiment, Original Turtle Trading System achieved an 18.59% average net profit margin (percentage of net profit over total revenue) and 35.94% average profitability (percentage of winning trades over total numbers of trades) in 87 trades through nearly one year. Extended Turtle Trading System achieved 114.41% average net profit margin and 52.75% average profitability in 41 trades through the same time interval. This research showed how Extended Turtle Trading System compared can improve over Original Turtle Trading System in trading cryptocurrencies.

Arbitrage trading systems for cryptocurrencies

Christian (Păuna 2018 ) introduced arbitrage trading systems for cryptocurrencies. Arbitrage trading aims to spot the differences in price that can occur when there are discrepancies in the levels of supply and demand across multiple exchanges. As a result, a trader could realise a quick and low-risk profit by buying from one exchange and selling at a higher price on a different exchange. Arbitrage trading signals are caught by automated trading software. The technical differences between data sources impose a server process to be organised for each data source. Relational databases and SQL are reliable solution due to the large amounts of relational data. The author used the system to catch arbitrage opportunities on 25 May 2018 among 787 cryptocurrencies on 7 different exchanges. The research paper (Păuna 2018 ) listed the best ten trading signals made by this system from 186 available found signals. The results showed that the system caught the trading signal of “BTG-BTC” to get a profit of up to 495.44% when arbitraging to buy in Cryptopia exchange and sell in Binance exchange. Another three well-traded arbitrage signals (profit expectation around 20% mentioned by the author) were found on 25 May 2018. Arbitrage Trading Software System introduced in that paper presented general principles and implementation of arbitrage trading system in the cryptocurrency market.

Characteristics of three cryptocurrency trading systems

Real-time trading systems use real-time functions to collect data and generate trading algorithms. Turtle trading system and arbitrage trading system have shown a sharp contrast in their profit and risk behaviour. Using Turtle trading system in cryptocurrency markets got high returns with high risk. Arbitrage trading system is inferior in terms of revenue but also has a lower risk. One feature that turtle trading system and arbitrage trading system have in common is they performed well in capturing alpha.

Technical analysis

Many researchers have focused on technical indicators (patterns) analysis for trading on cryptocurrency markets. Examples of studies with this approach include “Turtle Soup pattern strategy” (TradingstrategyGuides 2019 ), “Nem (XEM) strategy” (TradingstrategyGuides 2019 ), “Amazing Gann Box strategy” (TradingstrategyGuides 2019 ), “Busted Double Top Pattern strategy” (TradingstrategyGuides 2019 ), and “Bottom Rotation Trading strategy” (TradingstrategyGuides 2019 ). Table  7 shows the comparison among these five classical technical trading strategies using technical indicators. “Turtle soup pattern strategy” (TradingstrategyGuides 2019 ) used a 2-day breakout of price in predicting price trends of cryptocurrencies. This strategy is a kind of chart trading pattern. “Nem (XEM) strategy” combined Rate of Change (ROC) indicator and Relative Strength Index (RSI) in predicting price trends (TradingstrategyGuides 2019 ). “Amazing Gann Box” predicted exact points of increase and decrease in Gann Box which are used to catch explosive trends of cryptocurrency price (TradingstrategyGuides 2019 ). Technical analysis tools such as candlestick and box charts with Fibonacci Retracement based on golden ratio are used in this technical analysis. Fibonacci Retracement uses horizontal lines to indicate where possible support and resistance levels are in the market. “Busted Double Top Pattern” used a Bearish reversal trading pattern which generates a sell signal to predict price trends (TradingstrategyGuides 2019 ). “Bottom Rotation Trading” is a technical analysis method that picks the bottom before the reversal happens. This strategy used a price chart pattern and box chart as technical analysis tools.

Ha and Moon ( 2018 ) investigated using genetic programming (GP) to find attractive technical patterns in the cryptocurrency market. Over 12 technical indicators including Moving Average (MA) and Stochastic oscillator were used in experiments; adjusted gain, match count, relative market pressure and diversity measures have been used to quantify the attractiveness of technical patterns. With extended experiments, the GP system is shown to find successfully attractive technical patterns, which are useful for portfolio optimization. Hudson and Urquhart ( 2019 ) applied almost 15,000 to technical trading rules (classified into MA rules, filter rules, support resistance rules, oscillator rules and channel breakout rules). This comprehensive study found that technical trading rules provide investors with significant predictive power and profitability. Corbet et al. ( 2019 ) analysed various technical trading rules in the form of the moving average-oscillator and trading range break-out strategies to generate higher returns in cryptocurrency markets. By using one-minute dollar-denominated Bitcoin close-price data, the backtest showed variable-length moving average (VMA) rule performs best considering it generates the most useful signals in high frequency trading.

Grobys et al. ( 2020 ) examined a simple moving average trading strategy using daily price data for the 11 most traded cryptocurrencies over the period 2016-2018. The results showed that, excluding Bitcoin, technical trading rules produced an annualised excess return of 8.76% after controlling for average market returns. The analysis also suggests that cryptocurrency markets are inefficient. Al-Yahyaee et al. ( 2020 ) examined multiple fractals, long memory processes and efficiency assumptions of major cryptocurrencies using Hurst exponents, time-rolling MF-DFA and quantile regression methods. The results showed that all markets provide evidence of long-term memory properties and multiple fractals. Furthermore, the inefficiency of cryptocurrency markets is time-varying. The researchers concluded that high liquidity with low volatility facilitates arbitrage opportunities for active traders.

Pairs trading

Pairs trading is a trading strategy that attempts to exploit the mean-reversion between the prices of certain securities. Miroslav (Fil 2019 ) investigated the applicability of standard pairs trading approaches on cryptocurrency data with the benchmarks of Gatev et al. ( 2006 ). The pairs trading strategy is constructed in two steps. Firstly, suitable pairs with a stable long-run relationship are identified. Secondly, the long-run equilibrium is calculated and pairs trading strategy is defined by the spread based on the values. The research also extended intra-day pairs trading using high frequency data. Overall, the model was able to achieve a 3% monthly profit in Miroslav’s experiments (Fil 2019 ). Broek  (van den Broek and Sharif 2018 ) applied pairs trading based on cointegration in cryptocurrency trading and 31 pairs were found to be significantly cointegrated (within sector and cross-sector). By selecting four pairs and testing over a 60-day trading period, the pairs trading strategy got its profitability from arbitrage opportunities, which rejected the Efficient-market hypothesis (EMH) for the cryptocurrency market. Lintilhac and Tourin ( 2017 ) proposed an optimal dynamic pair trading strategy model for a portfolio of assets. The experiment used stochastic control techniques to calculate optimal portfolio weights and correlated the results with several other strategies commonly used by practitioners including static dual-threshold strategies. Li and Tourin ( 2016 ) proposed a pairwise trading model incorporating time-varying volatility with constant elasticity of variance type. The experiment calculated the best pair strategy by using a finite difference method and estimated parameters by generalised moment method.

Other systematic trading methods in cryptocurrency trading mainly include informed trading. Using USD / BTC exchange rate trading data, Feng et al. ( 2018 ) found evidence of informed trading in the Bitcoin market in those quantiles of the order sizes of buyer-initiated (seller-initiated) orders are abnormally high before large positive (negative) events, compared to the quantiles of seller-initiated (buyer-initiated) orders; this study adopts a new indicator inspired by the volume imbalance indicator (Easley et al. 2008 ). The evidence of informed trading in the Bitcoin market suggests that investors profit on their private information when they get information before it is widely available.

Emergent trading technologies

Copula-quantile causality analysis and Granger-causality analysis are methods to investigate causality in cryptocurrency trading analysis. Bouri et al. ( 2019 ) applied a copula-quantile causality approach on volatility in the cryptocurrency market. The approach of the experiment extended the Copula-Granger-causality in distribution (CGCD) method of Lee and Yang ( 2014 ) in 2014. The experiment constructed two tests of CGCD using copula functions. The parametric test employed six parametric copula functions to discover dependency density between variables. The performance matrix of these functions varies with independent copula density. Three distribution regions are the focus of this research: left tail (1%, 5%, 10% quantile), central region (40%, 60% quantile and median) and right tail (90%, 95%, 99% quantile). The study provided significant evidence of Granger causality from trading volume to the returns of seven large cryptocurrencies on both left and right tails. Bouri et al. ( 2020 ) examined the causal linkages among the volatility of leading cryptocurrencies via the frequency-domain test of Bodart and Candelon ( 2009 ) and distinguished between temporary and permanent causation. The results showed that permanent shocks are more important in explaining Granger causality whereas transient shocks dominate the causality of smaller cryptocurrencies in the long term. Badenhorst et al. ( 2019 ) attempted to reveal whether spot and derivative market volumes affect Bitcoin price volatility with the Granger-causality method and ARCH (1,1). The result shows spot trading volumes have a significant positive effect on price volatility while the relationship between cryptocurrency volatility and the derivative market is uncertain. Bouri et al. ( 2020 ) used a dynamic equicorrelation (DECO) model and reported evidence that the average earnings equilibrium correlation changes over time between the 12 leading cryptocurrencies. The results showed increased cryptocurrency market consolidation despite significant price declined in 2018. Furthermore, measurement of trading volume and uncertainty are key determinants of integration.

Several econometrics methods in time-series research, such as GARCH and BEKK, have been used in the literature on cryptocurrency trading. Conrad et al. ( 2018 ) used the GARCH-MIDAS model to extract long and short-term volatility components of the Bitcoin market. The technical details of this model decomposed the conditional variance into the low-frequency and high-frequency components. The results identified that S&P 500 realized volatility has a negative and highly significant effect on long-term Bitcoin volatility and S&P 500 volatility risk premium has a significantly positive effect on long-term Bitcoin volatility. Ardia et al. ( 2019 ) used the Markov Switching GARCH (MSGARCH) model to test the existence of institutional changes in the GARCH volatility dynamics of Bitcoin’s logarithmic returns. Moreover, a Bayesian method was used for estimating model parameters and calculating VaR prediction. The results showed that MSGARCH models clearly outperform single-regime GARCH for Value-at-Risk forecasting. Troster et al. ( 2019 ) performed general GARCH and GAS (Generalized Auto-regressive Score) analysis to model and predict Bitcoin’s returns and risks. The experiment found that the GAS model with heavy-tailed distribution can provide the best out-of-sample prediction and goodness-of-fit attributes for Bitcoin’s return and risk modeling. The results also illustrated the importance of modeling excess kurtosis for Bitcoin returns.

Charles and Darné ( 2019 ) studied four cryptocurrency markets including Bitcoin, Dash, Litecoin and Ripple. Results showed cryptocurrency returns are strongly characterised by the presence of jumps as well as structural breaks except the Dash market. Four GARCH-type models (i.e., GARCH, APARCH, IGARCH and FIGARCH) and three return types with structural breaks (original returns, jump-filtered returns, and jump-filtered returns) are considered. The research indicated the importance of jumps in cryptocurrency volatility and structural breakthroughs. Malladi and Dheeriya ( 2021 ) examined the time series analysis of Bitcoin and Ripple’s returns and volatility to examine the dependence of their prices in part on global equity indices, gold prices and fear indicators such as volatility indices and US economic policy uncertainty indices. Autoregressive-moving-average model with exogenous inputs model (ARMAX), GARCH, VAR and Granger causality tests are used in the experiments. The results showed that there is no causal relationship between global stock market and gold returns on bitcoin returns, but a causal relationship between ripple returns on bitcoin prices is found.

Some researchers focused on long memory methods for volatility in cryptocurrency markets. Long memory methods focused on long-range dependence and significant long-term correlations among fluctuations on markets. Chaim and Laurini ( 2019 ) estimated a multivariate stochastic volatility model with discontinuous jumps in cryptocurrency markets. The results showed that permanent volatility appears to be driven by major market developments and popular interest levels. Caporale et al. ( 2018 ) examined persistence in the cryptocurrency market by Rescaled range (R/S) analysis and fractional integration. The results of the study indicated that the market is persistent (there is a positive correlation between its past and future values) and that its level changes over time. Khuntia and Pattanayak ( 2018 ) applied the adaptive market hypothesis (AMH) in the predictability of Bitcoin evolving returns. The consistent test of  (Domínguez and Lobato 2003 ), generalized spectral (GS) of (Escanciano and Velasco 2006 ) are applied in capturing time-varying linear and nonlinear dependence in bitcoin returns. The results verified Evolving Efficiency in Bitcoin price changes and evidence of dynamic efficiency in line with AMH’s claims. Gradojevic and Tsiakas ( 2021 ) examined volatility cascades across multiple trading ranges in the cryptocurrency market. Using a wavelet Hidden Markov Tree model, authors estimated the transition probability of propagating high or low volatility at one time scale (range) to high or low volatility at the next time scale. The results showed that the volatility cascade tends to be symmetrical when moving from long to short term. In contrast, when moving from short to long term, the volatility cascade is very asymmetric.

Nikolova et al. ( 2020 ) provided a new method to calculate the probability of volatility clusters, especially for cryptocurrencies (high volatility of their exchange rates). The authors used the FD4 method to calculate the Hurst index of a volatility series and describe explicit criteria for determining the existence of fixed size volatility clusters by calculation. The results showed that the volatility of cryptocurrencies changes more rapidly than that of traditional assets, and much more rapidly than that of Bitcoin/USD, Ethereum/USD, and Ripple/USD pairs. Ma et al. ( 2020 ) investigated whether a new Markov Regime Transformation Mixed Data Sampling (MRS-MIADS) model can improve the prediction accuracy of Bitcoin’s Realised Variance (RV). The results showed that the proposed new MRS-MIDAS model exhibits statistically significant improvements in predicting the RV of Bitcoin. At the same time, the occurrence of jumps significantly increases the persistence of high volatility and switches between high and low volatility.

Katsiampa et al. ( 2018 ) applied three pair-wise bivariate BEKK models to examine the conditional volatility dynamics along with interlinkages and conditional correlations between three pairs of cryptocurrencies in 2018. More specifically, the BEKK-MGARCH methodology also captured cross-market effects of shocks and volatility, which are also known as shock transmission effects and volatility spillover effects. The experiment found evidence of bi-directional shock transmission effects between Bitcoin and both Ether and Litcoin. In particular, bi-directional shock spillover effects are identified between three pairs (Bitcoin, Ether and Litcoin) and time-varying conditional correlations exist with positive correlations mostly prevailing. In 2019, Katsiampa ( 2019 ) further researched an asymmetric diagonal BEKK model to examine conditional variances of five cryptocurrencies that are significantly affected by both previous squared errors and past conditional volatility. The experiment tested the null hypothesis of the unit root against the stationarity hypothesis. Once stationarity is ensured, ARCH LM is tested for ARCH effects to examine the requirement of volatility modeling in return series. Moreover, volatility co-movements among cryptocurrency pairs are also tested by the multivariate GARCH model. The results confirmed the non-normality and heteroskedasticity of price returns in cryptocurrency markets. The finding also identified the effects of cryptocurrencies’ volatility dynamics due to major news.

Hultman ( 2018 ) set out to examine GARCH (1,1), bivariate-BEKK (1,1) and a standard stochastic model to forecast the volatility of Bitcoin. A rolling window approach is used in these experiments. Mean absolute error (MAE), Mean squared error (MSE) and Root-mean-square deviation (RMSE) are three loss criteria adopted to evaluate the degree of error between predicted and true values. The result shows the following rank of loss functions: GARCH (1,1) > bivariate-BEKK (1,1) > Standard stochastic for all the three different loss criteria; in other words, GARCH(1,1) appeared best in predicting the volatility of Bitcoin. Wavelet time-scale persistence analysis is also applied in the prediction and research of volatility in cryptocurrency markets (Omane-Adjepong et al. 2019 ). The results showed that information efficiency (efficiency) and volatility persistence in the cryptocurrency market are highly sensitive to time scales, measures of returns and volatility, and institutional changes. Omane-Adjepong et al. ( 2019 ) connected with similar research by Corbet et al. ( 2018 ) and showed that GARCH is quicker than BEKK to absorb new information regarding the data.

Zhang and Li ( 2020 ) examined how to price exceptional volatility in a cross-section of cryptocurrency returns. Using portfolio-level analysis and Fama-MacBeth regression analysis, the authors demonstrated that idiosyncratic volatility is positively correlated with expected returns on cryptocurrencies.

As we have previously stated, Machine learning technology constructs computer algorithms that automatically improve themselves by finding patterns in existing data without explicit instructions (Holmes et al. 1994 ). The rapid development of machine learning in recent years has promoted its application to cryptocurrency trading, especially in the prediction of cryptocurrency returns. Some ML algorithms solve both classification and regression problems from a methodological point of view. For clearer classification, we focus on the application of these ML algorithms in cryptocurrency trading. For example, Decision Tree (DT) can solve both classification and regression problems. But in cryptocurrency trading, researchers focus more on using DT in solving classification problems. Here we classify DT as “Classification Algorithms”.

Common machine learning technology in this survey

Several machine learning technologies are applied in cryptocurrency trading. We distinguish these by the objective set to the algorithm: classification, clustering, regression, reinforcement learning. We have separated a section specifically on deep learning due to its intrinsic variation of techniques and wide adoption.

Classification algorithms Classification in machine learning has the objective of categorising incoming objects into different categories as needed, where we can assign labels to each category (e.g., up and down). Naive Bayes (NB) (Rish et al. 2001 ), Support Vector Machine (SVM) (Wang 2005 ), K-Nearest Neighbours (KNN) (Wang 2005 ), Decision Tree (DT) (Friedl and Brodley 1997 ), Random Forest (RF) (Liaw and Wiener 2002 ) and Gradient Boosting (GB) (Friedman et al. 2001 ) algorithms habe been used in cryptocurrency trading based on papers we collected. NB is a probabilistic classifier based on Bayes’ theorem with strong (naive) conditional independence assumptions between features (Rish et al. 2001 ). SVM is a supervised learning model that aims at achieving high margin classifiers connecting to learning bounds theory (Zemmal et al. 2016 ). SVMs assign new examples to one category or another, making it a non-probabilistic binary linear classifier (Wang 2005 ), although some corrections can make a probabilistic interpretation of their output (Keerthi et al. 2001 ). KNN is a memory-based or lazy learning algorithm, where the function is only approximated locally, and all calculations are being postponed to inference time  (Wang 2005 ). DT is a decision support tool algorithm that uses a tree-like decision graph or model to segment input patterns into regions to then assign an associated label to each region (Friedl and Brodley 1997 ; Fang et al. 2020 ). RF is an ensemble learning method. The algorithm operates by constructing a large number of decision trees during training and outputting the average consensus as predicted class in the case of classification or mean prediction value in the case of regression  (Liaw and Wiener 2002 ). GB produces a prediction model in the form of an ensemble of weak prediction models (Friedman et al. 2001 ).

Clustering algorithms Clustering is a machine learning technique that involves grouping data points in a way that each group shows some regularity  (Jianliang et al. 2009 ). K-Means is a vector quantization used for clustering analysis in data mining. K-means stores the k -centroids used to define the clusters; a point is considered to be in a particular cluster if it is closer to the cluster’s centroid than any other centroid (Wagstaff et al. 2001 ). K-Means is one of the most used clustering algorithms used in cryptocurrency trading according to the papers we collected. Clustering algorithms have been successfully applied in many financial applications, such as fraud detection, rejection inference and credit assessment. Automated detection clusters are critical as they help to understand sub-patterns of data that can be used to infer user behaviour and identify potential risks (Li et al. 2021 ; Kou et al. 2014 ).

Regression algorithms We have defined regression as any statistical technique that aims at estimating a continuous value (Kutner et al. 2005 ). Linear Regression (LR) and Scatterplot Smoothing are common techniques used in solving regression problems in cryptocurrency trading. LR is a linear method used to model the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables) (Kutner et al. 2005 ). Scatterplot Smoothing is a technology to fit functions through scatter plots to best represent relationships between variables (Friedman and Tibshirani 1984 ).

Deep Learning algorithms Deep learning is a modern take on artificial neural networks (ANNs) (Zhang et al. 2019 ), made possible by the advances in computational power. An ANN is a computational system inspired by the natural neural networks that make up the animal’s brain. The system “learns” to perform tasks including the prediction by considering examples. Deep learning’s superior accuracy comes from high computational complexity cost. Deep learning algorithms are currently the basis for many modern artificial intelligence applications (Sze et al. 2017 ). Convolutional neural networks (CNNs) (Lawrence et al. 1997 ), Recurrent neural networks (RNNs) (Mikolov et al. 2011 ), Gated recurrent units (GRUs) (Chung et al. 2014 ), Multilayer perceptron (MLP) and Long short-term memory (LSTM) (Cheng et al. 2016 ) networks are the most common deep learning technologies used in cryptocurrency trading. A CNN is a specific type of neural network layer commonly used for supervised learning. CNNs have found their best success in image processing and natural language processing problems. An attempt to use CNNs in cryptocurrency can be shown in  (Kalchbrenner et al. 2014 ). An RNN is a type of artificial neural network in which connections between nodes form a directed graph with possible loops. This structure of RNNs makes them suitable for processing time-series data (Mikolov et al. 2011 ) due to the introduction of memory in the recurrent connections. They face nevertheless for the vanishing gradients problem (Pascanu et al. 2013 ) and so different variations have been recently proposed. LSTM (Cheng et al. 2016 ) is a particular RNN architecture widely used. LSTMs have shown to be superior to nongated RNNs on financial time-series problems because they have the ability to selectively remember patterns for a long time. A GRU (Chung et al. 2014 ) is another gated version of the standard RNN which has been used in crypto trading (Dutta et al. 2020 ). Another deep learning technology used in cryptocurrency trading is Seq2seq, which is a specific implementation of the Encoder-Decoder architecture (Xu et al. 2017 ). Seq2seq was first aimed at solving natural language processing problems but has been also applied it in cryptocurrency trend predictions in Sriram et al. ( 2017 ).

Reinforcement learning algorithms Reinforcement learning (RL) is an area of machine learning leveraging the idea that software agents act in the environment to maximize a cumulative reward (Sutton and Barto 1998 ). Deep Q-Learning (DQN) (Gu et al. 2016 ) and Deep Boltzmann Machine (DBM) (Salakhutdinov and Hinton 2009 ) are common technologies used in cryptocurrency trading using RL. Deep Q learning uses neural networks to approximate Q-value functions. A state is given as input, and Q values for all possible actions are generated as outputs (Gu et al. 2016 ). DBM is a type of binary paired Markov random field (undirected probability graphical model) with multiple layers of hidden random variables (Salakhutdinov and Hinton 2009 ). It is a network of randomly coupled random binary units.

Research on machine learning models

In the development of machine learning trading signals, technical indicators have usually been used as input features. Nakano et al. ( 2018 ) explored Bitcoin intraday technical trading based on ANNs for return prediction. The experiment obtained medium frequency price and volume data (time interval of data is 15min) of Bitcoin from a cryptocurrency exchange. An ANN predicts the price trends (up and down) in the next period from the input data. Data is preprocessed to construct a training dataset that contains a matrix of technical patterns including EMA, Emerging Markets Small Cap (EMSD), relative strength index (RSI), etc. Their numerical experiments contain different research aspects including base ANN research, effects of different layers, effects of different activation functions, different outputs, different inputs and effects of additional technical indicators. The results have shown that the use of various technical indicators possibly prevents over-fitting in the classification of non-stationary financial time-series data, which enhances trading performance compared to the primitive technical trading strategy. (Buy-and-Hold is the benchmark strategy in this experiment.)

Some classification and regression machine learning models are applied in cryptocurrency trading by predicting price trends. Most researchers have focused on the comparison of different classification and regression machine learning methods. Sun et al. ( 2019 ) used random forests (RFs) with factors in Alpha01 (Kakushadze 2016 ) (capturing features from the history of the cryptocurrency market) to build a prediction model. The experiment collected data from API in cryptocurrency exchanges and selected 5-min frequency data for backtesting. The results showed that the performances are proportional to the amount of data (more data, more accurate) and the factors used in the RF model appear to have different importance. For example, “Alpha024” and “Alpha032” features appeared as the most important in the model adopted. (The alpha features come from paper “101 Formulaic Alphas” (Kakushadze 2016 ).) Vo and Yost-Bremm ( 2018 ) applied RFs in High-Frequency cryptocurrency Trading (HFT) and compared it with deep learning models. Minute-level data is collected when utilising a forward fill imputation method to replace the NULL value (i.e., a missing value). Different periods and RF trees are tested in the experiments. The authors also compared F-1 precision and recall metrics between RF and Deep Learning (DL). The results showed that RF is effective despite multicollinearity occurring in ML features, the lack of model identification also potentially leading to model identification issues; this research also attempted to create an HFT strategy for Bitcoin using RF.

Slepaczuk and Zenkova ( 2018 ) investigated the profitability of an algorithmic trading strategy based on training an SVM model to identify cryptocurrencies with high or low predicted returns. The results showed that the performance of the SVM strategy was the fourth being better only than S&P B&H strategy, which simply buys-and-hold the S&P index. (There are other 4 benchmark strategies in this research.) The authors observed that SVM needs a large number of parameters and so is very prone to overfitting, which caused its bad performance. Barnwal et al. ( 2019 ) used generative and discriminative classifiers to create a stacking model, particularly 3 generative and 6 discriminative classifiers combined by a one-layer Neural Network, to predict the direction of cryptocurrency price. A discriminative classifier directly models the relationship between unknown and known data, while generative classifiers model the prediction indirectly through the data generation distribution (Ng and Jordan 2002 ). Technical indicators including trend, momentum, volume and volatility, are collected as features of the model. The authors discussed how different classifiers and features affect the prediction. Attanasio et al. ( 2019 ) compared a variety of classification algorithms including SVM, NB and RF in predicting next-day price trends of a given cryptocurrency. The results showed that due to the heterogeneity and volatility of cryptocurrencies’ financial instruments, forecasting models based on a series of forecasts appeared better than a single classification technology in trading cryptocurrencies. Madan et al. ( 2015 ) modeled the Bitcoin price prediction problem as a binomial classification task, experimenting with a custom algorithm that leverages both random forests and generalized linear models. Daily data, 10-min data and 10-s data are used in the experiments. The experiments showed that 10-min data gave a better sensitivity and specificity ratio than 10-second data (10-second prediction achieved around 10% accuracy). Considering predictive trading, 10-min data helped show clearer trends in the experiment compared to 10-second backtesting. Similarly, Virk ( 2017 ) compared RF, SVM, GB and LR to predict the price of Bitcoin. The results showed that SVM achieved the highest accuracy of 62.31% and precision value 0.77 among binomial classification machine learning algorithms.

Different deep learning models have been used in finding patterns of price movements in cryptocurrency markets. Zhengyang et al. ( 2019 ) implemented two machine learning models, fully-connected ANN and LSTM to predict cryptocurrency price dynamics. The results showed that ANN, in general, outperforms LSTM although theoretically, LSTM is more suitable than ANN in terms of modeling time series dynamics; the performance measures considered are MAE and RMSE in joint prediction (five cryptocurrencies daily prices prediction). The findings show that the future state of a time series for cryptocurrencies is highly dependent on its historic evolution. Kwon et al. ( 2019 ) used an LSTM model, with a three-dimensional price tensor representing the past price changes of cryptocurrencies as input. This model outperforms the GB model in terms of F1-score. Specifically, it has a performance improvement of about \(7\%\) over the GB model in 10-min price prediction. In particular, the experiments showed that LSTM is more suitable when classifying cryptocurrency data with high volatility.

Alessandretti et al. ( 2018 ) tested Gradient boosting decision trees (including single regression and XGBoost-augmented regression) and the LSTM model on forecasting daily cryptocurrency prices. They found methods based on gradient boosting decision trees worked best when predictions were based on short-term windows of 5/10 days while LSTM worked best when predictions were based on 50 days of data. The relative importance of the features in both models are compared and an optimised portfolio composition (based on geometric mean return and Sharpe ratio) is discussed in this paper. Phaladisailoed and Numnonda ( 2018 ) chose regression models (Theil-Sen Regression and Huber Regression) and deep learning-based models (LSTM and GRU) to compare the performance of predicting the rise and fall of Bitcoin price. In terms of two common measure metrics, MSE and R-Square ( \(\hbox {R}^2\) ), GRU shows the best accuracy.

Researchers have also focused on comparing classical statistical models and machine/deep learning models. Rane and Dhage ( 2019 ) described classical time series prediction methods and machine learning algorithms used for predicting Bitcoin price. Statistical models such as Autoregressive Integrated Moving Average models (ARIMA), Binomial Generalized Linear Model and GARCH are compared with machine learning models such as SVM, LSTM and Non-linear Auto-Regressive with Exogenous Input Model (NARX). The observation and results showed that the NARX model is the best model with nearly 52% predicting accuracy based on 10 seconds interval. Rebane et al. ( 2018 ) compared traditional models like ARIMA with a modern popular model like seq2seq in predicting cryptocurrency returns. The result showed that the seq2seq model exhibited demonstrable improvement over the ARIMA model for Bitcoin-USD prediction but the seq2seq model showed very poor performance in extreme cases. The authors proposed performing additional investigations, such as the use of LSTM instead of GRU units to improve the performance. Similar models were also compared by Stuerner ( 2019 ) who explored the superiority of automated investment approach in trend following and technical analysis in cryptocurrency trading. Persson et al. ( 2018 ) explored the vector autoregressive model (VAR model), a more complex RNN, and a hybrid of the two in residual recurrent neural networks (R2N2) in predicting cryptocurrency returns. The RNN with ten hidden layers is optimised for the setting and the neural network augmented by VAR allows the network to be shallower, quicker and to have a better prediction than an RNN. RNN, VAR and R2N2 models are compared. The results showed that the VAR model has phenomenal test period performance and thus props up the R2N2 model, while the RNN performs poorly. This research is an attempt at optimisation of model design and applying to the prediction on cryptocurrency returns.

Deep neural network

Deep Neural Network architectures play important roles in forecasting. Researchers had applied many advanced deep neural network models in cryptocurrency trading like stacking (CNN + RNN) and Autoencoder-Decoder. In this subsection, we describe the cutting edge Deep Neural Network researches in cryptocurrency trading. Recent studies show the productivity of using models based on such architectures for modeling and forecasting financial time series, including cryptocurrencies. Livieris et al. ( 2020 ) proposed model called CNN-LSTM for accurate prediction of gold prices and movements. The first component of the model consists of a convolutional layer and a pooling layer, where complex mathematical operations are performed to develop the features of the input data. The second component uses the generated LSTM and the features of the dense layer. The results show that due to the sensitivity of the various hyperparameters of the proposed CNN-LSTM and its high complexity, additional optimisation configurations and major feature engineering have the potential to further improve the predictive power. More Intelligent Evolutionary Optimisation (IEO) for hyperparameter optimisation is core problem when tuning the overall optimization process of machine learning models (Huan et al. 2020 ). Lu et al. ( 2020 ) proposed a CNN-LSTM based method for stock price prediction. In In terms of MAE, RMSE and \(R^2\) metrics, the experimental results showed that CNN-LSTM has the highest prediction accuracy and the best performance compared with MLP, CNN, RNN, LSTM, and CNN-RNN.

Fang et al. ( 2021 ) applied an autoencoder-augmented LSTM structure in predicting the mid-price of 8 cryptocurrency pairs. Level-2 limit order book live data is collected and the experiment achieved 78% accuracy of price movements prediction in high frequency trading (tick level). This research improved and verified the view of Sirignano and Cont ( 2019 ) that universal models have better performance than currency-pair specific models for cryptocurrency markets. Moreover, “Walkthrough” (i.e., retrain the original deep learning model itself when it appears to no longer be valid) is proposed as a method to optimise the training of a deep learning model and shown to significantly improve the prediction accuracy. Yao et al. ( 2018 ) proposed a new method for predicting cryptocurrency prices based on deep learning techniques such as RNN and LSTM, taking into account various factors such as market capitalization, trading volume, circulating supply and maximum supply. The experimental results showed that the model performs well for a certain size of dataset. Livieris et al. ( 2020 ) combined three of the most widely used integration learning strategies: integrated averaging, bagging and stacking, with advanced deep learning models for predicting hourly prices of major cryptocurrencies. The proposed integrated model is evaluated using a state-of-the-art deep learning model as a component learner, which consists of a combination of LSTM, bidirectional LSTM and convolutional layers. The authors’ detailed experimental analysis shows that integrated learning and deep learning can effectively reinforce each other to develop robust, stable and reliable predictive models. Kumar and Rath ( 2020 ) analyzed how deep learning techniques such as MLP and LSTM can help predict the price trend of Ethereum. By applying day/hour/minute historical data, the LSTM model is more robust and accurate to long-term dependencies than the MLP while LSTM outperformed the MLP marginally but not very significantly.

Sentiment analysis

Sentiment analysis, a popular research topic in the age of social media, has also been adopted to improve predictions for cryptocurrency trading. This data source typically has to be combined with Machine Learning for the generation of trading signals.

Lamon et al. ( 2017 ) used daily news and social media data labeled on actual price changes, rather than on positive and negative sentiment. By this approach, the prediction on price is replaced with positive and negative sentiment. The experiment acquired cryptocurrency-related news article headlines from the website like “cryptocoinsnews” and twitter API. Weights are taken in positive and negative words in the cryptocurrency market. Authors compared Logistic Regression (LR), Linear Support Vector Machine (LSVM) and NB as classifiers and concluded that LR is the best classifier in daily price prediction with 43.9% of price increases correctly predicted and 61.9% of price decreases correctly forecasted. Smuts ( 2019 ) conducted a similar binary sentiment-based price prediction method with an LSTM model using Google Trends and Telegram sentiment. In detail, the sentiment was extracted from Telegram by using a novel measure called VADER  (Hutto and Gilbert 2014 ). The backtesting reached 76% accuracy on the test set during the first half of 2018 in predicting hourly prices.

Nasir et al. ( 2019 ) researched the relationship between cryptocurrency returns and search engines. The experiment employed a rich set of established empirical approaches including VAR framework, copulas approach and non-parametric drawings of time series. The results found that Google searches exert significant influence on Bitcoin returns, especially in the short-term intervals. Kristoufek ( 2013 ) discussed positive and negative feedback on Google trends or daily views on Wikipedia. The author mentioned different methods including Cointegration, Vector autoregression and Vector error-correction model to find causal relationships between prices and searched terms in the cryptocurrency market. The results indicated that search trends and cryptocurrency prices are connected. There is also a clear asymmetry between the effects of increased interest in currencies above or below their trend values from the experiment. Kim et al. ( 2016 ) analysed user comments and replies in online communities and their connection with cryptocurrency volatility. After crawling comments and replies in online communities, authors tagged the extent of positive and negative topics. Then the relationship between price and the number of transactions of cryptocurrency is tested according to comments and replies to selected data. At last, a prediction model using machine learning based on selected data is created to predict fluctuations in the cryptocurrency market. The results show the amount of accumulated data and animated community activities exerted a direct effect on fluctuation in the price and volume of a cryptocurrency.

Phillips and Gorse ( 2018 ) applied dynamic topic modeling and Hawkes model to decipher relationships between topics and cryptocurrency price movements. The authors used Latent Dirichlet allocation (LDA) model for topic modeling, which assumes each document contains multiple topics to different extents. The experiment showed that particular topics tend to precede certain types of price movements in the cryptocurrency market and the authors proposed the relationships could be built into real-time cryptocurrency trading. Li et al. ( 2019 ) analysed Twitter sentiment and trading volume and an Extreme Gradient Boosting Regression Tree Model in the prediction of ZClassic (ZCL) cryptocurrency market. Sentiment analysis using natural language processing from the Python package “Textblob” assigns impactful words a polarity value. Values of weighted and unweighted sentiment indices are calculated on an hourly basis by summing weights of coinciding tweets, which makes us compare this index to ZCL price data. The model achieved a Pearson correlation of 0.806 when applied to test data, yielding a statistical significance at the \(p < 0.0001\) level. Flori ( 2019 ) relied on a Bayesian framework that combines market-neutral information with subjective beliefs to construct diversified investment strategies in the Bitcoin market. The result shows that news and media attention seem to contribute to influence the demand for Bitcoin and enlarge the perimeter of the potential investors, probably stimulating price euphoria and upwards-downwards market dynamics. The authors’ research highlighted the importance of news in guiding portfolio re-balancing. Bouri and Gupta ( 2019 ) compared the ability of newspaper-based metrics and internet search-based uncertainty metrics in predicting bitcoin returns. The predictive power of Internet-based economic uncertainty-related query indices is statistically stronger than that of newspapers in predicting bitcoin returns.

Similarly, Colianni et al. ( 2015 ), Garcia and Schweitzer ( 2015 ), Zamuda et al. ( 2019 ) et al. used sentiment analysis technology applying it in the cryptocurrency trading area and had similar results. Colianni et al. ( 2015 ) cleaned data and applied supervised machine learning algorithms such as logistic regression, Naive Bayes and support vector machines, etc. on Twitter Sentiment Analysis for cryptocurrency trading. Garcia and Schweitzer ( 2015 ) applied multidimensional analysis and impulse analysis in social signals of sentiment effects and algorithmic trading of Bitcoin. The results verified the long-standing assumption that transaction-based social media sentiment has the potential to generate a positive return on investment. Zamuda et al. ( 2019 ) adopted new sentiment analysis indicators and used multi-target portfolio selection to avoid risks in cryptocurrency trading. The perspective is rationalized based on the elastic demand for computing resources of the cloud infrastructure. A general model evaluating the influence between user’s network Action-Reaction-Influence-Model (ARIM) is mentioned in this research. Bartolucci et al. ( 2019 ) researched cryptocurrency prices with the “Butterfly effect”, which means “issues” of the open-source project provides insights to improve prediction of cryptocurrency prices. Sentiment, politeness, emotions analysis of GitHub comments are applied in Ethereum and Bitcoin markets. The results showed that these metrics have predictive power on cryptocurrency prices.

Reinforcement learning

Deep reinforcement algorithms bypass prediction and go straight to market management actions to achieve high cumulated profit (Henderson et al. 2018 ; Liu et al. 2021 ). Bu and Cho ( 2018 ) proposed a combination of double Q-network and unsupervised pre-training using DBM to generate and enhance the optimal Q-function in cryptocurrency trading. The trading model contains agents in series in the form of two neural networks, unsupervised learning modules and environments. The input market state connects an encoding network which includes spectral feature extraction (convolution-pooling module) and temporal feature extraction (LSTM module). A double-Q network follows the encoding network and actions are generated from this network. Compared to existing deep learning models (LSTM, CNN, MLP, etc.), this model achieved the highest profit even facing an extreme market situation (recorded 24% of the profit while cryptocurrency market price drops by \(-64\%\) ). Juchli ( 2018 ) applied two implementations of reinforcement learning agents, a Q-Learning agent, which serves as the learner when no market variables are provided, and a DQN agent which was developed to handle the features previously mentioned. The DQN agent was backtested under the application of two different neural network architectures. The results showed that the DQN-CNN agent (convolutional neural network) is superior to the DQN-MLP agent (multilayer perceptron) in backtesting prediction. Lucarelli and Borrotti ( 2019 ) focused on improving automated cryptocurrency trading with a deep reinforcement learning approach. Double and Dueling double deep Q-learning networks are compared for 4 years. By setting rewards functions as Sharpe ratio and profit, the double Q-learning method demonstrated to be the most profitable approach in trading cryptocurrency. Sattarov et al. ( 2020 ) applied deep reinforcement learning and used historical data from BTC, LTC and ETH to observe historical price movements and acted on real-time prices. The model proposed by the authors helped traders to correctly choose one of the following three actions: buy, sell and hold stocks and get advice on the correct option. Experiments applying BTC via deep reinforcement learning showed that investors made a net profit of 14.4% in one month. Similarly, tests on LTC and ETH ended with 74% and 41% profits respectively. Koker and Koutmos ( 2020 ) pointed out direct reinforcement (DR) based model for active trading. Within the model, the authors attempt to estimate the parameters of the non-linear autoregressive model to achieve maximum risk-adjusted returns. Traders can take long or short positions in each of our sampled cryptocurrency markets, establish or hold them at the end of time interval t , and re-evaluate at the end of \(t+1\) . The results provide some preliminary evidence that cryptocurrency prices may not follow a purely random wandering process.

Atsalakis et al. ( 2019 ) proposes a computational intelligence technique that uses a hybrid Neuro-Fuzzy controller, namely PATSOS, to forecast the direction in the change of the daily price of Bitcoin. The proposed methodology outperforms two other computational intelligence models, the first being developed with a simpler neuro-fuzzy approach, and the second being developed with artificial neural networks. According to the signals of the proposed model, the investment return obtained through trading simulation is 71.21% higher than the investment return obtained through a simple buy and hold strategy. This application is proposed for the first time in the forecasting of Bitcoin price movements. Topological data analysis is applied to forecasting price trends of cryptocurrency markets in Kim et al. ( 2018 ). The approach is to harness topological features of attractors of dynamical systems for arbitrary temporal data. The results showed that the method can effectively separate important topological patterns and sampling noise (like bid-ask bounces, discreteness of price changes, differences in trade sizes or informational content of price changes, etc.) by providing theoretical results. Kurbucz ( 2019 ) designed a complex method consisting of single-hidden layer feedforward neural networks (SLFNs) to (1) determine the predictive power of the most frequent edges of the transaction network (a public ledger that records all Bitcoin transactions) on the future price of Bitcoin; and, (2) to provide an efficient technique for applying this untapped dataset in day trading. The research found a significantly high accuracy (60.05%) for the price movement classifications base on information that can be obtained using a small subset of edges (approximately 0.45% of all unique edges). It is worth noting that, Kondor et al. ( 2014 ), Kondor et al. ( 2014 ) firstly published some papers giving analysis on transaction networks on cryptocurrency markets and applied related research in identifying Bitcoin users (Juhász et al. 2018 ).

Abay et al. ( 2019 ) attempted to understand the network dynamics behind the Blockchain graphs using topological features. The results showed that standard graph features such as the degree distribution of transaction graphs may not be sufficient to capture network dynamics and their potential impact on Bitcoin price fluctuations. Omane-Adjepong et al. ( 2019 ) applied wavelet time-scale persistence in analysing returns and volatility in cryptocurrency markets. The experiment examined the long-memory and market efficiency characteristics in cryptocurrency markets using daily data for more than two years. The authors employed a log-periodogram regression method in researching stationarity in the cryptocurrency market and used ARFIMA-FIGARCH class of models in examining long-memory behaviour of cryptocurrencies across time and scale. In general, experiments indicated that heterogeneous memory behaviour existed in eight cryptocurrency markets using daily data over the full-time period and across scales (August 25, 2015 to March 13, 2018).

Portfolio, cryptocurrency assets and market condition research

Research among cryptocurrency pairs and related factors.

Ji et al. ( 2019 ) examined connectedness via return and volatility spillovers across six large cryptocurrencies (collected from coinmarketcap lists from August 7 2015 to February 22 2018) and found Litecoin and Bitcoin to have the most effect on other cryptocurrencies. The authors followed methods of Diebold and Yılmaz ( 2014 ) and built positive/negative returns and volatility connectedness networks. Furthermore, the regression model is used to identify drivers of various cryptocurrency integration levels. Further analysis revealed that the relationship between each cryptocurrency in terms of return and volatility is not necessarily due to its market size. Omane-Adjepong and Alagidede ( 2019 ) explored market coherence and volatility causal linkages of seven leading cryptocurrencies. Wavelet-based methods are used to examine market connectedness. Parametric and nonparametric tests are employed to investigate directions of volatility spillovers of the assets. Experiments revealed from diversification benefits to linkages of connectedness and volatility in cryptocurrency markets. Bouri et al. ( 2020 ) found the presence of jumps was detected in a series of 12 cryptocurrency returns, and significant jumping activity was found in all cases. More results underscore the importance of the jump in trading volume for the formation of cryptocurrency leapfrogging. Drożdż et al. ( 2020 ) examined the correlation of daily exchange rate fluctuations within a basket of the 100 highest market capitalization cryptocurrencies for the period October 1, 2015 to March 31, 2019. The corresponding dynamics mainly involve one of the leading eigenvalues of the correlation matrix, while the others are mainly consistent with the eigenvalues of the Wishart random matrix. The study shows that Bitcoin (BTC) was dominant during the period under consideration, signalling exchange rate dynamics at least as influential as the US dollar (USD).

Some researchers explored the relationship between cryptocurrency and different factors, including futures, gold, etc. Hale et al. ( 2018 ) suggested that Bitcoin prices rise and fall rapidly after CME issues futures consistent with pricing dynamics. Specifically, the authors pointed out that the rapid rise and subsequent decline in prices after the introduction of futures is consistent with trading behaviour in the cryptocurrency market. Kristjanpoller et al. ( 2020 ) focused on the asymmetric interrelationships between major currencies and cryptocurrencies. The results of multiple fractal asymmetric de-trending cross-correlation analysis show evidence of significant persistence and asymmetric multiplicity in the cross-correlation between most cryptocurrency pairs and ETF pairs. Bai and Robinson ( 2019 ) studied a trading algorithm for foreign exchange on a cryptocurrency Market using the Automated Triangular Arbitrage method. Implementing a pricing strategy, implementing trading algorithms and developing a given trading simulation are three problems solved by this research. Kang et al. ( 2019 ) examined the hedging and diversification properties of gold futures versus Bitcoin prices by using dynamic conditional correlations (DCCs) and wavelet coherence. DCC-GARCH model (Engle 2002 ) is used to estimate the time-varying correlation between Bitcoin and gold futures by modeling the variance and the co-variance but also this two flexibility. Wavelet coherence method focused more on co-movement between Bitcoin and gold futures. From experiments, the wavelet coherence results indicated volatility persistence, causality and phase difference between Bitcoin and gold. Qiao et al. ( 2020 ) used wavelet coherence and relevance networks to investigate synergistic motion between Bitcoin and other cryptocurrencies. The authors then tested the hedging effect of bitcoin on others at different time frequencies by risk reduction and downside risk reduction. The empirical results provide evidence of linkage and hedging effects. Bitcoin’s returns and volatility are ahead of other cryptocurrencies at low frequencies from the analysis, and in the long run, Bitcoin has a more pronounced hedging effect on other cryptocurrencies.  Dyhrberg ( 2016 ) applied the GARCH model and the exponential GARCH model in analysing similarities between Bitcoin, gold and the US dollar. The experiments showed that Bitcoin, gold and the US dollar have similarities with the variables of the GARCH model, have similar hedging capabilities and react symmetrically to good and bad news. The authors observed that Bitcoin can combine some advantages of commodities and currencies in financial markets to be a tool for portfolio management.

Baur et al. ( 2018 ) extended the research of Dyhrberg et al.; the same data and sample periods are tested (Dyhrberg 2016 ) with GARCH and EGARCH-(1,1) models but the experiments reached different conclusions. Baur et al. found that Bitcoin has unique risk-return characteristics compared with other assets. They noticed that Bitcoin excess returns and volatility resemble a rather highly speculative asset with respect to gold or the US dollar. Bouri et al. ( 2017 ) studied the relationship between Bitcoin and energy commodities by applying DCCs and GARCH (1,1) models. In particular, the results showed that Bitcoin is a strong hedge and safe haven for energy commodities. Kakushadze ( 2018 ) proposed factor models for the cross-section of daily cryptoasset returns and provided source code for data downloads, computing risk factors and backtesting for all cryptocurrencies and a host of various other digital assets. The results showed that cross-sectional statistical arbitrage trading may be possible for cryptoassets subject to efficient executions and shorting. Beneki et al. ( 2019 ) tested hedging abilities between Bitcoin and Ethereum by a multivariate BEKK-GARCH methodology and impulse response analysis within VAR model. The results indicated a volatility transaction from Ethereum to Bitcoin, which implied possible profitable trading strategies on the cryptocurrency derivatives market. Caporale and Plastun ( 2019 ) examined the week effect in cryptocurrency markets and explored the feasibility of this indicator in trading practice. Student t -test, ANOVA, Kruskal-Wallis and Mann-Whitney tests were carried out for cryptocurrency data in order to compare time periods that may be characterised by anomalies with other time periods. When an anomaly is detected, an algorithm was established to exploit profit opportunities (MetaTrader terminal in MQL4 is mentioned in this research). The results showed evidence of anomaly (abnormal positive returns on Mondays) in the Bitcoin market by backtesting in 2013-2016.

A number of special research methods have proven to be relevant to cryptocurrency pairs, which is reflected in cryptocurrency trading. Delfabbro et al. ( 2021 ) pointed out that cryprocurrency trading have similarities to gambling. Decisions are often based on limited information, short-term profit motives, and highly volatile and uncertain outcomes. The authors examined whether gambling and problem gambling are reliable predictors of reported cryptocurrency trading strength. Results showed that problem gambling scores (PGSI) and engaging in stock trading were significantly correlated with measures of cryptocurrency trading intensity based on time spent per day, number of trades and level of expenditure. In further research, Delfabbro et al. ( 2021 ) reviewed the specific structural features of cryptocurrency trading and its potential to give rise to excessive or harmful behaviour, including over-spending and compulsive checking. There are some similarities noted between online sports betting and day trading, but there are also some important differences. These include the 24/7 nature of trading, the global nature of the market and the powerful role of social media, social influences and non-balance sheet related events as determinants of price movement. Cheng and Yen ( 2020 ) investigated whether the economic policy uncertainty (EPU) index provided by Baker et al. ( 2016 ) can predict the returns of cryptocurrencies. The results suggest that China’s EPU Index can predict monthly returns for Bitcoin, whereas the EPU Index for the US or other Asian countries has no predictive power. In addition, China’s ban on cryptocurrency trading only affects bitcoin returns among major cryptocurrencies. Leirvik ( 2021 ) analysed the relationship between the particular volatility of market liquidity and the returns of the five largest cryptocurrencies by market capitalisation. The results showed that in general there is a positive correlation between the volatility of liquidity and the returns of large-cap cryptocurrencies. For the most liquid and popular cryptocurrencies, this effect does not exist: Bitcoin. Moreover, the liquidity of cryptocurrencies increases over time, but varies greatly over time.

Crypto-asset portfolio research

Some researchers applied portfolio theory for crypto assets. Corbet et al. ( 2019 ) gave a systematic analysis of cryptocurrencies as financial assets. Brauneis and Mestel ( 2019 ) applied the Markowitz mean-variance framework in order to assess the risk-return benefits of cryptocurrency portfolios. In an out-of-sample analysis accounting for transaction cost, they found that combining cryptocurrencies enriches the set of ‘low’-risk cryptocurrency investment opportunities. In terms of the Sharpe ratio and certainty equivalent returns, the 1/ N -portfolio (i.e., “naive” strategies, such as equally dividing amongst asset classes) outperformed single cryptocurrencies and more than 75% in terms of the Sharpe ratio and certainty equivalent returns of mean-variance optimal portfolios. Castro et al. ( 2019 ) developed a portfolio optimisation model based on the Omega measure which is more comprehensive than the Markowitz model and applied this to four crypto-asset investment portfolios by means of a numerical application. Experiments showed crypto-assets improves the return of the portfolios, but on the other hand, also increase the risk exposure.

Bedi and Nashier ( 2020 ) examined diversification capabilities of Bitcoin for a global portfolio spread across six asset classes from the standpoint of investors dealing in five major fiat currencies, namely US Dollar, Great Britain Pound, Euro, Japanese Yen and Chinese Yuan. They employed modified Conditional Value-at-Risk and standard deviation as measures of risk to perform portfolio optimisations across three asset allocation strategies and provided insights into the sharp disparity in Bitcoin trading volumes across national currencies from a portfolio theory perspective. Similar research has been done by Antipova ( 2019 ), which explored the possibility of establishing and optimizing a global portfolio by diversifying investments using one or more cryptocurrencies, and assessing returns to investors in terms of risks and returns. Fantazzini and Zimin ( 2020 ) proposed a set of models that can be used to estimate the market risk for a portfolio of crypto-currencies, and simultaneously estimate their credit risk using the Zero Price Probability (ZPP) model. The results revealed the superiority of the t-copula/skewed-t GARCH model for market risk, and the ZPP-based models for credit risk. Ji et al. ( 2019 ) examined the common dynamics of bitcoin exchanges. Using a connectivity metric based on the actual daily volatility of the bitcoin price, they found that Coinbase is undoubtedly the market leader, while Binance performance is surprisingly weak. The results also suggested that safer asset extraction is more important for volatility linkages between Bitcoin exchanges relative to trading volumes. Fasanya et al. ( 2020 ) quantified returns and volatility transmission between cryptocurrency portfolios by using a spillover approach and rolling sample analysis. The results showed that there is a significant difference between the behaviour of cryptocurrency portfolio returns and the volatility spillover index over time. Given the spillover index, the authors found evidence of interdependence between cryptocurrency portfolios, with the spillover index showing an increased degree of integration between cryptocurrency portfolios.

Trucíos et al. ( 247 ) proposed a methodology based on vine copulas and robust volatility models to estimate the Value-at-Risk (VaR) and Expected Shortfall (ES) of cryptocurrency portfolios. The proposed algorithm displayed good performance in estimating both VaR and ES. Hrytsiuk et al. ( 2019 ) showed that the cryptocurrency returns can be described by the Cauchy distribution and obtained the analytical expressions for VaR risk measures and performed calculations accordingly. As a result of the optimisation, the sets of optimal cryptocurrency portfolios were built in their experiments.

Jiang and Liang ( 2017 ) proposed a two-hidden-layer CNN that takes the historical price of a group of cryptocurrency assets as an input and outputs the weight of the group of cryptocurrency assets. This research focused on portfolio research in cryptocurrency assets using emerging technologies like CNN. Training is conducted in an intensive manner to maximise cumulative returns, which is considered a reward function of the CNN network. The performance of the CNN strategy is compared with the three benchmarks and the other three portfolio management algorithms (buy and hold strategy, Uniform Constant Rebalanced Portfolio and Universal Portfolio with Online Newton Step and Passive Aggressive Mean Reversion); the results are positive in that the model is only second to the Passive Aggressive Mean Reversion algorithm (PAMR). Estalayo et al. ( 2019 ) reported initial findings around the combination of DL models and Multi-Objective Evolutionary Algorithms (MOEAs) for allocating cryptocurrency portfolios. Technical rationale and details were given on the design of a stacked DL recurrent neural network, and how its predictive power can be exploited for yielding accurate ex-ante estimates of the return and risk of the portfolio. Results obtained for a set of experiments carried out with real cryptocurrency data have verified the superior performance of their designed deep learning model with respect to other regression techniques.

Bubbles and Crash Analysis

Bubbles and crash analysis is an important researching area in cryptocurrency trading. Phillips and Yu proposed a methodology to test for the presence of cryptocurrency bubble (Cheung et al. 2015 ), which is extended by Corbet et al. ( 2018 ). The method is based on supremum Augmented Dickey-Fuller (SADF) to test for the bubble through the inclusion of a sequence of forwarding recursive right-tailed ADF unit root tests. An extended methodology generalised SADF (GSAFD), is also tested for bubbles within cryptocurrency data. The research concluded that there is no clear evidence of a persistent bubble in cryptocurrency markets including Bitcoin or Ethereum. Bouri et al. ( 2019 ) date-stamped price explosiveness in seven large cryptocurrencies and revealed evidence of multiple periods of explosivity in all cases. GSADF is used to identify multiple explosiveness periods and logistic regression is employed to uncover evidence of co-explosivity across cryptocurrencies. The results showed that the likelihood of explosive periods in one cryptocurrency generally depends on the presence of explosivity in other cryptocurrencies and points toward a contemporaneous co-explosivity that does not necessarily depend on the size of each cryptocurrency.

Extended research by Phillips et al. ( 2015a , 2015b ) (who applied a recursive augmented Dickey-Fuller algorithm, which is called PSY test) and Enoksen and Landsnes ( 2019 ) studied possible predictors of bubble periods of certain cryptocurrencies. The evaluation includes multiple bubble periods in all cryptocurrencies. The result shows that higher volatility and trading volume is positively associated with the presence of bubbles across cryptocurrencies. In terms of bubble prediction, the authors found the probit model to perform better than the linear models.

Phillips and Gorse ( 2017 ) used Hidden Markov Model (HMM) and Superiority and Inferiority Ranking (SIR) method to identify bubble-like behaviour in cryptocurrency time series. Considering HMM and SIR method, an epidemic detection mechanism is used in social media to predict cryptocurrency price bubbles, which classify bubbles through epidemic and non-epidemic labels. Experiments have demonstrated a strong relationship between Reddit usage and cryptocurrency prices. This work also provides some empirical evidence that bubbles mirror the social epidemic-like spread of an investment idea. Caporale and Plastun ( 2018 ) examined the price overreactions in the case of cryptocurrency trading. Some parametric and non-parametric tests confirmed the presence of price patterns after overreactions, which identified that the next-day price changes in both directions are bigger than after “normal” days. The results also showed that the overreaction detected in the cryptocurrency market would not give available profit opportunities (possibly due to transaction costs) that cannot be considered as evidence of the EMH. Chaim and Laurini ( 2018 ) analysed the high unconditional volatility of cryptocurrency from a standard log-normal stochastic volatility model to discontinuous jumps of volatility and returns. The experiment indicated the importance of incorporating permanent jumps to volatility in cryptocurrency markets.

Cross et al. ( 2021 ) investigated the existence and nature of the interdependence of bitcoin, ethereum, litecoin and ripple during the cryptocurrency bubble of 2017-18. A generalized time-varying asset pricing model approach is proposed. The results showed that the negative news impact of the boom period in 2017 for LiteCoin and Ripple, which incurred a risk premium for investors, could explain the returns of cryptocurrencies during the 2018 crash.

Extreme condition

Differently from traditional fiat currencies, cryptocurrencies are risky and exhibit heavier tail behaviour. Katsiampa et al. ( 2018 ) found extreme dependence between returns and trading volumes. Evidence of asymmetric return-volume relationship in the cryptocurrency market was also found by the experiment, as a result of discrepancies in the correlation between positive and negative return exceedances across all the cryptocurrencies.

There has been a price crash in late 2017 to early 2018 in cryptocurrency (Yaya et al. 2018 ). Yaya et al. ( 2018 ) researched the persistence and dependence of Bitcoin on other popular alternative coins before and after the 2017/18 crash in cryptocurrency markets. The result showed that higher persistence of shocks is expected after the crash due to speculations in the mind of cryptocurrency traders, and more evidence of non-mean reversions, implying chances of further price fall in cryptocurrencies.

Manahov ( 2021 ) obtained millisecond data for major cryptocurrencies as well as the cryptocurrency indices Cryptocurrency IndeX (CRIX) and Cryptocurrencies Index 30 (CCI30) to investigate the relationship between cryptocurrency liquidity, herding behaviour and profitability during extreme price movements (EPM). Millisecond data was obtained for major cryptocurrencies as well as the cryptocurrency indices CRIX and CCI30 to investigate the relationship between cryptocurrency liquidity, herding behaviour and profitability during EPM. The experiments demonstrate that cryptocurrency traders (CTs) can promote EPM and demand liquidity even during periods of maximum EPM. The authors’ robustness checks suggest that herding behaviour follows a dynamic pattern with decreasing magnitude over time. Shahzad et al. ( 2021 ) investigated the interdependence of median-based and tail-based returns between cryptocurrencies under normal and extreme market conditions. The experiment used daily data and combines LASSO techniques with quantile regression within a network analysis framework. The main results showed that the interdependence of the tails is higher than the median, especially in the right tail. Fluctuations in market, size and momentum drive return connectivity and clustering coefficients under both normal and extreme market conditions. Chan et al. ( 2022 ) examined the extreme dependence and correlation between high-frequency cryptocurrency (Bitcoin and Ethereum, relative to the euro and the US dollar) returns and trading volumes in the extreme tails associated with booms and busts in cryptocurrency markets. Experiments with extreme value theory methods highlight how these results can help traders and practitioners who rely on technical indicators in their trading strategies - especially in times of extreme market volatility or irrational market booms.

Others related to Cryptocurrency Trading

Some other research papers related to cryptocurrency trading treat distributed in market behaviour, regulatory mechanisms and benchmarks.

Krafft et al. ( 2018 ) and Yang et al. ( 2018 ) analysed market dynamics and behavioural anomalies respectively to understand effects of market behaviour in the cryptocurrency market. Krafft et al. discussed potential ultimate causes, potential behavioural mechanisms and potential moderating contextual factors to enumerate possible influence of GUI and API on cryptocurrency markets. Then they highlighted the potential social and economic impact of human-computer interaction in digital agency design. Yang, on the other hand, applied behavioural theories of asset pricing anomalies in testing 20 market anomalies using cryptocurrency trading data. The results showed that anomaly research focused more on the role of speculators, which gave a new idea to research the momentum and reversal in the cryptocurrency market. Marchesi ( 2016 ) implemented a mechanism to form a Bitcoin price and specific behaviour for each type of trader including the initial wealth distribution following Pareto’s law, order-based transaction and price settlement mechanism. Specifically, the model reproduced the unit root attributes of the price series, the fat tail phenomenon, the volatility clustering of price returns, the generation of Bitcoins, hashing power and power consumption.

Leclair ( 2018 ) and Vidal-Tomás et al. ( 2019 ) analysed the existence of herding in the cryptocurrency market. Leclair applied herding methods of Hwang and Salmon ( 2004 ) in estimating the market herd dynamics in the CAPM framework. Vidal-Thomás et al. analyse the existence of herds in the cryptocurrency market by returning the cross-sectional standard (absolute) deviations. Both their findings showed significant evidence of market herding in the cryptocurrency market. Makarov and Schoar ( 2020 ) studied price impact and arbitrage dynamics in the cryptocurrency market and found that 85% of the variations in bitcoin returns and the idiosyncratic components of order flow (Liu et al. 2021 ) play an important role in explaining the size of the arbitrage spreads between exchanges. King and Koutmos ( 2021 ) examined the extent to which herding and feedback trading behaviour drive the price dynamics of nine major cryptocurrencies. The study documented heterogeneity in the types of feedback trading strategies used by investors in different markets and evidence of herding or “trend chasing” behaviour in some cryptocurrency markets.

In November 2019, Griffin et al. put forward a paper on the thesis of unsupported digital money inflating cryptocurrency prices (Griffin and Shams 2019 ), which caused a great stir in the academic circle and public opinion. Using algorithms to analyse Blockchain data, they found that purchases with Tether are timed following market downturns and result in sizeable increases in Bitcoin prices. By mapping the blockchains of Bitcoin and Tether, they were able to establish that one large player on Bitfinex uses Tether to purchase large amounts of Bitcoin when prices are falling and following the prod of Tether.

More researches involved benchmark and development in cryptocurrency market  (Hileman and Rauchs 2017 ; Zhou and Kalev 2019 ), regulatory framework analysis (Shanaev et al. 2020 ; Feinstein and Werbach 2021 ), data mining technology in cryptocurrency trading (Patil et al. 2018 ), application of efficient market hypothesis in the cryptocurrency market (Sigaki et al. 2019 ), Decentralized Exchanges (DEXs) and artificial financial markets for studying a cryptocurrency market (Cocco et al. 2017 ). Hileman and Rauchs ( 2017 ) segmented the cryptocurrency industry into four key sectors: exchanges, wallets, payments and mining. They gave a benchmarking study of individuals, data, regulation, compliance practices, costs of firms and a global map of mining in the cryptocurrency market in 2017. Zhou and Kalev ( 2019 ) discussed the status and future of computer trading in the largest group of Asia-Pacific economies and then considered algorithmic and high frequency trading in cryptocurrency markets as well. Shanaev et al. ( 2020 ) used data on 120 regulatory events to study the implications of cryptocurrency regulation and the results showed that stricter regulation of cryptocurrency is not desirable. Feinstein and Werbach ( 2021 ) collected raw data on global cryptocurrency regulations and used them to empirically test the trading activity of many exchanges against key regulatory announcements. No systematic evidence has been found that regulatory measures cause traders to flee or enter the affected regional jurisdictions according to authors’ analysis. Patil et al. ( 2018 ) used the average absolute error calculated between the actual and predicted values of the market sentiment of different cryptocurrencies on that day as a method for quantifying the uncertainty. They used the comparison of uncertainty quantification methods and opinion mining to analyse current market conditions. Sigaki et al. ( 2019 ) used permutation entropy and statistical complexity on the sliding time window returned by the price log to quantify the dynamic efficiency of more than four hundred cryptocurrencies. As a result, the cryptocurrency market showed significant compliance with efficient market assumptions. Aspris et al. ( 2021 ) surveyed the rapid rise of DEXs, including automated market makers. The study demonstrated the significant differences in the listing and trading characteristics of these tokens compared to their centralised equivalents. Cocco et al. ( 2017 ) described an agent-based artificial cryptocurrency market in which heterogeneous agents buy or sell cryptocurrencies. The proposed simulator is able to reproduce some real statistical properties of price returns observed in the Bitcoin real market. Marko (Ogorevc 2019 ) considered the future use of cryptocurrencies as money based on the long-term value of cryptocurrencies. Gandal and Halaburda ( 2014 ) analysed the influence of network effect on the competition of new cryptocurrency markets. Bariviera and Merediz-Sola ( 2020 ) gave a survey based on hybrid analysis, which proposed a methodological hybrid method for a comprehensive literature review and provided the latest technology in the cryptocurrency economics literature.

There also exists some research and papers introducing the basic process and rules of cryptocurrency trading including findings of Hansel ( 2018 ), Kate ( 2018 ), Garza ( 2019 ), Ward ( 2018 ) and Fantazzini ( 2019 ). Hansel ( 2018 ) introduced the basics of cryptocurrency, Bitcoin and Blockchain, ways to identify the profitable trends in the market, ways to use Altcoin trading platforms such as GDAX and Coinbase, methods of using a crypto wallet to store and protect the coins in their book. Kate ( 2018 ) set six steps to show how to start an investment without any technical skills in the cryptocurrency market. This book is an entry-level trading manual for starters learning cryptocurrency trading. Garza ( 2019 ) simulated an automatic cryptocurrency trading system, which helps investors limit systemic risks and improve market returns. This paper is an example to start designing an automatic cryptocurrency trading system. Ward ( 2018 ) discussed algorithmic cryptocurrency trading using several general algorithms, and modifications thereof including adjusting the parameters used in each strategy, as well as mixing multiple strategies or dynamically changing between strategies. This paper is an example to start algorithmic trading in cryptocurrency market. Fantazzini ( 2019 ) introduced the R packages Bitcoin-Finance and bubble, including financial analysis of cryptocurrency markets including Bitcoin.

A community resource, that is, a platform for scholarly communication, about cryptocurrencies and Blockchains is “Blockchain research network”, see (research network 2020 ).

Summary Analysis of Literature Review

This section analyses the timeline, the research distribution among technology and methods, the research distribution among properties. It also summarises the datasets that have been used in cryptocurrency trading research.

Figure  8 shows several major events in cryptocurrency trading. The timeline contains milestone events in cryptocurrency trading and important scientific breakthroughs in this area.

As early as 2009, Satoshi Nakamoto proposed and invented the first decentralised cryptocurrency, Nakamoto ( 2009 ). It is considered to be the start of cryptocurrency. In 2010, the first cryptocurrency exchange was founded, which means cryptocurrency would not be an OTC market but traded on exchanges based on an auction market system.

In 2013, Kristoufek ( 2013 ) concluded that there is a strong correlation between Bitcoin price and the frequency of “Bitcoin” search queries in Google Trends and Wikipedia. In 2014, Lee and Yang ( 2014 ) firstly proposed to check causality from copula-based causality in the quantile method from trading volumes of seven major cryptocurrencies to returns and volatility.

In 2015, Cheah and Fry ( 2015 ) discussed the bubble and speculation of Bitcoin and cryptocurrencies. In 2016, Dyhrberg explored Bitcoin volatility using GARCH models combined with gold and US dollars (Dyhrberg 2016 ).

From late 2016 to 2017, machine learning and deep learning technology were applied in the prediction of cryptocurrency return. In 2016, McNally ( 2016 ) predicted Bitcoin price using the LSTM algorithm. Bell ( 2016 ); Żbikowski ( 2016 ) applied SVM algorithm to predict trends of cryptocurrency price. In 2017, Jiang and Liang ( 2017 ) used double Q-network and pre-trained it using DBM for the prediction of cryptocurrencies portfolio weights.

From 2019 to 2020, several research directions including cross asset portfolios (Bedi and Nashier 2020 ; Castro et al. 2019 ; Brauneis and Mestel 2019 ), transaction network applications (Kurbucz 2019 ; Bouri et al. 2019 ), machine learning optimisation (Rane and Dhage 2019 ; Atsalakis et al. 2019 ; Zhengyang et al. 2019 ) have been considered in the cryptocurrency trading area.

In 2021, more regulation issues were put out the stage. On 18 May 2021, China banned financial institutions and payment companies from providing services related to cryptocurrency transactions, which led to a sharp drop in the price of bitcoin (Reuters 2021 ). In June 2021, El Salvador becomes the first country to accept Bitcoin as legal tender (MercoPress 2021 ).

Timeline of cryptocurrency trading research

Research Distribution among Properties

We counted the number of papers covering different aspects of cryptocurrency trading. Figure 9 shows the result. The attributes in the legend are ranked according to the number of papers that specifically test the attribute.

Over one-third (37.67%) of the papers research prediction of returns. Another one-third of papers focus on researching bubbles and extreme conditions and the relationship between pairs and portfolios in cryptocurrency trading. The remaining researching topics (prediction of volatility, trading system, technical trading and others) have roughly one-third share.

Research distribution among cryptocurrency trading properties

Research Distribution among Categories and Technologies

This section introduces and compares categories and technologies in cryptocurrency trading. When papers cover multiple technologies or compare different methods, we draw statistics from different technical perspectives.

Among all the 146 papers, 102 papers (69.86%) cover statistical methods and machine learning categories. These papers basically research technical-level cryptocurrency trading including mathematical modeling and statistics. Other papers related to trading systems on pure technical indicators and introducing the industry and its history are not included in this analysis. Among all 102 papers, 88 papers (86.28%) present statistical methods and technologies in cryptocurrency trading research and 13.72% papers research machine learning applied to cryptocurrency trading (cf. Fig.  10 ). It is interesting to mention that, there are 17 papers (16.67%) applying and comparing more than one technique in cryptocurrency trading. More specifically, Bach and Kasper ( 2018 ), Alessandretti et al. ( 2018 ), Vo and Yost-Bremm ( 2018 ), Phaladisailoed and Numnonda ( 2018 ), Siaminos ( 2019 ), Rane and Dhage ( 2019 ) used both statistical methods and machine learning methods in cryptocurrency trading.

Table  8 shows the results of search hits in all trading areas (not limited to cryptocurrencies). From the table, we can see that most research findings focused on statistical methods in trading, which means most of the research on traditional markets still focused on using statistical methods for trading. But we observed that machine learning in trading had a higher degree of attention. It might because the traditional technical and fundamental have been arbitraged, so the market has moved in recent years to find new anomalies to exploit. Meanwhile, the results also showed there exist many opportunities for research in the widely studied areas of machine learning applied to trade in cryptocurrency markets (cf. " Opportunities in cryptocurrency trading " section).

Research Distribution among Statistical methods

As from Fig.  10 , we further classified the papers using statistical methods into 6 categories: (i) basic regression methods; (ii) linear classifiers and clustering; (iii) time-series analysis; (iv) decision trees and probabilistic classifiers; (v) modern portfolios theory; and, (vi) Others.

Basic regression methods include regression methods (Linear Regression), function estimation and CGCD method. Linear Classifiers and Clustering include SVM and KNN algorithm. Time-series analysis include GARCH model, BEKK model, ARIMA model, Wavelet time-scale method. Decision Trees and probabilistic classifiers include Boosting Tree, RF model. Modern portfolio theory include Value-at-Risk (VaR) theory, expected-shortfall (ES), Markowitz mean-variance framework. Others include industry, market data and research analysis in cryptocurrency market.

The figure shows that basic Regression methods and time-series analysis are the most commonly used methods in this area.

Research Distribution among Machine Learning Categories

Papers using machine learning account for 13.7 (c.f Fig.  10 ) of the total. We further classified these papers into three categories: (vii) ANNs, (viii) LSTM/RNN/GRUs, and (ix) DL/RL.

The figure also shows that methods based on LSTM, RNN and GRU are the most popular in this subfield.

ANNs contains papers researching ANN applications in cryptocurrency trading such as back propagation (BP) NN. LSTM/RNN/GRUs include papers using neural networks that exploit the temporal structure of the data, a technology especially suitable for time series prediction and financial trading. DL/RL includes papers using Multilayer Neural Networks and Reinforcement Learning. The difference between ANN and DL is that generally, DL refers to an ANN with multiple hidden layers while ANN refers to simple structure neural network contained input layer, hidden layer (one or multiple), and an output layer.

Research distribution among cryptocurrency trading technologies and methods

Datasets used in Cryptocurrency Trading

Tables  9 – 11 show the details for some representative datasets used in cryptocurrency trading research. Table  9 shows the market datasets. They mostly include price, trading volume, order-level information, collected from cryptocurrency exchanges. Table  10 shows the sentiment-based data. Most of the datasets in this table contain market data and media/Internet data with emotional or statistical labels. Table  11 gives two examples of datasets used in the collected papers that are not covered in the first two tables.

The column “Currency” shows the types of cryptocurrencies included; this shows that Bitcoin is the most commonly used currency for cryptocurrency researches. The column “Description” shows a general description and types of datasets. The column “Data Resolution” means latency of the data (e.g., used in the backtest) – this is useful to distinguish between high-frequency trading and low-frequency trading. The column “Time range” shows the time span of datasets used in experiments; this is convenient to distinguish between the current performance in a specific time interval and the long-term effect. We also present how the dataset has been used (i.e., the task), cf. column “Usage”. “Data Sources” gives details on where the data is retrieved from, including cryptocurrency exchanges, aggregated cryptocurrency index and user forums (for sentiment analysis).

Alexander and Dakos ( 2020 ) made an investigation of cryptocurrency data as well. They summarised data collected from 152 published and SSRN discussion papers about cryptocurrencies and analysed their data quality. They found that less than half the cryptocurrency papers published since January 2017 employ correct data.

Opportunities in cryptocurrency trading

This section discusses potential opportunities for future research in cryptocurrency trading.

Sentiment-based research As discussed above, there is a substantial body of work, which uses natural language processing technology, for sentiment analysis with the ultimate goal of using news and media contents to improve the performance of cryptocurrency trading strategies.

Possible research directions may lie in a larger volume of media input (e.g., adding video sources) in sentiment analysis; updating baseline natural language processing model to perform more robust text preprocessing; applying neural networks in label training; extending samples in terms of holding period; transaction-fees; opinion dynamics (Zha et al. 2020 ) and, user reputation research.

Long-and-short term trading research There are significant differences between long and short time horizons in cryptocurrency trading. In long-term trading, investors might obtain greater profits but have more possibilities to control risk when managing a position for weeks or months. It is mandatory to control for risk on long term strategies due to the increase in the holding period, directly proportional to the risk incurred by the trader. On the other hand, the longer the horizon, the higher the risk and the most important the risk control. The shorter the horizon, the higher the cost and the lower the risk, so cost takes over the design of a strategy. In short-term trading, automated algorithmic trading can be applied when holding periods are less than a week. Researchers can differentiate between long-term and short-term trading in cryptocurrency trading by applying wavelet technology analysing bubble regimes (Phillips and Gorse 2018 ) and considering price explosiveness (Bouri et al. 2019 ) hypotheses for short-term and long-term research.

The existing work is mainly about showing the differences between long and short-term cryptocurrency trading. Long-term trading means less time would cost in trend tracing and simple technical indicators in market analysis. Short-term trading can limit overall risk because small positions are used in every transaction. But market noise (interference) and short transaction time might cause some stress in short term trading. It might also be interesting to explore the extraction of trading signals, time series research, application to portfolio management, the relationship between a huge market crash and small price drop, derivative pricing in cryptocurrency market, etc.

Correlation between cryptocurrency and others By the effects of monetary policy and business cycles that are not controlled by the central bank, cryptocurrency is always negatively correlated with overall financial market trends. There have been some studies discussing correlations between cryptocurrencies and other financial markets (Kang et al. 2019 ; Castro et al. 2019 ), which can be used to predict the direction of the cryptocurrency market.

Considering the characteristics of cryptocurrency, the correlation between cryptocurrency and other assets still requires further research. Possible breakthroughs might be achieved with principal component analysis, the relationship between cryptocurrency and other currencies in extreme conditions (i.e., financial collapse).

Bubbles and crash research . To discuss the high volatility and return of cryptocurrencies, current research has focussed on bubbles of cryptocurrency markets (Cheung et al. 2015 ), correlation between cryptocurrency bubbles and indicators like volatility index (VIX) (Enoksen and Landsnes 2019 ) (which is a “panic index” to measure the implied volatility of S&P500 Index Options), spillover effects in cryptocurrency market (Luu Duc Huynh 2019 ).

Additional research for bubbles and crashes in cryptocurrency trading could include a connection between the process of bubble generation and financial collapse and conducting a coherent analysis (coherent process analysis from the formation of bubbles to aftermath analysis of bubble burst), analysis of bubble theory by Microeconomics, trying other physical or industrial models in analysing bubbles in cryptocurrency market (i.e., Omori law  (Weber et al. 2007 )), discussing the supply and demand relationship of cryptocurrency in bubble analysis (like using supply and demand graph to simulate the generation of bubbles and simulate the bubble burst).

Game theory and agent-based analysis Applying game theory or agent-based modelling in trading is a hot research direction in the traditional financial market. It might also be interesting to apply this method to trading in cryptocurrency markets.

Public nature of Blockchain technology Investigations on the connections between the formation of a given currency’s transaction network and its price has increased rapidly in recent years; the growing attention on user identification  (Juhász et al. 2018 ) also strongly supports this direction. With an in-depth understanding of these networks, we may identify new features in price prediction and may be closer to understanding financial bubbles in cryptocurrency trading.

Balance between the opening of trading research literature and the fading of alphas Mclean et al. McLean and Pontiff ( 2016 ) pointed out that investors learn about mispricing in stock markets from academic publications. Similarly, cryptocurrency market predictability could also be affected by research papers in the area. A possible attempt is to try new pricing methods applying real-time market changes. Considering the proportion of informed traders increasing in the cryptocurrency market in the pricing process is another breaking point (looking for a balance between alpha trading and trading research literature).

Conclusions

We provided a comprehensive overview and analysis of the research work on cryptocurrency trading. This survey presented a nomenclature of the definitions and current state of the art. The paper provides a comprehensive survey of 146 cryptocurrency trading papers and analyses the research distribution that characterise the cryptocurrency trading literature. Research distribution among properties and categories/technologies are analysed in this survey respectively. We further summarised the datasets used for experiments and analysed the research trends and opportunities in cryptocurrency trading. Future research directions and opportunities are discussed in " Opportunities in cryptocurrency trading " section.

We expect this survey to be beneficial to academics (e.g., finance researchers) and quantitative traders alike. The survey represents a quick way to get familiar with the literature on cryptocurrency trading and can motivate more researchers to contribute to the pressing problems in the area, for example along the lines we have identified.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author upon request. Some datasets generated and/or analyzed during the current study are available in Google Scholar, arXiv and SSRN.

3commas: 3Commas Smart Trading terminal and auto trading bots. https://3commas.io/ . [Online, Accessed 26 Jan 2020] (2020)

Abay NC, Akcora CG, Gel YR, Kantarcioglu M, Islambekov UD, Tian Y, Thuraisingham B (2019) Chainnet: Learning on blockchain graphs with topological features. In: 2019 IEEE International Conference on Data Mining (ICDM), pp 946–951. IEEE

Adeyanju, C.: What crypto exchanges do to comply with KYC, AML and CFT regulations. https://cointelegraph.com/news/what-crypto-exchanges-do-to-comply-with-kyc-aml-and-cft-regulations . [Online, Accessed January 11, 2020] (2019)

Ahamad S, Nair M, Varghese B (2013) A survey on crypto currencies. In: 4th International Conference on Advances in Computer Science, AETACS, pp. 42–48. Citeseer

Alessandretti L, ElBahrawy A, Aiello LM, Baronchelli A (2018) Anticipating cryptocurrency prices using machine learning. Complexity

Alexander C, Dakos M (2020) A critical investigation of cryptocurrency data and analysis. Quantitative Finance 20(2):173–188

Google Scholar  

Al-Yahyaee KH, Mensi W, Ko H-U, Yoon S-M, Kang SH (2020) Why cryptocurrency markets are inefficient: The impact of liquidity and volatility. The North American Journal of Economics and Finance 52:101168

Antipova V (2019) Building and testing global investment portfolios using alternative asset classes. Master’s thesis, Vytautas Magnus University, Lithuania

Ardia D, Bluteau K, Rüede M (2019) Regime changes in bitcoin garch volatility dynamics. Financ Res Lett 29:266–271

Aspris A, Foley S, Svec J, Wang L (2021) Decentralized exchanges: The “wild west’’ of cryptocurrency trading. Int Rev Financ Anal 77:101845

Atsalakis GS, Atsalaki IG, Pasiouras F, Zopounidis C (2019) Bitcoin price forecasting with neuro-fuzzy techniques. Eur J Oper Res 276(2):770–780

Attanasio G, Cagliero L, Garza P, Baralis E (2019) Quantitative cryptocurrency trading: exploring the use of machine learning techniques. In: Proceedings of the 5th Workshop on Data Science for Macro-modeling with Financial and Economic Datasets, p. 1. ACM

Authority, F.C.: Contract for differences. https://www.fca.org.uk/firms/contracts-for-difference . [Online, Accessed January 29, 2020] (2019)

Bach WG, Kasper L (2018) On machine learning based cryptocurrency trading. Master’s thesis, Aalborg University, Denmark

Badenhorst JJ et al (2019) Effect of bitcoin spot and derivative trading volumes on price volatility. PhD thesis, University of Pretoria

Bai S, Robinson F (2019) Automated triangular arbitrage:: A trading algorithm for foreign exchange on a cryptocurrency market. Bachelor’s thesis, KTH Royal Institute of Technology, Sweden

Baker SR, Bloom N, Davis SJ (2016) Measuring economic policy uncertainty. Q J Econ 131(4):1593–1636

BAKKT: BAKKT markets. https://www.bakkt.com/index . [Online, Accessed February 11, 2020] (2020)

BAKKT: BAKKT terms of use. https://www.bakkt.com/terms-of-use . [Online, Accessed 11 Feb 2020] (2020)

Bariviera AF, Merediz-Sola I (2020) Where do we stand in cryptocurrencies economic research? a survey based on hybrid analysis. arXiv preprint arXiv:2003.09723

Barnwal A, Bharti H, Ali A, Singh V (2019) Stacking with neural network for cryptocurrency investment. arXiv preprint arXiv:1902.07855

Bartolucci S, Destefanis G, Ortu M, Uras N, Marchesi M, Tonelli R (2019) The butterfly affect: impact of development practices on cryptocurrency prices

Baur DG, Dimpfl T, Kuck K (2018) Bitcoin, gold and the us dollar-a replication and extension. Financ Res Lett 25:103–110

Bauriya A, Tikone A, Nandgaonkar P, Sakure KS (2019) Real-time cryptocurrency trading system. Int Res J Eng Technol (IRJET) 06

Bedi P, Nashier T (2020) On the investment credentials of bitcoin: a cross-currency perspective. Res Int Bus Financ 51:101087

Bell T (2016) Bitcoin trading agents. University of Southampton

Ben-Akiva M, McFadden D, Train K, Walker J, Bhat C, Bierlaire M, Bolduc D, Boersch-Supan A, Brownstone D, Bunch DS et al (2002) Hybrid choice models: progress and challenges. Mark Lett 13(3):163–175

Beneki C, Koulis A, Kyriazis NA, Papadamou S (2019) Investigating volatility transmission and hedging properties between bitcoin and ethereum. Res Int Bus Financ 48:219–227

Binance: Binance Partners with Coinfirm to Protect the Global Cryptocurrency Economy and Ensure Compliance with FATF AML Rules. https://www.binance.com/en/blog/386484403820867584/Binance-Partners-with-Coinfirm-to-Protect-the-Global-Cryptocurrency-Economy-and-Ensure-Compliance-with-FATF-AML-Rules . [Online, Accessed February 11, 2020] (2020)

Binance: Binance Review 2020: Pros, Cons, Fees, Features, and Safety. https://insidebitcoins.com/cryptocurrency-exchanges/binance-review/ . [Online, Accessed 11 Feb 2020] (2020)

Bitfinex: Bitfinex markets. https://www.bitfinex.com/ . [Online, Accessed 11 Feb 2020] (2020)

Bitfinex: Bitfinex terms of service. https://www.bitfinex.com/legal/terms . [Online, Accessed 11 Feb 2020] (2020)

Bitfinex: New York Court Rules That State Attorney Has Jurisdiction Over Bitfinex. https://cointelegraph.com/news/new-york-court-rules-that-state-attorney-has-jurisdiction-over-bitfinex . [Online, Accessed February 11, 2020] (2020)

Bitmex: Beginner’s Guide to BitMEX: Complete Review. https://blockonomi.com/bitmex-review/ . [Online, Accessed 11 Feb 2020] (2020)

Bitmex: Bitmex. https://www.bitmex.com/register . [Online, Accessed 11 Feb 2020] (2020)

Bitstamp: Bitstamp Review 2020. https://www.fxempire.com/crypto/exchange/bitstamp/review . [Online, Accessed 11 Feb 2020] (2020)

Bitstamp: Bitstamp who we are. https://www.bitstamp.net/about-us/ . [Online, Accessed 11 Feb 2020] (2020)

Bitstamp: Terms of Use. https://www.bitstamp.net/terms-of-use/sa . [Online, Accessed February 11, 2020] (2020)

Blackbird: Blackbird Bitcoin Arbitrage: a long/short market-neutral strategy. https://github.com/butor/blackbird . [Online, Accessed 26 Jan 2020] (2020)

Bloomberg: Coinbase Inc. https://www.bloomberg.com/profile/company/0776164D:US . [Online, Accessed 11 Feb 2020] (2020)

Bodart V, Candelon B (2009) Evidence of interdependence and contagion using a frequency domain framework. Emerg Mark Rev 10(2):140–150

Bouri E, Jalkh N, Molnár P, Roubaud D (2017) Bitcoin for energy commodities before and after the December 2013 crash: diversifier, hedge or safe haven? Appl Econ 49(50):5063–5073

Bouri E, Lau CKM, Lucey B, Roubaud D (2019) Trading volume and the predictability of return and volatility in the cryptocurrency market. Financ Res Lett 29:340–346

Bouri E, Shahzad SJH, Roubaud D (2019) Co-explosivity in the cryptocurrency market. Financ Res Lett 29:178–183

Bouri E, Lucey B, Roubaud D (2020) The volatility surprise of leading cryptocurrencies: Transitory and permanent linkages. Financ Res Lett 33:101188

Bouri E, Roubaud D, Shahzad SJH (2020) Do bitcoin and other cryptocurrencies jump together? Q Rev Econ Finance 76:396–409

Bouri E, Gupta R (2019) Predicting bitcoin returns: Comparing the roles of newspaper-and internet search-based measures of uncertainty. Finance Res Lett 101398

Bouri E, Vo XV, Saeed T (2020) Return equicorrelation in the cryptocurrency market: analysis and determinants. Finance Research Letters, 101497

Brauneis A, Mestel R (2019) Cryptocurrency-portfolios in a mean-variance framework. Financ Res Lett 28:259–264

Brunnermeier MK, Oehmke M (2013) Bubbles, financial crises, and systemic risk. In: Handbook of the Economics of Finance vol. 2, pp 1221–1288. Elsevier

Bu S-J, Cho S-B (2018) Learning optimal q-function using deep boltzmann machine for reliable trading of cryptocurrency. In: International Conference on Intelligent Data Engineering and Automated Learning, pp 468–480. Springer

Calo B, Johnson W (2002) Global trading system. Google Patents. US Patent App. 09/769,036

Capfolio: Capfolio cryptocurrency trading platform. https://www.capfol.io/ . [Online, Accessed 26 Jan 2020] (2020)

Caporale GM, Plastun A (2018) Price overreactions in the cryptocurrency market. CESifo Working Paper 6861, Munich. http://hdl.handle.net/10419/174984

Caporale GM, Plastun A (2019) The day of the week effect in the cryptocurrency market. Finance Res Lett 31

Caporale GM, Gil-Alana L, Plastun A (2018) Persistence in the cryptocurrency market. Res Int Bus Financ 46:141–148

Caporin M, McAleer M (2012) Do we really need both bekk and dcc? a tale of two multivariate garch models. J Econ Surv 26(4):736–751

Castro JG, Tito EAH, Brandão LET, Gomes LL (2019) Crypto-assets portfolio optimization under the omega measure. Eng Econom 1–21

Catalyst: An Algorithmic Trading Library for Crypto-Assets in Python. https://github.com/enigmampc/catalyst . [Online, Accessed 26 Jan 2020] (2020)

CBOE: CBOE history. http://www.cboe.com/aboutcboe/history . [Online, Accessed 11 Feb 2020] (2020)

CBOE: CBOE products. https://www.cboe.com . [Online, Accessed 11 Feb 2020] (2020)

CBOE: CFE Regulation. https://www.cboe.com/aboutcboe/legal-regulatory/departmental-overviews/cfe-regulation . [Online, Accessed February 11, 2020] (2020)

Ccxt: CCXT - CryptoCurrency eXchange Trading Library. https://github.com/ccxt/ccxt . [Online, Accessed 26 Jan 2020] (2020)

Chaim P, Laurini MP (2018) Volatility and return jumps in bitcoin. Econ Lett 173:158–163

Chaim P, Laurini MP (2019) Nonlinear dependence in cryptocurrency markets. N Am J Econ Finance 48:32–47

Chan S, Chu J, Zhang Y, Nadarajah S (2022) An extreme value analysis of the tail relationships between returns and volumes for high frequency cryptocurrencies. Res Int Bus Financ 59:101541

Chang C-C, Hsieh P-F, Wang Y-H (2015) Sophistication, sentiment, and misreaction. J Financ Quant Anal 50(4):903–928

Charles A, Darné O et al (2019) Volatility estimation for cryptocurrencies: further evidence with jumps and structural breaks. Econ Bull 39(2):954–968

Cheah E-T, Fry J (2015) Speculative bubbles in bitcoin markets? an empirical investigation into the fundamental value of bitcoin. Econ Lett 130:32–36

Cheng H-P, Yen K-C (2020) The relationship between the economic policy uncertainty and the cryptocurrency market. Financ Res Lett 35:101308

Cheng J, Dong L, Lapata M (2016) Long short-term memory-networks for machine reading. arXiv preprint arXiv:1601.06733

Cheung A, Roca E, Su J-J (2015) Crypto-currency bubbles: an application of the phillips–shi–yu (2013) methodology on mt. gox bitcoin prices. Appl Econ 47(23):2348–2358

Choi B (2012) ARMA model identification. Springer, New York

Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555

CME: CME Cryptocurrency products. https://www.cmegroup.com/trading/cryptocurrency-indices.html . [Online, Accessed 11 Feb 2020] (2020)

CME: CME Group Rules and Regulation Overview. https://www.cmegroup.com/education/courses/market-regulation/overview/cme-group-rules-and-regulation-overview.html . [Online, Accessed February 11, 2020] (2020)

CME: CME groups overview. https://www.cmegroup.com/company/history/ . [Online, Accessed 11 Feb 2020] (2020)

Cocco L, Concas G, Marchesi M (2017) Using an artificial financial market for studying a cryptocurrency market. J Econ Interac Coord 12(2):345–365

Cocco L, Marchesi M (2016) Modeling and simulation of the economics of mining in the bitcoin market. PloS one 11(10)

Coinbase: Coinbase Supported cryptocurrencies. https://help.coinbase.com/en/coinbase/getting-started/general-crypto-education/supported-cryptocurrencies.html . [Online, Accessed 11 Feb 2020] (2020)

Coinbase: Our path to listing SEC-regulated crypto securities. https://blog.coinbase.com/our-path-to-listing-sec-regulated-crypto-securities-a1724e13bb5a . [Online, Accessed February 11, 2020] (2020)

CoinMaketCap: Top 100 cryptocurrencies by market capitalization (2019). Accessed 20 Dec 2019

Coinmarketcap: Percentage of Total Market Capitalization. https://coinmarketcap.com/charts/#dominance-percentage . [Online, Accessed January 11, 2020] (2020)

Colianni S, Rosales S, Signorotti M (2015) Algorithmic trading of cryptocurrency based on twitter sentiment analysis. CS229 Project, 1–5

Conrad C, Custovic A, Ghysels E (2018) Long-and short-term cryptocurrency volatility components: A garch-midas analysis. J Risk Financ Manag 11(2):23

Corbet S, Meegan A, Larkin C, Lucey B, Yarovaya L (2018) Exploring the dynamic relationships between cryptocurrencies and other financial assets. Econ Lett 165:28–34

Corbet S, Lucey B, Yarovaya L (2018) Datestamping the bitcoin and ethereum bubbles. Financ Res Lett 26:81–88

Corbet S, Eraslan V, Lucey B, Sensoy A (2019) The effectiveness of technical trading rules in cryptocurrency markets. Financ Res Lett 31:32–37

Corbet S, Lucey B, Urquhart A, Yarovaya L (2019) Cryptocurrencies as a financial asset: a systematic analysis. Int Rev Financ Anal 62:182–199

Council FB (2021) The Main Roadblocks To Crypto Moving Mainstream. https://www.forbes.com/sites/forbesbusinesscouncil/2021/06/23/the-main-roadblocks-to-crypto-moving-mainstream/?sh=2e629de922b9 . [Online, Accessed: June 23, 2021]

Cross JL, Hou C, Trinh K (2021) Returns, volatility and the cryptocurrency bubble of 2017-18. Econ Model 105643

Cryptosignal: Automated Crypto Trading and Technical Analysis (TA) Bot. https://github.com/CryptoSignal/crypto-signal . [Online, Accessed 26 Jan 2020] (2020)

Ctubio: Ctubio - Cryptocurrency trading bot. https://github.com/ctubio/Krypto-trading-bot . [Online, Accessed 26 Jan 2020] (2020)

Delfabbro P, King D, Williams J, Georgiou N (2021) Cryptocurrency trading, gambling and problem gambling. Addict Behav 122:107021

Delfabbro P, King DL, Williams J (2021) The psychology of cryptocurrency trading: Risk and protective factors. J Behav Addict

Diebold FX, Yılmaz K (2014) On the network topology of variance decompositions: measuring the connectedness of financial firms. J Econom 182(1):119–134

Domínguez MA, Lobato IN (2003) Testing the martingale difference hypothesis. Econom Rev 22(4):351–377

Doran MD (2014) A forensic look at bitcoin cryptocurrency. PhD thesis, Utica College

Drożdż S, Minati L, Oświecimka P, Stanuszek M, Watorek M (2020) Competition of noise and collectivity in global cryptocurrency trading: route to a self-contained market. Chaos: Interdiscip J Nonlinear Sci 30(2):023122

Dutta A, Kumar S, Basu M (2020) A gated recurrent unit approach to bitcoin price prediction. J Risk Financ Manag 13(2):23

Dyhrberg AH (2016) Bitcoin, gold and the dollar-a garch volatility analysis. Financ Res Lett 16:85–92

Easley D, Engle RF, O’Hara M, Wu L (2008) Time-varying arrival rates of informed and uninformed trades. J Financ Econom 6(2):171–207

Elliott RJ, Van Der Hoek* J, Malcolm WP (2005) Pairs trading. Quant Finance 5(3):271–276

Engle R (2002) Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models. J Bus Econ Stat 20(3):339–350

Engle RF, Kroner KF (1995) Multivariate simultaneous generalized arch. Econom Theor 11(1):122–150

Enoksen FA, Landsnes CJ (2019) What can predict bubbles in cryptocurrency prices? Master’s thesis, University of Stavanger, Norway

Escanciano JC, Velasco C (2006) Generalized spectral tests for the martingale difference hypothesis. J Econom 134(1):151–185

Estalayo I, Del Ser J, Osaba E, Bilbao MN, Muhammad K, Gálvez A, Iglesias A (2019) Return, diversification and risk in cryptocurrency portfolios using deep recurrent neural networks and multi-objective evolutionary algorithms. In: 2019 IEEE Congress on Evolutionary Computation (CEC), pp 755–761. IEEE

Fang F, Chung W, Ventre C, Basios M, Kanthan L, Li L, Wu F (2021) Ascertaining price formation in cryptocurrency markets with machine learning. Eur J Finance 1–23

Fang F, Ventre C, Li L, Kanthan L, Wu F, Basios M (2020) Better model selection with a new definition of feature importance. arXiv preprint arXiv:2009.07708

Fantazzini D (2019) Quantitative finance with r and cryptocurrencies. Amazon KDP, ISBN-13, 978–1090685315

Fantazzini D, Zimin S (2020) A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies. J Ind Bus Econ 47(1):19–69

Farell R (2015) An analysis of the cryptocurrency industry

Fasanya IO, Oyewole O, Odudu T (2020) Returns and volatility spillovers among cryptocurrency portfolios. Int J Manag Finance

Feinstein BD, Werbach K (2021) The impact of cryptocurrency regulation on trading markets. Journal of Financial Regulation 7(1):48–99

Feng W, Wang Y, Zhang Z (2018) Informed trading in the bitcoin market. Financ Res Lett 26:63–70

Fil M (2019) Pairs trading in cryptocurrency markets. Univerzita Karlova, Fakulta sociálních věd

Flood R, Hodrick RJ, Kaplan P (1986) An evaluation of recent evidence on stock market bubbles. National Bureau of Economic Research Cambridge Mass, USA

Flori A (2019) News and subjective beliefs: a bayesian approach to bitcoin investments. Res Int Bus Financ 50:336–356

Forbes: Here’s What Caused Bitcoin’s ‘Extreme’ Price Plunge. https://www.forbes.com/sites/billybambrough/2020/03/19/major-bitcoin-exchange-bitmex-has-a-serious-problem/?sh=1be57a0d4f7d . [Online, Accessed: March 19, 2020] (2021)

Forbes: Is The Crypto Market Maturing? An Analysis For Entrepreneurs. https://www.forbes.com/sites/theyec/2021/06/01/is-the-crypto-market-maturing-an-analysis-for-entrepreneurs/?sh=1170160bba22 . [Online, Accessed: June 1, 2021] (2021)

Forbes: More Than \$600 Million Stolen In Ethereum And Other Cryptocurrencies-Marking One Of Crypto’s Biggest Hacks Ever. https://www.forbes.com/sites/jonathanponciano/2021/08/10/more-than-600-million-stolen-in-ethereum-and-other-cryptocurrencies-marking-one-of-cryptos-biggest-hacks-ever/?sh=502ce7387f62 . [Online, Accessed: August 10, 2021] (2021)

Forbes: Why Buffett Sees Bitcoin Bubble. https://www.forbes.com/sites/johnwasik/2017/11/06/why-buffett-sees-bitcoin-bubble/?sh=196c2a8062a8 . [Online, Accessed: November 6, 2017] (2017)

Fretrade: Freqtrade. https://github.com/freqtrade/freqtrade . [Online, Accessed 26 Jan 2020] (2020)

Friedl MA, Brodley CE (1997) Decision tree classification of land cover from remotely sensed data. Remote Sens Environ 61(3):399–409

Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189–1232

Friedman J, Tibshirani R (1984) The monotone smoothing of scatterplots. Technometrics 26(3):243–250

FT: Bitcoin: too good to miss or a bubble ready to burst? https://www.ft.com/crypto/ . [Online, Accessed: November 9, 2021] (2021)

Gandal N, Halaburda H (2014) Competition in the cryptocurrency market. CEPR Discussion Paper No. DP10157

Garcia D, Schweitzer F (2015) Social signals and algorithmic trading of bitcoin. R Soc Open Sci 2(9):150288

Garza P (2019) Formal automatic trading in the cryptocurrency era. PhD thesis, Politecnico di Torino

Gatev E, Goetzmann WN, Rouwenhorst KG (2006) Pairs trading: performance of a relative-value arbitrage rule. Rev Financ Stud 19(3):797–827

Gerritsen DF, Bouri E, Ramezanifar E, Roubaud D (2019) The profitability of technical trading rules in the bitcoin market. Finance Res Lett

Golang: A golang implementation of a console-based trading bot for cryptocurrency exchanges. https://github.com/saniales/golang-crypto-trading-bot . [Online, Accessed 26 Jan 2020] (2020)

Gradojevic N, Tsiakas I (2021) Volatility cascades in cryptocurrency trading. J Empir Financ 62:252–265

Grayblock: Elliptic-Curve Cryptography. https://medium.com/coinmonks/elliptic-curve-cryptography-6de8fc748b8b . [Online, Accessed December 29, 2019] (2018)

Griffin JM, Shams A (2019) Is bitcoin really un-tethered? Available at SSRN 3195066

Grobys K, Ahmed S, Sapkota N (2020) Technical trading rules in the cryptocurrency market. Financ Res Lett 32:101396

Gu S, Lillicrap T, Sutskever I, Levine S (2016) Continuous deep q-learning with model-based acceleration. In: International Conference on Machine Learning, pp 2829–2838

Guo T, Bifet A, Antulov-Fantulin N (2018) Bitcoin volatility forecasting with a glimpse into buy and sell orders. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 989–994. IEEE

Ha S, Moon B-R (2018) Finding attractive technical patterns in cryptocurrency markets. Memetic Comput 10(3):301–306

Hale G, Krishnamurthy A, Kudlyak M, Shultz P et al (2018) How futures trading changed bitcoin prices. FRBSF Econ Lett 12

Hansel D (2018) Cryptocurrency trading: How to make money by trading bitcoin and other cryptocurrency (volume 2)

Harwick C (2016) Cryptocurrency and the problem of intermediation. Independ Rev 20(4):569–588

Henderson P, Islam R, Bachman P, Pineau J, Precup D, Meger D (2018) Deep reinforcement learning that matters. In: Thirty-Second AAAI Conference on Artificial Intelligence

Hileman G, Rauchs M (2017) Global cryptocurrency benchmarking study. Cambridge Centre for Alternative Finance 33

Holmes G, Donkin A, Witten IH (1994) Weka: A machine learning workbench. In: Proceedings of ANZIIS’94-Australian New Zealand Intelligent Information Systems Conference, pp 357–361. IEEE

Hrytsiuk P, Babych T, Bachyshyna L (2019) Cryptocurrency portfolio optimization using value-at-risk measure. In: Strategies, Models and Technologies of Economic Systems Management (SMTESM 2019). Atlantis Press

Huan Y, Wu F, Basios M, Kanthan L, Li L, Xu B (2020) Ieo: Intelligent evolutionary optimisation for hyperparameter tuning. arXiv preprint arXiv:2009.06390

Hudson R, Urquhart A (2019) Technical analysis and cryptocurrencies. Available at SSRN 3387950

Hultman H (2018) Volatility forecasting an empirical study on bitcoin using garch and stochastic volatility models. Master’s thesis, Lund University, Sweden

Hutto CJ, Gilbert E (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Eighth International AAAI Conference on Weblogs and Social Media

Hwang S, Salmon M (2004) Market stress and herding. J Empir Financ 11(4):585–616

IntelliPaat: Supervised Learning vs Unsupervised Learning vs Reinforcement Learning. https://intellipaat.com/blog/supervised-learning-vs-unsupervised-learning-vs-reinforcement-learning/ . [Online, Accessed: September 14, 2021] (2021)

Ji Q, Bouri E, Lau CKM, Roubaud D (2019) Dynamic connectedness and integration in cryptocurrency markets. Int Rev Financ Anal 63:257–272

Jiang Z, Liang J (2017) Cryptocurrency portfolio management with deep reinforcement learning. In: 2017 Intelligent Systems Conference (IntelliSys), pp 905–913. IEEE

Jianliang M, Haikun S, Ling B (2009) The application on intrusion detection based on k-means cluster algorithm. In: 2009 International Forum on Information Technology and Applications, vol 1, pp 150–152. IEEE

Ji Q, Bouri E, Kristoufek L, Lucey B (2019) Realised volatility connectedness among bitcoin exchange markets. Finance Res Lett 101391

Johar S (2018) How do blockchain mining and transactions work explained in 7 simple steps. https://blog.goodaudience.com/how-a-miner-adds-transactions-to-the-blockchain-in-seven-steps-856053271476 . [Online, Accessed 26 Jan 2020]

Juchli M (2018) Limit order placement optimization with deep reinforcement learning: Learning from patterns in cryptocurrency market data. Master’s thesis, TU Delft Electrical Engineering, Netherlands

Juhász PL, Stéger J, Kondor D, Vattay G (2018) A bayesian approach to identify bitcoin users. PloS one 13(12)

Kaal WA (2020) Digital asset market evolution. J Corp Law 20–02

Kajtazi A, Moro A (2019) The role of bitcoin in well diversified portfolios: a comparative global study. Int Rev Financ Anal 61:143–157

Kakushadze Z (2018) Cryptoasset factor models. Algorithmic Finance (Preprint), 1–18

Kakushadze Z (2016) 101 formulaic alphas. Wilmott 2016(84):72–81

Kalchbrenner N, Grefenstette E, Blunsom P (2014) A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188

Kamrat S, Suesangiamsakul N, Marukatat R (2018) Technical analysis for cryptocurrency trading on mobile phones. In: 2018 3rd Technology Innovation Management and Engineering Science International Conference (TIMES-iCON), pp. 1–4 . IEEE

Kang SH, McIver RP, Hernandez JA (2019) Co-movements between bitcoin and gold: A wavelet coherence analysis. Physica A: Stat Mech Appl 120888

Kang K, Choo J, Kim Y (2019) Whose opinion matters? analyzing relationships between bitcoin prices and user groups in online community. Social Science Computer Review, 0894439319840716

Kat HM, Heynen RC (1994) Volatility prediction: A comparison of the stochastic volatility, garch (1, 1) and egarch (1, 1) models. J Derivat 2(2)

Kate C (2018) Cryptocurrency trading for beginners: 6-steps action plan to your first investment

Katsiampa P (2019) An empirical investigation of volatility dynamics in the cryptocurrency market. Res Int Bus Finance

Katsiampa P, Corbet S, Lucey BM (2018) Volatility spillover effects in leading cryptocurrencies: A bekk-mgarch analysis. Available at SSRN 3232912

Katsiampa P, Gkillas K, Longin F (2018) Cryptocurrency market activity during extremely volatile periods. https://doi.org/10.2139/ssrn.3220781

Kaufman PJ (2013) Trading Systems and Methods,+ Website, vol 591. Wiley, Hoboken

Keerthi SS, Shevade SK, Bhattacharyya C, Murthy KRK (2001) Improvements to Platt’s smo algorithm for svm classifier design. Neural Comput 13(3):637–649

Khuntia S, Pattanayak J (2018) Adaptive market hypothesis and evolving predictability of bitcoin. Econ Lett 167:26–28

Kim YB, Kim JG, Kim W, Im JH, Kim TH, Kang SJ, Kim CH (2016) Predicting fluctuations in cryptocurrency transactions based on user comments and replies. PLoS ONE 11(8):0161197

Kim K, Kim J, Rinaldo A (2018) Time series featurization via topological data analysis: an application to cryptocurrency trend forecasting. arXiv preprint arXiv:1812.02987

King T, Koutmos D (2021) Herding and feedback trading in cryptocurrency markets. Ann Oper Res 300(1):79–96

Koker TE, Koutmos D (2020) Cryptocurrency trading using machine learning. J Risk Financ Manag 13(8):178

Kondor D, Csabai I, Szüle J, Pósfai M, Vattay G (2014) Inferring the interplay between network structure and market effects in bitcoin. New J Phys 16(12):125003

Kondor D, Csabai I, Szule J, Psfai M, Vattay G (2014) Inferring the interplay between network structure and market effects in bitcoin. New J Phys 16(12):125003. https://doi.org/10.1088/1367-2630/16/12/125003

Article   Google Scholar  

Kondor D, Pósfai M, Csabai I, Vattay G (2014) Do the rich get richer? An empirical analysis of the bitcoin transaction network. PloS one 9(2):

Kou G, Peng Y, Wang G (2014) Evaluation of clustering algorithms for financial risk analysis using mcdm methods. Inf Sci 275:1–12

Kou G, Akdeniz ÖO, Dinçer H, Yüksel S (2021) Fintech investments in European banks: a hybrid it2 fuzzy multidimensional decision-making approach. Financ Innov 7(1):1–28

Kou G, Xu Y, Peng Y, Shen F, Chen Y, Chang K, Kou S (2021) Bankruptcy prediction for smes using transactional data and two-stage multiobjective feature selection. Decis Support Syst 140:113429

Krafft PM, Della Penna N, Pentland AS (2018) An experimental study of cryptocurrency market dynamics. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, p. 605. ACM

Kristjanpoller W, Bouri E, Takaishi T (2020) Cryptocurrencies and equity funds: Evidence from an asymmetric multifractal analysis. Physica A 545:123711

Kristoufek L (2013) Bitcoin meets google trends and wikipedia: Quantifying the relationship between phenomena of the internet era. Sci Rep 3:3415

Kumar D, Rath S (2020) Predicting the trends of price for ethereum using deep learning techniques. In: Artificial Intelligence and Evolutionary Computations in Engineering Systems, pp 103–114. Springer

Kurbucz MT (2019) Predicting the price of bitcoin by the most frequent edges of its transaction network. Econ Lett 184:108655

Kutner MH, Nachtsheim CJ, Neter J, Li W et al (2005) Applied linear statistical models, vol 5. McGraw-Hill, New York

Kwon D-H, Kim J-B, Heo J-S, Kim C-M, Han Y-H (2019) Time series classification of cryptocurrency price trend based on a recurrent lstm neural network. J Inform Process Syst 15(3)

Kyriazis NA (2019) A survey on efficiency and profitable trading opportunities in cryptocurrency markets. J Risk Financ Manag 12(2):67

Lamon C, Nielsen E, Redondo E (2017) Cryptocurrency price prediction using news and social media sentiment. SMU Data Sci Rev 1(3):1–22

Lawrence S, Giles CL, Tsoi AC, Back AD (1997) Face recognition: a convolutional neural-network approach. IEEE Trans Neural Networks 8(1):98–113

Leclair EM (2018) Herding in the cryptocurrency market. Econ 5029 final research, Carleton University, Canada

Lee T-H, Yang W (2014) Granger-causality in quantiles between financial markets: Using copula approach. Int Rev Financ Anal 33:70–78

Leirvik T (2021) Cryptocurrency returns and the volatility of liquidity. Finance Res Lett 102031

Li TN, Tourin A (2016) Optimal pairs trading with time-varying volatility. Int J Financ Eng 3(03):1650023

Li TR, Chamrajnagar A, Fong X, Rizik N, Fu F (2019) Sentiment-based prediction of alternative cryptocurrency price fluctuations using gradient boosting tree model. Front Phys 7:98

Liaw A, Wiener M et al (2002) Classification and regression by randomforest. R news 2(3):18–22

Li T, Kou G, Peng Y, Philip SY (2021) An integrated cluster detection, optimization, and interpretation approach for financial data. IEEE Trans Cybern

Lintilhac PS, Tourin A (2017) Model-based pairs trading in the bitcoin markets. Quant Finance 17(5):703–716

Liu W (2019) Portfolio diversification across cryptocurrencies. Financ Res Lett 29:200–205

Liu B, Polukarov M, Ventre C, Li L, Kanthan L (2021) Agent-based markets: Equilibrium strategies and robustness. In: Proceedings of the 2nd ACM International Conference on AI in Finance

Liu B, Polukarov M, Ventre C, Li L, Kanthan L (2021) Call markets with adaptive clearing intervals. In: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems, pp. 1587–1589

Livieris IE, Pintelas E, Pintelas P (2020) A cnn-lstm model for gold price time-series forecasting. Neural Comput Appl 32(23):17351–17360

Livieris IE, Pintelas E, Stavroyiannis S, Pintelas P (2020) Ensemble deep learning models for forecasting cryptocurrency time-series. Algorithms 13(5):121

Localbtc: Localbitcoins purchasing online. https://localbitcoins.com . [Online, Accessed: January 11, 2020] (2020)

Lucarelli G, Borrotti M (2019) A deep reinforcement learning approach for automated cryptocurrency trading. In: IFIP International Conference on Artificial Intelligence Applications and Innovations, pp 247–258. Springer

Lu W, Li J, Li Y, Sun A, Wang J (2020) A cnn-lstm-based model to forecast stock prices. Complexity 2020

Luu Duc Huynh T (2019) Spillover risks on cryptocurrency markets: A look from var-svar granger causality and student copulas. Journal of Risk and Financial Management 12(2):52

Ma F, Liang C, Ma Y, Wahab M (2020) Cryptocurrency volatility forecasting: a Markov regime-switching midas approach. J Forecast 39(8):1277–1290

Madan I, Saluja S, Zhao A (2015) Automated bitcoin trading via machine learning algorithms. http://cs229.stanford.edu/proj2014/Isaac%20Madan

Makarov I, Schoar A (2020) Trading and arbitrage in cryptocurrency markets. J Financ Econ 135(2):293–319

Malladi RK, Dheeriya PL (2021) Time series analysis of cryptocurrency returns and volatilities. J Econ Finance 45(1):75–94

Maltatoday: Why world leader crypto exchange Binance moved to Malta. https://www.maltatoday.com.mt/business/business_news/93170/why_world_leader_crypto_exchange_binance_moved_to_malta#.XlKZ8Gj7Q2x . [Online, Accessed 11 Feb 2020] (2020)

Manahov V (2021) Cryptocurrency liquidity during extreme price movements: is there a problem with virtual money? Quant Finance 21(2):341–360

Markowitz H (1952) Portfolio selection. J Financ 7(1):77–91

McLean RD, Pontiff J (2016) Does academic research destroy stock return predictability? J Financ 71(1):5–32

McNally S (2016) Predicting the price of bitcoin using machine learning. PhD thesis, Dublin, National College of Ireland

McNally S, Roche J, Caton S (2018) Predicting the price of bitcoin using machine learning. In: 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), pp 339–343. IEEE

MercoPress: Bitcoin legal tender in El Salvador, first country ever. https://en.mercopress.com/2021/06/10/bitcoin-legal-tender-in-el-salvador-first-country-ever . [Online, Accessed: June 10, 2021] (2021)

Merediz-Solà I, Bariviera AF (2019) A bibliometric analysis of bitcoin scientific production. Res Int Bus Financ 50:294–305

Meunier S (2018) Blockchain 101: What is blockchain and how does this revolutionary technology work? In: Transforming Climate Finance and Green Investment with Blockchains. Elsevier, pp 23–34

Mikolov T, Kombrink S, Burget L, Černockỳ J, Khudanpur S (2011) Extensions of recurrent neural network language model. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 5528–5531. IEEE

MIT: Bitcoin network dataset. https://senseable2015-6.mit.edu/bitcoin . [Online, Accessed January 11, 2020] (2015)

Molina J (2019) Develop your Crypto-Trading System Using Plain Logic, Part 1. https://medium.com/swlh/develop-your-crypto-trading-system-using-plain-logic-part-1-caac02f0a37d . Accessed 20 Sep 2019

Mukhopadhyay U, Skjellum A, Hambolu O, Oakley J, Yu L, Brooks R (2016) A brief survey of cryptocurrency systems. In: 2016 14th Annual Conference on Privacy, Security and Trust (PST), pp. 745–752. IEEE

Nakamoto S (2009) Bitcoin open source implementation of p2p currency. P2P foundation 18

Nakano M, Takahashi A, Takahashi S (2018) Bitcoin technical trading with artificial neural network. Physica A 510:587–609

Narayanan A, Bonneau J, Felten E, Miller A, Goldfeder S (2016) Bitcoin and cryptocurrency technologies: a comprehensive introduction. Princeton University Press, Princeton

Nasir MA, Huynh TLD, Nguyen SP, Duong D (2019) Forecasting cryptocurrency returns and volume using search engines. Financ Innov 5(1):2

Neter J, Kutner MH, Nachtsheim CJ, Wasserman W (1996) Applied Linear Statistical Models vol. 4. Irwin Chicago

Ng AY, Jordan MI (2002) On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. In: Advances in Neural Information Processing Systems, pp. 841–848

Nikolova V, Trinidad Segovia JE, Fernández-Martínez M, Sánchez-Granero MA (2020) A novel methodology to calculate the probability of volatility clusters in financial series: An application to cryptocurrency markets. Mathematics 8(8):1216

Nomics: Top Cryptocurrency Exchanges List. https://nomics.com/exchanges . [Online, Accessed: January 11, 2020] (2020)

Nti IK, Adekoya AF, Weyori BA (2020) A systematic review of fundamental and technical analysis of stock market predictions. Artif Intell Rev 53(4):3007–3057

Oberlechner T (2001) Importance of technical and fundamental analysis in the European foreign exchange market. Int J Finance Econ 6(1):81–93

Ogorevc M (2019) Cryptocurrency as money: A trading strategy solution. Available at SSRN 3436041

Omane-Adjepong M, Alagidede IP (2019) Multiresolution analysis and spillovers of major cryptocurrency markets. Res Int Bus Financ 49:191–206

Omane-Adjepong M, Alagidede P, Akosah NK (2019) Wavelet time-scale persistence analysis of cryptocurrency market returns and volatility. Physica A 514:105–120

Pascanu R, Mikolov T, Bengio Y (2013) On the difficulty of training recurrent neural networks. In: International Conference on Machine Learning, pp 1310–1318

Patil AP, Akarsh T, Parkavi A (2018) A study of opinion mining and data mining techniques to analyse the cryptocurrency market. In: 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS), pp. 198–203. IEEE

Păuna C (2018) Arbitrage trading systems for cryptocurrencies. Design principles and server architecture. Informatica Economica 22(2):35–42

Persson S, Slottje A, Shaw I (2018) Hybrid autoregressive-recurrent neural network architecture for algorithmic trading of cryptocurrencies. Cs230 deep learning thesis, Stanford University

Phaladisailoed T, Numnonda T (2018) Machine learning models comparison for bitcoin price prediction. In: 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE), pp 506–511. IEEE

Phillips RC, Gorse D (2017) Predicting cryptocurrency price bubbles using social media data and epidemic modelling. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp 1–7. IEEE

Phillips RC, Gorse D (2018) Mutual-excitation of cryptocurrency market returns and social media topics. In: Proceedings of the 4th International Conference on Frontiers of Educational Technologies, pp 80–86. ACM

Phillips RC, Gorse D (2018) Cryptocurrency price drivers: Wavelet coherence analysis revisited. PLoS ONE 13(4):0195200

Phillips PC, Shi S, Yu J (2015) Testing for multiple bubbles: Historical episodes of exuberance and collapse in the s&p 500. Int Econ Rev 56(4):1043–1078

Phillips PC, Shi S, Yu J (2015) Testing for multiple bubbles: limit theory of real-time detectors. Int Econ Rev 56(4):1079–1134

Poloniex (2020) Poloniex markets. https://poloniex.com/ . [Online, Accessed 11 Feb 2020]

Qiao X, Zhu H, Hau L (2020) Time-frequency co-movement of cryptocurrency return and volatility: evidence from wavelet coherence analysis. Int Rev Financ Anal 71:101541

Rane PV, Dhage SN (2019) Systematic erudition of bitcoin price prediction using machine learning techniques. In: 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), pp 594–598. IEEE

Rebane J, Karlsson I, Denic S, Papapetrou P (2018) Seq2seq rnns and arima models for cryptocurrency prediction: a comparative study. SIGKDD Fintech 18

Research Network B (2020) Platform for scholarly communication about cryptocurrencies and blockchains. https://www.blockchainresearchnetwork.org/ . [Online, Accessed: April 17, 2020]

Reuters: China bans financial, payment institutions from cryptocurrency business. https://www.reuters.com/technology/chinese-financial-payment-bodies-barred-cryptocurrency-business-2021-05-18/ . [Online, Accessed 18 May 2021] (2021)

Rish I et al (2001) An empirical study of the naive bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, vol. 3, pp 41–46

Rose C (2015) The evolution of digital currencies: Bitcoin, a cryptocurrency causing a monetary revolution. Int Bus Econ Res J (IBER) 14(4):617–622

Salakhutdinov R, Hinton G (2009) Deep boltzmann machines. In: Artificial Intelligence and Statistics, pp 448–455

Sattarov O, Muminov A, Lee CW, Kang HK, Oh R, Ahn J, Oh HJ, Jeon HS (2020) Recommending cryptocurrency trading points with deep reinforcement learning approach. Appl Sci 10(4):1506

Shahzad SJH, Bouri E, Ahmad T, Naeem MA (2021) Extreme tail network analysis of cryptocurrencies and trading strategies. Finance Res Lett 102106

Shanaev S, Sharma S, Ghimire B, Shuraeva A (2020) Taming the blockchain beast? regulatory implications for the cryptocurrency market. Res Int Bus Financ 51:101080

Sharma S, Krishma N, Raina E (2017) Survey paper on cryptocurrency. International Journal of Scientific Research in Computer Science, Engineering and Information Technology 2(3):307–310

Siaminos G (2019) Predicting the value of cryptocurrencies using machine learning time series analysis time series analysis time

Sigaki HY, Perc M, Ribeiro HV (2019) Clustering patterns in efficiency and the coming-of-age of the cryptocurrency market. Sci Rep 9(1):1440

Sirignano J, Cont R (2019) Universal features of price formation in financial markets: perspectives from deep learning. Quant Finance 19(9):1449–1459

Slepaczuk R, Zenkova M (2018) Robustness of support vector machines in algorithmic trading on cryptocurrency market. Cent Eur Econ J 5:186–205

Smuts N (2019) What drives cryptocurrency prices? An investigation of google trends and telegram sentiment. ACM SIGMETRICS Perform Evalu Rev 46(3):131–134

Sriram A, Jun H, Satheesh S, Coates A (2017) Cold fusion: Training seq2seq models together with language models. arXiv preprint arXiv:1708.06426

Stocksharp: StockSharp - trading platform. https://github.com/StockSharp/StockSharp . [Online, Accessed 26 Jan 2020] (2020)

Stuerner P (2019) Algorithmic cryptocurrency trading. PhD thesis, Ulm University

Sun J, Zhou Y, Lin J (2019) Using machine learning for cryptocurrency trading. In: 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS), pp 647–652. IEEE

Sutton RS, Barto AG et al (1998) Introduction to reinforcement learning, vol 135. MIT Press, Cambridge

Sze V, Chen Y-H, Yang T-J, Emer JS (2017) Efficient processing of deep neural networks: a tutorial and survey. Proc IEEE 105(12):2295–2329

Tapscott D, Tapscott A (2016) Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World. Penguin

TradingstrategyGuides: EOS Cryptocurrency Trading Strategy–Turtle Soup Pattern. https://tradingstrategyguides.com/eos-cryptocurrency-trading-strategy/ . [Online, Accessed January 29, 2020] (2019)

TradingstrategyGuides: Free OMNI Cryptocurrency Strategy–Amazing Gann Box. https://tradingstrategyguides.com/free-omni-cryptocurrency-strategy/ . [Online, Accessed January 29, 2020] (2019)

TradingstrategyGuides: IOTA Cryptocurrency Strategy–Busted Double Top Pattern. https://tradingstrategyguides.com/iota-cryptocurrency-strategy/ . [Online, Accessed January 29, 2020] (2019)

TradingstrategyGuides: Nem (XEM) Cryptocurrency Strategy–Momentum Pinball Setup. https://tradingstrategyguides.com/nem-xem-cryptocurrency-strategy/ . [Online, Accessed January 29, 2020] (2019)

TradingstrategyGuides: Tether Trading Strategy–Bottom Rotation Trading. https://tradingstrategyguides.com/tether-trading-strategy/ . [Online, Accessed January 29, 2020] (2019)

TradingView: Total Crypto Market Capitalization and Volume. https://www.tradingview.com/markets/cryptocurrencies/global-charts/ . [Online, Accessed September 10, 2021] (2021)

Troster V, Tiwari AK, Shahbaz M, Macedo DN (2019) Bitcoin returns and risk: A general garch and gas analysis. Financ Res Lett 30:187–193

Trucíos C, Tiwari AK, Alqahtani F (2019) Value-at-risk and expected shortfall in cryptocurrencies’ portfolio: A vine copula-based approach. Available at SSRN 3441892

UKTN: Bitcoin and the Challenges for Financial Regulation. https://www.uktech.news . [Online, Accessed: February 24, 2021] (2021)

van den Broek L, Sharif Z (2018) Cointegration-based pairs trading framework with application to the cryptocurrency market

Vidal-Tomás D, Ibáñez AM, Farinós JE (2019) Herding in the cryptocurrency market: Cssd and csad approaches. Financ Res Lett 30:181–186

Virk DS (2017) Prediction of bitcoin price using data mining. Master’s thesis, National College of Ireland

Vogelvang B (2005) Econometrics: theory and applications with eviews. Pearson Education, London

Vo A, Yost-Bremm C (2018) A high-frequency algorithmic trading strategy for cryptocurrency. J Comput Inform Syst 1–14

Wagstaff K, Cardie C, Rogers S, Schrödl S et al (2001) Constrained k-means clustering with background knowledge. In: Icml, vol 1, pp 577–584

Wang L (2005) Support vector machines: theory and applications, vol 177. Springer, New York

Wang H, He D, Ji Y (2017) Designated-verifier proof of assets for bitcoin exchange using elliptic curve cryptography. Future Generat Comput Syst

Ward M (2018) Algorithmic trading for cryptocurrencies. Departmental honors thesis, UtahState University, United States

Weber P, Wang F, Vodenska-Chitkushev I, Havlin S, Stanley HE (2007) Relation between volatility correlations in financial markets and omori processes occurring on all scales. Phys Rev E 76(1):016109

Wohlin C (2014) Guidelines for snowballing in systematic literature studies and a replication in software engineering. In: Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering, pp 1–10

Xu Z, Wang S, Zhu F, Huang J (2017) Seq2seq fingerprint: An unsupervised deep molecular embedding for drug discovery. In: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp 285–294

Yang H (2018) Behavioral anomalies in cryptocurrency markets. Available at SSRN 3174421. https://doi.org/10.2139/ssrn.3174421

Yao Y, Yi J, Zhai S, Lin Y, Kim T, Zhang G, Lee LY (2018) Predictive analysis of cryptocurrency price using deep learning. Int J Eng Technol 7(3.27):258–264

Yaya OS, Ogbonna EA, Olubusoye OE (2018) How persistent and dependent are pricing of bitcoin to other cryptocurrencies before and after 2017/18 crash?

Zamuda A, Crescimanna V, Burguillo JC, Dias JM, Wegrzyn-Wolska K, Rached I, Gonzlez H, Senkerik R, Pop C, Cioara T et al (2019) Forecasting cryptocurrency value by sentiment analysis: An hpc-oriented survey of the state-of-the-art in the cloud era. In: High-Performance Modelling and Simulation for Big Data Applications, pp 325–349. Springer

Żbikowski K (2016) Application of machine learning algorithms for bitcoin automated trading. In: Machine Intelligence and Big Data in Industry, pp 161–168. Springer

Zemmal N, Azizi N, Dey N, Sellami M (2016) Adaptive semi supervised support vector machine semi supervised learning with features cooperation for breast cancer classification. J Med Imaging Health Inform 6(1):53–62

Zha Q, Kou G, Zhang H, Liang H, Chen X, Li C-C, Dong Y (2020) Opinion dynamics in finance and business: a literature review and research opportunities. Financial Innovation 6(1):1–22

Zhang W, Li Y (2020) Is idiosyncratic volatility priced in cryptocurrency markets? Res Int Bus Financ 54:101252

Zhang S, Yao L, Sun A, Tay Y (2019) Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv 52(1). https://doi.org/10.1145/3285029

Zhengyang W, Xingzhou L, Jinjin R, Jiaqing K (2019) Prediction of cryptocurrency price dynamics with multiple machine learning techniques. In: Proceedings of the 2019 4th International Conference on Machine Learning Technologies, pp 15–19. ACM

Zhou H, Kalev PS (2019) Algorithmic and high frequency trading in asia-pacific, now and the future. Pac Basin Financ J 53:186–207

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Fang, F., Ventre, C., Basios, M. et al. Cryptocurrency trading: a comprehensive survey. Financ Innov 8 , 13 (2022). https://doi.org/10.1186/s40854-021-00321-6

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The financial crisis of 2008 breached society’s trust in banks and centralized financial institutions. Bitcoin appeared as a response to those transgressions, proving that the blockchain technology it implements is reliable. Since then, this technology has become widespread in the form of new cryptocurrencies and an increasing number of people have begun to invest in them. In the absence of a body to regulate the cryptocurrency market, users play with and encourage the fluctuations in their price, causing them to be treated as investment assets rather than transaction assets. This work carries out and then discusses a systematic study of the state of the art and of the general challenges faced by cryptocurrencies.

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Alzahrani, S., Daim, T.U.: Evaluation of the cryptocurrency adoption decision using hierarchical decision modeling (HDM). In: 2019 Portland International Conference on Management of Engineering and Technology (PICMET), pp. 1–7. IEEE (2019)

Google Scholar  

Barn, B., Barat, S., Clark, T.: Conducting systematic literature reviews and systematic mapping studies. In: Proceedings of the 10th Innovations in Software Engineering Conference, pp. 212–213 (2017)

Barone, R., Masciandaro, D.: Cryptocurrency or usury? Crime and alternative money laundering techniques. Eur. J. Law Econ. 47 (2), 233–254 (2019)

Article   Google Scholar  

Bortnikov, S.: The state sovereignty in questions of issue of cryptocurrency. In: International Scientific Conference Digital Transformation of the Economy: Challenges, Trends, New Opportunities, pp. 564–573. Springer, Berlin (2019)

Buocz, T., Ehrke-Rabel, T., Hödl, E., Eisenberger, I.: Bitcoin and the GDPR: allocating responsibility in distributed networks. Comput. Law Secur. Rev. 35 (2), 182–198 (2019)

Casado-Vara, R., Novais, P., Gil, A.B., Prieto, J., Corchado, J.M.: Distributed continuous-time fault estimation control for multiple devices in IoT networks. IEEE Access 7 , 11972–11984 (2019)

Castellanos-Garzón, J.A., Mezquita Martín, Y., José Luis Jaimes, S., López, S.M.: A data mining approach applied to wireless sensor networks in greenhouses. In: International Symposium on Distributed Computing and Artificial Intelligence, pp. 431–436. Springer, Berlin (2018)

Crosby, M., Pattanayak, P., Verma, S., Kalyanaraman, V., et al.: Blockchain technology: beyond bitcoin. Appl. Innov. 2 (6–10), 71 (2016)

Deng, H., Huang, R.H., Wu, Q.: The regulation of initial coin offerings in China: problems, prognoses and prospects. Eur. Bus. Organ. Law Rev. 19 (3), 465–502 (2018)

Dilek, S.: Cryptocurrencies in the digital era: the role of technological trust and its international effects. In: Blockchain Economics and Financial Market Innovation, pp. 453–474. Springer, Berlin (2019)

Earle, T.C.: Trust, confidence, and the 2008 global financial crisis. Risk Anal. Int. J. 29 (6), 785–792 (2009)

Eisenberger, I.: Innovation im recht (2016)

Eyal, I.: Blockchain technology: transforming libertarian cryptocurrency dreams to finance and banking realities. Computer 50 (9), 38–49 (2017)

Fatima, N.: Enhancing performance of a deep neural network by comparing optimizers experimentally. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. Salamanca 9 (2), 79–90 (2020). ISSN: 2255-2863

Feng, Q., He, D., Zeadally, S., Khan, M.K., Kumar, N.: A survey on privacy protection in blockchain system. J. Netw. Comput. Appl. 126 , 45–58 (2019)

Giechaskiel, I., Cremers, C., Rasmussen, K.B.: When the crypto in cryptocurrencies breaks: bitcoin security under broken primitives. IEEE Secur. Priv. 16 (4), 46–56 (2018)

González-Briones, A., Castellanos-Garzón, J.A., Mezquita Martín, Y., Prieto, J., Corchado, J.M.: A framework for knowledge discovery from wireless sensor networks in rural environments: a crop irrigation systems case study. Wirel. Commun. Mobile Comput. 2018 (2018)

Gros, D., Roth, F.: The financial crisis and citizen trust in the European central bank. CEPs working document (334) (2010)

Gupta, S., Meena, J., Gupta, O., et al.: Neural network based epileptic EEG detection and classification (2020)

Haffke, L., Fromberger, M., Zimmermann, P.: Cryptocurrencies and anti-money laundering: the shortcomings of the fifth AML directive (EU) and how to address them. J. Bank. Regul. 1–14 (2019)

Hawlitschek, F., Notheisen, B., Teubner, T.: The limits of trust-free systems: a literature review on blockchain technology and trust in the sharing economy. Electron. Commer. Res. Appl. 29 , 50–63 (2018)

Hussain, A., Hussain, T., Ali, I., Khan, M.R., et al.: Impact of sparse and dense deployment of nodes under different propagation models in manets (2020)

Johnson, B., Laszka, A., Grossklags, J., Moore, T.: Economic analyses of security investments on cryptocurrency exchanges. In: 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 1253–1262. IEEE (2018)

Kamps, J., Kleinberg, B.: To the moon: defining and detecting cryptocurrency pump-and-dumps. Crime Sci. 7 (1), 1–18 (2018). https://doi.org/10.1186/s40163-018-0093-5

Lazarenko, A., Avdoshin, S.: Financial risks of the blockchain industry: a survey of cyberattacks. In: Proceedings of the Future Technologies Conference, pp. 368–384. Springer, Berlin (2018)

Li, T., Chen, H., Sun, S., Corchado, J.M.: Joint smoothing and tracking based on continuous-time target trajectory function fitting. IEEE Trans. Autom. Sci. Eng. 16 (3), 1476–1483 (2018)

Li, T., Corchado, J.M., Sun, S.: Partial consensus and conservative fusion of Gaussian mixtures for distributed PHD fusion. IEEE Trans. Aerosp. Electron. Syst. 55 (5), 2150–2163 (2018)

Li, Y., Susilo, W., Yang, G., Yu, Y., Du, X., Liu, D., Guizani, N.: Toward privacy and regulation in blockchain-based cryptocurrencies. IEEE Netw. 33 (5), 111–117 (2019)

López, A.B.: Deep learning in biometrics: a survey. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 8 (4), 19–32 (2019)

Marella, V., Upreti, B., Merikivi, J., Tuunainen, V.K.: Understanding the creation of trust in cryptocurrencies: the case of bitcoin. Electron. Markets 1–13 (2020)

Martín, Y.M., Parra, J., Pérez, E., Prieto, J., Corchado, J.M.: Blockchain-based systems in land registry, a survey of their use and economic implications. CISIS 2020 , 13–22 (2020)

Mehar, M.I., Shier, C.L., Giambattista, A., Gong, E., Fletcher, G., Sanayhie, R., Kim, H.M., Laskowski, M.: Understanding a revolutionary and flawed grand experiment in blockchain: the dao attack. J. Cases Inf. Technol. (JCIT) 21 (1), 19–32 (2019)

Mezquita, Y., Alonso, R.S., Casado-Vara, R., Prieto, J., Corchado, J.M.: A review of k-NN algorithm based on classical and quantum machine learning. In: International Symposium on Distributed Computing and Artificial Intelligence, pp. 189–198. Springer, Berlin (2020)

Mezquita, Y., Casado-Vara, R., González Briones, A., Prieto, J., Corchado, J.M.: Blockchain-based architecture for the control of logistics activities: pharmaceutical utilities case study. Logic J. IGPL 29 (6), 974–985 (2021)

Mezquita, Y., Gil-González, A.B., Prieto, J., Corchado, J.M.: Cryptocurrencies and price prediction: a survey. In: International Congress on Blockchain and Applications, pp. 339–346. Springer, Berlin (2021)

Mezquita, Y., Gil-González, A.B., Martíndel Rey, A., Prieto, J., Corchado, J.M.: Towards a blockchain-based peer-to-peer energy marketplace. Energies 15 (9), 3046 (2022)

Mezquita, Y., González-Briones, A., Casado-Vara, R., Chamoso, P., Prieto, J., Corchado, J.M.: Blockchain-based architecture: a mas proposal for efficient agri-food supply chains. In: International Symposium on Ambient Intelligence, pp. 89–96. Springer, Berlin (2019)

Mezquita, Y., González-Briones, A., Casado-Vara, R., Wolf, P., Prieta, F.D.L., Gil-González, A.B.: Review of privacy preservation with blockchain technology in the context of smart cities. In: Sustainable Smart Cities and Territories International Conference, pp. 68–77. Springer, Berlin (2021)

Mezquita, Y., Parra, J., Perez, E., Prieto, J., Corchado, J.M.: Blockchain-based systems in land registry, a survey of their use and economic implications. In: Computational Intelligence in Security for Information Systems Conference, pp. 13–22. Springer, Berlin (2019)

Mezquita, Y., Valdeolmillos, D., González-Briones, A., Prieto, J., Corchado, J.M.: Legal aspects and emerging risks in the use of smart contracts based on blockchain. In: International Conference on Knowledge Management in Organizations, pp. 525–535. Springer, Berlin (2019)

Nakamoto, S., et al.: Bitcoin: a peer-to-peer electronic cash system (2008)

Petersen, K., Feldt, R., Mujtaba, S., Mattsson, M.: Systematic mapping studies in software engineering. In: 12th International Conference on Evaluation and Assessment in Software Engineering (EASE), vol. 12, pp. 1–10 (2008)

Pimpalkar, A.P., Raj, R.J.R.: Influence of pre-processing strategies on the performance of ml classifiers exploiting TF-IDF and bow features. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 9 (2), 49 (2020)

Saad, M., Choi, J., Nyang, D., Kim, J., Mohaisen, A.: Toward characterizing blockchain-based cryptocurrencies for highly accurate predictions. IEEE Syst. J. 14 (1), 321–332 (2019)

Sasson, E.B., Chiesa, A., Garman, C., Green, M., Miers, I., Tromer, E., Virza, M.: Zerocash: decentralized anonymous payments from bitcoin. In: 2014 IEEE Symposium on Security and Privacy, pp. 459–474. IEEE (2014)

Scholl, H.J., Bolívar, M.P.R.: Regulation as both enabler of technology use and global competitive tool: the Gibraltar case. Gov. Inf. Q. 36 (3), 601–613 (2019)

Tjio, H., Hu, Y.: Collective investment: land, crypto and coin schemes: regulatory ‘property’. Eur. Bus. Organ. Law Rev. 1–28 (2020)

Todorof, M.: Fintech on the dark web: the rise of cryptos. In: Era Forum, vol. 20, pp. 1–20. Springer, Berlin (2019)

Valdeolmillos, D., Mezquita, Y., González-Briones, A., Prieto, J., Corchado, J.M.: Blockchain technology: a review of the current challenges of cryptocurrency. In: International Congress on Blockchain and Applications, pp. 153–160. Springer, Berlin (2019)

Van Hout, M.C., Hearne, E.: New psychoactive substances (NPS) on cryptomarket fora: an exploratory study of characteristics of forum activity between NPS buyers and vendors. Int. J. Drug Policy 40 , 102–110 (2017)

Vergara, D., Extremera, J., Rubio, M.P., Dávila, L.P.: The proliferation of virtual laboratories in educational fields. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 9 (1), 85 (2020)

Weaver, N.: Risks of cryptocurrencies. Commun. ACM 61 (6), 20–24 (2018)

Wilson, C.: Cryptocurrencies: the future of finance? In: Contemporary Issues in International Political Economy, pp. 359–394. Springer, Berlin (2019)

Zetzsche, D.A., Buckley, R.P., Barberis, J.N., Arner, D.W.: Regulating a revolution: from regulatory sandboxes to smart regulation. Fordham J. Corp. Fin. L. 23 , 31 (2017)

Zubair, S., Al Sabri, M.A.: Hybrid measurement of the similarity value based on a genetic algorithm to improve prediction in a collaborative filtering recommendation system. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 10 (2), 165–182 (2021)

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The research of Yeray Mezquita is supported by the pre-doctoral fellowship from the University of Salamanca and co-funded by Banco Santander. This research was also partially supported by the project “Technological Consortium TO develop sustainability of underwater Cultural heritage (TECTONIC)”. Authors declare no conflicts of interest.

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Mezquita, Y., Plaza-Hernández, M., Abbasi, M., Prieto, J. (2023). Cryptocurrencies, Systematic Literature Review on Their Current Context and Challenges. In: Prieto, J., Benítez Martínez, F.L., Ferretti, S., Arroyo Guardeño, D., Tomás Nevado-Batalla, P. (eds) Blockchain and Applications, 4th International Congress . BLOCKCHAIN 2022. Lecture Notes in Networks and Systems, vol 595. Springer, Cham. https://doi.org/10.1007/978-3-031-21229-1_16

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Cryptocurrencies and future financial crime

• Arianna Trozze   ORCID: orcid.org/0000-0001-7383-4477 1 , 2 , 4 ,
• Josh Kamps 1 , 2 ,
• Eray Arda Akartuna 1 , 2 ,
• Florian J. Hetzel 2 ,
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Cryptocurrency fraud has become a growing global concern, with various governments reporting an increase in the frequency of and losses from cryptocurrency scams. Despite increasing fraudulent activity involving cryptocurrencies, research on the potential of cryptocurrencies for fraud has not been examined in a systematic study. This review examines the current state of knowledge about what kinds of cryptocurrency fraud currently exist, or are expected to exist in the future, and provides comprehensive definitions of the frauds identified.

The study involved a scoping review of academic research and grey literature on cryptocurrency fraud and a 1.5-day expert consensus exercise. The review followed the PRISMA-ScR protocol, with eligibility criteria based on language, publication type, relevance to cryptocurrency fraud, and evidence provided. Researchers screened 391 academic records, 106 of which went on to the eligibility phase, and 63 of which were ultimately analysed. We screened 394 grey literature sources, 128 of which passed on to the eligibility phase, and 53 of which were included in our review. The expert consensus exercise was attended by high-profile participants from the private sector, government, and academia. It involved problem planning and analysis activities and discussion about the future of cryptocurrency crime.

The academic literature identified 29 different types of cryptocurrency fraud; the grey literature discussed 32 types, 14 of which were not identified in the academic literature (i.e., 47 unique types in total). Ponzi schemes and (synonymous) high yield investment programmes were most discussed across all literature. Participants in the expert consensus exercise ranked pump-and-dump schemes and ransomware as the most profitable and feasible threats, though pump-and-dumps were, notably, perceived as the least harmful type of fraud.

Conclusions

The findings of this scoping review suggest cryptocurrency fraud research is rapidly developing in volume and breadth, though we remain at an early stage of thinking about future problems and scenarios involving cryptocurrencies. The findings of this work emphasise the need for better collaboration across sectors and consensus on definitions surrounding cryptocurrency fraud to address the problems identified.

Cryptocurrency fraud has become a growing concern worldwide. Between 2017 and 2018, the Australian Competition and Consumer Commission ( 2019 ) registered a 190% increase in losses for victims of scams involving cryptocurrencies. In 2019, the United Kingdom Financial Conduct Authority issued a warning to the public after cryptocurrency scam reports tripled (Financial Conduct Authority, 2019 ). This trajectory of criminals defrauding individuals who have purchased or transacted using cryptocurrencies (cryptocurrency ‘users’) suggests the cryptocurrency space offers yet unexploited opportunities for crime.

The rapid surge in defrauded cryptocurrency users appears to have outpaced corresponding research efforts. Yli-Huumo, et al. ( 2016 ) conducted a literature review to identify key blockchain research areas. Fourteen out of 41 reviewed papers addressed Bitcoin blockchain security challenges. However, only one publication examined fraud associated with blockchain ecosystems (Vasek & Moore, 2015 ). This points to a lack of research investigating deception and misrepresentation for financial gain as a challenge for cryptocurrencies, and the forms of fraud that might occur. The aim of this paper is to understand which types of cryptocurrency fraud have thus far been identified, which types might develop in the future, and how these threats are perceived by researchers and other stakeholders. To this end, we present findings from two complementary studies: a scoping review of the state of published knowledge relating to cryptocurrency fraud, and an expert consensus exercise involving participants from various stakeholder organisations.

A primer on cryptocurrencies

In this section, we provide a brief overview of the key principles of cryptocurrencies—with a focus on Bitcoin in particular—to provide context for the discussion of fraudulent exploitation that follows. While this outline is high-level, the interested reader is referred to both the original Bitcoin whitepaper (Nakamoto, 2008 ) or the textbook by Narayanan et al. ( 2016 ) for further details.

In 2008, an individual or group under the pseudonym Satoshi Nakamoto published a whitepaper entitled, ‘Bitcoin: A Peer-to-Peer Electronic Cash System’ (Nakamoto, 2008 ). This paper discussed a system through which parties could transact directly, without intermediary financial institutions. Bitcoin would rely on cryptography rather than central banks, law enforcement, and anti-counterfeiting measures to ensure security (Narayanan et al., 2016 ). Bitcoin’s market capitalisation has grown significantly since its implementation in 2009, and currently stands at $668 billion (CoinMarketCap, 2021 ). Bitcoin’s creation has sparked thousands of other cryptocurrencies which share similar tenets and technology; the total cryptocurrency market capitalisation is $1.6 trillion (CoinMarketCap, 2021 ).

Bitcoin and other cryptocurrencies share three common principles: decentralisation , pseudo-anonymity , and transparency . They are decentralised in that, rather than being governed by any single institution, they are administered via a peer-to-peer network, the majority of which must agree on which transactions and branch of a distributed digital ledger (the ‘blockchain’) are valid. They are pseudo-anonymous because, instead of usernames or account numbers, Bitcoin uses hashes of public keys to identify users, forming a system of ‘decentralised identity management’ decoupled from real-world identities. Cryptocurrencies are considered only pseudo-anonymous (rather than fully anonymous) due to the transparent nature of their transactions, despite not being explicitly connected with particular individuals and companies (Meiklejohn et al., 2016 ). Transparency results from the fact that all transactions that have ever occurred are recorded on the publicly available blockchain.

When someone creates a transaction, it is broadcast to all the peers in the network. To create a transaction, the user must have a pair of alphanumeric digital keys, comprising a public key (the hash of which identifies the user, and is analogous to an account address) and a private key (analogous to a PIN). Participants use their keys for digital signatures, to prove that they own the Bitcoin they are sending, and to specify the new owner.

The ‘miners’, a specialised subset of peers, collect contemporaneous transactions into a ‘block’ (one element of the ‘blockchain’). They compete to find a correct answer to a computationally hard puzzle—finding an input to a hash function which produces a particular output. Once one of the miners, after attempting many random inputs, finds a correct one, they broadcast the block to the network. This is referred to as Proof of Work (‘PoW’) because the nature of the puzzle means that, to find the correct input, the miner must have expended significant computational resources. Footnote 1

Miners are rewarded for their work—at the time of writing, the reward for finding a correct block is 6.25 Bitcoin (Conway, 2021 ). These 6.25 Bitcoin are created and enter circulation once the miner finds a block, through what is called a ‘coinbase’ transaction (until the maximum amount of Bitcoin, as specified in Nakamoto’s paper—21 million—are minted). The reward is halved approximately every 4 years.

After a candidate block has been broadcast, a consensus process begins to establish whether the block is valid and should be added to the main ledger. Other miners perform a computational test on the transactions within the block and the PoW from the original miner: if this test gives the correct output, the block is considered valid.

They then add the next blocks to whichever chain they think is the correct one. At any given time, there may be multiple branches of the blockchain, but generally the longest one is the most valid. Importantly, participants can only add blocks to the blockchain and are unable to change previous blocks. Once a transaction is executed, it is irreversible. This consensus mechanism prevents what is known as ‘double-spending’, whereby a user could attempt to spend the same Bitcoin again. Consensus on the most valid chain requires agreement of at least 51% of miners and is usually achieved after about six blocks (Narayanan et al., 2016 ). An attack in which a miner or group of miners attempts to manipulate this by controlling 51% of the hashing power—requiring tremendous computational resources—is referred to as a ‘51% attack’.

There are a variety of ways users can store their cryptocurrencies, which effectively means storing their private key. Storage can be either ‘hot’ (online) or ‘cold’ (offline). Offline storage may involve a physical wallet locked in a safe or a key stored locally in a file on one’s computer. Though cold storage is generally safer, if one loses his/her private key or it is stolen, the coins are lost forever. Online wallets are often hosted through custodial wallet provider services, which manage users’ private keys; in exchange, the user sacrifices some anonymity, security, and control. Cryptocurrency exchanges are another type of online service and enable users to convert between fiat currencies backed by governments and cryptocurrencies and among different cryptocurrencies. Many offer custodial online wallets.

While Bitcoin was the first cryptocurrency, and is the prototypical example of the concept, a range of alternative coins and services have subsequently been created for cryptocurrency users who desire more anonymity. For example, Monero obscures wallet addresses and transactions (Keller et al., 2021 ). Individuals may also use ‘mixers’ or ‘tumblers’ to further obfuscate the origin of their funds. (Möser et al., 2013 ).

Another particularly prominent project in the field is Ethereum, which is a distributed virtual machine. Ethereum accounts enable smart contracts, which are computer programmes that automatically execute contracts, in the form of if-else statements (e.g., if a product is received, then release the funds) (Narayanan et al., 2016 ). The smart contract code is publicly visible on the blockchain and immutable. Smart contracts allow parties to enter contracts without needing to trust one another, or a third party, for execution. Rather, the parties can be confident that the contract will be carried out as agreed, so long as they trust its code (Bartoletti et al., 2020 ).

Research approach

To determine the state of knowledge on which types of cryptocurrency fraud currently exist or will exist in the future, as well as the defining characteristics of these frauds, we conducted a scoping study in three steps. The first was a scoping review of published academic research on cryptocurrency fraud. This was followed by a 1.5-day in-person consensus exercise to elicit expert opinion on current and future threats, and to identify priorities for future work. The final step involved an updated search of the academic literature and a review of the grey literature.

Scoping review

Scoping reviews are a replicable method of knowledge synthesis when it is unclear what has been already published on a given topic (Arksey & O’Malley, 2005 ; Levac et al., 2010 ; Munn et al., 2018 ; Paré et al., 2015 ; Peters et al., 2015 ; Peterson et al., 2017 ; Pham et al., 2014 ). The objective of this scoping review was to describe current research into cryptocurrency fraud. For the purpose of this review, we consider a cryptocurrency to be any electronic payment system which uses cryptography to secure peer-to-peer transactions (Nakamoto, 2008 ). Moreover, we define fraud as misrepresentation to gain some (financial) advantage (Law & Martin, 2009 ).

This review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) protocol (Moher et al., 2009 ).

Eligibility criteria

To be considered for this scoping review, published studies had to meet various eligibility criteria. First, we limited our review to publications written in English as we relied entirely on our reviewers’ language skills. The academic literature portion of the scoping review exclusively focused on academic articles such as peer-reviewed journals and conference papers due to the study’s aim of mapping out current research activities. The grey literature review included reports, publications, and alerts. By implication, the review excludes publications such as blog posts, op-eds, presentations, newsletters, marketing materials, correspondence, and magazine or newspaper articles.

Second, studies eligible for this review had to address cryptocurrency fraud in some form. As a minimum, a publication had to discuss at least one scam type related to cryptocurrencies. However, it was not necessary to dedicate an entire publication to this topic. Additionally, publications from the grey literature needed to be authored by a governmental organisation or a private sector company—publications from non-governmental or religious organisations were excluded.

Finally, statements about frauds exploiting cryptocurrency environments had to be based on empirical evidence. Studies had to report at least anecdotal evidence of the scams. If a study did not meet one of the eligibility criteria, we excluded it.

The review used Google Scholar (GS) to identify academic studies for review and Google’s Search Engine to identify private and public sector publications potentially eligible for review. Footnote 2 One of the authors (AT) performed the final and most recent search on GS and Google’s Search Engine in November 2020.

Search strategy

Table 1 shows the search strings used. We split the search string into two separate queries because GS restricted searches to 256 characters. Footnote 3 Moreover, we used inverted commas to limit the search to exact key phrases to avoid retrieving too many irrelevant records. The searches included academic and legal articles but excluded patents and citations. Searches were not limited to a given period; most publications were released in the last decade owing to the recency of the topic.

We used the same two search strings to identify grey literature publications, with the addition of the following parameters: the file type should be a PDF and the text should be in English.

Selection of sources of evidence

Two reviewers (EA and FH) separately selected the publications eligible for the scoping review in two steps. First, each reviewer independently screened the title and abstract of the publications for language, publication type, and relevance to cryptocurrency fraud. To be regarded relevant, the title and abstract had to mention fraudulent behaviour linked to cryptocurrency technology. After completing the first round, the reviewers discussed disagreements and resolved them by consensus. Second, the two reviewers individually assessed the full texts of the articles to identify those that discussed cryptocurrency frauds and related empirical evidence. Any disagreements were resolved through discussion. This covered papers through June 2019. As this is a fast-moving field, the search was updated in November 2020. One reviewer (AT) conducted this subsequent search to capture articles released between June 2019 and November 2020, following the same process as the initial search.

In November 2020, one reviewer (AT) selected publications eligible for the grey literature scoping review in two steps. First, the reviewer screened the title and executive summary (or first section if none existed) of the publication for language, publication type, and relevance to cryptocurrency fraud. In addition, the reviewer searched the text of the source for ‘fraud’ and ‘scam’ and read the paragraph(s) including those words. To account for the fact that many sources did not have executive summaries, the reviewer adopted a permissive attitude at this stage; to be regarded as relevant, the content screened did not need to explicitly discuss fraudulent behaviour in detail. Rather, the reviewer included the publication if, from the content reviewed at this stage, the full text could reasonably be expected to discuss cryptocurrency fraud. Second, one reviewer assessed the full texts of publications meeting the initial criteria to identify those that discussed cryptocurrency frauds and related empirical evidence.

Data extraction process

Next, data were extracted from the included studies by one of the three reviewers. During the first round of the review, the data extraction form was tested by having the first two reviewers independently code 25 of the included studies. Disagreements were discussed and the form was updated accordingly. The final version of the data extraction form (see Table 2 ) was then used to extract information from all studies.

Study selection and characteristics

Figure  1 shows the PRISMA-ScR flow diagram (Moher et al., 2009 ), which summarises the study selection process for the two academic searches. GS identified 171 citations in the initial search and 220 in the November 2020 update. After removing duplicates, we screened 160 publications during the initial iteration and 167 in the update (i.e., a total of 327 unique publications). Based on the title and abstract, we excluded 114 records from the initial search and 107 from the second. As shown in Fig.  1 , a small number of studies were excluded because they were published in a language other than English, or because they were not academic publications, but most of those that were excluded did not address the topic of cryptocurrency fraud; rather, they were focused on topics like the technological and regulatory challenges of cryptocurrency ecosystems.

PRISMA-ScR flow diagram. *Articles were published on electronic pre-print service SSRN and appeared to be academic in nature from screening their titles and abstract but, upon full-text examination, were excluded. **One article was included in the initial search as a pre-print, but had since been formally published and was, therefore, excluded during the updated search as a duplicate

This left 46 studies from the initial search, of which 15 were excluded following full-text assessment. In total, 31 studies met the inclusion criteria and are included in the review from the initial search. We evaluated the full text of 60 publications during the November 2020 update, of which 32 were ultimately included. The four articles ultimately deemed not to be academic in nature were published on the electronic pre-print service SSRN; they appeared to be academic publications from their titles and abstract but, upon full-text examination, were excluded. The duplicate article was included in the initial iteration of the search as a pre-print but had since been formally published. The content had not changed and, therefore, it was excluded as part of the second full-text review. Overall, 63 total studies met the inclusion criteria and are included in this scoping review (see Appendix 1 : Table 3 or https://osf.io/7w9mu/?view_only=c9ad3a1e2ed54dae9b1a0fc2807f144f for summary details of these studies).

Figure  2 summarises the publication selection process for the grey literature. We identified 394 records through the Google search. After removing duplicate web addresses, we screened 377 publications. Based on the title and summary (or the first section of the documents), we excluded 249 records. Of these, one was published in a language other than English; 85 were academic publications; and 116 were ineligible types of publications. Footnote 4 Thirty-three sources were either not accessible or were excluded because opening them posed a privacy or security risk. Eleven sources did not address the topic of cryptocurrency fraud and three were, upon further inspection, duplicates.

PRISMA-ScR flow diagram for grey literature selection (search conducted November 2020)

This left 128 sources, of which 75 were excluded following full-text assessment. Fifty-three studies met the inclusion criteria and are included in the review (see Appendix 2 : Tables 4 and 5 or https://osf.io/7w9mu/?view_only=c9ad3a1e2ed54dae9b1a0fc2807f144f for summary details of these studies).

Types of fraud

In this section, we identify the specific forms of cryptocurrency fraud discussed and the definitions thereof.

The academic literature identified 29 different types of cryptocurrency fraud. Figure  3 lists all fraud types identified in the literature and the proportion of publications that discussed them, while Appendix 3 : Table 6 provides descriptions of the offences. Footnote 5 It is worth noting that in the literature reviewed, authors did not always clearly define or differentiate among types of fraud. Specifically, out of the 63 included academic studies, 30 (47.6%) fully reported definitions for all types of fraud discussed, while almost the same number of publications (33; 52.4%) did not. This lack of conceptual clarity is unfortunate as it impedes understanding of how the frauds are committed and how we might address them. For the benefit of the reader, where necessary, Appendix 3 : Table 6 includes definitions derived from additional sources.

Number of publications and expert consensus exercise participant votes per fraud type. *CPO/CTA fraud is an abbreviation for Commodity Pool Operator or Commodity Trading Advisor fraud. For more details, see Appendix 3 or https://osf.io/7w9mu/?view_only=c9ad3a1e2ed54dae9b1a0fc2807f144f

Academic publications most frequently referred to Ponzi schemes and (synonymous) high yield investment programmes (HYIPs). These scam types were discussed in 44.4% of the included studies. Eighteen (28.6%) publications analysed scams involving initial coin offerings (ICOs). Ten analyses (15.9%) covered phishing scams and nine (14.3%) discussed unspecified types of fraud. Seven (11.1%) studies covered pump-and-dump schemes and market manipulation. Six (9.5%) studies looked at exchange scams and five (7.9%) at scam wallet services. Four papers (4.8%) discussed each of the following types of fraud: fraudulent cryptocoins, smart contract honeypots / attacks, and mining scams. Three publications (4.8%) discussed mining malware and the same number addressed smart Ponzi schemes. Two (3.2%) publications discussed securities fraud and identity theft. Sixteen fraud categories were only mentioned in a single (1.6%) publication each. The second iteration of the search identified 17 new types of fraud from the literature.

Altogether, 36 of the grey literature publications came from private sector companies. These publications identified 32 different types of cryptocurrency fraud, 14 of which were not identified in the academic literature. Figure  3 shows these and the proportion of publications that discussed them, while Appendix 3 : Table 7 provides descriptions of any offences which were not previously defined in the academic literature. Footnote 6 Even more so than in the academic literature, authors did not clearly define or differentiate between types of fraud. Specifically, only four of the 36 studies (11.1%) fully reported definitions for all types of fraud discussed.

Most private sector studies (63.9%) referred to some unspecified type of fraud or scam. Fourteen (38.9%) publications analysed scams involving ICOs and 13 (36.1%) discussed Ponzi schemes or HYIPs. Nine (25.0%) studies covered phishing and seven (19.4%) covered mining malware. Four studies (11.1%) looked at SIM swapping, which did not appear in the academic literature, and which is defined in Appendix 3 : Table 7 . Four studies (11%) also discussed giveaway scams. Three studies (8.3%) discussed market manipulation, forex fraud, and/or exchange scams. Two studies (5.6%) looked at impersonation scams, mining scams, pump-and-dumps, and/or securities fraud. Eighteen fraud categories were mentioned in a single publication each (2.8%).

Seventeen different types of cryptocurrency fraud were identified in the public sector literature. Complete descriptions of these were provided for only four (23.5%). Definitions of frauds covered only in the public sector literature can be found in Appendix 3 : Table 8 . Footnote 7

The most frequently discussed were Ponzi schemes and HYIPs, which were covered in 58.8% of studies. This was followed closely by coverage of undefined or general fraud and scams (nine publications, 52.9%). Four publications (23.5%) addressed ICO fraud and three (17.6%) covered mining malware. Two studies each (11.8%) covered mining malware, pump-and-dumps, phishing, mining scams, and/or manipulation and market abuse. Nine types of fraud were only mentioned in a single (5.9%) publication each.

Expert consensus exercise

A recurring issue in the literature reviewed was the absence of clear definitions of the fraud types identified. In addition, few publications included any assessment of the level of risk presented by the offences: while the existence of a publication on a particular topic is evidence of research effort, this does not necessarily imply that the problem is severe or important. To address this gap, we held a 1.5-day ‘sandpit’ exercise in which we sought to elicit expert opinion on these issues from a diverse group of stakeholders in the field. The aim of the event was to complement our academically-focussed scoping study by obtaining subjective views on a range of issues: the cryptocurrency frauds identified in the literature, any additional threats not present in the literature, the potential for future crime, and the challenges and opportunities participants experienced or anticipated.

The sandpit activity was held in June 2019, with 27 high-profile representatives from the tech industry, the financial sector (HSBC, Nasdaq, Facebook), international financial intelligence units (from the UK, the Netherlands and Australia), law enforcement (Metropolitan Police, City of London Police, Her Majesty’s Prison and Probation Service, National Crime Agency, Defence Science and Technology Laboratory), as well as the World Bank and academic researchers (UCL, Georgia State University, Australian National University, Imperial College London). Findings from the first iteration of the scoping review were presented to inform the activity. However, to maximise the information provided by attendees, the findings from the scoping study were introduced as a way of providing an overview of the problem as it was represented in the published literature (at that time) and to frame the discussions, rather than a point of reference intended to limit their thinking.

Hereafter, we provide an overview of the planning considerations and structure of the event, as well as a summary of the activities, Footnote 8 their results, and key conclusions.

The event commenced with a general introduction and a presentation of the preliminary findings of the initial scoping review. All participants introduced themselves and described what they saw as the key problem with cryptocurrencies for their sector/area and what they thought were the key drivers and inhibitors of the adoption of cryptocurrencies.

Next, to ensure a common understanding of the overall topic, two invited talks were given: one providing an overview of the blockchain and cryptocurrencies, and the second offering an empirical example of a cryptocurrency fraud; in this case, pump-and-dump schemes (based on Kamps & Kleinberg, 2018 ).

Two group problem planning exercises formed the core part of day one. In predetermined groups (allocated to include members of each sector to facilitate cross-pollination of ideas), participants first engaged in the development of a fraud strategy. Their task was to devise a fraud/crime scheme with cryptocurrencies. Groups started in pairs to develop initial ideas, then joined another pair to decide on one fraud activity and further developed that idea in their group. These findings were then presented in a plenary setting. Next, in the second problem planning phase, each of the groups was assigned a fraud scheme from another group and had to devise mitigation steps. Specifically, the groups were tasked with thinking about what is already in place to mitigate cryptocurrency-related crime, what is needed for better mitigation efforts, and how they would address their allocated problem. As in the first problem planning phase, each group presented their mitigation ideas to the wider audience.

Day two was dedicated to analysing the problems identified. Participants were again allocated to groups, different from those of the first day to ensure that everyone interacted with as many others as possible. In roundtable discussions, the groups focused on the core problems identified on day one and were asked to indicate (on a scale from 1 = very low to 7 = very high) how harmful, profitable, feasible and defeatable they found each of the problems. These judgments were made using interactive polling software that allowed them to access the poll with their smartphone and see the (anonymised) results in real-time. Expert opinions concerning these four facets were particularly pertinent given the absence of such insights in the literature.

After a brief discussion of the findings, we proceeded in a plenary setting and focused on the wider problems associated with cryptocurrencies identified on day one. All participants were again asked to use the polling software to rank the issues according to their relevance. The problem analysis exercise closed with a ranking of the importance of the drivers and inhibitors identified during the introduction of the first day.

The activities of the event closed with three further questions about the future; another area about which the literature provided limited insight, and for which the answers to these questions were, therefore, critical to guiding future academic research. Specifically, we asked participants individually (using the polling software): (1) what they expected to see in the cryptocurrency space in ten years’ time; (2) what they definitely did not expect to happen; and (3) what would be needed to better address the potential criminal exploitation of cryptocurrencies in the broadest sense.

Summary of findings

Problem planning exercise and analysis of issues.

The initial problem planning exercise resulted in the identification/ production of various problem scenarios by the invited participants, as follows Footnote 9 :

Fake crypto wallets;

Pump-and-dump schemes;

Investment scams (includes ICO scams, Ponzi schemes, and HYIPs);

Cryptojacking (mining malware); and

Ransomware.

Problem analysis and evaluation

The problems identified in the scenario planning group exercises were discussed, and participants were asked to rate them (on a seven-point scale such as: 1 = not harmful at all to 7 = very harmful) regarding their harmfulness, profitability, feasibility, and defeatability. To capture their confidence in ratings made, participants were also asked to indicate the certainty in their judgments (1 = low certainty to 7 = high certainty). Participants completed the task individually using polling software. Footnote 10

To facilitate comparison with the results of our scoping review, the proportion of participants in this exercise who identified each type of fraud as a source of harm in the cryptocurrency space is also included in Fig.  3 . Figure  3 displays the proportion of participants who rated the harmfulness of each of these as ‘5’, ‘6’, or ‘7’. The expert consensus exercise participants did not differentiate between ICO scams and other HYIPs, but we have displayed their responses under the latter, more general category. There are clear discrepancies between the extent to which certain threats were identified by experts and the frequency with which they appear in the literature. While Ponzi schemes and other scams were common in both, two of the primary threats identified by experts—ransomware and wallet scams—were among those which only received modest attention in the literature. In contrast, the level of published material concerning issues such as phishing appeared disproportionate to its perceived risk.

The aggregate results for all four dimensions (averaged across participants) are shown in Fig.  4 . For each of the dimensions, the graph can be interpreted in much the same way: for example, the offences that participants perceived to be most harmful and for which they were the most certain of their judgement are in the top right of the figure.

Problem analysis on the harmfulness, profitability, feasibility, and defeatability dimensions (horizontal axis; judgment certainty on the vertical axis)

Overall, the problems discussed scored higher on their feasibility than they did on their defeatability. The tendency for participants to perceive defeating these problems as more difficult than devising the scams was a recurring topic during the exercise. While most offences were perceived to be profitable, participants were divided in terms of the degree of harm they posed. For all dimensions considered, participants expressed varying degrees of (un)certainty, suggesting a need for more knowledge on these offences.

In terms of the most highly ranked threats, pump-and-dump schemes and ransomware were perceived as the most profitable and most feasible. These were also the two offences for which participants tended to express the most confidence in their answers. Interestingly, perceptions differed for the perceived harm associated with pump-and-dump schemes, which were seen as the overall least harmful issue. An explanation for this disparity could be the perceived lack of victims for pump-and-dump operations. During the activity, participants voiced concerns that pump-and-dump operations are a known risk of which all cryptocurrency market participants should be aware.

Final three questions about the future

Participants emphasised the demand for better collaboration across sectors to address the problems discussed. Some highlighted the need for better sharing of intelligence and collaboration between cryptocurrency exchanges and public institutions. Unfortunately, others highlighted the same things as being unlikely to happen. Such diversity of opinion was also present for broader scenarios: specifically, while some anticipated a fully cashless society and easy-to-use cryptocurrency payments for everyday items in the next ten years, others saw the same scenario as unlikely.

Collectively, the findings demonstrate that we are at an early stage in thinking about future problems and scenarios involving cryptocurrencies. An area of agreement was the need for better collaboration between sectors since neither the private sector, nor the law enforcement or financial intelligence units can address the problem alone.

This scoping review represents the first summary of available research on cryptocurrency fraud and definitions of the types of fraud identified by the research. Findings suggest research on cryptocurrency fraud is rapidly developing both in volume and breadth. Criminals appear to be rapidly expanding into other areas of fraud and research has, so far, been unable to keep up. While it is unwise to conflate the volume of research on particular types of fraud with the magnitude of offending, the existence of empirical evidence of a number of different types of fraud about which there is little academic research supports this assertion. Key findings and limitations are discussed below, emphasising the need for further research on newly identified areas of cryptocurrency fraud and collaboration across stakeholders.

Cryptocurrency fraud as a cyber-enabled crime

Most sources portrayed cryptocurrency frauds as cyber-enabled frauds. Cyber-enabled crimes involve perpetrators using information and communication technologies to magnify the scale and reach of offences that could also be committed offline (McGuire & Dowling, 2013 ). In describing cryptocurrency frauds, researchers often refer to traditional financial frauds like Ponzi schemes (Bartoletti et al., 2018 ; Reddy & Minaar, 2018 ; Securities & Exchange Commission, 2013 ), market manipulation, and pump-and-dump schemes (Anderson et al., 2019 ; Chen et al., 2019a , 2019b , 2019c , 2019d ). These types of fraud are not new—Charles Ponzi first committed his namesake fraud in the 1920s, promising high returns for investments in stamps (Frankel, 2012 ). Pump-and-dump schemes have similarly plagued the stock market for centuries (Kamps & Kleinberg, 2018 ).

While the underlying characteristics of these frauds remain unchanged, implementation mechanisms have evolved. For example, there are significant parallels between ICOs and initial public offerings (Barnes, 2018 ; Baum, 2018 ) but, rather than shares being offered via a stock exchange, ICOs raise funds through the blockchain. Furthermore, smart contracts have transformed the way Ponzi schemes can be executed (Bartoletti et al., 2017 ; Chen et al., 2018a , 2018b , 2019a , 2019b , 2019c , 2019d ). Overall, however, the literature points to significant similarities between cryptocurrency frauds and traditional financial frauds (or at least the academic imagination conceptualises it this way).

Research refers to cryptocurrency frauds as cyber-dependent comparatively less often. Cyber-dependent crimes are offences that are only able to be committed using information and communication technologies (McGuire & Dowling, 2013 ). Crypto-mining and wallet and exchange service frauds can be categorised as such. For example, crypto-mining frauds involve malware which uses a victim’s computer to mine cryptocurrencies for the offender (Anderson et al., 2019 ; Conley et al., 2015 ). In the case of wallet and exchange service frauds, fraudsters impersonate legitimate versions of such services, only to later steal money from victims (Pryzmont, 2016 ; Samsudeen et al., 2019 ; Vasek, 2017 ; Vasek & Moore, 2015 ). These are, perhaps, the only two types of fraud identified which could be considered strictly crypto-dependent, as opposed to other crimes, such as ransomware, which—while cyber-dependent—are merely facilitated by cryptocurrencies. While these frauds were not particularly prominent in the literature, they illustrate how new technologies facilitate novel crime opportunities, not just in terms of the technologies themselves, but also through the supplementary services created alongside them. As cryptocurrency use becomes more mainstream, new cyber-dependent methods of fraud may emerge. There is particular potential for this to occur as the decentralised finance industry further develops (Schär, 2021 ).

The fraud types identified in the expert consensus exercise were more evenly split in terms of cyber-enabled and cyber-dependent crimes. Three of the crimes discussed (fake cryptocurrency wallets, cryptojacking, and ransomware) are cyber-dependent, while pump-and-dump schemes and investment scams are cyber-enabled.

Definitions in the literature

Insufficient reporting of definitions in the literature across all sectors (but especially in non-academic literature) was observed. One of the primary contributions of this study is, therefore, to provide a comprehensive list of definitions of the types of fraud identified in the literature (see Appendix 3 or https://osf.io/7w9mu/?view_only=c9ad3a1e2ed54dae9b1a0fc2807f144f ). In some cases, for example in legal sector sources (both academic and non-academic), this may be due to disciplinary norms. Legal scholars tend to assume fraud definitions refer to their statutory definition, which would be known to their intended audience.

There were also different definitions of certain types of fraud across the literature. For example, ‘credential stuffing’ was defined slightly differently in the private sector literature than its categorisation in academic literature (Krone et al., 2018 ; Navarro, 2019 ). Similarly, one academic study defined all ‘malware’ as ransomware (Xia et al., 2020a ). Furthermore, across the grey literature, types of crime ordinarily not considered fraud, per se—such as ransomware, embezzlement, and other malware—were all categorised as such.

In some cases, it was more difficult to synthesise the types of fraud due to disparities in definitions. For example, one article considered ‘imposter websites and apps’ an issue; it was unclear if the author intended this to be categorised as distinct from phishing or if this was another way to describe the same criminal act (Scheau et al., 2020 ). Similarly, one paper referred to ‘unfair and deceptive acts’ as a type of fraud but failed to define it. Without a definition and an understanding that this is likely to refer to the Federal Trade Commission Act (Federal Trade Commission Act, 2012 ), this could easily be misinterpreted as ‘unspecified fraud’ (Scott, 2020 ).

There were different levels of depth in definitions across sectors. For example, public sector literature included ‘market abuse’ when discussing ‘market manipulation’ (HM Treasury et al., 2018 ). Another public sector article referred to COVID-related scams very generally, while an academic article split them into several different categories (NHS National Services Scotland, 2020 ; Xia et al., 2020a ). Finally, some publications referred to individual types of fraud that were actually sub-categories of other types of fraud. For example, pump-and-dump schemes are one type of market manipulation.

Development of academic research over time

The most discussed types of fraud (Ponzi schemes/HYIPs and ICO scams) remained the same between the first iteration of our academic literature review and the update. The third most discussed was phishing, which was newly identified in the research during the second iteration.

Overall, 17 new types of fraud were identified in the updated literature review, all of which were cyber-enabled crimes. Since they were all cyber-enabled crimes (and not ‘new’, cyber-dependent crimes), it was surprising that these went unidentified in earlier literature. It is unclear if criminals are adapting to enforcement efforts and committing new types of fraud or if the research is simply ‘catching up’. Interestingly, besides phishing, only two other newly identified types of crime—securities fraud and identity theft—were mentioned in more than one publication.

Some of this change could be due to the fields responsible for publishing these papers. In the first iteration, there were more computer science papers; they would be less likely to pick up on legal issues like securities fraud.

There is a clear need for more research on these ‘newer’ types of fraud. This need is further supported by the expert consensus exercise participants’ varying degrees of (un)certainty about harmfulness, profitability, feasibility, and defeatability of the offences discussed. The volume of research is growing rapidly—this review identified more eligible publications published in the last year than in the first several years included in the first iteration of the academic literature review. Considering this rapid development, a follow-up expert consensus exercise could be useful, which we discuss in more detail below.

Differences among sectors

One of the primary conclusions from the sandpit exercise was the need for further collaboration among stakeholders. To address this, the updated version of this scoping review expanded to include grey literature sources.

Private sector literature was less specific in its discussion of fraud than other sectors—‘unspecified fraud’ was discussed most often. The prevalence of the ‘unspecified fraud’ categorisation also highlights the lack of transparency in many private sector publications, in both their methods and conclusions. ‘Unspecified fraud’ was followed by the same three most ‘popular’ types of fraud as in the academic literature—ICO scams, Ponzi Schemes / HYIPs, and phishing.

New types of fraud (e.g., SIM swapping, forex fraud, and securities fraud) were more commonly discussed in the grey literature than in the academic literature. This was true even though the academic review was recently updated. Notably, SIM swapping, forex fraud, and impersonation scams were completely absent from the academic literature. The remaining ‘new’ crimes identified in the private sector literature were only mentioned in one publication each and can be categorised as cyber-enabled crimes. These may, indeed, be crimes that have only recently emerged in the cryptocurrency space. In the public sector literature, Ponzi schemes, unspecified fraud, and ICO scams were the most frequently discussed types of fraud. Phishing was also frequently discussed in the public sector literature.

Focus of research and expert consensus exercise and prioritising future research

Across all sectors of published research, Ponzi Schemes/HYIPs and ICO scams were discussed the most often but were not considered particularly profitable or feasible by sandpit participants. It is possible that more research into—and a greater understanding of—these scams has made them less feasible, or at least has led to them being perceived as such. There was less certainty among participants surrounding the profitability of investment scams; future academic research (and collaboration with other stakeholders) could serve to reduce this uncertainty.

There could also, however, be a mismatch between research and practice. For example, our experts perceived crimes like ransomware and fake crypto wallets as profitable. Prior academic research has shown that ransomware, in particular, was not particularly so (Conti et al., 2018 ; Vasek et al., 2017 ). However, the applicability of this academic work might be limited by its age, as more recent, private sector sources have suggested ransomware has been increasing in recent years and that it has the potential to be very profitable and harmful (Chainalysis, 2021 ; CipherTrace, 2020 ). Beyond highlighting the need for further academic research on the impact of ransomware, this discrepancy emphasises the need for collaboration among stakeholders and academia in developing research agendas.

As noted by the expert participants of our sandpit activity, further collaboration is necessary across sectors to prioritise future research into cryptocurrency fraud. To facilitate this, a follow-up sandpit-style activity, informed by the updated and expanded scoping review, is recommended. It would be useful to gain experts’ insight into the harmfulness, feasibility, profitability, and defeatability of some of the ‘new’ cyber-enabled crimes we identified in the literature (e.g., securities fraud, identity theft, and wire fraud). Since the number of types of frauds has significantly increased, and many types of fraud were only mentioned in a single study, this is crucial to prioritising future research. The follow-up exercise should include broad participation from a variety of sectors to get a more comprehensive view of the current and future cryptocurrency-based fraud landscape. Finally, an emphasis on consensus surrounding definitions of various types of fraud would be useful. This could ultimately lead to the collaborative development of standards in the field, which could help prevent future frauds. Footnote 11

Limitations and Outlook

The limitations of this (and any) scoping review concern choices regarding the eligibility criteria and search strategy used. First, this scoping review was limited to GS. While it may be argued that using a single database may result in some publications being missed, we believe GS provides comprehensive coverage of the issues on which this review focuses. In designing the review, we conducted test runs across various databases, including ProQuest, Web of Science, and Scopus, with a combination of more general and more specific search terms. These searches returned a large volume of publications; however, many of the articles were merely news reports and the searches included many duplicates. In contrast to the other databases, GS returned the highest proportion of relevant, scientific work. While other similar studies (for example, Badawi and Jourdan ( 2020 )) identified a higher volume of publications, this is primarily due to their broader inclusion criteria.

We tested multiple alternative search strings but found that these resulted in large volumes of irrelevant material being identified. The final search strings were chosen through trial and error, and were deemed to best reduce irrelevant material, while remaining processable in a reasonable timeframe. We ultimately restricted the GS search to exact phrases (as opposed to texts including the keywords in an unconnected manner) because test queries identified too many potential (but irrelevant) records when the search terms were less specific. We acknowledge that we may have excluded relevant studies that alternative search strategies would have detected. However, the fact that we uncovered such a large range and number of scams and frauds means the implications of this on our overall conclusions are likely to be minimal. Furthermore, GS lacks wildcard character functionality. However, in our experience, using wildcards on other databases primarily resulted in more noise, rather than better findings. Moreover, using wildcards reduces control over the search to a certain extent. We ultimately sacrificed some potential coverage for greater precision, control, reliability, and transparency.

We limited our scoping review to research published in English. Of the 160 records screened in the first iteration of our review, only 14 were excluded because they were not in English (i.e., 10.9% of the total records excluded after removing duplicates). In the second iteration, of the 167 records reviewed, 20 were excluded because they were not in English (i.e., 14.8% of the total records excluded after removing duplicates). While future studies may benefit from including non-English sources, we do not feel their exclusion from this study meaningfully affected our conclusions.

Besides ‘classical’ journal publications, we also categorised electronic pre-prints, industry reports, and theses as eligible publication types. Some may argue that such publications lack peer-review and are therefore of lower academic value. However, while they would not be subject to the official peer-review process, they are likely to have undergone informal academic review. Furthermore, regardless of their peer-review status, they serve as an indicator of research effort within the field, which is what we sought to measure. On the other hand, we excluded blogs and other sources which might more expeditiously capture what is currently happening or likely to happen in the future in terms of cryptocurrency fraud. We acknowledge that there is a trade-off between timely identification of types of fraud through these types of sources and credibility and verifiability. Many blog posts do not involve rigorous analyses or empirical evidence, may exaggerate claims for marketing purposes or shock value, and do not undergo any outside review (formal or informal). We sought to understand scams and frauds that have been verified (including via some level of peer review in the case of academic publications) and well-researched, rather than identify speculated, future-oriented insights. If we had primarily consulted blogs, many of the insights reported in this paper would not have been identified due to the lack of detail compared to formal articles and reports. We ultimately strove for a balance between prompt identification of frauds and credibility by including electronic pre-prints, theses, and the like.

We also excluded non-governmental organisations’ research from our grey literature review. However, only four of the 128 full texts screened were excluded for this reason. Finally, only one researcher updated the academic literature scoping review and conducted the grey literature review. Ultimately, we designed a rigorous process that was easily replicable and, therefore, do not consider it to have impacted our results.

As literature on this topic develops, further analysis of the literature’s insights—specifically on the harmfulness, feasibility, profitability, and defeatability of the frauds as well as information on whether they are increasing, decreasing or otherwise—would be pertinent. Unfortunately, it was not possible to glean such information from the literature in either iteration of this scoping review as it was largely absent. The absence of these insights in the literature was one motivation for including these factors in the expert consensus exercise but it would, ultimately, be useful have these perspectives from the literature as well. We invite further studies to analyse these frauds in more depth. Since developments in this field are fast-paced, we also recommend regular updates to this scoping review to maintain an accurate view thereof.

In recent years, governments have reported an increase in frequency and scale of frauds involving cryptocurrencies. This review offers the first systematic study of research on what kinds of cryptocurrency fraud currently exist or are expected to exist in the future and, uniquely, systematically identifies expert practitioners’ assessments of these issues as well. The findings suggest scholarship on future problems and scenarios involving cryptocurrency fraud remains in its early stages, though research is rapidly developing (both in volume and scope). Even though many of the frauds identified in this research can be considered cyber-enabled (rather than cyber-dependent), the new ways in which they are being committed using cryptocurrencies necessitates future research.

Another notable finding was the lack of consistency (or existence at all) of definitions of the various types of fraud identified in the literature. One contribution of this study is, therefore, to provide definitions of all the types of fraud currently identified in the academic and grey literature (see Appendix 3 or https://osf.io/7w9mu/?view_only=c9ad3a1e2ed54dae9b1a0fc2807f144f ). Further consensus surrounding these definitions could lead to the collaborative development of standards in the cryptocurrency sector, which would facilitate prevention of future frauds.

This work can help guide research agendas and activities aimed at translating research into practice. Ultimately, the study emphasises the need for better collaboration across sectors in prioritising future research on and mitigations of frauds involving cryptocurrencies to better address the problems identified.

Availability of data and materials

The datasets generated and analysed in the current study are available in the Open Science Framework repository, https://osf.io/7w9mu/?view_only=c9ad3a1e2ed54dae9b1a0fc2807f144f .

Not all cryptocurrencies employ PoW. Other mechanisms, such as Proof of Stake, are used by other cryptocurrencies.

In designing the scoping review, we conducted test searches of multiple databases using various search strings with varying levels of specificity. Ultimately, Google Scholar provided the most comprehensive results, while reducing irrelevant noise in our results. In contrast to other publication aggregators, such as the Web of Science, GS is faster in indexing published work, especially from pre-print servers (i.e., where researchers make publications available without a paywall or before publication in conference proceedings or a journal). To map existing research on cryptocurrency fraud, this review required academic search engines and databases with broad coverage. Previous studies suggested that GS provides the best scope among the available databases. For instance, Gusenbauer ( 2019 ) compared the coverage of 12 databases and found GS to provide the most comprehensive range of academic publications. Martín-Martín, et al. ( 2018 ) analysed GS, Scopus, and Web of Science concluding that GS identified the largest proportion of citations across a broad spectrum of subject areas. Against this background, we tested our search on GS, ProQuest, Scopus, and Web of Science in April 2019. All of these databases, including GS, include results behind paywalls as well as open access sources. The results suggested GS was the only database with comprehensive coverage of academic publications. Given these findings, we selected GS as the information source for this scoping review on cryptocurrency fraud.

Note that Google Scholar does not support the wildcard function. However, from our test searches, using wildcard characters primarily resulted in more noise, rather than better findings.

Ineligible publication types included agendas marketing materials, blog posts, indices, infographics, statutes, magazines, reading lists, contracts, CVs, course catalogues, court cases, correspondence, op eds, press releases, news articles, websites, newsletters, and PowerPoints.

Definitions of all types of fraud identified in this scoping review can also be found at https://osf.io/7w9mu/?view_only=c9ad3a1e2ed54dae9b1a0fc2807f144f .

For a full list of definitions identified in this scoping review, see https://osf.io/7w9mu/?view_only=c9ad3a1e2ed54dae9b1a0fc2807f144f .

See also https://osf.io/7w9mu/?view_only=c9ad3a1e2ed54dae9b1a0fc2807f144f .

The full schedule of the event can be found at https://drive.google.com/file/d/1EqRYLUCTQ6Vn3oN_CQ0RSOa1Kgmpbq93/view .

We exclude here discussion of problems identified which specifically relate to money laundering or areas of crime other than fraud. Participants identified the following other crimes: money laundering using Bitcoin ATMs, cryptocurrency money mules, and cryptocurrency transaction extortion. More detail can be found in the following policy brief:

https://www.ucl.ac.uk/jill-dando-institute/sites/jill-dando-institute/files/ucl_cryptocurrencies_and_future_crime_policy_briefing_feb2021_compressed_1.pdf .

The Mentimeter polling software allowed us to display the questions using the Mentimeter app interface (using an Internet connection). That interface was displayed on a big screen, and each participant could use their smartphone to obtain access to answer the questions. Once they provided an answer, their response was visible (in anonymised form) on the screen so that the participants could see their judgments in real-time.

For example, many standards have been developed for the Internet; perhaps the cryptocurrency arena could learn from this example. See, for example, the work of the Internet Engineering Task Force.

Abbreviations

Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews

Proof of Work

Google Scholar

High yield investment programmes

Initial coin offerings

Commodity Pool Operator

Commodity Trading Advisor

Advertising Standards Authority & Committees of Advertising Practice. (2019). Using Technology for Good [Annual Report]. https://www.asa.org.uk/uploads/assets/68dd32b5-ae6a-4993-820a3ff8f1163b8e/c02c8290-0ceb-4946-a98e383d5ee79796/ASA-CAP-2019-Annaul-Report-Full-Version-Singles.pdf

Akin Gump Strauss Hauer & Feld LLP. (2018). Federal judge adopts CFTC position that cryptocurrencies are commodities . Akin Gump Strauss Hauer & Feld LLP. https://www.akingump.com/a/web/65522/aoiZE/litigation-alert-federal-judge-adopts-cftc-position-that-crypt.pdf

Anderson, R., Barton, C., Rainer, B., Clayton, R., Ga, C., Grasso, T., Levi, M., Moore, T., & Vasek, M. (2019). Measuring the Changing Cost of Cybercrime Our Framework for Analysing the Costs of Cybercrime. Workshop on the Economics of Information Security (WEIS) , 1–32.

Arksey, H., & O’Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology: Theory and Practice, 8 (1), 19–32. https://doi.org/10.1080/1364557032000119616

Article   Google Scholar  

Australian Competition & Consumer Commission. (2018). Targeting scams . http://www.keepmeposted.org.au/wp-content/uploads/2018/05/f1240_targeting-scams-report.pdf

Australian Competition and Consumer Commission. (2019). Targeting scams. Report of the ACCC on scams activity 2018 (Issue May).

Australian Competition & Consumer Commission. (2020). Targeting scams 2019 . https://www.accc.gov.au/system/files/1657RPT_Targeting%20scams%202019_FA.pdf

Badawi, E., & Jourdan, G.-V. (2020). Cryptocurrencies emerging threats and defensive mechanisms: A systematic literature review. IEEE Access . https://doi.org/10.1109/ACCESS.2020.3034816

Badawi, E., Jourdan, G.-V., Bochmann, G., & Onut, I.-V. (2020). An automatic detection and analysis of the bitcoin generator scam. IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), 2020 , 407–416.

Barnes, P. (2018). Crypto Currency and its Susceptibility to Speculative Bubbles, Manipulation, Scams and Fraud. Journal of Advanced Studies in Finance , 9 (2), 60. https://doi.org/10.14505//jasf.v9.2(18).03

Bartoletti, M., Carta, S., Cimoli, T., & Saia, R. (2017). Dissecting Ponzi schemes on Ethereum: Identification, analysis, and impact .

Bartoletti, M., Pes, B., & Serusi, S. (2018). Data mining for detecting bitcoin ponzi schemes. In Proceedings—2018 Crypto Valley Conference on Blockchain Technology, CVCBT 2018 , pp. 75–84. https://doi.org/10.1109/CVCBT.2018.00014

Bartoletti, M., Carta, S., Cimoli, T., & Saia, R. (2020). Dissecting Ponzi schemes on Ethereum: Identification, analysis, and impact. Future Generation Computer Systems, 102 , 259–277. https://doi.org/10.1016/j.future.2019.08.014

Baum, S. (2018). Cryptocurrency fraud: A look into the frontier of fraud .

Bolster. (2019). State of Phishing & Online Counterfeiting [Annual Report]. https://bolster.ai/assets/files/reports/2019.pdf

Boshmaf, Y., Al Jawaheri, H., & Al Sabah, M. (2019). BlockTag: Design and applications of a tagging system for blockchain analysis. In IFIP International Conference on ICT Systems Security and Privacy Protection , pp. 299–313.

Boshmaf, Y., Elvitigala, C., Al Jawaheri, H., Wijesekera, P., & Al Sabah, M. (2020). Investigating MMM Ponzi scheme on Bitcoin. In Proceedings of the 15th ACM Asia Conference on Computer and Communications Security , pp. 519–530.

Burrus, J. (2018). Fighting financial crime in the age of cryptocurrencies (p. 3). Refinitiv. https://www.refinitiv.com/content/dam/marketing/en_us/documents/expert-talks/world-check-expert-talk-fighting-financial-crime.pdf

Capobianco, A. (2019). Digital Disruption in Financial Markets—Note by the United States (p. 10). Organisation for Economic Co-operation and Development. https://www.justice.gov/atr/page/file/1313821/download

Chainalysis. (2019). Crypto Crime Report . Chainalysis. https://uploads-ssl.webflow.com/5a9360f88433cb00018022c2/5c4f67ee7deb5948e2941fda_Chainalysis%20January%202019%20Crypto%20Crime%20Report.pdf

Chainalysis. (2020). The 2020 Geography of Cryptocurrency Report . https://go.chainalysis.com/rs/503-FAP-074/images/2020-Geography-of-Crypto.pdf

Chainalysis. (2021, January 19). Crypto Crime Summarized: Scams and Darknet Markets Dominated 2020 by Revenue, But Ransomware Is the Bigger Story. Chainalysis Insights . https://blog.chainalysis.com/reports/2021-crypto-crime-report-intro-ransomware-scams-darknet-markets

Chen, W., Guo, X., Chen, Z., Zheng, Z., & Lu, Y. (2020). Phishing Scam Detection on Ethereum: Towards Financial Security for Blockchain Ecosystem. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence Special Track on AI in FinTech. , 4506–4512.

Chen, W., Guo, X., Chen, Z., Zheng, Z., Lu, Y., & Li, Y. (2020b). Honeypot contract risk warning on ethereum smart contracts. IEEE International Conference on Joint Cloud Computing, 2020 , 1–8.

Google Scholar  

Chen, W., Wu, J., Zheng, Z., Chen, C., & Zhou, Y. (2019a). Market Manipulation of Bitcoin: Evidence from Mining the Mt. Gox Transaction Network. In Proceedings—IEEE INFOCOM , 2019-April (November 2013), pp. 964–972. https://doi.org/10.1109/INFOCOM.2019.8737364

Chen, W., Xu, Y., Zheng, Z., Zhou, Y., Yang, J. E., & Bian, J. (2019b). Detecting ‘Pump & dump schemes’ on cryptocurrency market using an improved apriori algorithm. In Proceedings—13th IEEE International Conference on Service-Oriented System Engineering, SOSE 2019, 10th International Workshop on Joint Cloud Computing, JCC 2019 and 2019 IEEE International Workshop on Cloud Computing in Robotic Systems, CCRS 2019 , pp. 293–298. https://doi.org/10.1109/SOSE.2019.00050

Chen, W., Xu, Y., Zheng, Z., Zhou, Y., Yang, J. E., & Bian, J. (2019c). Detecting" Pump & Dump Schemes" on cryptocurrency market using an improved Apriori Algorithm. IEEE International Conference on Service-Oriented System Engineering (SOSE), 2019 , 293–2935.

Chen, W., Zheng, Z., Cui, J., Ngai, E., Zheng, P., & Zhou, Y. (2018). Detecting Ponzi Schemes on Ethereum . pp. 1409–1418. https://doi.org/10.1145/3178876.3186046

Chen, W., Zheng, Z., Ngai, E. C. H., Zheng, P., & Zhou, Y. (2019d). Exploiting blockchain data to detect smart ponzi schemes on ethereum. IEEE Access, 7 , 37575–37586. https://doi.org/10.1109/ACCESS.2019.2905769

Chen, Z., Khoa, L., Teoh, E., Nazir, A., Karuppiah, E., & Lam, K. (2018b). Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: A review. Knowledge and Information Systems, 57 (2), 245–285. https://doi.org/10.1007/s10115-017-1144-z

Chernin, A., Moran, N., & Mola, S. (2020). Trends in CFTC Virtual Currency Enforcement Actions: 2015-Q2 2020 (p. 14). Cornerstone Research. https://www.cornerstone.com/Publications/Reports/Trends-in-CFTC-Virtual-Currency-Enforcement-Actions-2015-Q2-2020/Trends-in-CFTC-Virtual-Currency-Enforcement-Actions-2015-Q2-2020.pdf

CipherTrace. (2018). Cryptocurrency Anti-Money Laundering Report (p. 22). https://ciphertrace.com/crypto-aml-report-2018q3.pdf

CipherTrace. (2020). ALERT: Malicious Crypto Browser Extension—Masked MetaMask . https://ciphertrace.com/alert-malicious-crypto-browser-extension-masked-metamask/

Cohen, D. O., & Crance, R. (2018). Taking a trip around the regulatory block: U.S. regulation of blockchain and digital assets (Financial Services Litigation Alert, p. 4). Schnader Harrison Segal & Lewis LLP. https://www.schnader.com/wp-content/uploads/2019/02/ALERT-Financial-Services-Blockchain-and-Cryptocurrency-Regulation-7-31-18.pdf

CoinMarketCap. (2021). Today’s cryptocurrency prices by market cap . CoinMarketCap. https://coinmarketcap.com/

Conley, B., Echert, J., Fuller, A., Lewis, H., & Lunday, C. (2015). Cryptocurrencies: An introduction for policy makers. Technology Law and Public Policy Clinic .

Conti, M., Gangwal, A., & Ruj, S. (2018). On the economic significance of ransomware campaigns: A Bitcoin transactions perspective. Computers & Security, 79 , 162–189. https://doi.org/10.1016/j.cose.2018.08.008

Conway, L. (2021). Bitcoin Halving . Investopedia. https://www.investopedia.com/bitcoin-halving-4843769

Corporate and Criminal Fraud Accountability Act of 2002, 18 United States Code § 1348 (2009). https://www.law.cornell.edu/uscode/text/18/1348

Coulter, B. B. (2018). CFTC Can Regulate Cryptocurrencies as Commodities (Burr Alert, p. 2). Burr Forman LLP. https://www.burr.com/wp-content/uploads/2018/03/ALERT_CFTC-Can-Regulate-Cryptocurrencies-as-Commodities_Coulter.pdf

Global Blockchain Business Council. (2020). Chain Reaction: Blockchain Enters the Mainstream [Annual Report]. Accenture. https://www.lw.com/thoughtLeadership/gbbc-report-blockchain-enters-mainstream

Crowell & Moring. (2018). Cryptocurrency in Small Bytes: The CFTC Chalks Up a Preliminary Win [Client Alert]. https://www.crowell.com/NewsEvents/AlertsNewsletters/all/Cryptocurrency-in-Small-Bytes-The-CFTC-Chalks-Up-a-Preliminary-Win/pdf

Crowley, B. J. (2018). The Legal and Regulatory Issues Surrounding Cryptocurrency .

Counterfeit Access Device and Computer Fraud and Abuse Act of 1984, 18 United States Code § 1029 (2015). https://www.law.cornell.edu/uscode/text/18/1029

Desai, V., Coderre, R., Kizzee, C., & Kalra, H. (2019). Retail and Hospitality Threat Trend report (No. 190147; p. 46). Accenture.

Devonshires. (2018). Litigation Know How: Staying Ahead of the Game . Devonshires. https://www.devonshires.com/wp-content/uploads/2017/07/Com-Lit-Brief-October-18.pdf

Dubois, S. M. (2019). A Baseline Assessment of Law Enforcement Cryptocurrency Investigations. University of Central Lancashire.

Duff, K. B., & Hays, G. (2018). Recent developments in the war on cryptocurrency fraud. The Receiver, 7 , 1–7.

Eastham, E. B. (2020). Morrison and cryptocurrencies: Is it time to revisit the extraterritorial application of rule 10B–5. Ga. J. Int’l & Comp. l., 48 , 563.

Elder, R., & Rotunda, J. (2018). Widespread Fraud Found in Cryptocurrency Offerings (Investor Alert: Cryptocurrencies). Texas State Securities Board. https://www.ssb.texas.gov/sites/default/files/CRYPTO%20SWEEP%20report%2002112019%20UPDATE.pdf

Elwell, C. K., Murphy, M. M., & Seitzinger, M. V. (2014). Bitcoin: Questions, Answers, and Analysis of Legal Issues (p. 20). Congressional Research Service.

Eversheds Sutherland Ltd. (2018). Navigating the issues Securities Enforcement Global Update . https://us.eversheds-sutherland.com/mobile/portalresource/lookup/poid/Z1tOl9NPluKPtDNIqLMRV56Pab6TfzcRXncKbDtRr9tObDdEpW3CmS3!/fileUpload.name=/Securities-Enforcement-Global-Update_Fall-2018.pdf

Federal Trade Commission Act, 15 United States Code § 45 (2012). https://www.law.cornell.edu/uscode/text/15/45

Financial Integrity Network. (2018). Virtual Currencies: Momentum Building For Regulation and Enforcement [Policy Alert].

Financial Conduct Authority. (2019). Cryptoasset investment scams . https://www.fca.org.uk/scamsmart/cryptoasset-investment-scams

Financial Conduct Authority. (2020). Forex trading scams . https://www.fca.org.uk/scamsmart/forex-trading-scams

Financial Conduct Authority. (2021). FCA Handbook . https://www.handbook.fca.org.uk/handbook/MAR/1/6.html?date=2016-03-07

Frankel, T. (2012). The ponzi scheme puzzle: A history and analysis of con artists and victims. Oxford University Press . https://doi.org/10.1093/acprof:osobl/9780199926619.001.0001

FSMA. (2020). Cryptocurrency fraud: The FSMA updates its list of suspicious sites (p. 1) [Warnings]. https://web3.cmvm.pt/SDI/IFs/app/docs/fsd852235.pdf

Gandal, N., Hamrick, J. T., Moore, T., & Oberman, T. (2018). Price manipulation in the Bitcoin ecosystem. Journal of Monetary Economics, 95 , 86–96. https://doi.org/10.1016/j.jmoneco.2017.12.004

Gao, B., Wang, H., Xia, P., Wu, S., Zhou, Y., Luo, X., & Tyson, G. (2020). Tracking Counterfeit Cryptocurrency End-to-end. ArXiv Preprint ArXiv:2011.02673.

Garg, S. C., Sawhney, A. P., Tyagi, A., & Kanungo, B. P. (2019). Report of the Committee to propose specific actions to be taken in relation to Virtual Currencies (Committee on Virtual Currencies). Department of Economic Affairs, Ministry of Finance. https://dea.gov.in/sites/default/files/Approved%20and%20Signed%20Report%20and%20Bill%20of%20IMC%20on%20VCs%2028%20Feb%202019.pdf

Goodlett, B. (2020). Texas cracks down on cryptocurrency fraud: 2020 developments (Accelerate, p. 2). DLA Piper.

Gusenbauer, M. (2019). Google Scholar to overshadow them all? Comparing the sizes of 12 academic search engines and bibliographic databases. In Scientometrics (Vol. 118, Issue 1). Springer International Publishing. https://doi.org/10.1007/s11192-018-2958-5

Halverson, G. (2020). Global Payments 2020–30 A quantum shift in the next decade [Submission to RBA Payments Boards]. McLean Roche Consulting Group. https://www.rba.gov.au/payments-and-infrastructure/submissions/review-of-retail-payments-regulation/mclean-roche-consulting.pdf

Hays, G. (2018). Receiver keeping up with the cryptos. The Receiver, 6 , 1–7.

Hedrich, W., Chalmers, B., Viet, P. H., & Koh, J. (2018). 14 Shades of risk in Asia-Pacific . Marsh & McLennan Companies.

Henning, P. J. (2019). A Taxonomy of Cryptocurrency Enforcement Actions (SSRN Scholarly Paper ID 3483198). Social Science Research Network. https://papers.ssrn.com/abstract=3483198

Higgins, S. (2017). A Digital Currency Scam is Misusing the Rothschild Family Name. CoinDesk . https://www.coindesk.com/rothschild-advisory-warns-fraudulent-digital-currency

HM Treasury, Financial Conduct Authority, & Bank of England. (2018). Cryptoassets Taskforce: Final report (p. 58). https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/752070/cryptoassets_taskforce_final_report_final_web.pdf

Identity Theft and Assumption Deterrence Act of 1998, Pub. L. No. 3007, 18 United States Code (2006). https://www.law.cornell.edu/uscode/text/18/1028

Insikt Group. (2018). Shifting Patterns in Internet Use Reveal Adaptable and Innovative North Korean Ruling Elite . https://www.recordedfuture.com/north-korea-internet-usage/

Internet Engineering Task Force. (n.d.). Internet standards . IETF. Retrieved 16 March 2021, from https://www.ietf.org/standards/

Jiaying, J. (2020). Regulating Blockchain? A Retrospective Assessment of China’s Blockchain Policies and Regulations. Tsinghua China L. Rev. , 12 .

Jung, E., Le Tilly, M., Gehani, A., & Ge, Y. (2019). Data mining-based ethereum fraud detection. IEEE International Conference on Blockchain (Blockchain), 2019 , 266–273.

Kamps, J., & Kleinberg, B. (2018). To the moon: Defining and detecting cryptocurrency pump-and-dumps. Crime Science . https://doi.org/10.1186/s40163-018-0093-5

Keller, P., Florian, M., & Böhme, R. (2021). Collaborative Deanonymization. ArXiv:2005.03535 [Cs] . http://arxiv.org/abs/2005.03535

Kethineni, S., & Cao, Y. (2019). The rise in popularity of cryptocurrency and associated criminal activity. International Criminal Justice Review . https://doi.org/10.1177/1057567719827051

Krone, M. J., Lukes, E. M., & McKibbin, C. (2018). Tales from the Crypt: Cryptocurrency is Here—How will crime insurers respond? The Fidelity Law Journal , XXIV , 86.

Lašas, K., Kasputytė, G., Užupytė, R., & Krilavičius, T. (2020). Fraudulent behaviour identification in ethereum blockchain. CEUR Workshop Proceedings [Electronic Resource]: IVUS 2020, Information Society and University Studies, Kaunas, Lithuania, 23 April, 2020: Proceedings. Aachen: CEUR-WS, 2020, Vol. 2698 .

Law, J., & Martin, E. A. (2009). Fraud. In J. Law & E. A. Martin (Eds.), Oxford dictionary of law (7th ed.). Oxford.

Leuz, C. (2018). Regulatory approaches to combat retail investor fraud . SEC Investor Advisory Committee. https://www.sec.gov/spotlight/investor-advisory-committee-2012/iac030818-leuz-remarks.pdf

Levac, D., Colquhoun, H., & O’Brien, K. K. (2010). Scoping studies: Advancing the methodology. Implementation Science, 5 (1), 69. https://doi.org/10.1186/1748-5908-5-69

LIFARS. (2018). Bitcoin & cryptocurrency investment prospects & hacking incidents . https://lifars.com/wp-content/uploads/2018/04/V2-Bitcoin-and-Cryptocurrency-Investment-Prospects-Hacking-Incidents.pdf

Lim, B., Murphy, G. E., Heda, K., Hannotin, C., Krauß, O., Popp, F., Maximenko, A., Klutchareva, E., & Low, C. (2019). Blockchain 2018 year-in-review . Debevoise & Plimptom.

Liu, X. F., Jiang, X.-J., Liu, S.-H., & Tse, C. K. (2020). Knowledge discovery in cryptocurrency transactions: A survey. ArXiv Preprint ArXiv:2010.01031.

Lockhart, J., & Nuesser, A. (2018). Taking Caution: Financial Consumersand the Cryptoasset Sector (Investor Office . Innovative Research Group Inc.

Lucking, D., & Aravind, V. (2019). Cryptocurrency as a Commodity: The CFTC’s Regulatory Framework. Global Legal Insights .

Mabille, C. (2020). Is cryptocurrency a store of value for the current crisis? Finatic. https://www.finatic.be/bitcoin.pdf

Malta Financial Services Authority. (2019). MSFA Warning—Bitcoin Future . https://web3.cmvm.pt/SDI/IFs/app/docs/fsd816904.pdf

Malyshev, P., Achilles, J., Ottenberg, J., & Stumacher, J. (2018). CFTC enforcement trends in 2017 and considerations for 2018. Journal of Investment Compliance, 19 (3), 17–21. https://doi.org/10.1108/JOIC-04-2018-0021

Manojlovic, D. (2019). Report to the Vancouver Police Board (No. 1902P03). https://vancouver.ca/police/policeboard/agenda/2019/0221/1902P04-Proposed-CAPG-Resolutions.pdf?TB_iframe=true&width=370.8&height=658.8

Martín-Martín, A., Orduna-Malea, E., Thelwall, M., & Delgado López-Cózar, E. (2018). Google Scholar, Web of Science, and Scopus: A systematic comparison of citations in 252 subject categories. Journal of Informetrics, 12 (4), 1160–1177. https://doi.org/10.1016/j.joi.2018.09.002

McAvoy, D., Queenin, C., & Becker, B. (2018). Court confirms CFTC jurisdiction over cryptocurrency fraud and that virtual currencies are commodities (Now + Next, p. 2). Nixon Peabody LLP. https://www.nixonpeabody.com/-/media/Files/Alerts/2018-March/Court-confirms-CFTC-jurisdiction-over-cryptocurrency-fraud.ashx?la=en

McGuire, M., & Dowling, S. (2013). Cyber crime: A review of the evidence. Research Report 75. Chapter 1: Cyber-dependent crimes. In Home Office Research Report 75 .

McGuire, M. (2019). Social Media Platforms and the Cybercrime Economy (Into the Web of Profit). Bromium. https://www.bromium.com/wp-content/uploads/2019/02/Bromium-Web-of-Profit-Social-Platforms-Report.pdf

Meiklejohn, S., Pomarole, M., Jordan, G., Levchenko, K., McCoy, D., Voelker, G. M., & Savage, S. (2016). A fistful of Bitcoins: Characterizing payments among men with no names. Communications of the ACM, 59 (4), 86–93. https://doi.org/10.1145/2896384

Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA Statement. PLoS Medicine, 6 (7), 1–6. https://doi.org/10.1371/journal.pmed.1000097

Moore V. United States, (U.S. Supreme Court 1895).

Möser, M., Bohme, R., & Breuker, D. (2013). An inquiry into money laundering tools in the Bitcoin ecosystem. APWG ECrime Researchers Summit . https://doi.org/10.1109/eCRS.2013.6805780

Munn, Z., Peters, M. D. J., Stern, C., Tufanaru, C., McArthur, A., & Aromataris, E. (2018). Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Medical Research Methodology, 18 (1), 1–7. https://doi.org/10.1186/s12874-018-0611-x

Murko, A., & Vrhovec, S. L. (2019). Bitcoin adoption: Scams and anonymity may not matter but trust into Bitcoin security does. In Proceedings of the Third Central European Cybersecurity Conference , pp. 1–6.

Murphy, E. V., Murphy, M. M., & Seitzinger, M. V. (2015). Bitcoin: Questions, answers, and analysis of legal issues .

Musiala, R. A. Jr., Goody, T. M., Reynolds, V., Tenery, L., McGrath, M., Rowland, C., & Sekhri, S. (2020). Cryptocurrencies: Forensic techniques to meet the challenge of new fraud and corruption risks | FVS Eye on Fraud . AICPA. https://future.aicpa.org/resources/download/cryptocurrencies-forensic-techniques-to-face-new-fraud-and-corruption-risks

Najmy, W. G. (2019). Enhanced Tax Compliance for Cryptoasset Transactions. Available at SSRN 3499659 .

Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System . 9.

Narayanan, A., Bonneau, J., Felten, E., Miller, A., & Goldfeder, S. (2016). Bitcoin and Cryptocurrency Technologies .

Navarro, R. R. (2019). Preventative fraud measures for cryptocurrency exchanges: Mitigating the risk of cryptocurrency scams [M.S., Utica College]. http://search.proquest.com/docview/2312801484/abstract/7B22A14839754F47PQ/1

Nelson, R. M. (2019). Examining regulatory frameworks for digital currencies and blockchain (Testimony No. 7–5700; p. 15). Congressional Research Service. https://www.banking.senate.gov/imo/media/doc/Nelson%20Testimony%207-30-19.pdf

Ngai, K. (2014). Regulating cryptocurrency to prevent fraud and money laundering .

NHS National Services Scotland. (2020). NHS counter fraud services rolling COVID-19 intelligence alert no. 14 . https://www.wihb.scot.nhs.uk/wp-content/uploads/2020/08/2020-08-03-CFS-Rolling-COVID-19-Intel-Alert-No14-Final.pdf

Nilsen, A. I. (2019). Limelight: Real-time detection of pump-and-dump events on cryptocurrency exchanges using deep learning . UiT Norges arktiske universitet.

Paré, G., Trudel, M. C., Jaana, M., & Kitsiou, S. (2015). Synthesizing information systems knowledge: A typology of literature reviews. Information and Management, 52 (2), 183–199. https://doi.org/10.1016/j.im.2014.08.008

Parisi, D., Goldman, G., Baris, J. G., Nallengara, L., Sahni, R. A., Greene, N., Szaja, P. J., Reynolds, B., Anderson, S. W., & Oosterbaan, J. (2018). Regulators and courts clarify virtual currency regulation, but overall framework remains murky . Shearman & Sterling. http://www.legalexecutiveinstitute.com/wp-content/uploads/2018/05/345PM-Regulators.pdf

Peters, M. D. J., Godfrey, C. M., Khalil, H., McInerney, P., Parker, D., & Soares, C. B. (2015). Guidance for conducting systematic scoping reviews. International Journal of Evidence-Based Healthcare, 13 (3), 141–146. https://doi.org/10.1097/XEB.0000000000000050

Peterson, J., Pearce, P. F., Ferguson, L. A., & Langford, C. A. (2017). Understanding scoping reviews: Definition, purpose, and process. Journal of the American Association of Nurse Practitioners, 29 (1), 12–16. https://doi.org/10.1002/2327-6924.12380

Pham, M. T., Rajić, A., Greig, J. D., Sargeant, J. M., Papadopoulos, A., & Mcewen, S. A. (2014). A scoping review of scoping reviews: Advancing the approach and enhancing the consistency. Research Synthesis Methods, 5 (4), 371–385. https://doi.org/10.1002/jrsm.1123

Phan, L., Li, S., & Mentzer, K. (2019). Blockchain technology and the current discussion on fraud. Computer Information Systems Journal .

Phillips, R., & Wilder, H. (2020). Tracing cryptocurrency scams: Clustering replicated advance-fee and Phishing Websites. ArXiv Preprint ArXiv:2005.14440.

Podgor, E. S. (2019). Cryptocurrencies and securities fraud: In need of legal guidance. Available at SSRN 3413384 .

Pryzmont, P. (2016). An empirical study of how Bitcoin related incidents impact its price volatility . August , 62.

PYMNTS.com & Trulioo. (2019). Keeping crime out of crypto (AML/KYC Tracker). https://www.pymnts.com/wp-content/uploads/2019/07/AML-KYC-Tracker-July-2019.pdf

Reddy, E., & Minaar, A. (2018). Cryptocurrency: A tool and target for cybercrime. Acta Criminologica: Southern African Journal of Criminology, 31 (3), 71–92.

Reik, T. (2019). Bitcoin Revisited . Sprott Asset Management USA, Inc. https://www.sprottusa.com/media/2497/sprott-gold-report-bitcoin-revisited.pdf

Rochemont, S. (2020). A Cashless Society in 2019 .

Rognone, L., Hyde, S., & Zhang, S. S. (2020). News sentiment in the cryptocurrency market: An empirical comparison with Forex. International Review of Financial Analysis, 69 , 101462.

Ropes & Gray. (2019). CFTC amends regulations applicable to asset managers including excluded and exempt CPOs and CTAs; action may be required . http://www.ropesgray.com/en/newsroom/alerts/2019/12/CFTC-Amends-Regulations-Applicable-to-Asset-Managers-Including-Excluded-and-Exempt-CPOs-and-CTAs

Samsudeen, Z., Perera, D., & Fernando, M. (2019). Behavioral analysis of bitcoin users on illegal transactions. Advances in Science, Technology and Engineering Systems Journal, 4 (2), 402–412. https://doi.org/10.25046/aj040250

Schär, F. (2021). Decentralized finance: On blockchain- and smart contract-based financial markets . Federal Reserve Bank of St. Louis. https://research.stlouisfed.org/publications/review/2021/02/05/decentralized-finance-on-blockchain-and-smart-contract-based-financial-markets

Scheau, M. C., Link to external site, this link will open in a new window, Crăciunescu, S. L., Brici, I., & Achim, M. V. (2020). A cryptocurrency spectrum short analysis. Journal of Risk and Financial Management; Basel , 13 (8), 184. https://doi.org/10.3390/jrfm13080184

Scott, A. P. (2020). Fintech: Overview of Financial Regulators and Recent Policy Approaches (No. R46333; p. 38). Congressional Research Service. https://crsreports.congress.gov/product/pdf/R/R46333

Securities and Exchange Commission. (2013). Ponzi schemes using virtual currencies. SEC Pub. No. 153 (7/13) .

Securities and Exchange Comimssion Philippines. (2020). SEC Advisory . https://www.sec.gov.ph/wp-content/uploads/2020/10/2020Advisory_BITCOIN-DIGITAL.pdf

Semenihin, A., & Kondrashin, A. (2018). Leading role of state as a regulator of crypto currency . 217 (ICSEAL), 329–334. https://doi.org/10.2991/icseal-18.2018.47

Štefanko, L. (2018). Cryptocurrency scams on Android (ESET Research Whitepapers, p. 15). ESET. https://www.welivesecurity.com/wp-content/uploads/2018/02/Cryptocurrency_Scams_on_Android.pdf

Shearman & Sterling. (2018). Financial Regulatory Developments Focus . 39 . https://www.shearman.com/-/media/Files/Perspectives/2018/10/Financial-Regulatory-Developments-Focus--Issue-39-October-5-2018-FIAFR-10052018.pdf?la=en&hash=9FFA4488F1F5BECB70A01332BA0C2FEA9310BCDA

Sureshbhai, P. N., Bhattacharya, P., & Tanwar, S. (2020). KaRuNa: A blockchain-based sentiment analysis framework for fraud cryptocurrency schemes. IEEE International Conference on Communications Workshops (ICC Workshops) . https://doi.org/10.1109/ICCWorkshops49005.2020.9145151

Torres, C. F., Steichen, M., & State, R. (2019). The art of the scam: demystifying honeypots in ethereum smart contracts .

Toyoda, K., Ohtsuki, T., & Mathiopoulos, P. T. (2017). Identification of high yielding investment programs in bitcoin via transactions pattern analysis . 2018-Janua , pp. 1–6. https://doi.org/10.1109/GLOCOM.2017.8254420

Toyoda, K., Ohtsuki, T., & Mathiopoulos, P. T. (2018). Multi-class bitcoin-enabled service identification based on transaction history summarization . September , pp. 1129–1136. https://doi.org/10.1109/Cybermatics

Toyoda, K., Ohtsuki, T., & Mathiopoulos, P. T. (2019). Time series analysis for bitcoin transactions: The case of Pirate@40’s HYIP scheme. IEEE International Conference on Data Mining Workshops, ICDMW , 2018-Novem , pp. 151–155. https://doi.org/10.1109/ICDMW.2018.00028

Toyoda, K., Mathiopoulos, P. T., & Ohtsuki, T. (2019a). A novel methodology for HYIP operators’ bitcoin addresses identification. IEEE Access, 7 , 74835–74848.

U.S. Securities and Exchange Commission. (n.d.). Insider Trading . Investor.Gov. Retrieved 4 March 2021, from https://www.investor.gov/introduction-investing/investing-basics/glossary/insider-trading

urRehman, M. H., Salah, K., Damiani, E., & Svetinovic, D. (2020). Trust in blockchain cryptocurrency ecosystem. IEEE Transactions on Engineering Management . https://doi.org/10.1109/TEM.2019.2948861

Vasek, M., Bonneau, J., Castellucci, R., Keith, C., & Moore, T. (2017). The Bitcoin Brain Drain: Examining the Use and Abuse of Bitcoin Brain Wallets. In J. Grossklags & B. Preneel (Eds.), Financial cryptography and data security (Vol. 9603, pp. 609–618). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-54970-4_36

Vasek, M. (2017). Measuring Bitcoin-based cybercrime . University of Tulsa.

Vasek, M., & Moore, T. (2018). Analyzing the Bitcoin Ponzi scheme ecosystem . https://doi.org/10.1007/978-3-662-58820-8_8

Vasek, M., & Moore, T. (2015). There’s no free lunch, even using Bitcoin: Tracking the popularity and profits of virtual currency scams. In R. Böhme & T. Okamoto (Eds.), Financial cryptography and data security (pp. 44–61). Springer.

Chapter   Google Scholar  

Vrazel, N. (2019). Betting it all on the flip of a coin: Regulating cryptocurrency initial coin offerings and protecting investors. S. Tex. l. Rev., 60 , 527.

Waxenbaum, S. (2019). The SEC and ICOs: Connections between digital assets and citrus groves. SSRN Electronic Journal . https://doi.org/10.2139/ssrn.3385064

Weber, K., Schütz, A. E., Fertig, T., & Müller, N. H. (2020). Exploiting the Human Factor: Social Engineering Attacks on Cryptocurrency Users. In P. Zaphiris & A. Ioannou (Eds.), Learning and Collaboration Technologies. Human and Technology Ecosystems (pp. 650–668). Springer International Publishing. https://doi.org/10.1007/978-3-030-50506-6_45

Webroot. (2018). Webroot Threat Research Review [White Paper]. https://www-cdn.webroot.com/2415/4662/3914/Threat-Roundup-Review-Whitepaper.pdf

Wright, J. (2018). Don’t @ Me: Hunting Twitter Bots at Scale (p. 43). Duo Security.

Wu, J., Yuan, Q., Lin, D., You, W., Chen, W., Chen, C., & Zheng, Z. (2020). Who are the phishers? Phishing scam detection on ethereum via network embedding. IEEE Transactions on Systems, Man, and Cybernetics: Systems .

Xia, P., Wang, H., Luo, X., Wu, L., Zhou, Y., Bai, G., Xu, G., Huang, G., & Liu, X. (2020). Don’t fish in troubled waters! characterizing coronavirus-themed cryptocurrency scams. ArXiv Preprint ArXiv:2007.13639.

Xia, P., Wang, H., Zhang, B., Ji, R., Gao, B., Wu, L., Luo, X., & Xu, G. (2020b). Characterizing cryptocurrency exchange scams. Computers & Security, 98 , 101993. https://doi.org/10.1016/j.cose.2020.101993

Xie, R. (2019). Why China had to “Ban” Cryptocurrency but the U.S. did not: A Comparative Analysis of Regulations on Crypto-Markets Between the U.S. and China .

Yli-Huumo, J., Ko, D., Choi, S., Park, S., & Smolander, K. (2016). Where is current research on Blockchain technology?—A systematic review. PLoS ONE, 11 (10), 1–27. https://doi.org/10.1371/journal.pone.0163477

Yuan, Q., Huang, B., Zhang, J., Wu, J., Zhang, H., & Zhang, X. (2020). Detecting Phishing scams on Ethereum based on transaction records. IEEE International Symposium on Circuits and Systems (ISCAS), 2020 , 1–5.

Zetzsche, D., Buckley, R., & Amer, D. (2017). The ICO Gold Rush: It’s a scam, it’s a bubble, it’s a super challenge for regulators. In EBI Working Paper Series (Issue 18).

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Acknowledgements

We thank the anonymous reviewers for their helpful comments.

This project was funded by the Dawes Centre for Future Crime at UCL and UK EPSRC Grant EP/S022503/1 that supports the Centre for Doctoral Training in Cybersecurity at UCL.

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Arianna Trozze, Josh Kamps, Eray Arda Akartuna, Bennett Kleinberg & Shane D. Johnson

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AT drafted the final manuscript, conducted the updated academic literature review, designed and conducted the grey literature review, and analysed and interpreted the results of all aspects of the scoping review. EA and FJH conducted the initial academic literature review and interpretation of data. JK, BK, FH, EA, and SDJ planned and carried out the expert consensus exercise reported in this study. BK, TD, JK, SDJ provided substantial feedback on the manuscript. All authors read and approved the final manuscript.

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See Table 3 .

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Definitions denoted with an asterisk (*), indicate types of fraud newly identified in the November 2020 update of the academic literature review. This information can also be found at https://osf.io/7w9mu/?view_only=c9ad3a1e2ed54dae9b1a0fc2807f144f . See Tables 6 , 7 and 8 .

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Trozze, A., Kamps, J., Akartuna, E.A. et al. Cryptocurrencies and future financial crime. Crime Sci 11 , 1 (2022). https://doi.org/10.1186/s40163-021-00163-8

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Analysis of the cryptocurrency adoption decision: literature review.

Saeed Alzahrani , Portland State University Tugrul Daim , Portland State University Follow

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Blockchains (Databases), Cryptocurrencies -- Decision making, Finance -- Technological innovations

Cryptocurrency is a recent and significant innovation in the financial industry. The goal is to offer a currency that is not tied, created, or backed by a government. Cryptocurrency use the Blockchain technology as the financial platform. Cryptocurrency adoption level has increased, and the market has grown dramatically. There have not been enough literature investigating the adoption and acceptance of the cryptocurrency by users. The aim of this paper is to fill the gap in the current literature by investigating the current cryptocurrency adoption level, adoption-influencing factors, providing an in-depth analysis of these factors and discussing some pitfalls surrounding the cryptocurrency adoption. We believe that despite the difficulty to find out an accurate number of cryptocurrency users, a good estimate can be made by studying the number of cryptocurrency exchange sites' users. In addition, the paper suggests that the main factors driving the adoption decision revealed from the literature review are the investment opportunity cryptocurrency forms, the anonymity of the transactions and privacy, the acceptance by businesses as a payment method, the fast transfer of funds, the low cost of transactions, and technological curiosity. The research findings help researchers, regulators, and cryptocurrency developers to better understand their consumer's intention toward cryptocurrency adoption.

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This is the publisher's final PDF. Copyright 2019 by PICMET. Paper delivered at the 2019 Proceedings of PICMET '19: Technology Management in the World of Intelligent Systems

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S. Alzahrani and T. U. Daim, "Analysis of the Cryptocurrency Adoption Decision: Literature Review," 2019 Portland International Conference on Management of Engineering and Technology (PICMET), Portland, OR, USA, 2019, pp. 1-11.

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