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Title: deciphering the blockchain: a comprehensive analysis of bitcoin's evolution, adoption, and future implications.

Abstract: This research paper provides a comprehensive analysis of Bitcoin, delving into its evolution, adoption, and potential future implications. As the pioneering cryptocurrency, Bitcoin has sparked significant interest and debate in recent years, challenging traditional financial systems and introducing the world to the power of blockchain technology. This paper aims to offer a thorough understanding of Bitcoin's underlying cryptographic principles, network architecture, and consensus mechanisms, primarily focusing on the Proof-of-Work model. We also explore the economic aspects of Bitcoin, examining price fluctuations, market trends, and factors influencing its value. A detailed investigation of the regulatory landscape, including global regulatory approaches, taxation policies, and legal challenges, offers insights into the hurdles and opportunities faced by the cryptocurrency. Furthermore, we discuss the adoption of Bitcoin in various use cases, its impact on traditional finance, and its role in the growing decentralized finance (DeFi) sector. Finally, the paper addresses the future of Bitcoin and cryptocurrencies, identifying emerging trends, technological innovations, and environmental concerns. We evaluate the potential impact of central bank digital currencies (CBDCs) on Bitcoin's future, as well as the broader implications of this technology on global finance. By providing a holistic understanding of Bitcoin's past, present, and potential future, this paper aims to serve as a valuable resource for scholars, policymakers, and enthusiasts alike.

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• 04 April 2022

Crypto and digital currencies — nine research priorities

• Andrew Urquhart 0 &
• Brian Lucey 1

Andrew Urquhart is professor of finance and financial technology at ICMA Centre, University of Reading, Henley Business School, Reading, UK.

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Brian Lucey is professor of international finance and commodities at Trinity Business School, Trinity College, Dublin, Ireland; Institute of Business Research, University of Economics, Ho Chi Minh City, Vietnam; and Institute for Industrial Economics, Jiangxi University of Economics and Finance, Nanchang, China.

Money is at a crossroads. A race is on to decide who creates it, who can access it and how, who controls it, and to what degree and how it is regulated. The outcome could decide whether governments have access to all our financial data, whether criminals can easily launder vast sums unseen, and whether the benefits of finance can be extended to the billions of people globally who lack access to banks.

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Nature 604 , 36-39 (2022)

doi: https://doi.org/10.1038/d41586-022-00927-5

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Foley, S., Karlsen, J. R. & Putnins, T. J. Rev. Finan. Stud. 32 , 1798–1853 (2019).

Shen, D., Urquhart, A. & Wang, P. Eur. Finan. Manage. 26 , 1294–1323 (2020).

Corbet, S., Lucey, B., Peat, M. & Vigne, S. Econ. Lett. 172 , 23–27 (2018).

Lucey, B. M., Vigne, S. A., Yarovaya, L. & Wang, Y. Finan. Res. Lett. 45 , 102147 (2022).

Easley, D., O’Hara, M. & Basu, S. J. Finan. Econ. 134 , 91–109 (2019).

Baur, D. G., Hong, K. & Lee, A. D. J. Int. Finan. Markets Inst. Money 54 , 177–189 (2018).

Agur, I., Ari, A. & Dell’Ariccia, G. J. Monet. Econ. 125 , 62–79 (2022).

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• Published: 07 February 2022

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.

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• Cryptocurrency
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Cryptocurrencies: market analysis and perspectives

• Published: 17 September 2019
• Volume 47 , pages 1–18, ( 2020 )

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• Alistair Milne 2 &
• Dmitri Vinogradov   ORCID: orcid.org/0000-0002-5426-1690 3 , 4  

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The papers in this special issue focus on the emerging phenomenon of cryptocurrencies. Cryptocurrencies are digital financial assets, for which ownership and transfers of ownership are guaranteed by a cryptographic decentralized technology. The rise of cryptocurrencies’ value on the market and the growing popularity around the world open a number of challenges and concerns for business and industrial economics. Using the lenses of both neoclassical and behavioral theories, this introductory article discusses the main trends in the academic research related to cryptocurrencies and highlights the contributions of the selected works to the literature. A particular emphasis is on socio-economic, misconduct and sustainability issues. We posit that cryptocurrencies may perform some useful functions and add economic value, but there are reasons to favor the regulation of the market. While this would go against the original libertarian rationale behind cryptocurrencies, it appears a necessary step to improve social welfare.

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1 Introduction

Cryptocurrencies continue to draw a lot of attention from investors, entrepreneurs, regulators and the general public. Much recent public discussions of cryptocurrencies have been triggered by the substantial changes in their prices, claims that the market for cryptocurrencies is a bubble without any fundamental value, and also concerns about evasion of regulatory and legal oversight. These concerns have led to calls for increased regulation or even a total ban. Further debates concern inter alia: the classification of cryptocurrencies as commodities, money or something else; the potential development of cryptocurrency derivatives and of credit contracts in cryptocurrency; the use of initial coin offerings (ICO) employing cryptocurrency technology to finance start-up initiatives; and the issue of digital currencies by central banks employing cryptocurrency technologies.

These discussions often shed more heat than light. There is as yet little clearly established scientific knowledge about the markets for cryptocurrencies and their impact on economies, businesses and people. This special issue of the Journal of Industrial and Business Economics aims at contributing to fill this gap. The collection of papers in the special issue offers six distinct perspectives on cryptocurrencies, written from both traditional and behavioural viewpoints and addressing both financial questions and broader issues of the relationship of cryptocurrencies to socio-economic development and sustainability.

Here in this introduction we set the stage by defining and discussing the main concepts and issues addressed in the papers collected in this special issue and previewing their individual contributions. Cryptocurrencies are digital financial assets, for which records and transfers of ownership are guaranteed by a cryptographic technology rather than a bank or other trusted third party. They can be viewed as financial assets because they bear some value (discussed below) for cryptocurrency holders, even though they represent no matching liability of any other party and are not backed by any physical asset of value (such as gold, for example, or the equipment stock of an enterprise). Footnote 1

As the word cryptocurrency, and the other terminology employing ‘coin’, ‘wallets’ in the original whitepaper proposing the supporting technology for Bitcoin (Nakamoto 2008 ) all suggest, the original developers consciously attempted to develop a digital transfer mechanism that corresponded to direct transfer of physical cash used for payments or other financial assets—such as a precious metals and ‘bearer bonds’—that like cash also change hands through physical transfer.

What about the arrangements used for financial assets recorded in digital form (such as bank deposits, equities or bonds but not bearer bonds or bank notes)? Ownership arrangements for these assets depend on the information system maintained by a financial institution (commercial bank, custodian bank, fund manager) determining who is entitled to any income or other rights it offers and has the right of sale or transfer. Originally these systems were paper based, but since the 1960s they have utilised first mainframe and more recently computer systems. Footnote 2 If there is a shortcoming in their information system, for example a breach of security that leads to theft or loss or failure to carry out an instruction for transfer, then the financial institution is legally responsible for compensating the owner of the asset.

In the case of cryptocurrencies, it is the supporting software that both verifies ownership and executes transfers. Footnote 3 There is no requirement for a ‘trusted third party’. Footnote 4 This approach though requires a complete historical record of previous cryptocurrency transfers, tracing back each holding of cryptocurrency to its initial creation. This historical record is based on a “blockchain”, a linking of records (“blocks”) to each other in such a way that each new block contains information about the previous blocks in the growing list (“chain”) of digital records. So that every participant in the cryptocurrency network sees the same transaction history, a new block is accepted by agreement across the entire network.

The applications of this technology are not necessarily finance-related; it can be applied to any form of record-keeping; however if the block refers to a financial transaction then each transaction in the blockchain, by definition, includes information about previous transactions, and thus verifies the ownership of the financial asset being transferred. Falsifying ownership, i.e. counterfeiting (which, one could imagine, is easy, as digital objects can be easily duplicated by copying), is impossible because one would have to alter preceding records in the whole chain. Since records are kept in the network of many users’ computers, a “distributed ledger”, this is rather unthinkable.

There is a substantial computer science literature on the supporting cryptocurrency technologies, including on the security of public key cryptography, efficient search tools for finding transactions on the blockchain, and the ‘consensus’ mechanisms used to establish agreement on ledger contents across the network. Footnote 5 Commentators expect new more efficient approaches will replace the mechanisms currently used in Bitcoin and other cryptocurrencies. Footnote 6 This though would not affect our definition of cryptocurrencies (as an asset and some technology which verifies ownership of the asset), which is independent of any particular technological implementation. Footnote 7

Cryptocurrencies can be seen as part of a broader class of financial assets, “cryptoassets” with similar peer-to-peer digital transfers of value, without involving third party institutions for transaction certification purposes. What distinguishes cryptocurrencies from other cryptoassets? This depends on their purpose, i.e. whether they are issued only for transfer or whether they also fulfil other functions. Within the overall category of cryptoassets, we can follow the distinctions drawn in recent regulatory reports, distinguishing two further sub-categories of cryptoassets, on top of cryptocurrencies: Footnote 8

Cryptocurrencies : an asset on a blockchain that can be exchanged or transferred between network participants and hence used as a means of payment—but offers no other benefits.

Within cryptocurrencies it is then possible to distinguish those whose quantity is fixed and price market determined (floating cryptocurrencies) and those where a supporting arrangement, software or institutional, alters the supply in order to maintain a fixed price against other assets (stable coins, for example Tether or the planned Facebook Libra).

Crypto securities : an asset on a blockchain that, in addition, offers the prospect of future payments, for example a share of profits.

Crypto utility assets : an asset on a blockchain that, in addition, can be redeemed for or give access to some pre-specified products or services.

A further distinguishing feature of crypto securities and crypto utility assets is that they are issued through a public sale (in so called initial coin offerings or ICOs). ICOs have been a substantial source of funding for technology orientated start-up companies using blockchain based business models. These classifications of cryptoassets are critical for global regulators, since they need to determine whether a particular cryptoasset should be regulated as an e-money, as a security or as some other form of financial instrument, especially in relation to potential concerns about investor protection in ICOs. Footnote 9

Floating cryptocurrencies account for the very large majority of the cryptoasset market capitalisation (Tether, a stablecoin, and Bitfinex’s UNUS SED LEO, a utility coin, are in the top 12 cryptoassets by market capitalisation, all the rest are floating cryptocurrencies). Table  1 summarises the market share of leading cryptocurrencies at the time of writing.

What is the value of cryptocurrencies? On the one hand, cryptocurrencies should be able to ease financial transactions through elimination of the intermediaries, reduction of transaction costs, accessibility to everyone connected to the Internet, greater privacy and security (see, e.g., discussions in Böhme et al. 2015 ; Richter et al., 2015 ). Footnote 10 On the other hand, the real economic value transferred in the transactions of freely floating cryptocurrencies such as Bitcoin’s BTC and Ethereum’s Ether remains unclear. Despite the exhaustive and unfalsifiable record of all previous transactions held cryptographically, as in the Bitcoin blockchain, the information only refers to nominal numbers, i.e. the amount of cryptocurrency units transferred. One can, however, get an idea of the market value of cryptocurrencies by looking at their exchange rates against existing fiat currencies. This is possible thanks to cryptocurrency exchanges, which provide a nearly continuous price record for all actively traded cryptocurrencies. Although the resulting exchange rates are highly volatile, they reveal that cryptocurrencies have a non-zero value for those prepared to pay fiat currency in order to purchase them.

What drives this value in the absence of a backing asset or an issuer’s liability? Some advocate it is the cost of “mining” (energy and time spent on computational efforts required to complete formation of a new block in the chain, and rewarded by a newly issued cryptocurrency unit), however the cost borne by one member of the network does not justify the value of the new cryptocurrency unit for other members of the network (see also Dwyer 2015 , who argues the cost of mining is sunk and as such should be disregarded in the market value analysis). Others claim their market value is driven by the speculative bubble; yet, strictly speaking, the bubble is manifested in upward price deviations from the fundamental value (see, e.g., Siegel 2003 , for a review of definitions), hence the bubble explanation is only partial and raises further questions about what drives investors’ beliefs that feed their demand and thus support the bubble.

If it is the ease and the speed of transactions, then new transaction technologies and fund transfer systems that greatly improved in the recent decade (such as Transferwise and similar systems) should have wiped out a big chunk of the cryptocurrency value, yet this does not seem to be the case. A possible answer may lie in the features that distinguish cryptocurrencies from other assets and payment systems. Privacy, or rather anonymity, is a prominent distinctive feature popping up in most discussions of cryptocurrencies. The value of a cryptocurrency is then effectively a measure of how much users value anonymity of their transactions. While anonymity may be attractive for illegal activities (and some research reviewed below suggests cryptocurrencies are often used for these purposes), one cannot rule out users may simply wish more privacy, trying to avoid the “Big Brother” effect of traditional transactions. Of course, there may be other factors, for example, fashion (users want to use the technology others are talking about), hi-tech appeal (the desire to use the most modern technology) or curiosity (the desire to try something new), among others, but these phenomena appear shorter-lived than the allure of anonymity.

A key development in the rise of cryptocurrencies and other cryptoassets has been the emergence of cryptoexchanges where anyone can open accounts and trade cryptoassets both against each other and against fiat currencies. In a survey by Hileman and Rauchs ( 2017 ), the US dollar, the Euro and the British Pound are currently most widely traded against cryptocurrencies, while the importance of the Chinese Renminbi (CNY) significantly diminished after the tightening of the regulation by the People’s Bank of China; about three-quarters of large exchanges provide trading support for two or more cryptocurrencies. Above, we highlighted that cryptoexchanges provide extensive cryptocurrency pricing and trading information in the public domain. The emergence of these exchanges has created an entire ‘ecosystem’ of services and participants, seeking to provide liquidity, exploit price discrepancies for profit and to support investment by both retail and professional investors.

Academic interest in cryptocurrencies started to soar in 2014 (see Fig.  1 ): the Scopus database lists 127 publications containing the word ‘Bitcoin’ in the title or abstract or keywords and 24 containing ‘cryptocurrency’ or ‘cryptocurrencies’ in 2014. In 2017 and especially in 2018 the number of publications grew fast, and in 2019 the trend is continuing. Interestingly, academic work focuses much more on the Bitcoin than on the more general topic of cryptocurrencies, although in 2018 and in 2019 the gap narrowed. It appears that—apart from the Bitcoin frenzy—there is a growing attention to the general phenomenon of cryptocurrencies. However, focusing only on the ‘Economics, Econometrics & Finance’ and ‘Business, Management & Accounting’ sections of Scopus reveals that the interest in the topic surged a few years later Footnote 11 , although the number of publications is still rather low: in 2018 there were just over 100 titles on the topic in the above fields. The remaining contributions come from the ‘Computer Science’, ‘Engineering’ and ‘Mathematics’ disciplines.

Publications listed on the Scopus database containing ‘Cryptocurrency/ies’ and ‘Bitcoin’ in the title or abstract or keywords. The graph reports the number of publications tracked by the Scopus database ( http://www.scopus.com ) accessed on August 10, 2019 containing the words “Cryptocurrency/ies” or “Bitcoin” in the title or abstract or keywords. The subsample ECON refers to the category Economics, Econometrics & Finance while the subsample BUS refers to Business, Management & Accounting

This special issue of the Journal of Industrial & Business Economics offers a multifaceted view on the cryptocurrency phenomenon. Contributions have been selected with the objective to extend the existing knowledge about cryptocurrencies, which themselves embody innovations and technological change, and may appear to be a lucrative form of fund raising for small businesses; extra emphasis is made on areas of the journal’s particular interest, such as environment, sustainability and social responsibility. The remainder of this editorial proceeds as follows. In Sect.  2 we describe the contributions that shed light on the relationship between cryptocurrencies and financial investments. In Sect.  3 we focus on behavioral issues, while in Sect.  4 we introduce the development of the socio-economic perspectives related to cryptocurrencies and discuss initial coin offerings as a potential source of funds for small businesses. Finally, Sect.  5 concludes discussing the research agenda for the future.

2 Cryptocurrencies and neoclassical finance

Cryptocurrencies can be used both as a means of payment and as a financial asset. Glaser et al. ( 2014 ) provide evidence that, at least for the Bitcoin, the main reason to purchase a cryptocurrency is speculative investment. Financial securities, such as ETNs (exchange traded notes) and CFDs (derivative products) that replicate Bitcoin’s price performance are made available by brokers, expanding the speculative investment opportunities to an even larger set of investors. With this in mind, it makes sense to evaluate cryptocurrencies as financial assets.

The cross-section of cryptocurrency returns has been analyzed in a number of papers. Urquhart ( 2016 ) shows that Bitcoin returns do not follow random walk, based on which he concludes the Bitcoin market exhibits a significant degree of inefficiency, especially in the early years of existence. Corbet et al. ( 2018 )analyze, in the time and frequency domains, the relationship between the return of three different cryptocurrencies and a variety of other financial assets, showing lack of relationship between crypto- and other assets. Liu and Tsyvinski ( 2018 ) investigate whether cryptocurrency pricing bears similarity to stocks: none of the risk factors explaining movements in stock prices applies to cryptocurrencies in their sample. Moreover, movements in exchange rates, commodity prices, or macroeconomic factors of traditional significance for other assets play little to none role for most cryptocurrencies. The latter invalidates the view on cryptocurrencies as substitutes to monies, or as a store of value (like gold), and rather stresses they are assets of their own class. The review of the literature in Corbet et al. ( 2019 ) summarizes the most interesting findings on the role of cryptocurrencies as a credible investment asset class and as a valuable and legitimate payment system.

The relative isolation of cryptocurrencies from more traditional financial assets suggests cryptocurrencies may offer diversification benefits for investors with short investment horizons. Bouri et al. ( 2017 ) as well as Baur et al. ( 2018 ) find that Bitcoin is suitable for diversification purposes as its returns are uncorrelated with those of most major assets. Interestingly, they provide empirical evidence of the predominant usage of Bitcoins as speculative assets, though this is done on the data on USD transactions only and thus likely reflects the behavior of U.S. cryptocurrency investors mainly. Relatedly, Adhami and Guegan ( 2020 ) find that similarly to cryptocurrencies, cryptotokens are also a useful diversification device though not a hedge.

One way to understand similarities and differences between cryptocurrencies and more traditional financial assets is to estimate relationships known for traditional assets. A pattern that has received a lot of attention in the finance literature is the co-movement of the trading volume and returns/volatility of financial assets (a by far non-exhaustive list of examples would include Admati and Pfleiderer ( 1988 ), Foster and Viswanathan ( 1993 ), and Andersen ( 1996 )—for equity markets; Bessembinder and Seguin ( 1993 )—for futures; notably, no clear evidence of such a relationship exists for currencies, i.e. for exchange rates, see, e.g. Côté 1994 ). This special issue includes a contribution by Figà-Talamanca ( 2020 ), who, inter alia , investigate this relationship for cryptocurrencies, along with the impact of “relevant events”, which are key disruptive changes to the market infrastructure. They find that Bitcoin trading volume does not affect its returns but detect a positive effect of Bitcoin trading volumes on return volatility. While their focus is mainly on market attention, these results highlight similar forces rule cryptocurrency markets and those for more traditional financial assets, again supporting the view of cryptocurrencies as investment assets. Footnote 12

The risk of holding cryptocurrencies is discussed in this special issue by Fantazzini and Zimin ( 2020 ). Cryptocurrency prices may drop dramatically because of a revealed scam or suspected hack, or other hidden problems. For example, on June 26th, 2019, the Bitcoin price lost more than 10 % of the value in a few minutes because of the crashes and outages of the Coinbase digital exchange. As a consequence, a cryptocoin may become illiquid and its value may substantially decline. Fantazzini and Zimin ( 2020 ) propose a set of models to estimate the risk of default of cryptocurrencies, which is back-tested on 42 digital coins. The authors make an important point in extending the traditional risk analysis to cryptocurrencies and making an attempt to distinguish between market risk and credit risk for them. The former, as typical in the finance literature, is associated with movements in prices of other assets. The latter is associated in traditional finance with the failure of the counterparty to repay, but as cryptocurrencies presume no repayments, defining credit risk for them is tricky. The authors’ approach is to see the “credit” risk of cryptocurrency in the possibility of them losing credibility among users, and thus becoming value-less, or “dead”. The authors find, notably, that the market risk of cryptocurrencies is driven by Bitcoin, suggesting some degree of homogeneity in the cryptomarket. As for the credit risk, the traditional credit scoring models based on the previous month trading volume, the one-year trading volume and the average yearly Google search volume work remarkably well, suggesting indeed a similarity between the newly defined credit risk for cryptocurrencies and the one traditionally used for other asset classes.

3 Cryptocurrencies and behavioral finance and economics

A large strand of the literature explains market phenomena that work against the neo-classical predictions, from the perspective of unquantifiable risk, or ambiguity. Most commonly, ambiguity is associated with the impossibility to assign probability values to events that may or may not occur. In the case of cryptocurrencies, this type of uncertainty may arise for two reasons: (1) the technology is rather complicated and opaque to unsophisticated traders, and (2) the fundamental value of cryptocurrencies is unclear. As we highlighted above, even if it is strictly positive, it is likely to derive from intangible factors and as such is rather uncertain. Dow and da Costa Werlang ( 1992 ) demonstrate that under pessimism (ambiguity aversion) uncertainty about fundamentals leads to zero trading in financial markets, yet this does not seem to apply to cryptocurrencies. In Vinogradov ( 2012 ) not only does the no-trade outcome depend on the degrees of optimism and pessimism, which may vary, but it also manifests only under high risk (in the standard sense). Still, again, although cryptocurrency returns exhibit high volatility, trade volumes are significant. In Caballero and Krishnamurthy ( 2008 ) uncertainty leads to “flights to quality” in traditional asset markets, which, if properly applied to cryptocurrencies, might also explain the crashes we recently observed.

Obtaining information is crucial to reduce uncertainty. Figà-Talamanca ( 2020 ) focus on a rather general definition of the demand for information, as manifested in the google search index. According to them, the intensity of the internet search for cryptocurrency-related keywords significantly affects cryptocurrency volatility (but not return); this impact vanishes once one controls for “relevant events”. These relevant events are effectively announcements of either restrictions (and even bans) on cryptocurrency usage, or of the widening of the cryptocurrency market. While we remain largely agnostic regarding what people find when they search for cryptocurrency related terms on the Internet, the events give us an indication of the type of information that actually matters for cryptocurrency investment decisions, and hence for pricing. In uncertainty, when finding relevant information is uneasy, investors might resort to watching and mimicking other, presumably better informed, investors’ decisions, resulting in herding (Trueman 1994 ; Devenow and Welch 1996 ), addressed in this special issue by Haryanto et al. ( 2020 ), see below.

Uncertainty and attitudes to it are not the only reasons why neoclassical predictions may fail. Shiller ( 2003 ) notes that market participants are humans and can make irrational systematic errors contrary to the assumption of rationality. Such errors affect prices and returns of assets, creating market inefficiencies. Studies in behavioral economics highlight inefficiencies, such as under- or over-reactions to information, as causes of market trends and, in extreme cases, of bubbles and crashes. Such reactions have been attributed to limited investor attention, overconfidence, mimicry and noise trading, explanations of many of which find roots in Kahneman and Tversky’s ( 1979 ) prospect theory, which postulates that decision makers evaluate outcomes from the perspective of their current endowment (and are predominantly loss-averse) and “revise” probabilities of outcomes when making decisions (predominantly overweighting probabilities of bad outcomes and underweighting those of good ones). The loss-aversion led Shefrin and Statman ( 1985 ) to formulate the ‘disposition effect’ in investment decisions: investors in traditional assets tend to keep assets that lose value too long and sell those that gain in value too early.

Three features distinguish cryptocurrency markets: investors are non-institutional, risk (volatility of returns) is high, and the fundamental value is unclear. Under these conditions behavioral biases should be even more pronounced than in traditional asset markets. In this special issue Haryanto et al. ( 2020 ) study the disposition effect and the herding behavior in the cryptocurrency realm by investigating the trading behavior at a cryptoexchange: they find a reverse disposition effect in bullish periods where the Bitcoin price increases while a positive disposition effect is observed in bearish periods. They also find that in different market conditions herding moves along with market trend (in the bullish market a positive market return increases herding, while in the bearish market a negative market return has the same effect). The reverse disposition effect in the bullish market indicates investors exhibit more optimism and expect returns to further grow, which is consistent with the exponential price growth in a bubble in the absence of a clearly defined fundamental value. This lack of clarity regarding the fundamental value is also supported by the asymmetric herding behavior: when the price grows in a bullish market, investors look at other market participants to see whether others also think the price will continue to grow (similarly but with the opposite sign for the bearish market).

This special issue also contributes to the debate on the existence of a ‘bubble’ in the cryptocurrency market (see Baek and Elbeck 2015 ; Cheah and Fry 2015 ). The contribution by Moosa ( 2020 ) highlights that the Bitcoin was in a bubble up to the end of 2017. The analysis claims that the volume of trading in Bitcoin can be explained predominantly in terms of price dynamics considering past price movements, particularly positive price changes, and that the path of the price is well described by an explosive process.

4 Socio-economic perspectives

Critiques emphasize cryptocurrencies are not exempt from frauds and scandals. For example, several millions in Bitcoin from the Japanese platform Mt. Gox in 2014 and $50 million in Ether during the Decentralized Autonomous Organization (DAO) attack in 2016 were stolen. Moreover, cryptocurrency payments, being largely unregulated, do not restrict any purchases, including those illegal. Böhme et al. ( 2015 ) provide summary data showing that, at least in the beginning of the Bitcoin era, most transactions were used for drug purchases. Foley et al. ( 2019 ) estimate that about 46 % of Bitcoin transactions are associated with illicit activities, but that the illegal share of Bitcoin activity declined over time with the emergence of more opaque cryptocurrencies. On top of that, users appear unprotected as payments are often irreversible, and an erroneous transfer cannot be cancelled, unlike credit card payments (Böhme et al. 2015 ).

On the positive side, the development of the cryptocurrency market contributes to the dynamics of access to finance (Adhami et al. 2018 ). The advent of the blockchain technology allowed entrepreneurial teams to raise capital in cryptocurrencies and fiat money (which has to be exchanged into a cryptocurrency) through the issuance of digital tokens (Initial Coin Offerings, ICOs) and the development of ‘smart contracts’ (Giudici and Adhami 2019 ). Tokens give their buyers a right to use certain services or products of the issuer, or to share profits, in which case they resemble equity. Special cryptoexchanges then serve the secondary market for tokens. The OECD ( 2019 ) lays out basic principles and typical steps of an ICO. An important distinction between tokens and cryptocurrencies is though that there is a liability or some sort of commitment behind the token, and this liability determines its value. Now that this cryptoasset bears more similarity with traditional assets, one would expect also the main predictions of neoclassical finance to come true. In fact, in a recent empirical study of cryptotokens, Howell et al. ( 2018 ) demonstrate the effects of asymmetric information on tokens trading: their liquidity and trading volume are positively associated with the information inflow. The latter is achieved through voluntary disclosure of information (including the operating budget and their business plans), and quality signaling (e.g. information on prior venture capital funding of the issuer).

Cryptocurrencies, which underlie the ICO procedure, are claimed to provide much more equitable and democratic access to capital as well as greater efficiency, compared to fiat money, allowing peer-to-peer transactions and avoiding the intermediation of banks (Nakamoto 2008 ; Karlstrøm 2014 ). This is normally done via an ICO, and could be a relevant opportunity for small business, which often experience a gap in funding and miss competences to relate with professional investors (Giudici and Paleari 2000 ). OECD ( 2019 ) also reports ICOs are a potential route for low cost finance for SMEs.

Will cryptocurrencies favor a process of “democratization” of funding? This has been widely discussed by practitioners and investors, with a great variety of views. For example, The World Economic Forum White Paper (WEF 2018 ), claims that cryptocurrencies and blockchain technologies could increase the worldwide trading volume, moving to better levels of service and lower transaction fees. To this extent, the contribution by Ricci ( 2020 ) in this special issue considers the geographical network of Bitcoin transactions in order to discover potential relationships between Bitcoin exchange activity among countries and national levels of economic freedom. The study shows that high levels of freedom to trade internationally, that guarantee low tariffs and facilitate international trade, are strongly connected to the Bitcoin diffusion. On the one hand, the freedom to trade internationally could increase the foreign trade through the use of alternative payment instruments capable of reducing transaction costs (like cryptocurrencies), on the other, low capital controls could encourage the use of cryptocurrencies for illegal conduct, such as money laundering.

The reward system for cryptocurrency ‘miners’ creates an incentive to leverage on computing power, increasing the consumption of energy. For example, Böhme et al. ( 2015 ) note that computational efforts of miners are costly, mainly because the proof-of-work calculations are “power-intensive, consuming more than 173 megawatts of electricity continuously. For perspective, that amount is … approximately $178 million per year at average US residential electricity prices.” The sustainability topic is raised in this special issue by Vaz and Brown ( 2020 ). They posit that there are significant sustainability issues in the cryptocurrency development exceeding potential benefits, that are captured typically by a few people. Therefore, they call for different institutional models with government and public engagement, as to avoid that the market is driven mostly by private money and profit motivations.

5 Conclusions

Growing attention has been paid to cryptocurrencies in the academic literature, discussing whether they are supposed to disrupt the economy or are a speculative bubble which could crash and burn or favor money laundering and criminals. In support of the first view, it is often argued they meet a market need for a faster and more secure payment and transaction system, disintermediating monopolies, banks and credit cards. Critics, on the other hand, point out that the unstable value of cryptocurrencies make them more a purely speculative asset than a new type of money.

The reality is somewhere in between these two positions, with cryptocurrencies performing some useful functions and hence adding economic value, and yet being potentially highly unstable. The trend is towards a regulation of cryptocurrencies, and more generally of all crypto-assets, and to their increased trading on organized and regulated exchanges. This would go against the original libertarian rationale that originated the Bitcoin but is a necessary step to provide protection for market participants and reduce moral hazard and information asymmetries.

How will future research build on the articles in this special issue and on other recent studies of cryptocurrencies? It is of course always difficult to anticipate substantial future research contributions, especially in relation to such a recent and novel phenomenon like cryptocurrencies. But we would argue that there are a few major issues that deserve continued attention from scholars in finance, economics and related disciplines.

One is the need for a much closer examination of the ‘market microstructure’ of cryptoexchanges. Some recent research already draws attention to the functioning of cryptoexchanges. For example, Gandal et al. ( 2018 ) investigate price manipulations at the Mt. Gox Bitcoin exchange; a notable by-product of their research is the finding that suspicious trading on one exchange led to equal price changes on other exchanges, suggesting traders can effectively engage in arbitrage activities across exchanges. Similarly, signs of efficiency are detected in Akyildirim et al. ( 2019 ) who investigate pricing of Bitcoin futures on traditional exchanges—Chicago Mercantile Exchange (CME) and the Chicago Board Options Exchange (CBOE). Importantly, in their study information flows and price discovery go from futures to spot markets, in contrast to previous results for traditional assets; a likely explanation is the difference in the type of traders at cryptoexchanges (that determine the spot price) and both CME and CBOE. Footnote 13 Yet more has to be learnt about cryptoexchanges. Their open nature distinguishes them from conventional stock exchanges and dealer markets with transactors directly accessing the market rather than relying on brokers as intermediaries. Is this open nature helpful, providing greater liquidity and narrowing trading spreads? Or does it disadvantage some investors, limiting regulatory oversight and allowing a core of participants to manipulate market prices at the expense of other investors? Do the technical arrangements supporting cryptoexchanges, notably the use of distributed ledger or blockchain technology which eliminates the need for post-trade settlement, lead to more efficient trading outcomes in terms of price, liquidity and speed of execution? Could these technologies also improve the efficiency of outcomes in conventional financial exchange?

The second issue, widely debated in the cryptocurrency literature, is whether cryptocurrencies have a fundamental own value. Dwyer ( 2015 ) conjectures the limitation of the quantity produced can create an equilibrium in which a digital currency has a positive value: this limitation is a form of commitment, replacing the implicit obligation of Central banks to exchange fiat money into gold. Hayes ( 2017 ) advocates the cost of production view on cryptocurrency pricing; yet, as we discussed earlier, from a market equilibrium perspective, being sunk cost (as in Dwyer 2015 ), it does not matter for the pricing of existing coins. Footnote 14 A concurrent work by Bolt and Van Oordt ( 2019 ) outlines three key elements of the cryptocurrency value: convertibility into fiat money or ability to buy goods and services, investors’ expectations, and factors that determine acceptance of the cryptocurrency in the future, by both vendors and buyers. Simultaneously, Schilling and Uhlig ( 2019 ) offer a model where cryptocurrencies are a reliable medium of exchange and compete against fiat money: this role implies the current price of cryptocurrencies is the expectation of their future value (a martingale), yet interestingly, competition and substitutability between the two imply in their analysis cryptocurrencies should disappear in the long run equilibrium. The authors admit that their analysis abstracts away such distinctive features of cryptocurrencies as “censorship resistance, transparency, and speed of trading”. Above we have provided a simplified argument explaining that cryptocurrencies may have a value by offering features, such as anonymity of transactions, not covered by traditional currencies. Many findings, also those included in this special issue, point towards the intangible nature of the cryptocurrency value. Knowing more about it, we would be better equipped to understand the price dynamics and, reciprocally, the price dynamics would improve our understanding of decisions made by investors. So far, we remain very much agnostic in this respect.

The third issue is the societal role of cryptocurrencies and their regulation. While many discussions of cryptocurrencies stress that they are free of regulation, and the desire to be unregulated was one of drivers behind their creation, there is considerable controversy both about the application of existing regulation to cryptocurrencies and other cryptoassets and also what if any new regulations may be needed to protect investors, prevent financial crime and ensure financial stability. Are crypto investments securities and therefore subject to securities law (in the US this has been determined by the so-called Howey test)? What about the regulation of cryptoexchanges and the problems of hacking with some prominent examples of theft and failure to enforce “know-your-customer” (KYC) and anti-money-laundering (ALM) regulations?

Globally, regulators are shifting towards a tougher stance. Some exchanges are seeking to engage with regulators and be fully compliant. Others prefer to operate outside of regulation. A simple argument is that one has to protect investors and users from financial and technological risks they face. However, as papers presented in this special issue demonstrate, cryptocurrencies differ from traditional assets, hence the validity of traditional arguments, such as systemic stability, consumer protection and promotion of competition, is not clear. As our literature review and papers in this special issue underscore, cryptocurrencies do not comove with other assets; they help diversification and do not pose an immediate danger for systemic stability. There appears to be a significant and growing degree of competition between different cryptocurrencies and cryptoexchanges, and yet we have to understand whether and why such a competition is desirable for the society.

Similarly, we need to understand whether there is a need to protect consumers. In traditional asset markets and in banking such protection improves allocation of resources and promotes economic growth and welfare, which is not straightforwardly applicable to cryptocurrencies and existing other cryptoassets. An extra dimension that arises from the studies in our special issue is the sustainability and environmental impact of cryptocurrencies, and this is again different from other asset classes.

Last but not the least, yet another major issue is how cryptocurrency technologies may affect conventional fiat currency issued by central banks. Footnote 15 Emerging literature on the competition between cryptocurrencies and fiat money raises concerns that the emergence of privately issued cryptocurrencies could weaken the monetary policy tools employed by the central bank and result in welfare losses (Zhu and Hendry 2018 ; Schilling and Uhlig 2019 ). Fernández-Villaverde and Sanches ( 2019 ) find that when private currency competes with a central bank issued e-money the former should vanish in equilibrium, yet it remains unclear what happens if cryptocurrencies are not a perfect substitute to fiat money. Footnote 16 Cukierman ( 2019 ), building on the analysis by Roubini ( 2018 ), brings the discussion to a further level by discussing the potential also for cryptocurrency issue by the central bank being used to implement fully reserved or narrow banking and thus to promote financial stability.

We hope this special issue contributes to our understanding of cryptocurrencies and surrounding issues. We also reckon it helps generate knowledge and materials useful for practitioners and scholars, involved in studying and shaping the cryptocurrency market for the future. Very possibly this will evolve and become very different from what we observe today, but for sure already now cryptocurrencies embody an innovation capable of moving our financial markets and economies forward in terms of efficiency and growth. We just need to learn using this innovation properly.

From the accounting perspective, cryptocurrencies are investment assets, sometimes even treated similarly to stocks for accounting purposes (Raiborn and Sivitanides 2015 ).

Milne ( 2015 ) provides a history of the information systems used in securities markets.

A more detailed yet still accessible overview of the key features of the current technology behind cryptocurrencies can be found in Böhme et al. ( 2015 ). Narayanan et al. ( 2016 ) provide a detailed textbook description. A key feature is that ownership is identified with a public cryptographic key. The matching private cryptographic key can then be used both to confirm ownership of the associated public key and to instruct transfers of the cryptocurrency to other public keys. The number of these keys is effectively unlimited. In the case of Bitcoin these keys are 256-bit binary numbers, so in consequence there are 2 256 possible public keys; an almost unimaginably large number.

Third parties may still play a role in the functioning of a cryptocurrency. For example, 5 % of XRP, the cryptocurrency that supports Ripple international payments platform is held by Ripple themselves, and their decisions to buy or sell affect market supply. Third parties also support stablecoins such as Tether or Facebook’s proposed Libra currency.

Blockchains are validated and updated within peer-to-peer networks using a ‘consensus mechanism’ (for example “proof of work” or “proof of stake”, see Tschorsch and Scheuermann 2016 ) that prevents members of the network from creating a false version of history. This consensus then supports a fully decentralized secure verification of ownership and exchange (Pilkington 2016 ; Goldstein et al. 2019 ). In the case of Bitcoin, the term block was originally used because its consensus mechanism (‘mining’) is applied to append ‘blocks’ of around 1000 transactions at a time to the chain of transaction records.

For a review of several prominent consensus mechanisms see Baliga ( 2017 ).

Ripple (XRP) is an example of a cryptoasset that does not use blockchain. However, it has a different purpose designed primarily to mediate conversions from currency to currency, or from any asset A to asset B.

For example (Bank of England, Financial Conduct Authority, and HM Treasury 2018 ; ESMA 2019 ; EBA 2019 ) and also (Hacker and Thomale 2017 ). The term ‘token’ is often used as a shorthand reference to cryptoassets, especially for crypto securities and crypto utility assets (e.g. Adhami and Guegan 2020 ), though Milne ( 2018 ) argues that this usage can be misleading, disguising similarities with more conventional financial assets.

Recent discussion of these issues includes FSB ( 2018 ), FCA ( 2019 ) and Blandin et al. ( 2019 ).

Note that transactions in cryptocurrencies are subject to such restrictions as the lack of reversibility, i.e. an erroneous transaction cannot be cancelled as soon as it is written in the block. More traditional payment systems, such as bank transfers and credit card payments, are more flexible in this respect.

This delay may also reflect slower publication process in our field, with most papers going through a few not so fast rounds of revisions (let alone rejections) before they get published. Huisman and Smits ( 2017 ) review recent evidence on the duration of the publication process; their sample shows, for example, that tit takes twice as long to publish in Economics than, e.g. in Medicine, with an average first response time in Economics and Finance being 16–18 weeks (comparable to Azar’s 2007 , estimate of 3–6 months). Their sample does not account for the number of previous rejections though. John Cochrane witnesses most of his publications were rejected 2–3 times before getting eventually published ( https://johnhcochrane.blogspot.com/2017/09/a-paper-and-publishing.html ); further anecdotal evidences are in Shepherd ( 1995 ).

“Similar forces” here does not mean similar factors: like Liu and Tsyvinski ( 2018 ), Figà-Talamanca ( 2020 ) find a strong dependence of cryptocurrency returns of their past values, which distinguishes them from other asset classes.

Interestingly, CBOE futures present an informational advantage over the CME alternative, possibly because of the smaller size of contracts and hence the larger number of investors actively trading.

It may matter though for the decision to mine new coins (the marginal cost of coin production should be below market price, which stands for the marginal profit). Hayes ( 2017 ) also points at the difficulty of the mining algorithm as a driver of cryptocurrency prices. This measure may be an indicator of the reliability of the cryptographic technology behind the cryptocurrency, and thus part of the fundamental value, as it represents security of transactions, valued by the users.

Pieters ( 2020 , forthcoming) provides a useful wider review of central banks and digital payments technologies.

Fernández-Villaverde and Sanches ( 2019 ) also advance an interesting idea that cryptocurrencies, being “private money”, create limits for monetary policy and, at the same time, provides market discipline for the government.

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Giancarlo Giudici

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Giudici, G., Milne, A. & Vinogradov, D. Cryptocurrencies: market analysis and perspectives. J. Ind. Bus. Econ. 47 , 1–18 (2020). https://doi.org/10.1007/s40812-019-00138-6

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Received : 07 September 2019

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Accepted : 11 September 2019

Published : 17 September 2019

Issue Date : March 2020

DOI : https://doi.org/10.1007/s40812-019-00138-6

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Special Report

Cryptos on  the rise 2022

• Introduction
• 2. Beyond Bitcoin
• 3. Oversight in a crypto world
• 4. Compendium: Cryptocurrency regulations by country

Crypto-assets and the vast universe of associated products and services have grown rapidly in recent years and are becoming increasingly interlinked with the regulated financial system. Policymakers appear to be struggling to keep track of risks posed by a sector where most activities are unregulated, or at best lightly regulated.

Financial stability risks could soon become systemic in some countries, according to the International Monetary Fund (IMF).

There is also concern that uncoordinated regulatory actions may facilitate potentially destabilizing capital flows. The IMF estimates cryptos’ market capitalization at $2.5 trillion. This may be an indication of the significant economic value of the underlying technological innovations such as the blockchain, although it might also reflect froth in an environment of stretched valuations.

Cryptos’ potential to transform the traditional financial system means the associated challenges are attracting considerable regulatory attention. The focus is twofold: cryptos’ possible impact on financial stability and the need to protect vulnerable customers.

The principal challenge is the need for an internationally coherent policy approach, including definitions and jurisdictional perimeters, and in terms of exchanges, prevention of market manipulation and systemic risks. Lending and payment risks, banking, payments and anti-money laundering (AML) risks, tax policy and tax evasion risks, securities fraud and scams, together with cyber security, hacking and privacy risk will all need to be addressed.

The increasing regulatory challenges are exacerbated by the growing public awareness, acceptance and use of cryptos. From the U.S. perspective, research published [1]   in November 2021 by Pew Research, a nonpartisan think tank in Washington, reported 16% of respondents saying they personally have invested in, traded or otherwise used cryptocurrencies. Newsweek Magazine cited a survey in January 2022 by the crypto firm New York Digital Investment Group, estimating the total number of Americans who own cryptos at 46 million (about 14% of the population).

In the UK, in June 2021, the UK Financial Conduct Authority published its fourth consumer research publication on crypto-assets ownership [2]  which found heightened public interest in, and media coverage of, cryptos, with 78% of adults now having heard of cryptocurrencies. Around 2.3 million now own crypto-assets, up from around 1.9 million in 2020.

The UK regulator also found attitudes have shifted, as cryptocurrencies appear to have become more normalized — fewer crypto users regard them as a gamble (38%, down from 47%) and more see them as an alternative or complement to mainstream investments, with half of crypto users saying they intend to invest more in the future.

In the European Union, as of February 2022, the total market capitalization of crypto-assets is reported [3]  as having increased eightfold in the last two years to around 1.5 trillion euros now, although around 1 trillion euros below its peak in November 2021. The suggestion is that crypto-assets are beginning to gain mainstream acceptability, with ownership peaking at 6% of Slovakians and 8% of Dutch nationals reported as owning crypto-assets.

This report is a follow-up to Regulatory Intelligence’s “Cryptos on Rise” special report [4]  published in 2021. That report highlighted the need for policymakers, regulators and firms all to play their part in ensuring that cryptos are as "safe" as possible, not only in terms of investment risk but also with regards to regulatory certainty and cyber resilience.

The 2022 special report expands beyond cryptocurrencies such as bitcoin. Considering the need to develop a regulatory framework, it investigates other crypto-related instruments, such as central bank digital currencies (CBDCs), non-fungible tokens (NFTs) and stablecoins, and highlights policy work in key countries. It examines some of the misconceptions which persist about cryptos, as well as the ramifications for financial stability and the future of money. It also considers changing structural models for financial institutions emerging from the crypto world, as represented by decentralized autonomous organizations (DAOs).

As with the 2021 report there is a compendium which analyzes the tax, legal and regulatory status of cryptos in various jurisdictions.

[1]   https://www.pewresearch.org/fact-tank/2021/11/11/16-of-americans-say-they-have-ever-invested-in-traded-or-used-cryptocurrency/

[2]   https://www.fca.org.uk/publications/research/research-note-cryptoasset-consumer-research-2021  

[3]   https://www.esma.europa.eu/sites/default/files/library/esma50-164-5533_keynote_speech_-_verena_ross_-_keeping_on_track_in_an_evolving_digital_world.pdf  

[4]  https://www.thomsonreuters.com.sg/en/resources/cryptos-on-the-rise.html

Chapter Two

Beyond Bitcoin

Central bank digital currencies.

There are some structural similarities between crypto-assets and central bank digital currencies, but CBDCs are best described as the digital equivalent of a country’s fiat currency. As a result, they are often seen as an alternative or competitor to cryptos. The most advanced CBDC thus far is China’s digital yuan. During the 2022 Beijing Winter Olympic Games athletes, coaches and media made digital payments via smartphone apps, payment cards, or wristbands.

From the crypto regulatory landscape in the compendium of this report, it is apparent that many of the early movers on CBDCs also adopt restrictive stances or outright bans on other cryptos. Prime examples include China, Russia, Iran and Venezuela.

The G7 countries have been deliberately cautious about CBDCs’ potential, particularly with regards to retail CBDCs used by the public. The G7 has reiterated that the decision on whether to launch a CBDC is for each country to make, and no G7 jurisdiction has yet done so. In a 2021 survey of central banks [5] , the Bank for International Settlements (BIS) found that 86% are actively researching the potential for CBDCs, 60% are experimenting with the technology and 14% are deploying pilot projects.

[5]  https://www.bis.org/about/bisih/topics/cbdc.htm  

Retail CBDC

A retail CBDC would be a digital form of central bank money, denominated in the national unit of account, distinct from electronic reserves (which cannot be accessed by individuals) and physical cash. As a direct liability of the central bank, CBDCs would also be distinct from commercial bank money. If issued, CBDCs, as a form of central bank money, could act as both a liquid, safe settlement asset and as an anchor for the payments system.

Not crypto-assets

The G7 is clear that CBDCs are not crypto-assets. Crypto-assets are not issued by a central bank, can be highly volatile, and are not widely used for payments. CBDCs are fundamentally different from privately issued digital currencies such as stablecoins, which are a liability of private entities that seek to maintain stability in their price (typically in relation to stable assets such as fiat currency). CBDCs can be considered in two parts:

• the CBDC itself, an instrument issued by the central bank that can be transferred as a means of payment or held as a store of value; and
• the wider “ecosystem” in which a CBDC operates, including the supporting infrastructure that allows CBDC balances to be managed and payments made.

This wider infrastructure could involve both public and private participants (such as banks, digital wallet providers or other payment entities).

Public policy principles

In October 2021 the G7 published [6]  a set of 13 public policy principles for possible future retail CBDCs. Principles 1-8 cover foundational issues and principles 9-13 cover the opportunities. The “foundational issues” are those that any CBDC must demonstrate if it is to command the confidence and trust of users. These include the preservation of monetary and financial stability, the protection of users’ privacy, strong standards of operational and cyber resilience, the avoidance of financial crime and sanctions evasion, and environmental sustainability.

The G7 principles also highlight the potential for CBDCs to support safe and efficient transactions. They make it a political priority to harness opportunities and address the monetary and financial stability risks, as well as ensure trust in the financial system. The G7 notes that CBDCs could also advance public policy goals, including digital-economy innovation, financial inclusion and reducing frictions in cross-border payments.

[6]  https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1025235/G7_Public_Policy_Principles_for_ Retail_CBDC_FINAL.pdf

A divided UK stance

The UK’s stance on CBDC is at best unproven and at worst divided. In January 2022, the parliamentary Economic Affairs Committee published a report which concluded that there is no convincing case for UK to have a CBDC. The committee found that while a CBDC may provide some advantages, it could present significant challenges for financial stability and the protection of privacy.

The committee report [7]  builds on a November 2021 joint statement [8]  by the Bank of England and HM Treasury, which announced the next steps on the exploration of a UK CBDC. Specifically, the Bank and HM Treasury intend to launch a consultation in 2022 which will set out their assessment of the case for a UK CBDC. The consultation will form part of a “research and exploration” phase and will seek to inform policy development in the next few years.

The committee report adds several challenges and questions to the proposed consultation and evaluation process. The report’s findings, however, make it clear that the UK has some way to go before the case has been made for a UK retail CBDC. It also recommends that the UK government and Bank of England take action to shape international standards which suit the UK’s values and interests, particularly with regards to privacy, security and operational standards.

The U.S. approach to CBDCs

The potential for a CBDC in the United States took a step forward in February when the findings of a project by the Federal Reserve Bank of Boston and the Massachusetts Institute of Technology (MIT) were released. The project, dubbed “Project Hamilton,” achieved its preliminary goals of using emerging technology to deliver, in theory, high-speed transactions within a resilient infrastructure.

[7]   https://publications.parliament.uk/pa/ld5802/ldselect/ldeconaf/131/131.pdf

[8]   https://www.bankofengland.co.uk/news/2021/november/statement-on-central-bank-digital-currency-next-steps

Separately, the Federal Reserve Board in January opened debate [9]  on the merits of a CBDC. The “white paper” said creating an official digital version of the U.S. dollar could give Americans more, and speedier, payment options, but it would also present financial stability risks and privacy concerns. The paper, however, made no policy recommendations and offered no clear signal about where the Fed stands on whether to launch a CBDC.

The Federal Reserve’s Board said it would not proceed with creating a CBDC “without clear support from the executive branch and from Congress, ideally in the form of a specific authorizing law.”

Leaders of the Boston Fed/MIT project said the next phase will explore alternative designs and look more closely at other issues such as security and programmability. They will also look at ways to balance privacy issues with concerns about compliance.

“There are still many remaining challenges in determining whether or how to adopt a central bank payment system for the United States,” said Neha Narula, director of MIT’s Digital Currency Initiative.

In March 2022, the White House issued an Executive Order requiring the government to ensure the responsible development of digital assets and to assess the risks and benefits associated with of creating a central bank digital dollar.

[9]   https://www.reuters.com/business/fed-lays-out-risks-benefits-cbdc-paper-takes-no-policy-stance-2022-01-20/

Stablecoins

A stablecoin is any cryptocurrency designed to have a stable price, typically through being reserved, backed, or pegged to an underlying asset such as a commodity or currency, or through algorithmic mechanisms to its reference asset. The potential use cases for stablecoins are far-reaching and potentially disruptive to the established banking and payments industries.

Regulators are developing their approach to stablecoins. In October 2021, the international Financial Stability Board (FSB) published [10]  a progress report on the implementation of the high-level recommendations with regards to the regulation, supervision and oversight of global stablecoin (GSC) arrangements.

The progress report concluded that “cross-border cooperation and coordination” were the highest regulatory priorities, followed by further work regarding when a so-called stablecoin may be appropriately identified as a GSC.

Individual jurisdictions are developing their own approaches to stablecoins. The Hong Kong Monetary Authority (HKMA) published a discussion paper [11]  on crypto-assets and stablecoins inviting views from the industry and public on the relevant regulatory approach.

The paper, which closed for comments at the end of March 2022, sets out the HKMA’s thinking on the regulatory approach for crypto-assets, particularly payment-related stablecoins. The HKMA has considered, among other things, the international recommendations, the market and regulatory landscape locally and in other major jurisdictions, and the characteristics of payment-related stablecoins.

The paper considers five policy options across the entire spectrum, from “no action” to a “blanket ban.” 

United States 

Stablecoins are a likely early crypto priority for U.S. regulators. In November 2021, the President’s Working Group on Financial Markets, a government-industry body, released a report on stablecoins [12]  that urged Congress to pass new legislation to “fill regulatory gaps.”

In the meantime, U.S. market regulators are prepared to play a leading role in stablecoin oversight, Gary Gensler, the chair of the Securities and Exchange Commission (SEC), said in announcing the working group’s report.

Similarly, the federal Financial Stability Oversight Council, chaired by Janet Yellen, Treasury secretary, noted in its annual report [13]  in December 2021 that it “will further assess and monitor the potential risks of stablecoins and recommends that its members consider appropriate actions within each member’s jurisdiction to address those risks while continuing to coordinate and collaborate on issues of common interest.”

Gensler cited similarities between stablecoins and stable value funds and said the SEC and the Commodity Futures Trading Commission (CFTC) “will deploy the full protections of the federal securities laws and the Commodity Exchange Act to these products and arrangements, where applicable.”

The SEC and CFTC are also likely to play an integral role in the oversight of crypto trading platforms or exchanges. Market structure, potential market manipulation, scams and investment and trading activities will be priorities.

Concerns about investor protection have already been voiced by several prominent members of Congress. The SEC and CFTC will also oversee investor protection and overall policing and enforcement, with input from the Consumer Financial Protection Bureau (CFPB).

Some use cases for stablecoins will “trigger obligations under federal consumer financial protection laws, including the prohibition on unfair, deceptive, or abusive acts or practices,” said Rohit Chopra, chair of the CFPB.

The CFPB has launched a review of stablecoins’ potential to cause harm in three main areas: concentrated market power, systemic risk and consumer abuse.

The banking regulators will play a role in regulating stablecoins because of their potential uses in payments, borrowing, lending and deposit-like functions.

[10]  https://www.fsb.org/wp-content/uploads/P071021.pdf

[11]  https://www.hkma.gov.hk/media/eng/doc/key-information/press-release/2022/20220112e3a1.pdf

[12]  https://www.sec.gov/news/statement/gensler-statement-presidents-working-group-report-stablecoins-110121

[13]  https://home.treasury.gov/system/files/261/FSOC2021AnnualReport.pdf

The Monetary Authority of Singapore (MAS) has repeatedly cautioned that investing in cryptocurrencies is risky, and unsuitable for retail investors. Cryptocurrency funds are not authorized for sale to retail investors in Singapore.

In December 2019, MAS issued a public consultation seeking views on the interactions between money, e-money and cryptocurrencies, including stablecoins, and the appropriate regulatory treatment for cryptocurrencies, particularly stablecoins.

The consultation sought views on the defining characteristics of e-money and cryptocurrency, considered the potential ability of stablecoins to function as money, and discussed its relevance in the regulatory class of e-money or cryptocurrency.

The differing regulatory priorities for e-money and cryptocurrency services have different implications for how stablecoins would be regulated if placed in either of these categories.

E-money services are regulated for the safeguarding of customers’ money, whereas cryptocurrency services are regulated for AML risk, with a disclosure requirement to warn customers of the risk of loss. Other issues were also touched on, such as whether a global stablecoin should be regulated differently from other stablecoins and how the stabilization mechanism should be regulated.

The consultation received mixed views over whether a stablecoin was a single-currency or multi-currency stablecoin and whether there was a claim on the issuer of the stablecoin. There were also varying views regarding whether stablecoins should be treated as a payment instrument or an investment product, depending on the assets backing the stablecoins.

MAS intends to continue its work on reviewing the appropriate regulatory treatment for stablecoins, such as the treatment under different legislation, taking into consideration its practical use and risks, and informed by the continuing work of the international standard-setting bodies.

Non-fungible tokens

A non-fungible token (NFT) is a unique digital code stored on a blockchain, a form of distributed or digital ledger. Non-fungible tokens represent rights to the particular asset. The term "non-fungible" distinguishes NFTs from other digital assets that are fungible or interchangeable, such as bitcoin.

The use cases for NFTs are far-reaching as they provide an ability to authenticate virtually anything where there is a need to establish authenticity and ownership. Their popularity thus far has centred on the art and collectibles world — NFTs representing works of art, collectibles, video clips, or other digital media have exploded in price and popularity — but other potential uses include real-estate and auto titles, coupons, transit, or event tickets.

NFT and blockchain technology can also be useful in logistics and supply-chain applications, where metadata and timestamps can authenticate and help track the origins and journeys of commodities.

Critics may see the NFT market as yet another speculative bubble, but proponents point to broader applications in other industrial, legal and commercial uses that could be transformative.

The popularity of NFTs has raised concerns that the marketplace could be fertile ground for illicit activities such as scams, cybercrime, price manipulation, or money laundering. Indeed, many are baffled as to why so much money is spent on items that do not physically exist.

NFTs have been noticeably absent from the regulatory policy debate so far. How far financial regulators ultimately attempt to expand the perimeters of their authority — potentially into this new digital art and collectibles world, or even beyond into commercial applications — remains to be seen.

With such broad technological utility and complexity, regulation will be complex and likely to be challenged in courts. It appears obvious that AML requirements should apply in some areas. Another question is whether an NFT is deemed a financial instrument or security. Many legal experts already agree that if an NFT is fractionalized, thus representing partial ownership, or has royalty streams of income associated with it, it will likely be deemed a security and thus subject to regulatory oversight.

European Union

In September 2021, the European Union introduced a proposal to regulate crypto-assets. The Markets in Crypto-Assets Regulation (MiCA), if adopted, will regulate all issuers and service providers dealing with crypto-assets.

NFTs were explicitly excluded from MICA’s scope. Article 4 (2) of the draft provides that issuers of “crypto-assets that are unique and non-fungible” do not need to publish or register a white paper for them. MICA does state, however, that fractional NFTs should not be considered unique and would therefore be subject to MiCA.

United States

The United States has yet to issue direct guidance on NFTs as their use cases and potential value remain to be clarified. The structure of NFTs and the intellectual property rights, such as rights to use, copy and display, and whether revenue streams are associated, are just some of the legal uncertainties.

There is no direct state regulatory framework or guidance on NFTs, but several states, including New York and Louisiana, which do have virtual currency regulations could attempt to hold NFTs under their purview.

The U.S. Treasury’s anti-money laundering arm has yet to issue guidance specific to NFTs but has published general guidance related to how the Bank Secrecy Act and related regulations relate to virtual currencies that might apply to NFTs.

Established financial services firms and venues are getting into NFTs. In February 2022, the New York Stock Exchange filed an application to register the term “NYSE” for a marketplace for NFTs, appearing to take a step closer to setting up an online trading place for cryptocurrencies and NFTs.

If the NYSE launches a new marketplace, it will compete with SuperRare, Rarible and NFT marketplace OpenSea, which was valued at $13.3 billion after its latest private funding round.

A spokesperson for the NYSE said, however, that it has no immediate plans to launch cryptocurrency or NFT trading.

The NYSE minted its first set of NFTs in April 2021 commemorating the first trades of six “notable” listings. 

Investors in Hong Kong have shown considerable interest in NFTs. Projects have been launched at a steady pace, attracting enthusiastic bidders. Bricks and mortar marketplaces such as Sotheby’s and Christie’s have auctioned NFTs to buyers in Hong Kong, either as standalone items or as add-ons to luxury items such as watches, as well as facilitating bidding for locally produced NFT art.

NFT activity in Hong Kong has been further buoyed by regulatory uncertainty in mainland China. Financial authorities there have yet to clarify whether a recently implemented ban on all cryptocurrency transactions includes producing, selling or trading NFTs. As a result, some Chinese digital art and entertainment creators have turned to Hong Kong to issue NFTs.

The Securities and Futures Commission (SFC) has stated that virtual assets fall within the legal definition of securities or derivatives and are therefore subject to local securities laws. Cryptocurrency trading platforms such as Binance have withdrawn from Hong Kong after receiving written warnings from the SFC. The regulator’s move to assert jurisdiction over platforms suggests that it firmly considers virtual assets, such as cryptocurrencies and tokens that function as securities, to fall within its jurisdiction.

The natural next question is whether financial regulators will also consider NFTs as a class of virtual assets that fall within their jurisdiction. They have yet to issue regulations specifically concerning NFTs, although recent legislative developments in Hong Kong have tended to apply certain regulatory requirements, such as anti-money laundering and counter-terrorist financing rules, to all classes of virtual assets.

Chapter Three

Oversight in a crypto world

Financial stability and regulatory challenges.

The identification, monitoring and management of risks continue to concern and on occasion confound regulators and firms alike. The challenges include operational and financial integrity risks from crypto-asset exchanges and wallets, investor protection, and inadequate reserves and inaccurate disclosure for some stablecoins. Moreover, in emerging markets and developing economies, the advent of crypto can accelerate what the IMF has badged “cryptoization”— when these assets replace domestic currency and circumvent exchange restrictions and capital account management measures.

Financial stability

The FSB raised [14]  potentially serious concerns about financial stability in a recent paper. Given the international and diverse nature of the crypto-asset markets, it has advocated that regulatory authorities prioritize cross-border and cross-sectoral cooperation. Financial stability risks could escalate rapidly, and the FSB is clear that a ”timely and pre-emptive evaluation of possible policy responses” is required.

The need for policymaking pre-emption and cooperation is seen as increasingly urgent as, while crypto-assets account for only a small portion of overall financial system assets, they are growing rapidly. Direct connections between crypto-assets and systemically important financial institutions and core financial markets are rapidly evolving, opening the door to the potential for regulatory gaps, fragmentation or arbitrage.

A lack of consistency

The cross-sector, cross-border nature of cryptos limits the effectiveness of national approaches. Countries are adopting different strategies, and existing regulations may not allow for national approaches that comprehensively cover all elements of these assets. Importantly, many crypto service providers operate across borders, making the task for supervision and enforcement even more difficult.

[14]   https://www.fsb.org/wp-content/uploads/P160222.pdf

A particular challenge is a lack of consistency between, or absence of, definitions related to new technology applications. There are also legal and jurisdictional questions to be resolved. As an example, the U.S. CFTC and the courts have established that bitcoin is a commodity. The banking regulators see cryptos as a form of payment subject to their purview. The SEC, as the lead U.S. financial services regulator, however, sees things differently.

U.S. Executive Order and SEC take steps toward crypto regulation

In March 2022, the White House issued an Executive Order which emphasized the importance of digital assets and the need for coordination and cooperation between government departments, agencies and regulators. The Order said, “We must reinforce United States leadership in the global financial system and in technological and economic competitiveness, including through the responsible development of payment innovations and digital assets.”

The Order took a holistic approach to addressing risks and harnessing the potential benefits of digital assets. It emphasized six key priorities: consumer and investor protection, financial stability; illicit finance; U.S. leadership in the global financial system and economic competitiveness; financial inclusion; and responsible innovation.

New proposed rules from the SEC related to alternative trading systems (ATSs) have raised speculation in the crypto industry that the regulatory expansion could include blockchain and cryptocurrency platforms.

The proposal does not specifically reference cryptocurrencies or blockchain. However, a reference to “communication protocol systems” could apply to trading venues of all types, such as unregulated platforms according to several attorneys.

The rule proposal [15]  announced in January 2022 may have come as a surprise to the crypto and blockchain industries, some elements of which perceived it as an early shot in what will be a long and complex regulatory battle.

Alternative trading systems are SEC-regulated electronic trading systems that match orders for buyers and sellers of securities. Trading in U.S. government securities on such platforms has grown significantly in recent years. The level of regulatory oversight and investor transparency at these venues has not matched similar platforms for corporate bonds or equity securities.

The proposed rules are intended to protect investors and enhance cybersecurity in ATSs that trade U.S. Treasury securities. They expand on a similar 2020 proposal under Jay Clayton, former SEC chair.

The 650-page document raised about a dozen significant issues, according to Hester Peirce, an SEC commissioner. Peirce cited [16]  a reach to “currently unregulated communication protocol systems” and noted that the proposal “goes well beyond government securities, or even fixed-income securities; key parts of the proposal affect trading venues that make any type of security available for trading.”

Critics have said the proposal could include wallets, block explorers that allow users to call smart contracts, and other market participants including virtually every blockchain-based application. The proposal considers definitions such as “orders” “trading interests,” and “communication protocol systems” in place of “exchanges.”

[15]   https://www.sec.gov/rules/proposed/2022/34-94062.pdf

[16]   https://www.sec.gov/news/statement/peirce-ats-20220126 

Crypto advertising

Supervisory approaches to the advertising of cryptos to retail investors vary considerably among jurisdictions.

In February 2022, the UK FCA updated its prohibition [17]  on the retail marketing, distribution and sale of crypto-asset derivatives and crypto-asset exchange-traded notes. The UK is also consulting on further potential restrictions.

[17]   https://www.handbook.fca.org.uk/handbook/COBS/22/6.pdf

Crypto advertising in the United States is big business. When celebrity Kim Kardashian was paid to ask her 250 million Instagram followers to speculate on crypto tokens by “joining the Ethereum Max Community,” she disclosed that her post was an advertisement. She did not, however, have to disclose that Ethereum Max — not to be confused with the cryptocurrency ethereum — was a speculative digital token created a month before, one of hundreds of such tokens that fill the crypto-exchanges.

On television, meanwhile, potential retail investors in cryptos can watch movie stars pitch them in prime-time slots during major sporting events. Sporting venues have been re-named after crypto trading platforms, most notably Crypto.com, which paid $700 million for the naming rights of the Staples Center, home of the NBA’s Los Angeles Lakers, for a 20-year term. During the 2022 Super Bowl, four cryptocurrency commercials aired. A one-minute advertisement costing nearly $14 million, which featured nothing more than a floating QR code, drove more than 20 million hits to Coinbase’s landing page within one minute, according to Bitcoin Magazine.

Regulators in the United States have thus far focused their attention and enforcement efforts on unregistered securities offerings, and fraudulent scams. However, with investor protection and risk disclosures as core tenets, stricter advertising regulations surrounding cryptos are likely inevitable.

The Spanish securities regulator (CNMV) said in January that would begin to regulate rampant advertising of crypto-assets, including by social media influencers, to ensure investors are aware of risks. New regulations [18] set out requirements for the content and format of promotional messages for crypto-asset campaigns.

Advertisers and companies that market crypto-assets will have to inform the CNMV at least 10 days in advance about the content of campaigns targeting more than 100,000 people.

In November 2021, the CNMV scolded soccer star Andres Iniesta after he promoted the cryptocurrency exchange platform Binance on his Twitter and Instagram accounts, telling him that he should be thoroughly informed about cryptocurrencies before making any investment in them or recommending others to do so.

The Monetary Authority of Singapore in January published guidelines [19]  “discouraging” cryptocurrency trading by the general public and giving effect to MAS’ expectations that cryptocurrency service providers should not promote their services to the general public in Singapore.

Also in January 2022, Russia’s central bank proposed to ban the use and mining of cryptocurrencies on Russian territory, citing threats to financial stability, citizens’ wellbeing and its monetary policy sovereignty. Russia has argued for years against cryptocurrencies, saying they could be used in money laundering or to finance terrorism. It eventually gave them legal status in 2020 but banned their use as a means of payment.

The Russian central bank stated that speculative demand primarily determined cryptocurrencies’ rapid growth and that they carried characteristics of a financial pyramid, warning of potential bubbles in the market, threatening financial stability and citizens. The bank has proposed to prevent financial institutions from carrying out any operations with cryptocurrencies and said mechanisms should be developed to block transactions aimed at buying or selling cryptocurrencies for fiat currencies. The proposed ban would include crypto exchanges.

[18]   https://cnmv.es/DocPortal/Legislacion/Circulares/Circular_1_2022_EN.pdf

[19]   https://www.mas.gov.sg/-/media/MAS-Media-Library/regulation/guidelines/PSO/ps-g02-guidelines-on-provision-of-digital-payment-token-ser­vices-to-the-public/Guidelines-on-Provision-of-Digital-Payment-Token-Services-to-the-Public-PS-G02.pdf

The DFSA advises consumers and potential investors to exercise caution and undertake due diligence to understand the risks involved when buying crypto-assets. Risks include:

• Fraud:  Criminals often use crypto-assets and new technology to perpetrate fraudulent schemes by misleading customers as to the nature of the product on offer and “take the money and run” shortly after the token is issued. Also, fraudsters may entice customers by touting crypto-assets as an investment or an “opportunity” to get into a cutting-edge space without any real benefit behind the offer.
• Volatility:  Crypto-asset valuation and pricing can be difficult because of volatility and lack of real underlying assets, and holders may suffer significant losses if the price of the crypto-asset drops quickly.
• Liquidity:  Illiquid or flat market structures can make it hard to sell or trade crypto-assets. It may also be difficult to exit the market and “cash out.”
• Information:  Information may be missing, inaccurate, incomplete and unclear with respect to the project and associated risks. Documents may be technical and require additional knowledge to understand the characteristics of the crypto-assets and what the holder is (not) getting.
• Money laundering:  Crypto-asset platforms commonly rely on complex infrastructures using several entities (spanning across jurisdictions) to transfer funds and/or execute payments. This can mean that AML/CTF compliance, supervision and enforcement may not be effective. Consumers should exercise caution when dealing with crypto-asset entities, unless they are sure that the entities are properly regulated, to be protected against financial misconduct or wrongdoing.”  -- Extract from Dubai Financial Services Authority statement on crypto-assets, November 2021.
• Trust:  Trust is a particular challenge with regards to the increasingly widespread use of cryptos, especially as cryptos are seen to be eroding or replacing existing monetary norms such as fiat currency. Policymakers are beginning to consider the possible economic and regulatory ramifications of the adoption of digital currencies, together with the potential impact on the international monetary system.

Trust is primarily needed to maintain the societal conventions regarding the use of money. Part of that convention is that central banks provide, and critically are seen to provide, an open, neutral, trusted and stable platform. Private companies use their ingenuity and dynamism to develop new payment methods and financial products and services. This combination has been a powerful driver of innovation and welfare. The successful symbiosis cannot be taken for granted, however, and some recent developments may threaten money’s essence as a public good, if taken too far.

In a speech [20]   entitled “Digital currencies and the soul of money,” Agustín Carstens, general manager of the Bank for International Settlements, offers three plausible scenarios for the future of money:

Big Tech stablecoins compete with national currencies and also against each other, fragmenting the monetary system.

The elusive promise of crypto and decentralized finance, or “DeFi,” which claims to offer a financial system free from powerful intermediaries but may deliver something very different.

The realization of the vision of an open monetary and financial system that harnesses technology for the benefit of all.

[20]   https://www.bis.org/speeches/sp220118.htm

Carstens is an advocate of the third scenario, with an ideal of incumbent financial institutions, Big Techs and new innovative entrants all competing in an open marketplace that guarantees interoperability, building on central bank public goods. This is also the goal of the BIS Innovation Hub [21] .

Gatekeeping the gatekeepers — big tech and banking licenses

The growing interconnectedness between the traditional financial system and cryptos is demonstrated by the potential for, and the implications of, Big Tech firms and other digital asset firms taking stakes in or owning banks and financial services companies.

In January 2022, a paper by the Bank for International Settlements’ Financial Stability Institute assessed [22]  the benefits and risks of extending banking licenses to Big Techs and fintechs. The findings are based on publicly available licensing requirements in seven jurisdictions covering Asia, Europe and North America.

The paper compares the merits of bank ownership by tech firms in relation to ownership by commercial or industrial non-financial companies (NFCs).

The perceived benefits of allowing tech firms to operate with a banking license are “compelling but require scrutiny,” the paper says. Unburdened by legacy infrastructure, tech firms can offer superior technology and user-friendly apps that may allow them to reach more consumers and perform various aspects of the banking business (onboarding, deposit-taking, lending, payments) more efficiently than incumbents, including commercial or industrial NFCs that may own banks.

Collectively, their technology-centric approach to the delivery of financial services is expected to advance some authorities’ broader goals of fostering financial inclusion, promoting competition and delivering better outcomes for society. Nevertheless, as part of the authorization process — and subsequently through continuing supervision — authorities need to examine the ability and willingness of tech firms to deliver on their stated objectives.

A particular policy concern is whether the risks of allowing tech firms to own banks can be offset through licensing requirements without undermining the potential benefits they bring to consumers. Policy responses may differ across countries, but they are likely to be guided by three main considerations: the policy priorities of each jurisdiction; the inherent risks posed across and within each group of tech firms; and the applicability of the existing licensing regime in addressing the risks of tech-owned banks.

[21]   https://www.bis.org/about/bisih/about.htm

[22]   https://www.bis.org/fsi/publ/insights39.pdf

Warning from history

The UK has a stark warning for policymakers regarding the risks associated with non-financial services owners or controllers of banks, in a report on the Co-operative Bank’s failure in 2013. The report [23]  by Sir Christopher Kelly, which considered the events leading to the Co-operative Bank’s capital shortfall, highlights lessons relevant to the policy debate on tech firms owning or controlling banks or other financial services firms.

It found that mistakes had not stemmed from regulatory grey areas or misinterpretations of risk, regulation or compliance. Rather, the Co-operative Group’s board lacked the skills, knowledge or understanding required to manage a bank. It did not know what management information to expect, did not understand the role of the regulator and fundamentally did not understand banking.

The potential relevance to, say, a Big Tech owning a bank is clear. In the words of Kelly, “one of the most surprising features of this whole episode is that the board seemed unaware of its limitations.”

Policymakers will need to ensure there is credible deterrence inherent in the approach to tech firm bank ownership and specifically that any senior manager who is unaware of or ignores their regulatory responsibilities will be vulnerable to investigation and sanction.

[23]   https://assets.ctfassets.net/5ywmq66472jr/3LpckmtCnuWiuuuEM2qAsw/9bc99b1cd941261bca5d674724873deb/kelly-review.pdf

Decentralized autonomous organizations

The blockchain-based economy has spawned a new structure of financial institution called the “digital autonomous organization.” This type of organization, based on computerized “smart contracts” recorded on a blockchain, raises significant issues regarding governance and accountability.

Decentralized autonomous organizations 

The emergence of decentralized autonomous organizations (DAOs) represents a revolutionary change in the ways people and businesses can organize. DAOs leverage blockchain technology and are decentralized models of control and governance. They are characterized by transparency, clarity of rule, and process-driven decisions, primarily using smart contracts on distributed ledgers. Once a DAO has been established, via a blockchain, participants take ownership of its token, which allows them to participate in the system. Token holders can propose changes, and can vote on those changes, with the subsequent actions being taken “leaderlessly.” There are no chief executives, chief financial officers or chief technical officers, only code and community.

Close to 5,000 DAOs have been formed to date, and this is expected to grow exponentially. Many involve pooling digital money together to purchase assets, both physical and digital. ConstitutionDAO was established seven days prior to the auctioning of one of the 11 remaining copies of the U.S. Constitution. The intent was to purchase and house it at a protected public location. Participants in the DAO contributed money in ETH (Ethereum token), raising $45 million. Separately, the AssangeDAO raised $53 million for the criminal defense of Julian Assange. These are just two examples of how quickly DAOs can be created, and of how powerful they can be.

Central to a DAO is transparency. Anyone can see which individual (wallet address) owns tokens. Tokens allow for people to vote on proposals. Anyone can create a proposal. Simply stated, and in an ideal setting, it is egalitarian. One challenge to the model, however, is its democratic nature which can make DAOs overly deliberate and result in a slower process compared with more traditional organizations.

The regulatory landscape for DAOs is nearly non-existent at the state level. Wyoming, which has led the United States on regulation for blockchain and cryptocurrency, recently codified rules for DAOs residing in the state. A DAO could, therefore, be created under the laws of the State of Wyoming. No other state enables this yet. Further, there is a movement afoot for corporations in the cryptocurrency sector to dissolve and become DAOs. With potentially hawkish regulation on the horizon for cryptocurrency, DAOs, by their very nature, are code-based, self-running, leaderless entities running via a decentralized network, which permits actions based on how users interact under brassbound, predefined rules. Theoretically, under the current regulatory landscape there is nothing the law can do about such an entity. A corporation converted to a DAO would no longer be in control of the platform, which reverts to a completely new decentralized model, unlike anything regulated currently.

The SEC is reportedly looking into true DAOs such as Uniswap, which operates in the decentralized finance (DeFi) sector as a decentralized exchange (DEX) and is a code-based organization that matches buyers and sellers of cryptocurrency. One area of focus is lending pools, where users will provide their assets for other users to trade, which produces healthy yields, just as banks provide interest on assets. This may fall into the Howey Test investment contract realm.

Financial crime

There is also concern that crypto firms can, and are, being used as conduits for facilitating financial crime. Many such firms, if not most, are outside the regulatory perimeter and have often found stepping into the regulated world challenging. One example of this is Binance, which has suffered multiple setbacks in its attempts to become regulated in several jurisdictions.

New research shows that decentralized finance (DeFi) protocols in particular are becoming an increasingly significant route for money launderers. The January 2022 update [24] from data provider Chainalysis reported that $8.6 billion worth of cryptocurrency was laundered in 2021 — a figure that has fluctuated from $6.6 billion in 2020 to $10.9 billion in 2019.

The 2021 figure represents a 30% increase in money laundering activity compared with 2020, although, as the update points out, “such an increase is unsurprising given the significant growth of both legitimate and illicit cryptocurrency activity in 2021.” Chainalysis also notes that the numbers only account for funds derived from “cryptocurrency-native” crime. This refers to cyber-criminal activity such as darknet market sales or ransomware attacks in which profits are virtually always derived in cryptocurrency rather than fiat currency. It is more difficult to measure how much fiat currency derived from offline crime — traditional drug trafficking, for example — is converted into cryptocurrency to be laundered. 

[24]   https://blog.chainalysis.com/reports/2022-crypto-crime-report-preview-cryptocurrency-money-laundering/

The U.S. Department of Justice (DOJ) announced recently that it had seized a record $3.6 billion in bitcoin tied to the 2016 hack of digital currency exchange Bitfinex and had arrested a husband-and-wife team on money laundering charges.

The couple allegedly conspired to launder 119,754 bitcoin stolen after a hacker broke into Bitfinex and initiated more than 2,000 unauthorized transactions. DOJ officials said the transactions at the time were valued at $71 million in bitcoin, but with the rise in the currency’s value, the value now is more than $4.5 billion. Bitfinex said in a statement it was working with the DOJ to “establish our rights to a return of the stolen bitcoin.”

This showed that cryptocurrency was “not a safe haven for criminals,” said Lisa Monaco, deputy attorney general.

In another high-profile example last year, former partners and associates of the ransomware group REvil [25] caused a widespread gas shortage on the U.S. East Coast when it used encryption software called DarkSide to launch a cyber attack on the Colonial Pipeline. The DOJ recovered some $2.3 million in cryptocurrency ransom that Colonial paid to the hackers just days later.

Cases like these demonstrate that the DOJ “can follow money across the blockchain, just as we have always followed it within the traditional financial system,” said Kenneth Polite, assistant attorney general of the DOJ’s Criminal Division. This showed that cryptocurrency was “not a safe haven for criminals,” said Lisa Monaco, deputy attorney general.

Transparency

Overall, cyber-criminals have laundered more than $33 billion worth of cryptocurrency since 2017, with most of the total over time moving to centralized exchanges. For comparison, the UN (United Nations) Office on Drugs and Crime estimates that between $800 billion and $2 trillion of fiat currency is laundered each year — as much as 5% of GDP worldwide, whereas money laundering accounted for just 0.05% of all cryptocurrency transaction volume in 2021.

The biggest difference between fiat and cryptocurrency-based money laundering is that, due to the inherent transparency of blockchains, it is much easier to trace how criminals move cryptocurrency between wallets and services in their efforts to convert their funds into cash.

For the first time since 2018, centralized exchanges did not receive most of the funds sent by illicit addresses, taking in just 47%. Instead, the illicit funds were routed through DeFi protocols, which received 17% of all funds sent from illicit wallets in 2021, up from 2% the previous year. That translates to a 1,964% year-over-year increase in total value received by DeFi protocols from illicit addresses, reaching a total of $900 million in 2021. Mining pools, high-risk exchanges and mixers also saw substantial increases in value received from illicit addresses. 

[25]   https://www.reuters.com/technology/exclusive-governments-turn-tables-ransomware-gang-revil-by-pushing-it-offline-2021-10-21/

The increasing concern about DeFi was highlighted in 2021 when the U.S. Treasury Department’s Office of Foreign Assets Control (OFAC) sanctioned Suex and Chatex, two DeFi “gateway services” that regularly laundered funds from ransomware operators, scammers, and other cyber criminals.

In a different vein, HM’s Revenue & Customs in the UK is reported to have seized NFTs for the first time in February 2022 as part of a fraud investigation.

That said, the Belgian financial services regulator reported [26] that fraud linked specifically to cryptocurrencies fell 11% between 2020 and 2021.

Cryptos are undoubtedly being used in financial crime, but it still appears that, for instance, cryptocurrencies are substantially less likely to be used for money laundering than fiat currency. That said, the war in Ukraine has raised further questions and concerns about the potential for cryptos to be used in the avoidance of, or non-compliance with, sanctions.

[26]   https://www.fsma.be/en/news/fraudulent-online-trading-platforms-53-cent-increase-reports

The way forward

Policymakers are all-too aware of the need for a coherent approach to cryptos. “Global crypto regulation should be comprehensive, consistent and coordinated,” according to the IMF.

Specifically, the international regulatory framework should provide a level playing field along the activity and risk spectrum. The IMF believes this should have the following elements:

• Crypto-asset service providers that deliver critical functions should be licensed or authorized.      This would include storage, transfer, settlement and custody of reserves and assets, among others, as with existing rules for financial service providers.
• Requirements should be tailored to the main use cases of crypto-assets and stablecoins.
• Authorities should provide clear requirements on regulated financial institutions concerning their exposure to and engagement with crypto. 

Firms and their risk and compliance officers must engage with policymakers and regulators to ensure the best possible supervisory approach. Fast-moving digital transformation and adoption, even in limited terms, of innovative new technology, products and solutions will require skill sets to keep pace.

In addition to crypto, respondents to Regulatory Intelligence’s Fintech, regtech and role of compliance report for 2022 [27] highlighted a swath of other technological skills, including artificial intelligence and machine learning, cyber resilience and digital ledger technology, as being future knowledge requirements for risk and compliance functions.

[27]   https://legal.thomsonreuters.com/en/insights/reports/fintech-regtech-and-the-role-of-compliance-in-2022/form

A positive and transformative force?

Cryptos have huge potential to be a positive and transformative force for the future of financial services. The point was made in a November 2021 speech [28] by Carolyn A Wilkins, an external member of the Financial Policy Committee at the Bank of England.

Wilkins said she saw crypto-assets as the bedrock of the emerging financial ecosystem. The opportunities and risks extend well past the crypto-assets themselves to encompass a rapidly expanding range of financial services, from lending to insurance, she said. The future of this new frontier will depend critically on the regulatory response to these new activities and how fast the traditional financial system modernizes, and there will need to be major investment in domestic and cross-border payments, as well as digital governance, she said.

Tipping point

In many countries, cryptos appear to be at a legal and regulatory tipping point. Concerns about financial stability and vulnerable customers, together with the apparently persistent misperceptions about financial crime, are driving policymakers to consider significant action. Policymakers must, however, balance these considerations with the benefits which could be derived from the more widespread adoption of cryptos.

Other countries, meanwhile, are welcoming cryptos with seemingly few regulatory concerns. Cryptos’ borderless nature makes this even more challenging, as is evidenced by the near-overnight relocation of miners and crypto firms out of China. Most countries are reluctant to stifle innovation, but it would be politically unacceptable to deliberately risk either wholesale financial stability or widespread retail customer detriment.

There is an urgent need for a coherent approach to the regulation and oversight of cryptos; otherwise, there is a danger that they will fail to achieve their potential, and the world will lose the considerable benefits they could bring.

[28]   https://www.bankofengland.co.uk/speech/2021/november/carolyn-a-wilkins-keynote-speaker-at-autorite-des-marches-financiers-annual-meeting

Chapter Four

Compendium: Cryptocurrency regulations by country

In 2021 digital assets moved from the fringes of the economy and began to enter the mainstream, prompting more widespread public adoption. Commercials for crypto trading platforms blanket network television in the United States and the sector has become a focus of everyday conversation.

In November 2021, with bitcoin prices peaking around the $60,000 level, the total value of all cryptocurrencies surpassed $3 trillion, an increase from approximately $500 billion in December 2020. Today there are more than 16,000 individual cryptocurrencies in circulation, led by bitcoin. Total daily trading volumes are now estimated to be more than $275 billion on more than 400 platforms.

2021 was a transformative year for digital assets, and the stage is set for regulators to build a framework to govern this massive new market. Thus far, the regulatory response is best described as ad-hoc, rhetorical or driven by enforcement in some instances. The challenge in such a new and disruptive area will likely take years to finalize. Adding to the challenge is the ambiguous nature of digital assets themselves and the lack of standardized definitions, thus creating questions of overlap and jurisdiction.

The regulation of this new sector will require international coordination and engagement with the industry as it presents an opportunity for progress. An overly restrictive approach could stifle innovation and drive the industry to more welcoming jurisdictions, as the new digital universe is inherently global and borderless.

The regulatory framework is evolving rapidly and changing quickly. Some jurisdictions have imposed outright bans while others are staunch advocates.

Complete restrictions are rare and difficult to enforce, but regulators are scrambling to clarify rules to keep pace with crypto’s popularity.

Many market participants are desperately seeking a more defined regulatory framework and thus, certainty. This will mean new rules, regulations, or at a minimum official guidance. The race to regulate is now underway.

Crypto-assets, cryptocurrencies, central bank digital currencies and non-fungible tokens make up the new “crypto” universe, and each provides unique benefits, as well as regulatory challenges and complexities. This compendium to the report provides a summary of the regulatory picture in each jurisdiction. The summary below is grouped by region and focuses primarily on cryptocurrencies such as bitcoin. It provides an overview for each country, the regulatory state of play and links to the primary financial regulatory authorities or other relevant information.

Much of the regulatory framework is still developing, and regulations and restrictions also vary depending on uses such as payments, investments, derivatives, and tax status. Most countries have generally found ways to tax gains or income derived from cryptocurrencies, and some have more specific obligations than others. Few pure “tax havens” remain.

North America

Canada has approved bitcoin exchange-traded funds (ETFs). Canadian Securities Administrators (CSA) [29] and the Investment Industry Regulatory Organization of Canada (IIROC) [30] have issued guidance requiring crypto trading platforms and dealers in Canada to register with the local provincial regulators. In 2021 Canada adopted a clear registration regime for trading platforms that offer custodial services to Canadian clients. Several firms have registered under the new rules. Canada has also provided guidance on advertising and marketing of cryptos. The Ontario Securities Commission has actively enforced the regulations against several unregistered foreign trading platforms.

The Canada Revenue Authority (CRA) generally treats cryptocurrency like a commodity for purposes of the Income Tax Act.

Cryptocurrencies are prohibited in Mexico. The government and the financial authority, CNBV, enacted a set of fintech laws [31] in March 2018 that developed a regulatory framework and “sandbox” for virtual assets. The country has, however, taken a conservative approach to virtual assets with their relationship to existing financial system.

In June 2021 financial authorities said crypto-assets are not legal tender and not considered currencies under existing laws, warning that financial institutions that operate with them are subject to sanctions. “The financial authorities reiterate their warnings ... on the risks inherent in the use of so-called ‘virtual assets’ as a means of exchange, as a store of value or as another form of investment,” the statement said.

“The country’s financial institutions are not authorized to carry out and offer to the public operations with virtual assets, such as bitcoin, Ether, XRP and others in order to maintain a healthy distance between them and the financial system.”

Despite the restrictions, some of population has embraced cryptocurrencies. Mexico’s largest crypto exchange, Bitsos, has more than one million users on its platform.

Mexico’s Federal AML Law was amended in March 2018 to include transactions with “virtual assets” and considers them vulnerable activities under Financial Action Task Force (FATF) purposes.

The tax framework for cryptocurrencies is expected to change as there is no official position.

[29]   https://www.securities-administrators.ca/news/canadian-securities-regulators-outline-regulatory-framework-for-compliance-for-crypto-asset-trading-platforms/

[30]   https://www.iiroc.ca/news-and-publications/notices-and-guidance/joint-csaiiroc-staff-notice-21-329-guidance-crypto-asset-trading-platforms-compliance-regulatory

[31]   http://www.diputados.gob.mx/LeyesBiblio/ref/lritf.htm

The regulatory framework for cryptocurrencies is evolving despite overlap and differences in viewpoints between agencies. Although the Securities and Exchange Commission [32] (SEC) is widely seen as the most powerful regulator, Treasury’s FinCEN [33] , the Federal Reserve Board [34] and the Commodity Futures Trading Commission [35] (CFTC) have issued their own differing interpretations and guidance. An Executive Order from the White House [36] released in March directs the agencies to coordinate their regulatory efforts.

The SEC often views many cryptos as securities, the CFTC calls bitcoin a commodity, and Treasury calls it a currency. To iron out the regulatory differences, confusion about definitions, and jurisdiction, the President’s Working Group and the Financial Stability Oversight Council will play important roles in the development of a future regulatory framework.

The Internal Revenue Service (IRS) defines cryptocurrencies as “a digital representation of value that functions as a medium of exchange, a unit of account, and/or a store of value” and has issued tax guidance [37] accordingly. The IRS requires investors to disclose yearly cryptocurrency activity on their tax returns.

The United States is home to the largest number of crypto investors, exchanges, trading platforms, crypto mining firms and investment funds.

Central and South America

In Argentina, investing in cryptocurrencies is legal. It has become a large industry and accounts for a considerable portion of the country’s savings and assets. The government has issued regulations regarding cryptocurrencies related to taxation and AML/CFT. The government has proposed legislation which would create a legal and regulatory framework for crypto-assets as a means of payments, investments and transactions.

Argentina agreed with the IMF that it would adopt a program of fiscal, monetary and financial stability as it refinanced external debt in January. The promise may lead to higher taxes on cryptos.

The Argentina Securities and Exchange Commission [38] (CNV) will be the regulatory body with oversight responsibilities. It plans to maintain a national registry of operations, with transactions reported to the Financial Information Unit for compliance with AML requirements.

Argentina’s Federal Administration of Public Income and central bank have requested more information from domestic crypto exchanges and banks. Gains from cryptos are generally taxable at a 4% to 6.5% rate on gross income for each digital currency transaction.

[32]   https://www.sec.gov/files/digital-assets-risk-alert.pdf

[33]   https://home.treasury.gov/news/press-releases/sm1216

[34]   https://www.federalreserve.gov/econres/notes/feds-notes/tokens-and-accounts-in-the-context-of-digital-currencies-122320.htm

[35]   https://www.cftc.gov/PressRoom/PressReleases/8291-20

[36]   https://www.whitehouse.gov/briefing-room/presidential-actions/2022/03/09/executive-order-on-ensuring-responsible-development-of-digital-assets/

[37]   https://www.irs.gov/newsroom/irs-virtual-currency-guidance

[38]   https://www.argentina.gob.ar/cnv

The Bolivian government banned the use of cryptocurrencies such as bitcoin in 2014, in the belief that it would facilitate tax evasion and monetary instability. “It is illegal to use any kind of currency that is not issued and controlled by a government or an authorized entity,” Bolivia’s central bank [39] (BCB) said.

Bolivia has refrained from cracking down on or criminalizing the holding or trading cryptos, but it has not allowed businesses and brokers seeking to provide crypto-related services in the country. The BCB has publicly said, “…crypto-assets may not be operated through the Bolivian financial system. They do not operate with the authorization of the BCB or the Financial System Supervision Authority.” The BCB has said the measures were necessary to protect the public from “risks, frauds and swindles.”

In 2021 as the Brazilian real struggled, many Brazilians turned to cryptos. According to CoinMarketCap, approximately 10 million Brazilians now participate in the crypto market. Legislators in Brazil have proposed a series of regulations in the past several years and created a regulatory “sandbox.” Brazilian lawmakers have also passed legislation [40] requiring “virtual asset service providers to follow rules of communication of financial transactions, with identification of customers and recordkeeping.”

The Brazilian Securities and Exchange Commission [41] (CVM) has approved several crypto ETFs. The government has declared that bitcoin is an asset and therefore is subject to capital gains taxes. Brazil has said that existing AML laws extend to virtual currencies in certain contexts.

The Special Department of Federal Revenue of Brazil [42] has published a document on cryptocurrency taxes in the country.

The Central Bank of Brazil [43] said a CBDC, the digital real, could be launched as early as 2023.

Lawmakers in Chile are working to develop a regulatory and oversight framework for cryptocurrencies and to potentially recognize bitcoin as legal form of payment [44] . The government is also working on a CBDC. With a growing number of cryptocurrency exchanges in the country, and in the absence of a legal framework, the Central Bank and the Financial Market Commission [45] has said that existing regulations are applicable to cryptocurrencies.

The Chilean Internal Revenue Service (SII) is the only institution so far to have issued legislation on cryptocurrencies in Notice no 963, issued on May 14, 2018, [46] . The SII released a determination on the taxation of income obtained from buying and selling cryptocurrencies. It said that Tax Form 22 would require the declaration “from the sale of foreign currencies of legal course or assets digital/virtual, such as cryptocurrencies (for example, bitcoins).”

[39]   https://www.bcb.gob.bo/

[40]   https://www.camara.leg.br/noticias/811726-comissao-aprova-pena-maior-para-lavagem-de-dinheiro-com-moedas-virtuais

[41]   https://www.gov.br/cvm/en

[42]   http://normas.receita.fazenda.gov.br/sijut2consulta/link.action?visao=anotado&idAto=100592

[43]   https://www.bcb.gov.br/en/pressdetail/2397/nota

[44]   https://www.senado.cl/appsenado/templates/tramitacion/index.php?boletin_ini=14708-03

The Colombian government has prohibited banks from providing financial services to cryptocurrency companies. The country’s restrictive approach has created a challenge for the industry as firms may not use banking institutions.

The Banco de la República [47] , the country’s monetary, exchange and credit authority, and the Superintendencia Financiera de Colombia (SFC) [48] , the government agency responsible for overseeing financial regulation and market systems, released statements on cryptos. The authorities said cryptos are not legal tender or valid investments for supervised entities, and that firms are not authorized to advise or manage them.

The Superintendency of Corporations in Colombia [49] has stated that companies can legally purchase cryptos such as bitcoin, although such “intangible assets” are unregulated. The country’s tax authority, the Directorate of National Taxes and Customs (DIAN) [50] , said “virtual currencies are not money for legal purposes. However, in the context of mining activity, insofar as they are received in exchange for services and/or commissions, they correspond to income and, in any case, to goods that can be valued and generate income for those who obtain them as from be part of your patrimony and take effect in tax matters.”

There is no specific legislation or prohibition on the use of cryptocurrencies, but warnings from the government have led banks to deactivate cryptocurrency-related accounts and created an environment which makes it impossible for cryptocurrency-oriented companies to operate.

In January 2018, the Central Bank of Ecuador [51] informed citizens that bitcoin “is not a means of payment authorized for use in the country.” It clarified that bitcoin is not backed by any authority as its value is based on speculation. Financial transactions are not controlled, supervised, or regulated by any entity in the country, and this presents a financial risk to those who use it.

Despite this warning, the central bank has said that “the purchase and sale of cryptocurrencies — such as bitcoin — through the internet is not prohibited.”

In January 2022, Guillermo Avellan, the manager of the Central Bank of Ecuador, said there are plans to issue regulations later this year, which would bring clarity and contribute to the prevention of financial crimes such as money laundering.

[45]   https://www.cmfchile.cl/portal/principal/613/w3-article-25729.html

[46]   https://www.sii.cl/normativa_legislacion/jurisprudencia_administrativa/ley_impuesto_renta/2018/ja963.htm

[47]   https://www.banrep.gov.co/es/publicaciones/documento-tecnico-criptoactivos

[48]   https://www.superfinanciera.gov.co/jsp/index.jsf

[49]  https://www.supersociedades.gov.co/nuestra_entidad/normatividad/normatividad_conceptos_juridicos/OFICIO_100-237890_DE_2020.pdf

[50]   https://www.dian.gov.co/Prensa/ComunicadosPrensa/009-DIAN-realiza-acciones-de-fiscalizacion-a-operacion-con-criptoactivos-BITCOIN.pdf

[51]   https://www.bce.fin.ec/index.php/boletines-de-prensa-archivo/item/1028-comunicado-oficial-sobre-el-uso-del-bitcoin

El Salvador

El Salvador has established itself as a pioneer in cryptocurrencies with its 2021 adoption [52] of bitcoin as legal tender in the country. President Nayib Bukele has fully embraced bitcoin with promises of no income tax on cryptos and plans to build a geo-thermal powered city to try to attract bitcoin mining.

The International Monetary Fund, has urged El Salvador to reverse course, citing concerns about the country’s financial stability. The move to legal tender status is widely seen as a risky experiment, with credit rating agencies downgrading the country’s debt ratings. The move has also raised concerns related to AML and KYC compliance.

In December 2022, a new cryptocurrency law was introduced which seeks to define crypto-assets and regulate crypto transactions. The proposed law, “Crypto-asset Marketing Framework,” was introduced in the Peruvian Congress under the number N° 1042/2021-CR [53] , The law is seen as a first step to establish regulatory clarity for virtual asset service providers and others involved in blockchain and cryptography. The law proposes the creation of a public register and provides that registrants must operate lawfully in the country. It also considers the use of crypto-assets to create and incorporate companies and proposes that the assets could be considered property or intangible assets.

Thus far, the government has warned that no supervision is provided by the Securities Agency [54] (SMV), the Banking, Insurance and Pension Fund Manager Agency [55] (SBS), or the Peruvian Central Reserve Bank [56] (BCRP).

The BCRP has said that these financial assets are not legal tender, nor are they supported by central banks, so they fail fully to meet the functions of money as a medium of exchange, unit of account and store of value.

[52]   https://www.asamblea.gob.sv/sites/default/files/documents/decretos/8EE85A5B-A420-4826-ABD0-463380E2603B.pdf

[53]   https://wb2server.congreso.gob.pe/spley-portal-service/archivo/OTM0MA==/pdf/PL0104220211220

[54]   https://www.smv.gob.pe/

[55]   https://www.sbs.gob.pe/

[56]   https://www.bcrp.gob.pe/en

There is no specific legislation on cryptocurrencies. The Uruguayan Chamber of FinTech [57] has, however, announced the formation of a cryptocurrency committee to analyze what future regulations might look like. The country is widely viewed as bitcoin- and blockchain-friendly with no regulations specifically banning or permitting the use of cryptocurrencies.

On October 1, 2021, the Central Bank of Uruguay issued a statement about virtual assets and outlined a process for regulating cryptos. Peru has actively embraced the industry with a view of achieving a regulatory approach that is in line with international organizations.

The central bank clarified that the assets are not considered legal tender and that a regulatory framework would be very different from that of El Salvador.

Prior to 2018, law enforcement arrested and seized assets of bitcoin miners but has now declared cryptocurrencies such as bitcoin legal. The Superintendency of Crypto-assets and Related Activities of Venezuela (SUPCACVEN) is the governmental agency in charge of regulations, control and protection of crypto-assets.

On September 21, 2020, Venezuela legalized bitcoin mining. Miners must, however, be registered and all activities must be overseen through the “National Mining Pool,” with the government in charge of distributing the rewards from such activities.

The government has also created its own cryptocurrency called the Petro, which is backed by the value of Venezuelan oil.

The Financial Market Authority (FMA) has warned [58] investors that cryptocurrencies are risky and that the FMA does not supervise or regulate virtual currencies, including bitcoin, or cryptocurrency trading platforms. The FMA’s regulations follow Austria’s implementation of the Fifth Money Laundering Directive (AMLD5), defining crypto-assets as “financial instruments.” The FMA regulations provide registration requirements with respect to the issuance and selling of virtual currencies as well as transferring them, trading and exchange platforms for them as well as providers of custodian wallets.

Cryptocurrencies are legal but are not considered as legal tender. The Austrian Ministry of Finance [59] classes cryptocurrencies as “other (intangible) commodities.” As part of a nationwide tax overhaul, Austria will apply a 27.5% capital gains tax on digital currencies, bringing the treatment of cryptos into line with that of stocks and bonds, to “streamline” conditions between asset classes.

As a member of the EU, regulations and guidance issued by the European supervisory authorities (the European Banking Authority (EBA), the European Insurance and Occupational Pensions Authority (EIOPA) and the European Securities and Markets Authority (ESMA)) apply. Virtual currencies are defined by the European Central Bank (ECB) as “a digital representation of value, not issued by a central bank, credit institution or e-money institution, which, in some circumstances, can be used as an alternative to money.”

[57]   https://fintech.org.uy/

[58]   https://www.fma.gv.at/en/bitcoins/

[59]   https://www.bmf.gv.at/en.html

Bailiwick of Guernsey

The territory of Guernsey within the British Isles is known as a Crown Dependency but is not part of the United Kingdom; rather, it is a self-governing possession of the British Crown. The Guernsey Financial Services Commission (GFSC) is the body responsible for the regulation of the finance sector.

The GFSC has warned of the risks associated with cryptos, although it has taken a light regulatory approach. According to the GFSC website [60] , “Virtual or crypto currencies could interact with our regulatory laws in a number of ways and therefore any application would need to be assessed on its individual merits. We will assess any application by the same criteria we use for other asset types or structures, which means we would look to ensure that key controls are appropriate — for example, around custody, liquidity, valuation of assets and investor information.”

The GFSC has said it will assess applications on individual merits against the criteria used for asset types or structures, because cryptocurrencies, “could interact with regulatory laws in a number of ways.” Applicants must demonstrate how they will comply with AML/CTF laws and rules. The GFSC has also said it would be cautious about approving applications for ICOs, and also about the establishment of any kind of digital currency exchange within the jurisdiction.

Guernsey has announced plans for crypto-asset regulations later this year. The laws are expected to include a licensing regime for VASPs. Guernsey has approved a bitcoin fund.

[60]   https://www.gfsc.gg/faqs-0

Bailiwick of Jersey

The territory of Jersey within the British Isles is known as a Crown Dependency but is not part of the United Kingdom; rather, it is a self-governing possession of the British Crown. In 2016 amendments to the Proceeds in Crime Law categorized virtual currency as a form of currency.

Financial services business such as exchanges are subject to Jersey’s AML requirements and must comply with the island’s laws, regulations, policies and procedures related to AML/CTF.

Virtual currency exchanges are a supervised business and are required to register with, and fall under the supervision of, the Jersey Financial Services Commission [61] (JFSC).

Mining of cryptos on a small scale in Jersey is not taxable [62] , although the exchange of cryptocurrencies to and from conventional currencies and other cryptocurrencies will be liable to income tax, if it is considered to be “trading.”

The Belgian Financial Services and Markets Authority [63] and the National Bank of Belgium are the primary regulatory bodies for financial services in Belgium. The regulators have published guidance and warnings to the public that cryptocurrencies are not legal tender and have also issued statements regarding scams and investor protection. Belgium has, however, fostered a strong fintech community involved in digital assets and blockchain. The minister of justice has announced plans to establish a legal framework related to cryptos.

In February 2022 Belgium announced new rules [64] for certain virtual asset service providers. The rules, which take effect in May 2022, will require service providers “to meet a series of conditions, including ones relating to their professional integrity and compliance with the anti-money laundering legislation.”

Gains on cryptocurrencies are taxable by as “miscellaneous income.”

As a member of the EU, regulations issued by the EBA, EIOPA and ESMA apply. Virtual currencies are defined by the ECB as “a digital representation of value, not issued by a central bank, credit institution or e-money institution, which, in some circumstances, can be used as an alternative to money.”

The Bulgarian National Bank [65] and the Bulgarian Commission for Financial Supervision [66] have not defined cryptocurrencies as financial instruments or electronic money. Cryptocurrencies and bitcoin mining are not illegal and not regulated.

Bulgarian regulators have issued various standard warnings to the public and potential investors about risks associated with digital assets and initial coin offerings, and has not defined cryptocurrencies as financial instruments or legal tender for payments.

The Bulgarian National Revenue Agency [67] has issued a statement to define tax treatment for businesses and individuals and declare activities. Gains on cryptocurrency gains are taxed at 10%.

As a member of the EU, Bulgaria is one of only eight countries that has not adopted the euro, although national bank officials have said they intend to adopt the euro in 2024. Other EBA, EIOPA and ESMA regulations and guidance apply.

[61]   https://www.jerseyfsc.org/

[62]   https://www.gov.je/TaxesMoney/IncomeTax/Technical/Guidelines/Pages/CryptocurrenciesTreatment.aspx

[63]   https://www.fsma.be/en

[64]   https://www.fsma.be/en/news/cryptocurrencies-new-rules-certain-service-providers

[65]   https://www.bnb.bg/

[66]   https://www.fsc.bg/en/

Czech Republic

In the Czech Republic, cryptocurrency is largely unregulated and is regarded as a commodity rather than a currency. It is not an official means of payment.

The Czech National Bank [68] permits Czech banks to offer crypto-related services as long as they comply with AML regulations. The Czech Republic has said cryptocurrencies present no danger to the banking system and has deferred to EU directives. The Czech Republic has, however, implemented a stricter legal model than AMLD5 requiring that every cryptocurrency-related firm be regulated by the Czech government. AML regulations apply to anyone that provides cryptocurrency services, including “those who buy, sell, store, manage, or mediate the purchase or sale of cryptocurrencies or provide other services related to such currencies as a business.”

Gains on cryptos are taxed at rates between 15 and 19%.

The Danish Financial Supervisory Authority [69] is the main regulator in Denmark. Cryptocurrency regulation is, however, influenced by EU law. An amendment in January 2020 to the Danish Act on Measures to Prevent Money Laundering and Financing of Terrorism [70] defines a virtual currency as “a digital representation of value that is not issued or guaranteed by a central bank or a public authority, is not necessarily attached to a legally established currency and does not possess a legal status of currency or money, but is accepted by natural or legal persons as a means of exchange and which can be transferred, stored and traded electronically.”

There is no regulation of mining for virtual currencies in Denmark.

Denmark amended the AML Act in 2020 to implement AMLD5, which is designed to bring virtual currencies within the scope of the 4MLD.

The Danish central bank, the Nationalbanken [71] , is researching the development of a digital currency, the “e-krone.”

[67]   https://www.iota-tax.org/organization/national-revenue-agency

[68]  https://www.cnb.cz/en/public/media-service/speeches-conferences-seminars/presentations-and-speeches/Cryptoassets-Central-Banks-and-the- Current-Monetary-System-pdf-754-kB/

[69]   https://www.dfsa.dk/

[70]   https://www.dfsa.dk/Rules-and-Practice/AML_act_guide

[71]   https://www.nationalbanken.dk/en/publications/Pages/2017/12/Central-bank-digital-currency-in-Denmark.aspx

Estonia has been an early crypto frontrunner, with more than 1,300 crypto exchanges. In January 2021 the Ministry of Finance in Estonia proposed regulations for virtual currency service providers. The new regulations require “virtual currency service” firms to have their registered office, management and place of business located in Estonia. Such firms include wallets and trading platforms.

Although virtual currencies are not subject to securities regulation in the EU, the new draft rules attempt to address some of the regulatory issues and tighten regulation on virtual asset service providers. Firms will be subject to the supervision of the Financial Supervision Authority [72] , which will require minimum capital standards, IT standards, audits and reporting. All license holders are required to re-apply for a new license.

In December 2021, Estonia’s minister of finance published an informational page [73] addressing commonly asked questions about the proposed bill. “The legislation does not contain any measures to ban customers from owning and trading virtual assets and does not in any way require customers to share their private keys to wallets,” the minister said.

The proposed bill is seen as Estonia’s answer to the FATF guidance on regulating VASPs.

Income derived from cryptocurrencies in Estonia is taxable by the county’s Tax and Custom Board [74] .

In May 2019, Finland’s Financial Supervisory Authority [75] (FSA) began regulating virtual currency exchange providers, wallets and issuers of virtual currencies. Registration is required to ensure compliance with statutory requirements surrounding reliability of the provider, protection of client money, segregation of assets, marketing and compliance with AML/CFT regulations.

The FSA has warned consumers of the risky, volatile and speculative nature of the investments.

The Finnish FSA has published stricter rulings regarding crypto marketing saying “Only registered virtual currency providers can market virtual currencies and related services in Finland. The marketing of virtual currencies in Finnish and in Finland is only allowed for entities registered as virtual currency providers in Finland.”

The list of supervised entities [76] operating in the cryptocurrency and digital currency sector is small, with fewer than 10 companies registered; although, the FSA does not advise on or restrict Finnish customers visiting foreign websites.

Finland has joined the European Blockchain Partnership [77] and agreed to AMLD5.

[72]   https://www.fi.ee/en/finantsinspektsioon/financial-innovation/virtual-currencies-and-ico/information-entities-engaging-virtual-currencies-and-icos

[73]   https://www.fin.ee/en/faq-how-will-new-estonian-draft-legislation-affect-virtual-assets-and-crypto#can-i-be-fined-for-o

[74]  https://www.emta.ee/eng/private-client/declaration-income/other-income/taxation-private-persons-virtual

[75]   https://www.finanssivalvonta.fi/en/publications-and-press-releases/supervision-releases/2019/virtual-currency-providers-to-be-supervised-by-the-fin-fsa--briefing-for-virtual-currency-providers-on-15-may/

[76]   https://www.finanssivalvonta.fi/en/registers/supervised-entities/

[77]   https://digital-strategy.ec.europa.eu/en/news/european-countries-join-blockchain-partnership

In April 2019, the French National Assembly adopted the Plan d’Action pour la Croissance et la Transformation de Enterprises [78] (PACTE – Action Plan for Business Growth and Transformation) that will establish a framework for digital asset services providers. France’s Financial Market Authority [79] (AMF) has adopted new rules and regulations for cryptocurrency service providers and ICOs, related to the (PACTE). Ordinance № 2020-1544 [80] , was issued on December 9, 2020, to compliment France’s cryptocurrency regulations.

In June 2021, the regulations were finalized and went into effect. Firms are now subject to mandatory registration and subject to stricter KYC regulations. The rules established new AML/CFT rules related to digital assets. They imposed new requirements on crypto exchanges and prohibit anonymous accounts, expand AML/CFT and KYC obligations to better harmonize the French AML framework with Financial Action Task Force [81] (FATF) principles and respond to new risks associated with digital assets.

Lawmakers in France have recently debated changing the tax structure related to cryptos. Cryptos are taxed similar to movable property. Occasional traders are charged a flat tax of 30% while miners and professional traders are taxed 45%.

The German government was one of the first countries to provide legal certainty to financial institutions, allowing them to hold crypto-assets. Regulations stipulate that citizens and legal entities can buy or trade crypto-assets as long as it is done through licensed exchanges and custodians. Firms must be licensed with the German Federal Financial Supervisory Authority [82] (BaFin).

BaFin views and classifies cryptos as “units of account” within the meaning of the German Banking Act. They are therefore not legal tender, money, or foreign exchange notes or coins. The regulators have agreed, however, that they are deemed “crypto-assets” in accordance with the definition of financial instruments.

Germany has signed up to requirements under AMLD5. It has established licensing requirements for custody services. Crypto-assets are, however, based on agreement and accepted as a means of exchange or payment or as an investment, and can be transferred, stored, and traded electronically.

The German Federal Central Tax Office considers cryptocurrencies as private money for tax purposes. For individuals, gains of less than 600 euros held for less than a year are considered tax-free. Sales of cryptos held for more than a year are tax-exempt in Germany. If neither of the conditions are met, the gains are taxed subject to ordinary income rates.

[78]   https://www.gouvernement.fr/en/pacte-the-action-plan-for-business-growth-and-transformation

[79]   https://www.amf-france.org/en

[80]   https://www.legifrance.gouv.fr/jorf/id/JORFTEXT000042636234

[81]   https://www.fatf-gafi.org/

[82]   https://www.bafin.de/SharedDocs/Veroeffentlichungen/EN/Merkblatt/BA/mb_Hinweise_zum_Erlaubnisantrag_fuer_das_Kryptoverwahrgeschaeft_en.html

In the midst of the Greek debt crisis in 2015 bitcoin exploded in popularity in the country. Crypto regulation centers around Europe-wide directives. The Bank of Greece has issued and adopted European warnings and the country joined the European Blockchain Partnership.

The Hellenic Capital Market Commission [83] views cryptocurrencies as portfolio assets and not currency. It requires providers of digital wallets, custody services and exchange services between cryptos and fiat currencies such as ATMs to be registered. The registry is seen as an important first step in the country’s regulatory efforts. As an EU member state, Greece has agreed to follow any EU initiatives and to AMLD5.

The Bank of Greece set up an Innovation Hub or “sandbox” to enable fintech activities and became a member of the European Forum for Innovation Facilitators (EFIF) in April 2019.

There is no dedicated tax regime for blockchain or cryptocurrencies, although taxation for mining is considered income from commercial enterprises and the profits that will arise after deducting the operating expenses are taxed according to the general provisions and the applicable tax rates. Holders of cryptocurrencies are taxed at a rate of 15% plus a progressive increase as income from capital gains.

As an autonomous Danish dependent territory under the Kingdom of Denmark, financial services, banking, and crypto laws and regulations in Greenland are within the scope of the Danish regime.

The National Bank of Hungary, the Magyar Nemzeti Bank (MNB), [84] has issued a public statement warning citizens who use or invest in cryptocurrencies such as bitcoin about their unregulated nature and associated risks. The MNB published a report [85] on fintech and digitalization in April 2020 that included an analysis of the fintech sector, profitability and services across the fintech market.

Cryptocurrencies are not recognized as legal tender and regulations are underdeveloped in Hungary as there are no laws specifically regulating crypto activities. Hungary has, however, joined the European Blockchain Partnership and agreed to AMLD5.

Taxes on crypto mining and trading were lowered in 2022 to 15% of income. Exchanges from crypto to crypto are not taxable events. The taxes apply only when cryptos are converted to fiat currency. The 15% rate is favorable compared with the rest of Europe.

[83]    http://www.hcmc.gr/el_GR/web/portal/mlaundering1

[84]    https://www.mnb.hu/foreign-warnings

[85]   https://www.mnb.hu/letoltes/fintech-es-digitalizacios-jelente-s-final-eng.pdf

The Central Bank of Ireland [86] has issued warnings on the risks associated with cryptocurrencies such as bitcoin and Ether. It points out that they are unregulated, with a particular warning about ICOs. Cryptos are not considered as money or as equivalent to fiat currency in Ireland, and they are not backed by either the Irish government or the Central Bank. Ireland has taken a “wait-and-see” approach with regards to implementing domestic crypto regulation; rather, it has followed guidance from international regulators, most notably EU supervisory authorities.

Ireland’s Department of Finance has proposed the creation of a new blockchain working group to help create a coordinated approach to crypto regulation. The group published a report, “Virtual Currencies And Blockchain Technology.” [87]  Ireland has joined the European Blockchain Partnership and agreed to AMLD5.

Ireland’s Office of the Revenue Commissioners released a manual [88] on the tax treatment of various transactions under cryptocurrencies. It clarified that ordinary tax rules apply, and that cryptocurrency mining would generally not be subject to VAT. Generally, profits and losses from crypto transaction are taxable as normal income. There is some uncertainty as to capital gains tax and whether they are held as “investments” under “Badges of Trade” and related case law.

Isle of Man

The Isle of Man within the British Isles is known as a Crown Dependency but is not part of the United Kingdom; rather, it is a self-governing possession of the British Crown. The Isle of Man is considered one of the most attractive locations for crypto companies because of its secure data centers, low-cost electricity and its friendly regulatory and tax environment.

The Isle of Man Financial Services Authority (FSA) and the Digital Isle of Man, an executive agency within the government’s enterprise department, published guidance [89] aimed at giving companies greater clarity when setting up blockchain-related business in the jurisdiction.

Cryptocurrencies such as bitcoin are considered securities and fall outside regulatory oversight. Companies involved with the assets must, however, register with the FSA and comply with AML/CTF requirements. Tokens or cryptocurrencies that offer a store of value or access to services and are not a form of e-money would be unregulated.

[86]   https://www.centralbank.ie/consumer-hub/consumer-notices/consumer-warning-on-virtual-currencies

[87]   https://www.gov.ie/en/publication/d59daf-virtual-currencies-and-blockchain-technology/?referrer=http://www.finance.gov.ie/wp-content/up­loads/2018/03/Virtual-Currencies-and-Blockchain-Technology-March-2018.pdf

[88]  https://www.revenue.ie/en/tax-professionals/tdm/income-tax-capital-gains-tax-corporation-tax/part-02/02-01-03.pdf

[89]   https://www.iomfsa.im/media/2720/regulatory-perimeter-for-tokens.pdf

In February 2022, Italy published [90] new AML rules for crypto firms which outline registration and reporting requirements for VASPs that align with the EU AMLD5 and the Financial Action Task Force (FATF) guidelines for crypto firms.

The new rules also require virtual asset service providers to register in a special roster for crypto firms. Registration is required if firms offer any digital asset-related services in the country.

Italy joined the European Blockchain Partnership (EBP) along with 22 other countries in April 2018. The EBP was established to enable member states to work together with the European Commission on blockchain technology.

Cryptocurrencies and blockchain are regulated at the legislative level in Italy under Legislative Act no. 90. The decree in 2017 grouped cryptocurrency exchanges with foreign currency exchanges. Although the decree states that cryptocurrencies are not issued by the central bank and are not correlated with other currencies, it is a virtual currency used as a medium of exchange for goods and services.

Latvia’s Financial and Capital Market Commission [91] has warned investors that in Latvia there is no regulatory framework for cryptocurrencies. Nor are there any particular prohibitions or obligations to obtain special licenses. Furthermore, bitcoin and other cryptos are not classified as currency of any state.

Commercial activities related to the purchase and distribution of bitcoins or similar cryptocurrencies are not considered financial instruments or money issuance, nor are they payment services. Those conducting crypto activities are not licensed or registered with the Commission.

In the past several years Latvia has launched an effort to improve its AML regulations. In 2019 it expanded the role of the Financial and Capital Market Commission to cover AML/CTF and impose beneficial ownership requirements on local limited companies, foundations, unions and other enterprises.

The Latvian Finance Ministry imposes a 20% tax on capital gains from cryptocurrencies.

Latvia has signed a declaration joining the European Blockchain Partnership.

The Bank of Lithuania defined [92] cryptocurrencies in 2017. Also known as virtual currencies, cryptocurrencies such as bitcoin are unregulated and are not guaranteed by the central bank.

Lithuania requires crypto firms to register with the country’s Center of Registers. Registrants must adopt comprehensive KYC and AML procedures and are expected to inform the Financial Crime Investigation Service (FCIS) about large transfers. Companies that are registered as virtual currency exchange operators are not supervised as financial service providers. They have no right to provide any financial services, including investment services. The list of financial institutions authorized to provide investment services is published on the Bank of Lithuania website [93] .

A June 2020 report [94] from Moneyval — the Council of Europe’s committee of experts on the evaluation of AML/CFT measures — found Lithuania had made progress toward eliminating gaps in its regulation and supervision of cryptocurrency and claimed to have gone beyond requirements in AMLD5.

In July 2021, the Bank of Lithuania warned [95] an exchange operator about unlicensed investment services in the country and ordered that publicly available information must not be misleading.

The Lithuania State Tax Inspectorate considers cryptos as “property” and levies a 15% rate on the gains. Income from mining activities is only considered as income upon the sale of the cryptos after mining.

[90]  https://www.gazzettaufficiale.it/atto/serie_generale/caricaDettaglioAtto/originario?atto.dataPubblicazioneGazzetta=2022-02-17&atto.codiceRe­dazionale=22A01127&elenco30giorni=false

[91]   https://www.fktk.lv/en/

[92]   https://www.lb.lt/uploads/documents/files/Pozicijos%20del%20virtualiu%20valiutu%20ir%20VV%20zetonu%20platinimo%20EN.pdf

The Netherlands

The Dutch Central National Bank De Nederlandsche N.V. (DNB) [96] requires crypto firms to register with it. Dutch regulations require VASPs to provide identifying information on themselves and their customers. The DNB also supervises crypto service providers’ compliance with the Sanctions Act 1977.

The DNB defines cryptos as “a digital representation of value that is not issued or guaranteed by a central bank or a public authority, is not necessarily attached to a legally established currency and does not possess a legal status of currency or money but is accepted by natural or legal persons as a means of exchange and which can be transferred, stored and traded electronically.”

In May 2020 the Dutch Implementation Act amended Dutch AML rules and implemented 5MLD.

The Netherlands does not impose taxes on capital gains, but rather imposes a deemed interest on the value of all assets minus all liabilities. The deemed interest is taxable against a flat rate of 31% (in 2021, 30% in 2020).

[93]   https://www.lb.lt/en/sfi-financial-market-participants

[94]   https://www.fatf-gafi.org/media/fatf/documents/reports/fur/Moneyval-1st-Follow-Up-Report-Lithuania.pdf

[95]   https://www.lb.lt/en/news/bank-of-lithuania-issued-warning-regarding-binance-uab-and-other-crypto-asset-service-providers

[96]  https://www.dnb.nl/en/sector-information/supervision-sectors/crypto-service-providers/registration-of-crypto-service-providers/

Cryptocurrencies are legal . They are defined as an asset and not any type of money. Norway has been an attractive location for blockchain start-ups.

The Financial Supervisory Authority of Norway “Finanstilsynet” [97] and the country’s Ministry of Finance has established money laundering regulations which apply to “Norwegian providers of virtual currency exchange and storage services.”

The legislation requires firms such as storage services and exchanges that convert cryptos to fiat currency to comply with AML rules, but it does not impose regulatory obligations on other crypto services.

“Finanstilsynet will ensure that virtual currency exchange and storage providers comply with the money laundering rules. However, FSA does not have any tasks monitoring other areas of these providers, such as investor protection,” the regulator said.

In June 2021, Finanstilsynet published a warning [98] which said, “Most cryptocurrencies are subject to extreme price fluctuations. The risk of loss is high… Price formation is in many cases not transparent.” It also warned of significant criminal activity. “Scammers use spam, computer viruses, fake drawings and a variety of other techniques to deceive consumers,” the warning stated.

Bitcoin profits are subject to wealth tax and use of cryptos falls under sales tax regulations

The Central Bank of Norway is exploring the development of a CBDC.

Like many other countries in Europe, Poland has not regulated cryptos outside EU requirements. The National Bank of Poland and the Polish Financial Supervision Authority [99] (KNF) have warned of the risks associated with cryptocurrencies. The KNF has said that the cryptocurrency market is not a regulated or supervised market. “The KNF does not authorize, supervise or exercise any other supervisory powers in relation to the trade in cryptocurrencies. Some entities operating in the cryptocurrency market are authorized to provide payment services, in particular to settle payments made with legal tender (fiat money) in exchange for the cryptocurrencies being bought or sold.”

Poland’s AML regime adopted AMLD5, which had a significant impact on the approach to crypto businesses. The main goal was to increase transparency and protection from suspicious transactions. As of October 31, 2021, companies were required to register with the Ministry of Finance. Registration is not connected with any controlling aspect, however, and does not grant authority to operate or provide legal security.

Poland has signed a declaration joining the European Blockchain Partnership.

Cryptocurrencies are not considered legal tender. Gains on digital assets are subject to capital gains taxes and VAT. Polish tax rates on cryptos are 19% plus an additional 4% for those with income in excess of PLN 1 million.

[97]   https://www.finanstilsynet.no/en/

[98]   https://www.finanstilsynet.no/nyhetsarkiv/nyheter/2021/forbrukere-og-kryptovaluta/

[99]   https://www.knf.gov.pl/en/news?articleId=71711&p_id=19#:~:text=In%20the%20light%20of%20the,to%2C%20the%20trade%20in%20cryptocurrencies .

Despite having issued warnings about the risks related to cryptos, Portugal is widely seen as the most crypto-friendly country in Europe. The legal status of cryptocurrency in Portugal was officially clarified in a statement [100] by the Portuguese tax authorities and was subsequently reaffirmed by the Journal de Negocios [101] . Portugal does, however, follow EU regulation as has agreed to AMLD5.

In April 2020, the Portuguese government published a Digital Transition Action Plan [102] which included 12 pillars, the most important of which were the digital empowerment of people, the digital transformation of companies, the digitization of the state. The plan also established a flexible regulatory environment for technology testing and development.

A 2016 law ruled that because cryptocurrencies are not considered currencies, they are not legal tender and are therefore untaxable. The country’s non-habitual tax regime (NHR) has attracted many crypto traders as it allows for exemptions and reductions in tax for a 10-year period for individuals of high cultural or economic worth. “An exchange of cryptocurrency for ’real’ currency constitutes an on-demand, VAT-free exercise of services,” the Portuguese tax authorities have said.

Like its neighbor Portugal, Spain was a notable early hot spot for cryptocurrencies among EU members, with merchants accepting payments and bitcoin kiosks in the streets. Despite having no formal legal status, virtual currencies in Spain are taxable as income and under VAT.

In 2021 the Spanish Securities and Exchange Commission, the Comision Nacional del Mercado de Valores (CNMV) and the Bank of Spain issued a joint statement warning of the risks and volatility associated with cryptos. The joint statement [103] also highlighted that, from a legal standpoint, cryptocurrencies are not a means of payment and are not backed by a central bank or other customer protection mechanisms or authority.

Spain issued Royal Decree Law 5/2021 [104] which included a provision giving the CNMV power to regulate advertising related to cryptocurrencies. In January 2022, the CNMV published a circular [105] saying it would begin to regulate rampant advertising of crypto assets, including by social media influencers, to make sure investors are aware of risks.

[100]   https://www.audico.pt/wp-content/uploads/2019/08/57_INFORMACAO_14436.pdf

[101]   https://www.jornaldenegocios.pt/economia/impostos/detalhe/troca-e-remuneracao-de-criptomoeda-isentas-de-iva?ref=Economia_outros

[102]   https://eportugal.gov.pt/en/noticias/governo-lanca-plano-de-acao-para-a-transicao-digital

[103]   https://www.cnmv.es/portal/verDoc.axd?t=%7B52286f9f-c592-4418-9559-b75bf97115d2%7D

[104]   https://www.boe.es/buscar/act.php?id=BOE-A-2021-3946

The Financial Supervisory Authority (FSA) and the central bank have publicly declared that bitcoin is legal but not an official form of payment or legal tender. From a tax perspective they are viewed as an asset, not a currency or cash.

The FSA has warned [106] of the risks associated with cryptos and investment products with cryptos as underlying assets such as exchange-traded products (ETPs). Sweden has imposed registration requirements that mean custodians, wallet providers and exchanges must comply with the Swedish Currency Exchange Act. The act requires certain types of financial institutions (which are otherwise largely unregulated and unsupervised) to comply with AML provisions.

The scope of the Currency Exchange Act now includes custodian wallet providers and providers of virtual currency exchange services in accordance with the implementation of AMLD5.

Mining activities are not regulated under Swedish law. There are no licensing or registration requirements specifically applicable to virtual currency mining activities.

Sweden’s Central Bank, the Riksbanken, has been a leader in developing a CBDC, the e-krona.

Swedish income tax law has different categories of income such as employment income, self-employment income, business income and investment income. Capital gains are treated as investment income. Sweden imposes capital gains tax on cryptocurrencies at a flat rate of 30%. Losses are deductible up to 70%. Income tax is based on a progressive model with average rates around 32%.

Switzerland

Switzerland is known as one of the most cryptocurrency-friendly nations in the world. Switzerland’s financial markets regulator, the Swiss Financial Market Supervisory Authority [107] (FINMA) has defined licensing requirements for cryptocurrency businesses of all types including bitcoin kiosk operations, and has created requirements for blockchain companies.

Cryptocurrency businesses are subject to AML regulations and licensing requirements under FINMA. FINMA’s regulatory environment complies with the FATF’s digital asset regulation issued in June 2019.

Switzerland further improved its regulations surrounding tokens with the July 2021 implementation of the Federal Act on the Adaptation of Federal Law to Developments in Distributed Ledger Technology [108] (the DLT Act).

In Switzerland capital gains arising from a “private wealth asset” are exempt from income tax. This applies to capital gains from cryptos. Realized gains arising from the disposal of cryptocurrency are therefore not subject to tax. Losses arising from the disposal of cryptocurrency assets are not tax-deductible. Under Swiss tax law, cryptocurrencies are considered items that can be valued and traded. They are therefore assets that are subject to wealth tax. Tax rates vary.

[105]   http://www.cnmv.es/Portal/verDoc.axd?t=%7b1cbaf61c-57c2-4830-bd6a-071f806795e2%7d

[106]   https://www.fi.se/en/published/press-releases/2021/fi-warns-consumers-of-risks-connected-to-crypto-asset-products/

[107]  https://www.finma.ch/en/~/media/finma/dokumente/dokumentencenter/myfinma/faktenblaetter/faktenblatt-virtuelle-waehrungen.pdf?la=en

In the midst of a financial, currency and debt crisis, Turkey’s regulatory environment surrounding cryptos is a very mixed picture. Although it is not “illegal” to own cryptos, authorities have demanded user information from crypto trading platforms and regulators frequently cite crypto as a form of evasion for capital controls and taxes.

In April 2021, Turkey’s Central Bank [109] banned the use of cryptocurrencies saying they may be used, directly or indirectly, to pay for goods and services.

In May 2021, President Erdoğan issued a decree that added [110] cryptocurrency exchanges to a list of institutions that must operate under AML/CTF regulations. Despite the harsh rhetoric, bans on use in payments, and lack of any regulatory supervisory authority, public interest by Turkey’s citizens has soared as they are increasingly adopting and using cryptocurrencies.

The Financial Crimes Investigation Board (MASAK) oversees crypto service providers on AML and compliance issues. The Capital Markets Board (SPK) governs the crypto market, including ICOs and token offerings.

MASAK published [111] a guide for crypto asset service providers and President Erdogan have announced that a bill regulating digital assets is forthcoming.

Turkey is developing a digital central bank currency.

Ukraine is one of the top countries in usage of cryptocurrencies. In September 2021, the Ukrainian Parliament adopted a draft Law No. 3637 “On Virtual Assets” which introduced a basic regulation regarding all virtual assets. The law establishes general provisions regarding ownership, conduct of businesses, their circulation, and liabilities. The law uses the term “virtual asset” as which covers any type of crypto asset. Under the law, a virtual asset means a set of electronic data which has certain value and exists in the system of virtual assets circulation.

The law stipulates and distinguishes cryptos as assets and that they are not to be used as instruments of payments. It further distinguishes between “secured” or “unsecured” virtual assets. Secured virtual assets are secured by fiat currency and unsecured are any other type of virtual asset. Secured assets presumably would include stablecoins and unsecured would include other cryptos such as bitcoin.

The bill was passed [112] in February 2022 and signed into law by President Volodymyr Zelensky in March 2022. After the Russian invasion of Ukraine, the country received more than $100 million in crypto donations to support the country’s defense effort.

[108]   https://www.newsd.admin.ch/newsd/message/attachments/60601.pdf

[109]   https://www.tcmb.gov.tr/wps/wcm/connect/en/tcmb+en

[110]   https://www.reuters.com/technology/turkey-adds-crypto-firms-money-laundering-terror-financing-rules-2021-05-01/

[111]  https://panel.cetinkaya.com/dcdc07d0-3637-461c-b59a-c68460d5bb20_Masak%20Guide%20Translation%20PDF.pdf

United Kingdom

The UK Financial Conduct Authority [113] (FCA), HM Treasury and the Bank of England make up the country’s Crypto-assets Taskforce.

The FCA has created regulations to cover KYC, AML and CFT tailored for crypto-assets. It has also created regulations to cover VASPs, but has been careful to not stifle innovation.

Crypto exchanges must register with the FCA unless they have applied for an e-money license. Cryptocurrencies are not considered legal tender and taxes are levied based on activities. The FCA has banned the trading of cryptocurrency derivatives.

The Law Commission published a call for evidence [114] on digital assets in April 2021. The request seeks input from stakeholders ahead of publication of a consultation paper on digital assets which will make proposals for new legislation.

In February 2022, the UK government and the FCA published complementary reform proposals to bring financial promotions for some “qualifying crypto-assets” into HM Treasury’ financial promotions regime and into the FCA financial promotions rules.

There is no specific UK regulatory regime that captures the activities of crypto miners.

Although there is no specific UK tax legislation applicable to cryptos, HM Revenue and Customs has set out its view of the treatment based on normal principles. Receipt of cryptos from an employer are treated as “money’s worth” and are taxed as income based on the value of the assets at the time of receipt. Where cryptos are held as personal investments, capital gains tax applies upon disposal. In cases where frequent trading is involved, income tax rather than capital gains may apply.

[112]   https://www.kmu.gov.ua/en/news/parlament-uhvaliv-zakon-pro-virtualni-aktivi-zgidno-z-propoziciyami-prezidenta

[113]   https://www.fca.org.uk/news/press-releases/fca-provides-clarity-current-cryptoassets-regulation

[114]   https://www.lawcom.gov.uk/project/digital-assets/#digital-assets-call-for-evidence

Pacific region, Asia, and Australia

In 2018 new laws for digital currency exchange providers were implemented by the Australian Transaction Reports and Analysis Centre (AUSTRAC) [115] , the financial intelligence agency and AML/CTF regulator.

Firms are required to register and implement KYC policies, report suspicious transactions and comply with AML legislation.

In December 2021, Australia said it will create a licensing framework for cryptocurrency exchanges and consider launching a retail CBDC as part of an overhaul of its payment industry. Josh Frydenberg, the Treasurer, said the government would begin consultation in early 2022 on establishing a licensing framework for digital exchanges, allowing the purchase and sale of crypto-assets by consumers in a regulated environment.

The government would also consult on regulating businesses that hold crypto-assets on behalf of consumers, and on the feasibility of a central bank digital currency, Frydenberg said.

Taxes on cryptos in Australia [116] , generally are subject to capital gains taxes which range from 19 to 45%.

The Bangladesh Central Bank issued warnings in 2014 and 2017 related to transactions in cryptocurrencies and warned violations could be punishable by up to 12 years in jail under existing money laundering and terrorist financing regulations. Despite prohibitions on the use of cryptocurrencies, Bangladesh has proposed a national blockchain strategy, [117] perhaps signaling a change in the future. Concerns about a foreign flight of local capital are a major concern hindering cryptos, however.

Despite an international reputation for being hostile to cryptos, some attorneys argue that the acts of parliament fall short of criminalizing or even banning cryptos. Despite the restrictions, there are no verified reports of arrests, charges or convictions, related to the use of cryptos.

The People’s Bank of China [118] banned financial institutions from dealing in cryptocurrencies in 2013 and later expanded the ban to cover crypto exchanges and ICOs. China was the epicenter for mining because of low electricity costs. At its peak it was estimated that more than 65% of bitcoin mining was taking place in China.

The government considered a ban on crypto mining, but in 2019 reconfirmed that it would remain legal. In May 2021, China’s Financial Stability and Development Committee, the financial regulatory agency under Vice-Premier Liu He, said the Chinese government would “crack down on bitcoin mining and trading behaviour, and resolutely prevent the transfer of individual risks to the society.”

Most experts now estimate Chinese mining to be, in effect, near zero.

Despite the PBOC’s embrace of blockchain technology and efforts to be on the forefront of developing the central bank’s digital currency, the digital yuan, the ban on mining and all other crypto-related activities was one of the most noteworthy events in cryptos in 2021.

[115]  https://www.austrac.gov.au/new-australian-laws-regulate-cryptocurrency-providers

[116]  https://www.ato.gov.au/General/Other-languages/In-detail/Information-in-other-languages/Cryptocurrency-and-tax/

[117]  https://bcc.portal.gov.bd/sites/default/files/files/bcc.portal.gov.bd/page/bdb0a706_e674_4a40_a8a8_7cfccf7e9d9b/2020-10-19-15-03-391a6d­9d1eb062836b440256cee34935.pdf

[118]   http://www.pbc.gov.cn/english/130437/index.html

Hong Kong has long been vying to establish itself as a fintech innovation hub. The Hong Kong Securities and Futures Commission (SFC) [119] has, however, enacted a strict regulatory framework and licensing requirements for VASPs.

It has also proposed a ban on crypto trading for retail investors under which only professional investors who have more than HK$8 million in assets would be allowed to trade.

Hong Kong’s regulation of crypto has been unclear in recent years. China’s ban on cryptos has caused uneasiness in Hong Kong, with many fintech and crypto firms leaving or downsizing operations in the region.

Hong Kong began to take steps to close legal loopholes which have allowed crypto exchanges to operate. In January 2022, however, the Hong Kong Monetary Authority (HKMA) issued two papers: one on stablecoins [120] and another on crypto-related exchange-traded funds [121] .

Bitcoin is defined as a virtual commodity and not legal tender. There are no capital gains taxes and AML/CFT laws apply to every individual or business in Hong Kong, irrespective of activity and are in accordance with FATF requirements.

In Indonesia virtual currencies are not considered legal tender. In 2019 the Indonesian Commodity Futures Trading Regulatory Agency (Bappebti) approved regulation no. 5/2019, [122] which legally recognizes and regulates bitcoin and other cryptocurrencies as commodities. Derivative transactions and cryptocurrency exchanges are also subject to regulatory requirements of Bappebti.

The regulation defines a crypto-asset as “an intangible commodity in the form of a digital asset that uses cryptography, a peer-to-peer network and distributed-ledger technology to regulate the creation of new units, verify transactions and ensure transaction security without the involvement of a third-party intermediary.”

Bank Indonesia, the country’s central bank, has banned the use of cryptocurrencies as a payment tool.

Indonesia has also banned financial firms from facilitating crypto sales. Indonesia’s Financial Services Authority (OJK) said it has “strictly prohibited financial service institutions from using, marketing and/or facilitating crypto asset trading,” the regulator said in a statement [123] posted on Instagram.

The ministry is facilitating the establishment of a separate bourse for digital assets, called the Digital Futures Exchange, which officials say will be launched in the first quarter of 2022.

It warned that the value of crypto-assets often fluctuates and that people buying into the digital assets should fully understand the risks.

The warning follows similar concerns by the central banks of Thailand [124] and Singapore [125] .

Japan has one of the most progressive and developed regulatory regimes for cryptocurrencies. Cryptocurrency exchanges must be registered and comply with traditional AML/CFT and other regulations. They are regulated under the Payment Services Act (PSA), which defines “cryptocurrency” as a property value and not a legal tender. The PSA defines “crypto-assets” as payment methods that are not denominated in fiat currency and can be used to pay unspecified persons.

In December 2017, Japan’s National Tax Agency [126] ruled that gains on cryptocurrencies should be categorized as “miscellaneous income” and taxed accordingly. There have been several new regulations and amendments to the PSA, and to the Financial Instruments and Exchange Act [127] (FIEA), introducing the term “crypto-asset,” and regulating crypto derivatives trading. Cryptocurrency custody service providers (that do not sell or purchase crypto-assets) fall under the scope of the PSA, while cryptocurrency derivatives businesses fall under the scope of the FIEA.

In April 2020, Japan was the first country to create self-regulatory bodies, the Japanese Virtual Currency Exchange Association [128] (JVCEA) and the Japan STO Association [129] . The JVCEA and the STO Association promote regulatory compliance and play a significant role in establishing best practices and ensuring compliance with regulations.

In Japan, gains associated with cryptos are considered miscellaneous income. Tax rates on crypto gains vary and depend on individual income. Rates can be as high as 55%.

[119]   https://apps.sfc.hk/edistributionWeb/gateway/EN/news-and-announcements/news/doc?refNo=19PR105

[120]   https://www.hkma.gov.hk/media/eng/doc/key-information/press-release/2022/20220112e3a1.pdf

[121]   https://apps.sfc.hk/edistributionWeb/gateway/EN/circular/intermediaries/supervision/doc?refNo=22EC10

[122]   http://bappebti.go.id/resources/docs/peraturan/sk_kep_kepala_bappebti/sk_kep_kepala_bappebti_2019_02_01_w9i365pf_id.pdf

[123]   https://www.instagram.com/p/CZIgoP2PjI2/

[124]   https://www.reuters.com/markets/funds/thai-central-bank-says-doesnt-support-digital-assets-payments-2021-12-01/

[125]   https://www.reuters.com/technology/singapore-cbank-issues-guidelines-discourage-crypto-trading-by-public-2022-01-17/

[126]   https://www.nta.go.jp/english/

[127]   https://www.fsa.go.jp/en/policy/fiel/

[128]   https://www.asahi.com/articles/ASL4R3VLKL4RULFA00M.html

[129]   https://www.fsa.go.jp/news/r1/shouken/20200430.html

The Securities Commission Malaysia (SC) issued guidelines on the regulation of various digital currency platforms operating in the country. The Capital Markets and Services (Prescription of Securities) (Digital Currency and Digital Token) Order 2019 [130] ruled that digital tokens are “securities” for purposes of securities laws.

Digital currency is defined as “a digital representation of value recorded on a distributed digital ledger that functions as a medium of exchange and is interchangeable with any money, including through the crediting and debiting of an account.” All exchange offerings and digital asset custodians are required to register and “assess and conduct the necessary due diligence on the issuer, review the issuer’s proposal and the disclosures in the whitepaper, and assess the issuer’s ability to comply with the requirements of the Guidelines and the SC’s Guidelines on Prevention of Money Laundering and Terrorism Financing.”

The position on the taxation of cryptos in Malaysia is unclear. The Inland Revenue Board of Malaysia (IRB) has not issued definitive guidelines on the taxation of cryptos.

With regards to cryptocurrency transactions, the IRB has cited Section 3 of the Income Tax Act 1967 and indicated that the provision can be applied to active cryptocurrency traders.

The IRB has said further that several factors may determine whether profits from crypto activities would be subject to income tax.

New Zealand

The Financial Markets Authority of New Zealand (FMA) [131] has determined that additional obligations will apply to certain activities considered “financial services” include exchanges, wallets, deposits, broking and ICOs involving crypto-assets that are classed as “financial products” under the FMC Act of 2013 [132] .

However, the FMA said in September 2021, “Cryptocurrencies are not legal tender (money that must be accepted as payment) in most countries and do not exist physically as notes and coins. They are also not viewed as financial products so are not regulated in New Zealand.”

The Inland Revenue Department [133] of New Zealand considers cryptocurrencies as “property,” with gains and losses taxable as income.

[130]   https://www.sc.com.my/api/documentms/download.ashx?id=8c8bc467-c750-466e-9a86-98c12fec4a77

[131]   https://www.fma.govt.nz/compliance/role/cryptocurrencies/

[132]   https://www.legislation.govt.nz/act/public/2013/0069/latest/DLM4090578.html

[133]   https://www.ird.govt.nz/cryptoassets/taxing

Philippines

The Philippine Central Bank, the Bangko Sentral ng Pilipinas (BSP) requires [134] VASPs to register. The BSP has developed an AML framework in line with FATF guidelines.

The BSP licensing requirements include exchanges of virtual assets and fiat currency. All transactions are treated as cross-border wire transfers and crypto service providers are expected to comply with relevant BSP rules. Additionally, BSP licensed firms must comply with rules for money service businesses such as liquidity risk management, IT risk management and consumer protection.

The BSP has published and updated FAQs for the public related to virtual currencies.

The National Internal Revenue Code (NIRC) of the Philippines states that any income of an individual or corporation, in whatever form, obtained in the Philippines is taxable.

Cryptocurrencies are regulated by the Monetary Authority of Singapore [135] (MAS). The Payment Services Act of 2019 regulates traditional and cryptocurrency payments and exchanges. The Securities and Futures Act is also applicable to public offerings and issues of digital tokens.

A May 2020 Guide to Digital Token Offerings [136] published by the MAS details the regulations surrounding digital tokens and their applicability to securities, collective investments, derivative contracts and the determination of whether a token is a type of “capital market product.” The AML/CFT provisions under the PSA address the risk of financial crimes and promotes best practices, including KYC, to help crypto businesses comply with the new regulatory framework.

In February 2022, the MAS issued Guidelines to Discourage Cryptocurrency Trading by General Public [137] . The new guidelines clarify the expectations that digital payment token (DPT) service providers should not engage in marketing or advertising of DPT services to the general public in Singapore.

The Inland Revenue Authority [138] has said, “Businesses that choose to accept digital tokens such as bitcoins for their remuneration or revenue are subject to normal income tax rules. They will be taxed on the income derived from or received in Singapore. Tax deductions will be allowed, where permissible, under our tax laws.”

[134]   https://www.bsp.gov.ph/Media_and_Research/Primers%20Faqs/VC.pdf

[135]   https://www.mas.gov.sg/regulation/acts/payment-services-act

[136]   https://www.mas.gov.sg/regulation/explainers/a-guide-to-digital-token-offerings

[137]   https://www.mas.gov.sg/news/media-releases/2022/mas-issues-guidelines-to-discourage-cryptocurrency-trading-by-general-public

[138]   https://www.iras.gov.sg/taxes/corporate-income-tax/income-deductions-for-companies/taxable-non-taxable-income#:~:text=Trading%20in%20 Digital%20Tokens,-Businesses%20that%20buy&text=Businesses%20that%20buy%20digital%20tokens,are%20not%20subject%20to%20tax.

South Korea

South Koreans were early bitcoin pioneers and have been enthusiastic traders and investors in cryptos. In 2021, total trading volumes for cryptos in South Korea surpassed that of the domestic equities market. Regulators in South Korea have taken a cautious approach to cryptocurrency exchanges and companies. Companies are subject to equivalent AML and tax obligations as other financial institutions.

Following several large crypto-exchange hacks, South Korea passed the “Act on Reporting and Using Specified Financial Transaction Information,” also known as the Financial Transaction Reports Act [139] (FTRA), which requires VASPs to register and comply with AML regulations.

South Korea has sought to ensure market integrity compliance with the FATF. Regulators have also emphasized the importance of safety of trading platforms. New rules went into effect in 2021 requiring all crypto service providers to register with the Korean Financial Services Commission. Platforms must also comply with AML obligations and acquire an Information Security Management System (ISMS) certificate [140] from the Korea Internet & Security Agency (KISA).

In South Korea virtual assets are categorized under “other income” for tax purposes. In late 2020, South Korea authorized an initiative to tax crypto trading profits in 2022. Gains will be taxed at a rate of 20%. Korea’s National Tax Service has also widened the crypto tax law to include foreign crypto exchanges and businesses.

The amended law will tax 20% of profit from crypto transactions in excess of 2.5 million Korean won, or about $2,200. Korea’s National Tax Service (NTS) has since expanded [141] the crypto tax law on accounts by domestic investors to foreign crypto exchanges and businesses.

Taiwan’s Central Bank and Financial Supervisory Commission [142] (FSC) have warned that cryptocurrencies are not currencies, but rather commodities and have no legal protection. The FSC has been empowered under the country’s Money Laundering Control Act [143] and Terrorism Financing Prevention Act to require users on trading platforms to register their “real names.” The FSC implemented new money laundering regulations for the nation’s cryptocurrency exchanges, requiring them to report transactions valued at more than NT$500,000 ($17,770),

The FSC has required platform operators operating STO business to obtain a securities dealer’s license and comply with the securities business prevention system Money Laundering and Anti- Terrorism (AML/CFT) regulations. Banks must report suspicious anonymous transactions.

There are no regulations on crypto mining.

With the exodus from China following the government crackdown, many expected Taiwan to be a beneficiary; but, many still view Singapore as more crypto-friendly.

The trading of cryptos on a platform within Taiwan may be deemed a sale of services and thus subject to Taiwan business tax.

[139]   https://www.kofiu.go.kr/eng/legislation/financial.do#:~:text=The%20Financial%20Transaction%20Reports%20Act,%2Fanalysis%2Fdissemina­tion%20of%20STRs.

[140]  https://www.kisa.or.kr/eng/main.jsp

[141]   https://forkast.news/headlines/south-korea-tax-overseas-crypto-asset-accounts/

[142]   https://www.fsc.gov.tw/ch/home.jsp?id=96&parentpath=0,2&mcustomize=news_view.jsp&dataserno=202104200003&dtable=News

[143]   https://law.moj.gov.tw/ENG/LawClass/LawAll.aspx?pcode=G0380131

The Securities and Exchange Commission of Thailand regulates cryptocurrencies under an Emergency Decree on Digital Asset Businesses B.E. 2561 [144] issued in 2018. Under the decree, digital asset businesses are required to apply for a license, monitor for unfair trading practices, and are considered “financial institutions” for AML purposes among others.

The Thailand Central Bank has said repeatedly that it does not support use of crypto as payments. In January 2022, the central bank and market regulator announced plans to ban digital asset operators from facilitating use of crypto as a means of payment for goods and services.

Digital asset business operators have expanded their businesses to cover services related to the use of digital assets as payments, which may result in a wider adoption of such activity, they said in a joint statement [145] .

That could potentially affect financial stability and the overall economic system, they said in the statement.

A public hearing on the new rule will be held until February 8 before it will be effective, Charuphan Intararoong, assistant secretary-general at the Securities and Exchange Commission (SEC), told a news conference. It will not yet cover use of digital assets as payments between merchants and customers, while trading of crypto assets is still allowed, Charuphan said.

“Investors, consumers, and citizens can still trade digital assets for investment as usual,” she said.

The central bank and relevant agencies will consider allowing digital assets that are beneficial to the country to operate, however, said Siritida Panomwon Na Ayudhya, assistant central bank governor, without elaborating.

Trading and use of cryptocurrencies have gained momentum in Thailand, with retailers and real estate developers accepting digital assets as payments.

Gains are taxed as income and subject to the highest tax bracket of 35%.

[144]   https://www.sec.or.th/TH/Documents/DigitalAsset/enactment_digital_2561_summary_en.pdf

[145]  https://www.bot.or.th/en/news-and-media/news/news-20220125.html

Russia, Middle East, Africa, and other countries

The 2018 Financial Law of Algeria prohibits the use of any cryptocurrencies as well as the purchase, sale, use, and possession of virtual currencies.

In 2020 the Bahamas passed the Digital Assets and Registered Exchange Bill (DARE) putting in place a framework for digital assets. The law creates opportunities for FinTech firms and facilitates the registration of exchanges and other business involved with digital tokens.

The legal framework [146] is being heralded as one of the most comprehensive regulatory structures and standards in the world while also welcoming to the industry.

The Bahamas Central Bank was the first to launch a CBDC, the Bahamian “Sand Dollar” [147] in October 2020.

The Bahamas are considered an investor-friendly tax haven where there is no income or capital gains tax.

The offshore finance and insurance center Bermuda, has adopted a business-friendly approach to the oversight of cryptos and related businesses. The Digital Asset Business Act [148] and the Companies and Limited Liability Company Initial Coin Offering Amendment Act, passed in 2018, defines digital assets and provide standards governing ICOs and digital asset businesses.

The Bermuda Monetary Authority (BMA) has issued requirements [149] through the Digital Asset Business Act creating a licensing regime for custodians, service providers, trading platforms and other crypto businesses.

Initial coin offerings are classified as a restricted business activity that requires approval from the BMA. Digital asset businesses are required to register and comply with AML/CTF regulations, specifically, the Proceeds of Crime Acts.

There are no specific taxes on income, capital gains, or other taxes on digital assets in Bermuda.

[146]   https://www.bahamas.gov.bs/wps/portal/public/gov/government/news/

[147]   https://www.sanddollar.bs/

[148]   https://www.bma.bm/digital-assets-supervision-regulation

[149]   https://www.bma.bm/digital-assets-supervision-regulation

Cayman Islands

In May 2020, Cayman Islands lawmakers enacted several new legislative acts [150] regulating the cryptocurrency industry. The centerpiece, the Virtual Asset Service Provider (VASP) Law, makes it mandatory for digital asset businesses to be registered with the Cayman Islands Monetary Authority (CIMA).

The Cayman’s crypto regulations provided regulatory certainty for VASPs and align with international AML/CFT regulations to protect consumers and to meet the requirements of the FATF recommendations.

The Caymans have no income, inheritance, gift, capital gains, or corporate taxes with respect to the issuance, holding, or transfer of digital assets.

The Egyptian government banned trading of cryptos in 2018 because of religious decrees under Islamic law. Despite the ban, several international crypto trading platforms have reported significant user growth in the country in recent years. The Central Bank of Egypt [151] has cited the importance of art 206 of the Central Bank and Banking System Law promulgated by Law No. 194 of 2020. The law prohibits the issuance, trading, promotion, platforms, and other activities related to cryptos.

In 2018 the Reserve Bank of India [152] banned cryptocurrency trading and prohibited Indian banks from dealing with cryptocurrency exchanges following consumer protection, AML and market integrity concerns. In 2020, however, the Indian Supreme Court struck down the ban, and clarified that no prohibition exists.

Despite widespread concerns, skepticism, and prior bans on cryptocurrencies, India has encouraged innovation and the use of blockchain. It has also begun work on a state-backed CBDC, the digital rupee.

A proposed crypto regulatory framework was published [153] on the website of the Lok Sabha in 2021. The Cryptocurrency and Regulation of Official Digital Currency Bill, 2021 was dropped in the final days of the session but will likely resurface in the future.

The Advertising Standards Council of India announced new guidance [154] related to the advertising of cryptos and NFTs in February 2022. The new rules, which come into effect on April 1, prohibit the use of the words “currency, securities, custodian, and depositories” in advertisements, as consumers often associate the terms with regulated products.

[150]   https://www.cfatf-gafic.org/home/what-s-happening/649-cayman-islands-adopts-regulatory-framework-for-virtual-asset-services

[151]   https://www.cbe.org.eg/en/Pages/default.aspx

[152]   https://www.rbi.org.in/

[153]   http://loksabhadocs.nic.in/bull2mk/2021/23.11.21.pdf

[154]  https://ascionline.in/images/pdf/vda-guidelines-press-release-feb-23.pdf

The Iranian Central Bank [155] has authorized banks and currency exchanges to use crypto-currencies mined by licensed crypto miners in the county. Although mining is legal, the country takes a heavy-handed approach requiring firms to sell cryptos to the central bank to fund imports.

The country has issued more than 1,000 licenses to crypto miners and shut down unlicensed firms. Trading outside the country has been banned, to stop capital flight. The use of cryptos for payments has also been banned.

In early 2022, the country said [156] it was exploring the possible use of cryptos for international trade, which potentially would allow some businesses to make international payments using cryptos.

The Israeli Securities Authority has ruled that cryptocurrency is a security [157] (link in Hebrew) subject to Israel’s Securities Laws.

The regulator has warned [158] the public of the risks associated with cryptocurrencies.

On November 14, 2021, an anti-money laundering order [159] regulating transactions in digital currencies came into effect. The new law is seen as the first step toward the need for entities dealing in digital currencies to have a permanent operating license.

The Israel Money Laundering and Terror Financing Prohibition Authority has taken a similar approach to AML/CTF requirements as FATF.

The Israel Tax Authority defines cryptocurrency as an asset and levies 25% on capital gains.

The Central Bank of Kenya [160] issued a public notice in December 2015 warning that bitcoin and other cryptos are unregulated and not guaranteed by any government or central bank. The notice said no entity is licensed to offer money remittance services and products using virtual currencies.

Despite of lack of any regulatory framework, Kenya is considered as one of the leading markets for Bitcoin.

The Central Bank is reportedly considering a CBDC.

[155]  https://www.cbi.ir/default_en.aspx

[156]   https://www.mehrnews.com/news/5396149/

[157]   https://www.isa.gov.il/%d7%92%d7%95%d7%a4%d7%99%d7%9d%20%d7%9e%d7%a4%d7%95%d7%a7%d7%97%d7%99%d7%9d/Corporations/Staf_Positions/Preliminary_Inquiries/Prospectuses/Documents/T3121.pdf

[158]   https://www.isa.gov.il/sites/ISAEng/Pages/unregulated-investments.aspx

[159]   https://perma.cc/JN4X-F7P5

[160]   https://www.centralbank.go.ke/images/docs/media/Public_Notice_on_virtual_currencies_such_as_Bitcoin.pdf

Despite a law in 2017 banning cryptos in Morocco, the public continues to operate underground, circumventing the restrictions.

The Morocco Foreign Exchange Office [161] has said it does not support “hidden payment systems” not backed by government institutions. However, the country’s central bank has reportedly confirmed [162] , that it is exploring a CBDC.

The two primary financial regulators in Nigeria view cryptos differently. The Central Bank of Nigeria [163] has barred banks and financial institutions from dealing in cryptos. The central bank has argued that cryptos are unregulated and not legal tender. Meanwhile the Nigerian Securities and Exchange Commission [164] (SEC) has sought to regulate cryptocurrency investments on the grounds that they qualify as securities transactions.

Both regulators said they had identified certain risks within the digital asset sector, without explaining further.

The central bank has argued that cryptocurrencies, which are unregulated and not legal tender, are risky for the user.

Use of bitcoin, the original and biggest cryptocurrency, has boomed in Nigeria in recent years, especially among small businesses, as the weakening naira currency makes it difficult to get the U.S. dollars needed to import goods or services.

The Central Bank of Nigeria officially launched the “eNaira,” its CBDC, on October 25, 2021.

There is no Nigerian legislation clarifying the tax treatment of transactions involving virtual currencies.

[161]   https://www.finances.gov.ma/en/The_Ministry/Pages/The-Foreign-Exchange-Office.aspx

[162]   https://www.ledgerinsights.com/morocco-central-bank-confirms-exploring-digital-currency-cbdc/

[163]   https://www.cbn.gov.ng/

[164]   https://sec.gov.ng/

[165]   http://publication.pravo.gov.ru/Document/View/0001202007310056?index=0&rangeSize=1

In 2020, Russian President Vladimir Putin signed a law [165] that regulates digital financial asset transactions. Under the law, which took effect on January 1, 2021, digital currencies are recognized as a payment means and investment. The digital currency cannot be used to pay for any goods and services, however.

Digital currencies were previously banned. Russian banks and exchanges can become exchange operators of digital financial assets if they register with the Bank of Russia.

The Central Bank of Russia [166] has also begun a pilot program to develop a digital central bank currency, the Digital Ruble. The central bank has staunchly opposed cryptos, while Russia’s Ministry of Finance has pushed for regulations on cryptos.

The Ministry of Finance introduced a bill “On Digital Currency” [167] in February 2022, which creates a “mechanism for organizing the circulation of digital currencies.”

Despite the regulatory confusion, Russia is considered a significant player, and estimates peg Russian ownership of cryptos at approximately 12% of the international crypto economy.

Saudi Arabia

The Saudi Central Bank and Minster of Finance have warned [168] “against dealing or investing in virtual currencies including cryptocurrencies as they are not recognized by legal entities in the kingdom. They are outside the scope of the regulatory framework and are not traded by financial institutions locally. Such crypto currencies have been associated with fraudulent activities and attract concern that they may be used in illegal and illegitimate financial activities in addition to their high-investment risks related to frequent price fluctuations.”

While the Saudi Central Bank has warned the public of the risks associated with cryptocurrencies, and that they are not legal tender, bitcoin is accepted by small businesses and merchants, and the government has taken a very light regulatory approach thus far. In recent years, Saudi Arabia has worked with the United Arab Emirates to attract crypto companies to the region. Cryptos are sure to play and important role in the country’s long-term effort to diversify its economy and become an innovation hub — “Saudi Vision 2030.”

The Saudi Central Bank has begun to use blockchain technology in its activities in the banking sector and to keep pace with market trends. It has also created a regulatory sandbox [169] for collaboration on new digital banking services and blockchain education programs.

South Africa

The South African Reserve Bank [170] , the Financial Sector Conduct Authority (FCSA) and the National Treasury, together with an Intergovernmental FinTech Working Group [171] , have published plans to develop a registration regulatory framework. The plans would codify FATF AML recommendations.

The regulatory framework is expected in 2022 and comes as a response to major crypto scams where investors have been defrauded. The FCSA aims to also address how cryptos will interact with traditional financial services and overall financial stability. Virtual currency is not considered legal tender in South Africa.

The South African Revenue Service considers cryptocurrencies such as bitcoin to be intangible assets rather than currency or property. They are taxed as long-term or short-term income ranging from 18% to 40% allowing for deduction of costs.

United Arab Emirates

The UAE is estimated to be the third-largest crypto market in the Middle East, with total transaction values estimated at approximately $26 billion. The Dubai Financial Services Authority included a crypto regulatory framework in its 2021 business plan for firms operating in the Dubai International Financial Center.

In early 2022 the UAE announced a licensing program to be rolled out early in the year. The UAE also said it wants to build and attract a mining ecosystem in the region. The UAE Securities and Commodities Authority issued [172] its regulation in 2020, which seeks to provide clarity as to how crypto and other digital assets may be used as a stored value when purchasing various goods and services.

The Financial Services Regulatory Authority (FSRA) of Abu Dhabi Global Market has enhanced its “Guidance for the Regulation of Crypto Asset Activities.” [173]

The UAE and Saudi Arabia are reportedly working on research for a CBDC dubbed “Project Aber.”

[166]   https://www.cbr.ru/press/event/?id=9761

[167]   https://tass.com/economy/1406879?

[168]   https://www.sama.gov.sa/en-US/News/Pages/news21082019.aspx

[169]   https://www.sama.gov.sa/en-us/news/pages/news-575.aspx

[170]   https://www.resbank.co.za/en/home/quick-links/frequently-asked-questions

[171]   https://www.ifwg.co.za/Pages/default.aspx

[172]   https://www.sca.gov.ae/en/regulations/drafts.aspx#page=1

[173]   https://www.adgm.com/media/announcements/adgm-enhances-guidance-on-regulation-of-crypto-asset-activities

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About the authors

SUSANNAH HAMMOND

  Susannah Hammond is Senior Regulatory Intelligence Expert for Thomson Reuters Regulatory Intelligence with more than 25 years of wide-ranging compliance, regulatory and risk experience in international and UK financial services. She is co-author of “Conduct and Accountability in Financial Services: A Practical Guide” published by Bloomsbury Professional.

  Todd Ehret is a Senior Regulatory Intelligence Expert for Thomson Reuters Regulatory Intelligence. He has more than 25 years’ experience in the financial industry where he held key positions in trading, operations, accounting, audit, and compliance for broker-dealers, asset managers, private equity, and hedge funds. Before joining Thomson Reuters he served as a Chief Compliance Officer and Chief Operating Officer at a Registered Investment Adviser/Hedge Fund for nearly a decade.

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