The first system was Bitcoin  · Ethereum and Hyperledger Fabric smart contracts differ in multiple aspects. While Solidity is the well-known. Ethereum: A secure decentralized generalized transaction ledger [OL]. . bettingfootball.website Google Scholar. . We searched Google Scholar, ACM Digital Library, IEEE Xplore Digital Library, Springer Online Library, and arXiv, to find literature related to Ethereum. FOREX BROKER REVIEWS FXCM DEMO
Atzei et al. At the top, vulnerabilities are classified according to where they appear: in the source code usually Solidity , at machine level in the bytecode or related to instruction semantics , or at blockchain level. A mapping to actual examples of attacks and vulnerable smart contracts completes the taxonomy. Although this work is referenced in several other papers, we have found some issues and inconsistencies regarding the classification of concrete vulnerabilities.
For example, the vulnerability type called unpredictable state is illustrated by an example that is viewed in most other work as an instance of transaction order dependency. At the same time another example for problems associated with dynamic libraries is assigned to the same class.
It can be argued that these two examples exhibit different vulnerabilities, as the underlying causes are inherently different. Dika extends the taxonomy of Atzei et al. Grishchenko et al. If a bytecode satisfies such a property, it is provably free of the corresponding vulnerabilities.
As the properties usually are too complex to be established automatically, the authors consider simpler criteria that imply the properties. The project neither defines the listed vulnerabilities nor explains how the vulnerabilities were selected and ranked. Several studies like Durieux et al. Currently, the registry holds 36 vulnerabilities, with descriptions, references, suggestions for remediation and sample Solidity contracts.
While several taxonomies build on the early classification of Atzei et al. EVM vs. Solidity or cause vs. So far, none of the taxonomies has seen wide adoption. Table 7 maps our ten classes, omitting vulnerabilities that have no counterpart in the other taxonomies. We find a correspondence for 34 vulnerabilities, while 20 vulnerabilities documented in literature remain uncovered. Mapping of classifications for vulnerabilities.
The mapping is not exact in the sense that categories in the same line of the table may overlap only partially. SWC covers a range of 36 vulnerabilities, but 22 of our categories are missing. Both community classifications seem inactive: SWC was last updated in March , and the DASP 10 website with the first iteration of the project is dated For other summaries, differing in breadth and depth, see the surveys Almakhour et al.
We discuss four groups of methods: static code analysis, dynamic code analysis, formal specification and verification, and miscellany. The distinction between static analysis and formal methods is to some extent arbitrary, as the latter are mostly used in a static context. Moreover, methods like symbolic execution regularly use formal methods as a black box.
A key difference is the aspiration of formal methods to be rigorous, requiring correctness and striving for completeness. In this sense abstract interpretation should be rather considered a formal method, but it resembles symbolic execution and therefore is presented there. The analysis starts either from the source or the machine code of the contract.
In most cases, the aim is to identify code patterns that indicate vulnerabilities. Some tools also compute input data to trigger the suspected vulnerability and check whether the attack has been effective, thereby eliminating false positives. To put the various methods into perspective, we take a closer look at the process of compiling a program from a high-level language like Solidity to machine code Aho et al.
The sequence of characters first becomes a stream of lexical tokens comprising e. The parser transforms the linear stream of tokens into an abstract syntax tree AST and performs semantic checks. Now several rounds of code analysis, code optimization, and code instrumentation may take place, with the output in each round again in IR. This last step linearizes any hierarchical structures left, by arranging code fragments into a sequence and by converting control flow dependencies to jump instructions.
Such representations are readily available when starting from source code, as AST and IR are by-products of compilation. This approach is fast, but lacks accuracy if a vulnerability cannot be adequately characterized by such patterns. Recovering a control flow graph CFG from machine code is inherently more complex.
Its nodes correspond to the basic blocks of a program. A basic block is a sequence of instructions executed linearly one after the other, ending with the first instruction that potentially alters the flow of control, must notably conditional and unconditional jumps. Nodes are connected by a directed edge if the corresponding basic blocks may be executed one after the other.
The reachability of code is difficult to determine, as indirect jumps retrieve the target address from a register or the stack, where it has been stored by an earlier computation. Backward slicing resolves many situations by tracking down the origins of the jump targets. If this fails, the analysis has the choice between over- and under-approximation, by either treating all blocks as potential successors or by ignoring the undetectable successors.
Some tools go on by transforming the CFG and a specification of the vulnerability to a restricted form of Horn Logic called DataLog, which is not computationally universal, but admits efficient reasoning algorithms see e. Soufle, Starting from the CFG, decompilation attempts to reverse also the other phases of the compilation process, with the aim to obtain source from machine code. The result is intended for manual inspection by humans, as it usually is not fully functional and does not compile.
Any operation on such symbols results in a symbolic expression that is passed to the next operation. In the case of a fork, all branches are explored, but they are annotated with complementary symbolic conditions that restrict the symbols to those values that will lead to the execution of the particular branch.
At intervals, an SMT Satisfiability Modulo Theory solver is invoked to check whether the constraints on the current path are still simultaneously satisfiable. If they are contradictory, the path does not correspond to an actual execution trace and can be skipped. Otherwise, exploration continues. When symbolic execution reaches code that matches a vulnerability pattern, a potential vulnerability is reported. If, in addition, the SMT solver succeeds in computing a satisfying assignment for the constraints on the path, it can be used to devise an exploit that verifies the existence of the vulnerability.
The effectiveness of symbolic execution is limited by several factors. First, the number of paths grows exponentially with depth, so the analysis has to stop at a certain point. Second, some aspects of the machine are difficult to model precisely, like the relationship between storage and memory cells, or complex operations like hash functions.
Third, SMT solvers are limited to certain types of constraints, and even for these, the evaluation may time out instead of detecting un satisfiability. Symbolic execution of the same path then yields formal constraints characterizing the path. After negating some constraint, the SMT solver searches for a satisfying assignment. Using it as the input for the next cycle leads, by construction, to the exploration of a new path. This way, concolic execution achieves a better coverage of the code.
Propagation rules define how tags are transformed by the instructions. Some vulnerabilities can be identified by inspecting the tags arriving at specific code locations. Taint analysis is often used in combination with other methods, like symbolic execution.
They may report vulnerabilities where there are none false positives, unsoundness , and may fail to detect vulnerabilities present in the code false negatives, incompleteness. The first limitation arises from the inability to specify necessary conditions for the presence of vulnerabilities that can be effectively checked.
The second one is a consequence of the infeasibly large number of computation paths to explore, and the difficulty to come up with sufficient conditions that can be checked. Abstract interpretation Cousot and Cousot, aims at completeness by focusing on properties that can be evaluated for all execution traces.
As an example, abstract interpretation may split the integer range into the three groups zero, positive, and negative values. Instead of using symbolic expressions to capture the precise result of instructions, abstract interpretation reasons about how the property of belonging to one of the three groups propagates with each instruction. This way it may be possible to show that the divisors in the code always belong to the positive group, ruling out division by zero, for any input. The challenge is to come up with a property that is strong enough to entail the absence of a particular vulnerability, but weak enough to allow for the exploration of the search space.
Contrary to symbolic execution and most other methods, this approach does not indicate the presence of a vulnerability, but proves that a contract is definitely free from a certain vulnerability safety guarantee. The most common method is testing, where the code is run with selected inputs and its output is compared to the expected result. Fuzzing is a technique that runs a program with a large number of randomized inputs, in order to provoke crashes or otherwise unexpected behavior.
Code instrumentation augments the program with additional instructions that check for abnormal behavior or monitor performance during runtime. An attempt to exploit a vulnerability then may trigger an exception and terminate execution. As an example, a program could be systematically extended by assertions ensuring that arithmetic operations do not cause an overflow. Machine instrumentation is similar to code instrumentation, but adds the additional checks on machine level, enforcing them for all contracts.
Some authors go even further by proposing changes to the transaction semantics or the Ethereum protocol, in order to prevent vulnerabilities. While interesting from a conceptual point of view, such proposals are difficult to realize, as they require a hard fork affecting also the contracts already deployed. Mutation testing is a technique that evaluates the quality of test suites. The source code of a program is subjected to small syntactic changes, known as mutations, which mimic common errors in software development.
For example, a mutation might change a mathematical operator or negate a logical condition. If a test suite is able to detect such artificial mistakes, it is more likely that it also finds real programming errors. Modeling smart contracts on an even higher level of abstraction offers additional benefits, like formal proofs of contract properties. The core logic of many blockchain applications can be modeled as finite state machines FSMs , with constraints guarding the transitions.
As FSMs are simple formal objects, techniques like model checking can be used to verify properties specified in variants of computation tree logic. Once the model is finished, tools translate the FSM to conventional source code, where additional functionality can be added. The high cost of errors and the small size of blockchain programs makes them a promising target for formal verification approaches. Unlike testing, which detects the presence of bugs, formal verification aims at proving the absence of bugs and vulnerabilities.
As a necessary prerequisite, the execution environment and the semantics of the programming language or the machine need to be formalized. Then functional and security properties can be added, expressed in some specification language. Finally, automated theorem provers or semi-automatic proof assistants can be used to show that the given program satisfies the properties. Bhargavan et al. From the specification, the K framework is able to generate tools like interpreters and model-checkers, but also deductive program verifiers.
Horn logic is a restricted form of first-order logic, but still computationally universal. It forms the basis of logic-oriented programming and is attractive as a specification language, as Horn formulas can be read as if-then rules. Techniques like long-short term memory LSTM modeling, convolution neural networks or N-gram language models may achieve high test accuracy. A common challenge is to obtain a labeled training set that is large enough and of sufficient quality.
Formal reasoning and constraint solving is most frequently employed, due to the many tools integrating formal methods as a black box, like constraint solvers to prune the search space or Datalog reasoners to check intermediate representations. Proper formal verification, automated or via proof assistants, is rare, even though smart contracts, due to their limited size and the value at stake, seem to be a promising application domain. This may be due to the specific knowledge required for this approach.
Number of analysis tools employing a particular method. Next in popularity are the construction of control flow graphs 46, In the Supplementary Material , we describe the tools and list their functionalities and methods. Number of analysis tools providing a particular functionality. Code level. More than half of the tools analyze Solidity code 86, More than half of the tools 79, Some tools go the extra length of verifying the vulnerabilities they found by providing exploits or suggesting remedies.
Almost a third 41, Analysis Type. The vast majority of tools , The development of new tools has increased rapidly since , with more than half of them published open source. Over a third of the open source tools 25 received updates in , while 19 tools were updated within the first 7 months of Publication and maintenance of tools. The numbers for include the first 7 months only. Many tools were developed as a proof-of-concept for a method described in a scientific publication, and have not been maintained since their release.
While this is common practice in academia, potential users prefer tools that are maintained and where reported issues are addressed in a timely manner. Table 8 lists twenty tools that have been around for some time, are maintained, and are apparently used. More precisely, we include a tool if it was released in or earlier, shows continuous update activities, and has some filed issues that were addressed by the authors of the tool.
We exclude newer tools, since they have not yet a substantial maintenance history. Tools published in or before that are maintained and in use. For each of them, Table 9 lists the publication, the project name and a link to a repository if available. The number of contracts per test set fourth column varies between 6 and 47 More than half of the projects also include contracts that were manually verified to be true or false positives with respect to some property, in order to serve as a ground truth.
Their number is given in the fifth column. Three collections additionally provide exploits, marked in the last column. The first group in the table refers to collections of vulnerable contracts written in Solidity. The second group in this table comprises projects aimed at the analysis of vulnerabilities, but without accompanying tool. They partially provide Ethereum addresses, Solidity sources, and analysis results.
The third and largest group consists of tools that provide test data. Two tools, VerX and Zeus, offer only analysis results, but neither the bytecode, the source code nor the deployment address of the contracts analyzed, which makes it hard to verify the results. While reentrancy is an early and well analyzed vulnerability, most others have received significantly less attention. This uneven coverage is also reflected in the tools, which address vulnerabilities in diverse combinations, with reentrancy being the most prominent one.
Collaborative efforts like the SWC registry are valuable resources, but as plain collections lack structural information and usability. We find several proposals from the community and from scholars regarding Ethereum-specific taxonomies, none of which can be considered established. Despite the narrow scope, blockchain and Ethereum, we do not perceive a convergence of taxonomies. In fact, the proposed taxonomies often are complementary rather than extensions or refinements.
This makes it difficult to map the different taxonomies to each other and leaves room for discussion. One reason may be the continued rapid development on all levels, including blockchain protocols and blockchain programming. Another one are the different angles for categorizing vulnerabilities. For detection, it is natural to consider the causes of vulnerabilities, as this is what tools can search for, like storage locations accessible to anyone.
A second dimension are the effects of vulnerabilities, like denial of service or unauthorized withdrawal. Different causes can result in the same effect, while a technical cause may contribute to various effects. To this end, blockchain is considered as a promising technology that aims at addressing the aforementioned challenges by eliminating the single point of failure, enhancing transparency, and enforcing rules using smart contracts.
In this context, several studies using blockchain-enabled smart contracts [ 38 , 87 , 97 , ] have been proposed, as shown in Table For instance, Zichichi et al. Smart contracts are used to control and manage funds without the need for a trusted third entity. LikeStarter assigns Likoins i. These tokens can be employed and converted to buy artifacts and they provide users with voting capabilities i.
Non-profit-centric solutions Blockchain technology is needed in a cross-organizational collaboration area suffering from a decline in trust from involved parties e. Table 13 presents some examples of non-profit-centric solutions including volunteer system [ 19 ], philanthropic-related systems [ 32 , 82 , 91 , 94 ], e-voting service [ 75 ], system for educational institutions [ 86 ], and copyright protection [ ].
For instance, Cheng et al. VOLTimebank provides a channel for volunteers to serve the elderly and gives volunteers a way to exchange the services they can offer today with the services that they hope to get in the future. Thus, some efforts have been made to map the charity collection process on blockchain technology, for instance, Farooq et al. The design uses smart contracts and digital wallets to transfer money in real-time with complete data security and an auditable trail of every transaction.
These smart contracts have been introduced to securely transfer donations to individual beneficiaries, organization, and their associated projects. The deployed smart contract is responsible for sharing the copyright parameters. Despite the benefits of blockchain and smart contracts in reforming operations in a wide variety of industries, namely supply chain, insurance, and charity, certain challenges to their widespread adoption still exist.
These challenges include legal issues, lack of standards and protocols, privacy issues, and error intolerance. Arguments that smart contracts are no panacea for all financial use cases doubt the applicability of smart contracts to certain scenarios as far as agreement type and scale. Discussion We discuss below the study results and present challenges and future development trends in smart contract research. Other legal issues can be cited including, i each country has its own laws and regulations, hence, it is complicated to ensure compliance will all regulations, ii law clauses or conditions are not quantifiable, thus it is still complicated to model these conditions in smart contracts so that they are appropriate and quantifiable for a machine to execute them, and iii governments are interested in a regulated and controlled use of the blockchain technology in many applications, however, this means that the untrustworthy network will regress to a third-party trusted network, losing part of its essence [ 79 ].
For this purpose, oracles are used as trusted third parties that retrieve off-chain information and then push that information to the blockchain at predetermined times. For instance, an oracle might be unable to push out the necessary information, provide erroneous data, or go out of business. Therefore, smart contracts will need to account for these eventualities before their adoption can become more widespread [ 51 ].
Immutability issue The immutability feature is an important characteristic of smart contracts. Indeed, once a smart contract is deployed, the code cannot be changed by any party. However, the dark side of the immutability concept in smart contracts lies mainly in the fact that in the event of any errors made in the code, the immutability feature of a smart contract prevents it from being rectified.
Similarly, if circumstances change e. Therefore, extensive and possibly expensive reviews of the smart contract performed by experts before its deployment in a blockchain are required to address the immutability issue. Another limitation in the blockchain itself that impacts the smart contracts is the irreversible nature of the blockchain, thus once the smart contracts are deployed, they cannot be changed.
Moreover, any blockchain nodes can be hacked or misused to report erroneous data that will be logged on the blockchain in an immutable manner. Scalability issue Scalability is the primary concern for many blockchain networks. For instance, the Ethereum blockchain can verify 14 transactions per second, which is slow as compared with Visa that can handle up to 24, transactions per second.
Indeed, the scalability issue leads to network congestion, increased commission fees for transactions, and an increase in the time required to confirm the transactions [ 80 ]. In order to address the scalability issue, extensive research focusing on increasing the number of transactions per second by smart contract platforms is required in the future. However, the transaction verification depends on the consensus mechanism used by the smart contract platforms.
Therefore, scalability depends on consensus mechanisms, which is another issue in smart contracts. Consensus mechanism issue The consensus mechanism plays the leading role to maintain security, scalability, and decentralization in the blockchain networks at the same time. Although the PoW algorithm enables security in the blockchain, it wastes resources.
Thus, many organizations switch from the PoW algorithm to new consensus mechanisms that promise lower fees for transactions as well as lower energy costs for the block production process. Therefore, future studies can use new consensus mechanisms, such as proof-of-activity PoA or delegated proof-of-stake DPoS in order to test them and eventually improve their quality. Future Development Trends Future development trends of smart contracts are introduced from two aspects namely, Layer 2 protocols, and contract management solutions.
Layer 2 protocols In order to address the aforementioned challenges faced by smart contracts, a viable solution, called Layer 2 is appeared to tackle the blockchain scalability problem. While Layer 1 is the used term to describe the underlying main blockchain architecture, Layer 2 is an overlaying network that lies on top of the underlying blockchain.
Indeed, Layer 2 refers to the multiple solutions or protocols being built on top of an existing blockchain system. The main goal of Layer 2 protocols is to solve the transaction speed and scaling difficulties that are being faced by the major cryptocurrency networks. Ethereum Plasma is a series of smart contracts, which allows for many blockchains within a root blockchain.
The root blockchain enforces the state in the Plasma chain. The root chain is the enforcer of all computation globally but is only computed and penalized if there is proof of fraud. Many Plasma blockchains can co-exist with their business logic and smart contract terms.
Indeed, Plasma enables persistently operating decentralized applications at a high scale [ 76 ]. Contract management solutions Smart contracts encompass far more than just the benefits of blockchain technology.
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Bouri et al. 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. Relatedly, Adhami and Guegan 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. 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 Cryptocurrency prices may drop dramatically because of a revealed scam or suspected hack, or other hidden problems.
As a consequence, a cryptocoin may become illiquid and its value may substantially decline. Fantazzini and Zimin 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 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.
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 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 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. Obtaining information is crucial to reduce uncertainty. 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.
Uncertainty and attitudes to it are not the only reasons why neoclassical predictions may fail. Shiller 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.
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. 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.
The contribution by Moosa highlights that the Bitcoin was in a bubble up to the end of 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.
Socio-economic perspectives Critiques emphasize cryptocurrencies are not exempt from frauds and scandals. For example, several millions in Bitcoin from the Japanese platform Mt. Moreover, cryptocurrency payments, being largely unregulated, do not restrict any purchases, including those illegal. Foley et al. On the positive side, the development of the cryptocurrency market contributes to the dynamics of access to finance Adhami et al.
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. 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. The latter is achieved through voluntary disclosure of information including the operating budget and their business plans , and quality signaling e.
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 This has been widely discussed by practitioners and investors, with a great variety of views.
For example, The World Economic Forum White Paper WEF , 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 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.
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. 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.
Some recent research already draws attention to the functioning of cryptoexchanges. For example, Gandal et al. 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. 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 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 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 , it does not matter for the pricing of existing coins. Simultaneously, Schilling and Uhlig 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.
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? 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 ; Schilling and Uhlig Footnote 16 Cukierman , building on the analysis by Roubini , 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. In general, there are two types of accounts: externally owned accounts, controlled by private keys, and contract accounts, controlled by their contract code.
An externally owned account has no code, and one can send messages from an externally owned account by creating and signing a transaction; in a contract account, every time the contract account receives a message its code activates, allowing it to read and write to internal storage and send other messages or create contracts in turn.
Messages and Transactions The term "transaction" is used in Ethereum to refer to the signed data package that stores a message to be sent from an externally owned account. Transactions contain: The recipient of the message A signature identifying the sender The amount of ether to transfer from the sender to the recipient An optional data field A STARTGAS value, representing the maximum number of computational steps the transaction execution is allowed to take A GASPRICE value, representing the fee the sender pays per computational step The first three are standard fields expected in any cryptocurrency.
The data field has no function by default, but the virtual machine has an opcode using which a contract can access the data; as an example use case, if a contract is functioning as an on-blockchain domain registration service, then it may wish to interpret the data being passed to it as containing two "fields", the first field being a domain to register and the second field being the IP address to register it to.
The contract would read these values from the message data and appropriately place them in storage. In order to prevent accidental or hostile infinite loops or other computational wastage in code, each transaction is required to set a limit to how many computational steps of code execution it can use. The fundamental unit of computation is "gas"; usually, a computational step costs 1 gas, but some operations cost higher amounts of gas because they are more computationally expensive, or increase the amount of data that must be stored as part of the state.
There is also a fee of 5 gas for every byte in the transaction data. The intent of the fee system is to require an attacker to pay proportionately for every resource that they consume, including computation, bandwidth and storage; hence, any transaction that leads to the network consuming a greater amount of any of these resources must have a gas fee roughly proportional to the increment.
Messages Contracts have the ability to send "messages" to other contracts. Messages are virtual objects that are never serialized and exist only in the Ethereum execution environment. A message contains: The sender of the message implicit The recipient of the message The amount of ether to transfer alongside the message An optional data field A STARTGAS value Essentially, a message is like a transaction, except it is produced by a contract and not an external actor. A message is produced when a contract currently executing code executes the CALL opcode, which produces and executes a message.
Like a transaction, a message leads to the recipient account running its code. Thus, contracts can have relationships with other contracts in exactly the same way that external actors can. Note that the gas allowance assigned by a transaction or contract applies to the total gas consumed by that transaction and all sub-executions. For example, if an external actor A sends a transaction to B with gas, and B consumes gas before sending a message to C, and the internal execution of C consumes gas before returning, then B can spend another gas before running out of gas.
If not, return an error. Subtract the fee from the sender's account balance and increment the sender's nonce. If there is not enough balance to spend, return an error. Transfer the transaction value from the sender's account to the receiving account.
If the receiving account does not yet exist, create it. If the receiving account is a contract, run the contract's code either to completion or until the execution runs out of gas. If the value transfer failed because the sender did not have enough money, or the code execution ran out of gas, revert all state changes except the payment of the fees, and add the fees to the miner's account.
Otherwise, refund the fees for all remaining gas to the sender, and send the fees paid for gas consumed to the miner. For example, suppose that the contract's code is: if! Suppose that the contract's storage starts off empty, and a transaction is sent with 10 ether value, gas, 0. The process for the state transition function in this case is as follows: Check that the transaction is valid and well formed.
If it is, then subtract 2 ether from the sender's account. Subtract 10 more ether from the sender's account, and add it to the contract's account. Run the code. In this case, this is simple: it checks if the contract's storage at index 2 is used, notices that it is not, and so it sets the storage at index 2 to the value CHARLIE. If there was no contract at the receiving end of the transaction, then the total transaction fee would simply be equal to the provided GASPRICE multiplied by the length of the transaction in bytes, and the data sent alongside the transaction would be irrelevant.
Note that messages work equivalently to transactions in terms of reverts: if a message execution runs out of gas, then that message's execution, and all other executions triggered by that execution, revert, but parent executions do not need to revert. This means that it is "safe" for a contract to call another contract, as if A calls B with G gas then A's execution is guaranteed to lose at most G gas.
Finally, note that there is an opcode, CREATE, that creates a contract; its execution mechanics are generally similar to CALL, with the exception that the output of the execution determines the code of a newly created contract.
Code Execution The code in Ethereum contracts is written in a low-level, stack-based bytecode language, referred to as "Ethereum virtual machine code" or "EVM code". The code consists of a series of bytes, where each byte represents an operation.
In general, code execution is an infinite loop that consists of repeatedly carrying out the operation at the current program counter which begins at zero and then incrementing the program counter by one, until the end of the code is reached or an error or STOP or RETURN instruction is detected.
Unlike stack and memory, which reset after computation ends, storage persists for the long term. The code can also access the value, sender and data of the incoming message, as well as block header data, and the code can also return a byte array of data as an output. The formal execution model of EVM code is surprisingly simple.
For example, ADD pops two items off the stack and pushes their sum, reduces gas by 1 and increments pc by 1, and SSTORE pushes the top two items off the stack and inserts the second item into the contract's storage at the index specified by the first item. Although there are many ways to optimize Ethereum virtual machine execution via just-in-time compilation, a basic implementation of Ethereum can be done in a few hundred lines of code.
Blockchain and Mining The Ethereum blockchain is in many ways similar to the Bitcoin blockchain, although it does have some differences. The main difference between Ethereum and Bitcoin with regard to the blockchain architecture is that, unlike Bitcoin, Ethereum blocks contain a copy of both the transaction list and the most recent state.
Aside from that, two other values, the block number and the difficulty, are also stored in the block. The basic block validation algorithm in Ethereum is as follows: Check if the previous block referenced exists and is valid. Check that the timestamp of the block is greater than that of the referenced previous block and less than 15 minutes into the future Check that the block number, difficulty, transaction root, uncle root and gas limit various low-level Ethereum-specific concepts are valid.
Check that the proof-of-work on the block is valid. Let TX be the block's transaction list, with n transactions. If it is, the block is valid; otherwise, it is not valid. The approach may seem highly inefficient at first glance, because it needs to store the entire state with each block, but in reality efficiency should be comparable to that of Bitcoin. The reason is that the state is stored in the tree structure, and after every block only a small part of the tree needs to be changed.
Thus, in general, between two adjacent blocks the vast majority of the tree should be the same, and therefore the data can be stored once and referenced twice using pointers ie. A special kind of tree known as a "Patricia tree" is used to accomplish this, including a modification to the Merkle tree concept that allows for nodes to be inserted and deleted, and not just changed, efficiently.
Additionally, because all of the state information is part of the last block, there is no need to store the entire blockchain history - a strategy which, if it could be applied to Bitcoin, can be calculated to provide x savings in space. A commonly asked question is "where" contract code is executed, in terms of physical hardware. This has a simple answer: the process of executing contract code is part of the definition of the state transition function, which is part of the block validation algorithm, so if a transaction is added into block B the code execution spawned by that transaction will be executed by all nodes, now and in the future, that download and validate block B.
Applications In general, there are three types of applications on top of Ethereum. The first category is financial applications, providing users with more powerful ways of managing and entering into contracts using their money. This includes sub-currencies, financial derivatives, hedging contracts, savings wallets, wills, and ultimately even some classes of full-scale employment contracts.
The second category is semi-financial applications, where money is involved but there is also a heavy non-monetary side to what is being done; a perfect example is self-enforcing bounties for solutions to computational problems. Finally, there are applications such as online voting and decentralized governance that are not financial at all. Token Systems On-blockchain token systems have many applications ranging from sub-currencies representing assets such as USD or gold to company stocks, individual tokens representing smart property, secure unforgeable coupons, and even token systems with no ties to conventional value at all, used as point systems for incentivization.
Token systems are surprisingly easy to implement in Ethereum. The key point to understand is that all a currency, or token system, fundamentally is, is a database with one operation: subtract X units from A and give X units to B, with the proviso that i A had at least X units before the transaction and 2 the transaction is approved by A. All that it takes to implement a token system is to implement this logic into a contract. The basic code for implementing a token system in Serpent looks as follows: def send to, value : if self.
A few extra lines of code need to be added to provide for the initial step of distributing the currency units in the first place and a few other edge cases, and ideally a function would be added to let other contracts query for the balance of an address. But that's all there is to it. Theoretically, Ethereum-based token systems acting as sub-currencies can potentially include another important feature that on-chain Bitcoin-based meta-currencies lack: the ability to pay transaction fees directly in that currency.
The way this would be implemented is that the contract would maintain an ether balance with which it would refund ether used to pay fees to the sender, and it would refill this balance by collecting the internal currency units that it takes in fees and reselling them in a constant running auction. Users would thus need to "activate" their accounts with ether, but once the ether is there it would be reusable because the contract would refund it each time.
Financial derivatives and Stable-Value Currencies Financial derivatives are the most common application of a "smart contract", and one of the simplest to implement in code. The simplest way to do this is through a "data feed" contract maintained by a specific party eg. NASDAQ designed so that that party has the ability to update the contract as needed, and providing an interface that allows other contracts to send a message to that contract and get back a response that provides the price.
Given that critical ingredient, the hedging contract would look as follows: Wait for party A to input ether. Wait for party B to input ether. Such a contract would have significant potential in crypto-commerce. Up until now, the most commonly proposed solution has been issuer-backed assets; the idea is that an issuer creates a sub-currency in which they have the right to issue and revoke units, and provide one unit of the currency to anyone who provides them offline with one unit of a specified underlying asset eg.
The issuer then promises to provide one unit of the underlying asset to anyone who sends back one unit of the crypto-asset. This mechanism allows any non-cryptographic asset to be "uplifted" into a cryptographic asset, provided that the issuer can be trusted. In practice, however, issuers are not always trustworthy, and in some cases the banking infrastructure is too weak, or too hostile, for such services to exist.
Financial derivatives provide an alternative. Here, instead of a single issuer providing the funds to back up an asset, a decentralized market of speculators, betting that the price of a cryptographic reference asset eg. ETH will go up, plays that role. Unlike issuers, speculators have no option to default on their side of the bargain because the hedging contract holds their funds in escrow. Note that this approach is not fully decentralized, because a trusted source is still needed to provide the price ticker, although arguably even still this is a massive improvement in terms of reducing infrastructure requirements unlike being an issuer, issuing a price feed requires no licenses and can likely be categorized as free speech and reducing the potential for fraud.
Identity and Reputation Systems The earliest alternative cryptocurrency of all, Namecoin , attempted to use a Bitcoin-like blockchain to provide a name registration system, where users can register their names in a public database alongside other data.
The major cited use case is for a DNS system, mapping domain names like "bitcoin. Other use cases include email authentication and potentially more advanced reputation systems. Here is the basic contract to provide a Namecoin-like name registration system on Ethereum: def register name, value : if! Anyone can register a name with some value, and that registration then sticks forever. A more sophisticated name registration contract will also have a "function clause" allowing other contracts to query it, as well as a mechanism for the "owner" ie.
One can even add reputation and web-of-trust functionality on top. Decentralized File Storage Over the past few years, there have emerged a number of popular online file storage startups, the most prominent being Dropbox, seeking to allow users to upload a backup of their hard drive and have the service store the backup and allow the user to access it in exchange for a monthly fee.
However, at this point the file storage market is at times relatively inefficient; a cursory look at various existing solutions shows that, particularly at the "uncanny valley" GB level at which neither free quotas nor enterprise-level discounts kick in, monthly prices for mainstream file storage costs are such that you are paying for more than the cost of the entire hard drive in a single month.
Ethereum contracts can allow for the development of a decentralized file storage ecosystem, where individual users can earn small quantities of money by renting out their own hard drives and unused space can be used to further drive down the costs of file storage.
The key underpinning piece of such a device would be what we have termed the "decentralized Dropbox contract". This contract works as follows. First, one splits the desired data up into blocks, encrypting each block for privacy, and builds a Merkle tree out of it. One then makes a contract with the rule that, every N blocks, the contract would pick a random index in the Merkle tree using the previous block hash, accessible from contract code, as a source of randomness , and give X ether to the first entity to supply a transaction with a simplified payment verification-like proof of ownership of the block at that particular index in the tree.
When a user wants to re-download their file, they can use a micropayment channel protocol eg. An important feature of the protocol is that, although it may seem like one is trusting many random nodes not to decide to forget the file, one can reduce that risk down to near-zero by splitting the file into many pieces via secret sharing, and watching the contracts to see each piece is still in some node's possession. If a contract is still paying out money, that provides a cryptographic proof that someone out there is still storing the file.
The members would collectively decide on how the organization should allocate its funds. Methods for allocating a DAO's funds could range from bounties, salaries to even more exotic mechanisms such as an internal currency to reward work. This essentially replicates the legal trappings of a traditional company or nonprofit but using only cryptographic blockchain technology for enforcement.
The requirement that one person can only have one membership would then need to be enforced collectively by the group. A general outline for how to code a DAO is as follows. The simplest design is simply a piece of self-modifying code that changes if two thirds of members agree on a change. Although code is theoretically immutable, one can easily get around this and have de-facto mutability by having chunks of the code in separate contracts, and having the address of which contracts to call stored in the modifiable storage.
In a simple implementation of such a DAO contract, there would be three transaction types, distinguished by the data provided in the transaction: [0,i,K,V] to register a proposal with index i to change the address at storage index K to value V [1,i] to register a vote in favor of proposal i [2,i] to finalize proposal i if enough votes have been made The contract would then have clauses for each of these. It would maintain a record of all open storage changes, along with a list of who voted for them.
It would also have a list of all members. When any storage change gets to two thirds of members voting for it, a finalizing transaction could execute the change. A more sophisticated skeleton would also have built-in voting ability for features like sending a transaction, adding members and removing members, and may even provide for Liquid Democracy -style vote delegation ie.
This design would allow the DAO to grow organically as a decentralized community, allowing people to eventually delegate the task of filtering out who is a member to specialists, although unlike in the "current system" specialists can easily pop in and out of existence over time as individual community members change their alignments.
An alternative model is for a decentralized corporation, where any account can have zero or more shares, and two thirds of the shares are required to make a decision. A complete skeleton would involve asset management functionality, the ability to make an offer to buy or sell shares, and the ability to accept offers preferably with an order-matching mechanism inside the contract. Delegation would also exist Liquid Democracy-style, generalizing the concept of a "board of directors".
Further Applications 1. Savings wallets. Suppose that Alice wants to keep her funds safe, but is worried that she will lose or someone will hack her private key. Alice and Bob together can withdraw anything. If Alice's key gets hacked, she runs to Bob to move the funds to a new contract. If she loses her key, Bob will get the funds out eventually. If Bob turns out to be malicious, then she can turn off his ability to withdraw.
Crop insurance. One can easily make a financial derivatives contract but using a data feed of the weather instead of any price index. If a farmer in Iowa purchases a derivative that pays out inversely based on the precipitation in Iowa, then if there is a drought, the farmer will automatically receive money and if there is enough rain the farmer will be happy because their crops would do well. This can be expanded to natural disaster insurance generally.
A decentralized data feed. For financial contracts for difference, it may actually be possible to decentralize the data feed via a protocol called " SchellingCoin ". SchellingCoin basically works as follows: N parties all put into the system the value of a given datum eg. Everyone has the incentive to provide the answer that everyone else will provide, and the only value that a large number of players can realistically agree on is the obvious default: the truth.
Smart multisignature escrow. Bitcoin allows multisignature transaction contracts where, for example, three out of a given five keys can spend the funds. Additionally, Ethereum multisig is asynchronous - two parties can register their signatures on the blockchain at different times and the last signature will automatically send the transaction. Cloud computing. The EVM technology can also be used to create a verifiable computing environment, allowing users to ask others to carry out computations and then optionally ask for proofs that computations at certain randomly selected checkpoints were done correctly.
This allows for the creation of a cloud computing market where any user can participate with their desktop, laptop or specialized server, and spot-checking together with security deposits can be used to ensure that the system is trustworthy ie.
Although such a system may not be suitable for all tasks; tasks that require a high level of inter-process communication, for example, cannot easily be done on a large cloud of nodes. Other tasks, however, are much easier to parallelize; projects like SETI home, folding home and genetic algorithms can easily be implemented on top of such a platform.
Peer-to-peer gambling. Any number of peer-to-peer gambling protocols, such as Frank Stajano and Richard Clayton's Cyberdice , can be implemented on the Ethereum blockchain. The simplest gambling protocol is actually simply a contract for difference on the next block hash, and more advanced protocols can be built up from there, creating gambling services with near-zero fees that have no ability to cheat.
Prediction markets. Provided an oracle or SchellingCoin, prediction markets are also easy to implement, and prediction markets together with SchellingCoin may prove to be the first mainstream application of futarchy as a governance protocol for decentralized organizations. On-chain decentralized marketplaces, using the identity and reputation system as a base.
The motivation behind GHOST is that blockchains with fast confirmation times currently suffer from reduced security due to a high stale rate - because blocks take a certain time to propagate through the network, if miner A mines a block and then miner B happens to mine another block before miner A's block propagates to B, miner B's block will end up wasted and will not contribute to network security.
Thus, if the block interval is short enough for the stale rate to be high, A will be substantially more efficient simply by virtue of its size. With these two effects combined, blockchains which produce blocks quickly are very likely to lead to one mining pool having a large enough percentage of the network hashpower to have de facto control over the mining process.
As described by Sompolinsky and Zohar, GHOST solves the first issue of network security loss by including stale blocks in the calculation of which chain is the "longest"; that is to say, not just the parent and further ancestors of a block, but also the stale descendants of the block's ancestor in Ethereum jargon, "uncles" are added to the calculation of which block has the largest total proof-of-work backing it. To solve the second issue of centralization bias, we go beyond the protocol described by Sompolinsky and Zohar, and also provide block rewards to stales: a stale block receives Transaction fees, however, are not awarded to uncles.
It cannot be an ancestor of B An uncle must be a valid block header, but does not need to be a previously verified or even valid block An uncle must be different from all uncles included in previous blocks and all other uncles included in the same block non-double-inclusion For every uncle U in block B, the miner of B gets an additional 3.
This limited version of GHOST, with uncles includable only up to 7 generations, was used for two reasons. First, unlimited GHOST would include too many complications into the calculation of which uncles for a given block are valid. Second, unlimited GHOST with compensation as used in Ethereum removes the incentive for a miner to mine on the main chain and not the chain of a public attacker.
Fees Because every transaction published into the blockchain imposes on the network the cost of needing to download and verify it, there is a need for some regulatory mechanism, typically involving transaction fees, to prevent abuse. The default approach, used in Bitcoin, is to have purely voluntary fees, relying on miners to act as the gatekeepers and set dynamic minimums. This approach has been received very favorably in the Bitcoin community particularly because it is "market-based", allowing supply and demand between miners and transaction senders determine the price.
The problem with this line of reasoning is, however, that transaction processing is not a market; although it is intuitively attractive to construe transaction processing as a service that the miner is offering to the sender, in reality every transaction that a miner includes will need to be processed by every node in the network, so the vast majority of the cost of transaction processing is borne by third parties and not the miner that is making the decision of whether or not to include it.
Hence, tragedy-of-the-commons problems are very likely to occur. However, as it turns out this flaw in the market-based mechanism, when given a particular inaccurate simplifying assumption, magically cancels itself out. The argument is as follows.
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