Pedro Bação, António Portugal Duarte, Hélder Sebastião, Srdjan Redžepagić
This paper investigates the information transmission between the most important cryptocurrencies -Bitcoin, Litecoin, Ripple, Ethereum and Bitcoin Cash. We use a VAR modelling approach, upon which the Geweke’s feedback measures and generalized impulse response functions are computed. This methodology allows us to fully characterize the direction, intensity and persistence of information flows between cryptocurrencies. At the availabledata granularity, most of information transmission is contemporaneous, that is, it occurs within a day. However, it seems that there are some lagged feedback effects, mainly from other cryptocurrencies to Bitcoin. The generalized impulse-response functions confirm that there is a strong contemporaneous correlation and that there is not much evidence of lagged effects. The exception appears to be related to the overreaction of Bitcoin returns to contemporaneous shocks
Purpose The purpose of this paper is to examine price overreactions in the case of the following cryptocurrencies: bitcoin, litecoin, ripple and dash. Design/methodology/approach A number of parametric ( t -test, ANOVA, regression analysis with dummy variables) and non-parametric (Mann–Whitney U -test) tests confirm the presence of price patterns after overreactions: the next day price changes in both directions are bigger than after “normal” days. A trading robot approach is then used to establish whether these statistical anomalies can be exploited to generate profits. Findings The results suggest that a strategy based on counter-movements after overreactions is not profitable, whilst one based on inertia appears to be profitable but produces outcomes not statistically different from the random ones. Therefore, the overreactions detected in the cryptocurrency market do not give rise to exploitable profit opportunities (possibly because of transaction costs) and cannot be seen as evidence against the efficient market hypothesis (EMH). Originality/value The overreactions detected in the cryptocurrency market do not give rise to exploitable profit opportunities (possibly because of transaction costs) and cannot be seen as evidence against the EMH.
The invention of blockchain technology has radically changed the perception of how monetary systems can be structured and operated. Central banks and state authorities mostly refuse to acknowledge that cryptocurrencies are money, yet the number of payment transactions using cryptocurrencies is increasing and cryptocurrencies form a non-negligible stake of wealth. As with other economic phenomena, cryptocurrencies shall be addressed in the financial statements of the entities using them, albeit without any accounting guidance in current financial reporting standards. This paper fills this void by suggesting, comparing, and assessing potential accounting models under IFRS. Based on evidence from literature review, as well as recent time-series data on the price volatility of cryptocurrencies, the paper shows that fair value accounting is the most relevant source of useful information for users of financial statements when cryptocurrencies are acquired for investment purposes. Furthermore, the paper identifies scenarios under which cryptocurrencies shall be treated as (foreign) currencies, even though financial system regulators do not consider cryptocurrencies as being money (fiat currency).
This discussion paper examines the recent history of cryptocurrency thefts and exchanges shutdowns, focusing specifically on the largest cryptoinstrument: Bitcoin. The examination of thefts and shutdowns are intended to draw academic attention to the accountability deficits that pervade the cryptocurrency space, and the findings of the paper suggest that a much more robust accountability, transparency, and oversight architecture must be put in place vis-a-vis cryptocurrencies.
Drawing on an empirical study of cryptocurrency white papers, this paper proposes an actor-based taxonomy of cryptocurrency blockchains. First, it describes the evolution of blockchain architecture with reference to the economic services that blockchains supply. Second, it discusses the socio-technical platform of blockchains as proposed in cryptocurrency white papers. Third, it analyses the socio-economic consequences of these technically diverse blockchain platforms, by proposing a taxonomy of their digital architectures in reference to two groups of actors that maintain blockchain infrastructure: transactioners and accountants. Defining cryptocurrency as data money, and locating cryptocurrency ownership as the possession of an exclusive right to move data privately in a public or private space, the paper describes a blockchain as a digital actor-network platform that makes it possible to define and distribute these data transfer rights.
The recent evolution of cryptocurrencies has been characterized by bubble-like behavior and extreme volatility. While it is difficult to assess an intrinsic value to a specific cryptocurrency, one can employ recently proposed bubble tests that rely on recursive applications of classical unit root tests. This paper extends this approach to the case where volatility is time varying, assuming a deterministic long-run component that may take into account a decrease of unconditional volatility when the cryptocurrency matures with a higher market dissemination. Volatility also includes a stochastic short-run component to capture volatility clustering. The wild bootstrap is shown to correctly adjust the size properties of the bubble test, which retains good power properties. In an empirical application using eleven of the largest cryptocurrencies and the CRIX index, the general evidence in favor of bubbles is confirmed, but much less pronounced than under constant volatility.
Julio C. Mendoza-Tello, Higinio Mora, Francisco A. Pujol, Miltiadis D. Lytras
The deployment of cryptocurrencies in e-commerce has reached a significant number of transactions and continuous increases in monetary circulation; nevertheless, they face two impediments: a lack of awareness of the technological utility, and a lack of trust among consumers. E-commerce carried out through social networks expands its application to a new paradigm called social commerce. Social commerce uses the content generated within social networks to attract new consumers and influence their behavior. The objective of this paper is to analyze the role played by social media in increasing trust and intention to use cryptocurrencies in making electronic payments. It develops a model that combines constructs from social support theory, social commerce, and the technology acceptance model. This model is evaluated using the partial least square analysis. The obtained results show that social commerce increases the trust and intention to use cryptocurrencies. However, mutual support among participants does not generate sufficient trust to adequately promote the perceived usefulness of cryptocurrencies. This research provides a practical tool for analyzing how collaborative relationships that emerge in social media can influence or enhance the adoption of a new technology in terms of perceived trust and usefulness. Furthermore, it provides a significant contribution to consumer behavior research by applying the social support theory to the adoption of new information technologies. These theoretical and practical contributions are detailed in the final section of the paper.
This article is a short introduction to cryptocurrencies and blockchain technology. The focus of the introduction is on Bitcoin, but many elements are shared by other blockchain implementations and alternative cryptoassets.
Nektarios Aslanidis, Aurelio F. Bariviera, Oscar Martínez
This letter explores the behavior of conditional correlations among main cryptocurrencies, stock and bond indices, and gold, using a generalized DCC class model. From a portfolio management point of view, asset correlation is a key metric in order to construct efficient portfolios. We find that: (i) correlations among cryptocurrencies are positive, albeit varying across time; (ii) correlations with Monero are more stable across time; (iii) correlations between cryptocurrencies and traditional financial assets are negligible.
Abstract We present stylized facts on the asset pricing properties of cryptocurrencies: summary statistics on cryptocurrency return properties and measures of common variation for secondary market returns on 222 digital coins. In our sample, secondary market returns of all other currencies are strongly correlated with Bitcoin returns. We also provide some investment characteristics of a sample of 64 initial coin offerings.
Nowadays, blockchain has become one of the most cutting-edge technologies, which has been widely concerned and researched. However, the quantum computing attack seriously threatens the security of blockchain, and related research is still less. Targeting at this issue, in this paper, we present the definition of post-quantum blockchain (PQB) and propose a secure cryptocurrency scheme based on PQB, which can resist quantum computing attacks. First, we propose a signature scheme based on lattice problem. We use lattice basis delegation algorithm to generate secret keys with selecting a random value, and sign message by preimage sampling algorithm. In addition, we design the first-signature and last-signature in our scheme, which are defined as double-signature. It is used to reduce the correlation between the message and the signature. Second, by combining the proposed signature scheme with blockchain, we construct the PQB and propose this cryptocurrency scheme. Its security can be reduced to the lattice short integer solution (SIS) problem. At last, through our analysis, the proposed cryptocurrency scheme is able to resist the quantum computing attack and its signature satisfies correctness and one-more unforgeability under the lattice SIS assumption. Furthermore, compared with previous signature schemes, the sizes of signature and secret keys are relatively shorter than that of others, which can decrease the computational complexity. These make our cryptocurrency scheme more secure and efficient.
Jethin Abraham, Daniel Higdon, John B. Nelson, Juan G. Ibarra
In this paper, we present a method for predicting changes in Bitcoin and Ethereum prices utilizing Twitter data and Google Trends data. Bitcoin and Ethereum, the two largest cryptocurrencies in terms of market capitalization represent over \$160 billion dollars in combined value. However, both Bitcoin and Ethereum have experienced significant price swings on both daily and long term valuations. Twitter is increasingly used as a news source influencing purchase decisions by informing users of the currency and its increasing popularity. As a result, quickly understanding the impact of tweets on price direction can provide a purchasing and selling advantage to a cryptocurrency user or a trader. By analyzing tweets, we found that tweet volume, rather than tweet sentiment (which is invariably overall positive regardless of price direction), is a predictor of price direction. By utilizing a linear model that takes as input tweets and Google Trends data, we were able to accurately predict the direction of price changes. By utilizing this model, a person is able to make better informed purchase and selling decisions related to Bitcoin and Ethereum.
We use the GARCH-MIDAS model to extract the long- and short-term volatility components of cryptocurrencies. As potential drivers of Bitcoin volatility, we consider measures of volatility and risk in the US stock market as well as a measure of global economic activity. We find that S&P 500 realized volatility has a negative and highly significant effect on long-term Bitcoin volatility. The finding is atypical for volatility co-movements across financial markets. Moreover, we find that the S&P 500 volatility risk premium has a significantly positive effect on long-term Bitcoin volatility. Finally, we find a strong positive association between the Baltic dry index and long-term Bitcoin volatility. This result shows that Bitcoin volatility is closely linked to global economic activity. Overall, our findings can be used to construct improved forecasts of long-term Bitcoin volatility.
Abstract We document numerous occurrences of pump-and-dump schemes (P&Ds) targeting cryptocurrencies, which tend to trigger short-term episodes that feature dramatic increases in prices, volume, and volatility, followed by quick reversals. The evidence we document, including price run-ups before P&Ds start, suggests wealth transfers from outsiders to insiders. Our findings based on wallet-level data are consistent with the reasoning that gambling preferences, overconfidence, and naïve reinforcement learning help explain P&D participation. Finally, exploiting two natural experiments in which exchanges altered P&D policies, we find evidence consistent with the idea that P&Ds contribute to reduced cryptocurrency liquidity and lower prices.
Laura Alessandretti, Abeer ElBahrawy, Luca Maria Aiello, Andrea Baronchelli
Machine learning and AI-assisted trading have attracted growing interest for the past few years. Here, we use this approach to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. We analyse daily data for $1,681$ cryptocurrencies for the period between Nov. 2015 and Apr. 2018. We show that simple trading strategies assisted by state-of-the-art machine learning algorithms outperform standard benchmarks. Our results show that nontrivial, but ultimately simple, algorithmic mechanisms can help anticipate the short-term evolution of the cryptocurrency market.
Abstract We establish that cryptocurrency returns are driven and can be predicted by factors that are specific to cryptocurrency markets. Cryptocurrency returns are exposed to cryptocurrency network factors but not cryptocurrency production factors. We construct the network factors to capture the user adoption of cryptocurrencies and the production factors to proxy for the costs of cryptocurrency production. Moreover, there is a strong time-series momentum effect, and proxies for investor attention strongly forecast future cryptocurrency returns.
Blockchain-based distributed computing platforms enable the trusted execution of computation - defined in the form of smart contracts - without trusted agents. Smart contracts are envisioned to have a variety of applications, ranging from financial to IoT asset tracking. Unfortunately, the development of smart contracts has proven to be extremely error prone. In practice, contracts are riddled with security vulnerabilities comprising a critical issue since bugs are by design non-fixable and contracts may handle financial assets of significant value. To facilitate the development of secure smart contracts, we have created the FSolidM framework, which allows developers to define contracts as finite state machines (FSMs) with rigorous and clear semantics. FSolidM provides an easy-to-use graphical editor for specifying FSMs, a code generator for creating Ethereum smart contracts, and a set of plugins that developers may add to their FSMs to enhance security and functionality.
Alex Biryukov, Dmitry Khovratovich, Sergei Tikhomirov
Identity is a fundamental concept for the financial industry. In order to comply with regulation, financial institutions must verify the identity of their customers. Identities are currently handled in a centralized way, which diminishes users' control over their personal information and threats their privacy. Blockchain systems, especially those with support for smart contracts (e.g., Ethereum), are expected to serve as a basis of more decentralized systems for digital identity management. We propose a design of a privacy-preserving KYC scheme on top of Ethereum. It would let providers of financial services leverage the potential of blockchain technology to increase effciency of customer onboarding while complying with regulation and protecting users' privacy.