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Since Bitcoin was first proposed in late 2008 and went live in 2009, hundreds of research papers have been published trying to understand the behaviour of cryptocurrencies and their impact on financial markets. Their size and importance to the financial sector has increased substantially also has the number of challenges they face and the negative externalities they have caused. This article reviews the related cryptocurrency literature and introduces articles included in this special issue on this theme which were presented at the 2020 Cryptocurrency Research Conference. We conclude by offering possible future research directions. [ABSTRACT FROM AUTHOR]

Purpose: Finance continuously evolves as the technological innovation progresses in the society. Numerous prior studies have discussed emerging financial services due to this innovation. However, limited scholarly work has evaluated the trends and state of the art of financial innovation. Therefore, this study aims to review recent literature on financial innovation by using a bibliometric and content-analysis approach. Design/methodology/approach: Documents for this study are sampled from financial innovation, a journal focusing on recent innovations in finance. A total of 354 peer-reviewed articles published in eight years (2015–2022) are first examined and mapped using the bibliometrix package in RStudio software. Furthermore, content analysis was performed to investigate the adopted research methods and types, and produce directions for future studies. Findings: The trend of financial innovation research kept increasing, with China as the leader in publication quantity, affiliation productivity and paper citation acquisition. Topics related to "FinTech," "Bitcoin" and "Covid-19" have been the most discussed topics by financial innovation researchers. FinTech and Bitcoin studies are expected to grow in emerging countries like China, India and Pakistan. The study also indicates that most financial innovation studies use quantitative research methods and are categorized as empirical papers. Originality/value: This study contributes to the finance literature by comprehensively evaluating current research on financial innovation using one specific journal in the field. Also, this study examines financial innovation literature using different approaches from previous bibliometric financial innovation studies. [ABSTRACT FROM AUTHOR]

Demand for blockchains such as Bitcoin and Ethereum is far larger than supply, necessitating a mechanism that selects a subset of transactions to include "on-chain" from the pool of all pending transactions. This article investigates the problem of designing a blockchain transaction fee mechanism through the lens of mechanism design. We introduce two new forms of incentive compatibility that capture some of the idiosyncrasies of the blockchain setting, one (MMIC) that protects against deviations by profit-maximizing miners and one (OCA-proofness) that protects against off-chain collusion between miners and users. This study is immediately applicable to a major change to Ethereum's transaction fee mechanism, made on August 5, 2021, based on a proposal called "EIP-1559." Originally, Ethereum's transaction fee mechanism was a first-price (pay-as-bid) auction. EIP-1559 suggested making several tightly coupled changes, including the introduction of variable-size blocks, a history-dependent reserve price, and the burning of a significant portion of the transaction fees. We prove that this new mechanism earns an impressive report card: it satisfies the MMIC and OCA-proofness conditions, and is also dominant-strategy incentive compatible (DSIC) except when there is a sudden demand spike. We also introduce an alternative design, the "tipless mechanism," which offers an incomparable slate of incentive-compatibility guarantees—it is MMIC and DSIC, and OCA-proof unless in the midst of a demand spike. [ABSTRACT FROM AUTHOR]

Blockchain is the foundation of cryptocurrencies, and most cryptocurrency technologies are decentralized. This study explains the topic of scalability in blockchains and presents a comparative analysis of numerous blockchain metrics with real-time data. This study performed this using a blockchain simulation (BlockSim) and then looked at effective techniques that may be utilized to overcome the limitation by comparing the simulator and real-world circumstances. This paper proposes an effective algorithm that improves the scalability of the Bitcoin network through efficient transaction deferment. This study also proposes an algorithm that improves the current Bitcoin protocols using inventory messaging (INV) and transaction deferment or adjournment. These improvements are compatible with the existing Bitcoin Network protocols. The improved algorithm was simulated using BlockSim and the AnyLogic Multi Paradigm Simulation Engine. The simulators were configured with 1000 nodes interconnected in a Bitcoin-like peer-to-peer network. The result of the simulation shows that the adjourned transaction protocol provides for a controlled reduction in the number of messages required to propagate a transaction at the cost of a modest increase in transaction propagation time. This system can manage the tradeoff between the quantity of messages and propagation latency by adjusting the threshold and timeout values, thereby improving the network's overall scalability. [ABSTRACT FROM AUTHOR]

This paper documents inconsistent terminologies and misleading analogies in current discussions of digital money and payments. It offers a more consistent framework for understanding the potential of technological innovation in providing the functions of money and payments: as media of exchange, stores of value, and units of account and the implications of cryptographic technologies underpinning cryptocurrencies for the future of money and payments. These could support efficiency gains in money and payments, but decentralization is not inherent to their application. Radical reform leading to improved economic outcomes is conceivable, but not through disruptive displacement of existing institutional arrangements. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to improve how investors can better manage their exposure to bitcoin (BTC), given the growing importance of BTC and the accompanying high volatility of BTC. This paper tests whether altcoins can serve as safe havens and diversifiers against exposure to BTC. Design/methodology/approach: Using daily returns of altcoins and BTC from 2014 to early 2022, this paper examines the relationship between altcoins and BTC in a GARCH regression framework. Findings: This paper finds that altcoins act as reliable safe havens during periods of extremely negative BTC returns and provide BTC investors with diversification benefits during normal periods. The safe haven effect of altcoins is superior to that of conventional assets. This paper presents evidence that this safe haven property of altcoins can be attributed to the informational efficiency channel, which arose from the increased adoption of BTC by institutional investors. Research limitations/implications: The study uses a data set from 2014 to early 2022. While the sample is among the largest samples in the literature on crypto assets and includes adequate BTC tail events to test the hypotheses, it may not capture more recent changes in the crypto markets. Practical implications: The findings suggest that BTC investors can enjoy diversification and safe haven protections by including altcoins in their portfolios. Originality/value: This paper's focus on alternative cryptocurrencies (altcoins) as potential diversifiers and safe havens is original. The hypothesis about altcoins being better alternatives during extreme negative movements in BTC prices is a unique contribution. The test of the role of the information efficiency channel further enhances the paper's originality. [ABSTRACT FROM AUTHOR]

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Numerous variables are involved in determining the market capitalization and price of cryptocurrencies. So, It is impossible to ignore the feedback of each variable on the other ones. Also, Bitcoin management is challenging due to inter-dependencies and feedback between economic and social drivers. To better understand the dynamics that different variables have on Bitcoin, a system dynamics approach has been developed to investigate the complex interactions between factors related to Bitcoin and paper money and how these factors may affect the stability of Bitcoin. This paper aims to examine the impact of government support for Bitcoin in the future, news and sentiments effect on Bitcoin market price, and the future orientation of Bitcoin. For this purpose, a simulation period is considered from 2017 to 4500 days later. Moreover, In this study, policies have been designed to check the impact of policies on variables in the long term by keeping other variables constant. The results show that if governments support Bitcoin as an official trading currency in the future, the market price of Bitcoin and its trading volume will grow upward and fluctuate so that its market price will go up to about 90 thousand dollars. [ABSTRACT FROM AUTHOR]

In 2021, President Nayib Bukele introduced Bitcoin as legal tender in El Salvador. This paper examines the relationship between right-wing populism and Bitcoin. Through textual and visual analyses, the paper shows how Bukele weaves the promises and techno-utopian ideology surrounding Bitcoin into classic populist narratives. Adopting Bitcoin adds the promise of technology-driven development and the realization of a techno-utopia to the populist narrative template, allowing Bukele to construct a populist myth of himself as a tech-savvy visionary. This brand of crypto-populism represents a novel addition to the right-wing populist repertoire, capitalizing on the promises of an ongoing digital revolution. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate the factors that make people want to hold cryptocurrency. Besides prior experience with holding crypto, this paper considers various expectations and conjectures about the future as key determinants. Design/methodology/approach: Data for this study come from an online survey in the USA. Econometric analyses help to quantify the relative importance of drivers of demand for cryptocurrency. Findings: Survey respondents will more likely hold cryptocurrency in the future the more they expect cryptocurrency to replace government money, to increase transparency in monetary affairs and to yield high profits. Importantly, demand is shown to be driven by the anticipation that nonmonetary uses of the Blockchain technology will have a spillover effect on the Bitcoin price. By contrast, subjective expectations of a crypto-induced financial crisis dampen demand. Econometric analyses show that differences in the future demand among people with and without prior holdings of cryptocurrency largely stem from differences in their expectations. Originality/value: By relating individuals' expectations to their plans, the present approach offers more insights than the mere attitude surveys already available. This paper's insights on crypto demand drivers indicate that regulators should be wary about signaling safety of an asset whose fundamental value is still uncertain. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to investigate the safe haven feature of Bitcoin, gold and two gold-backed cryptocurrencies (DGX and PAXG) against energy and agricultural commodities (crude oil, natural gas and wheat) during the COVID-19 pandemic, the Russia–Ukraine conflict and the Silicon Valley Bank (SVB) collapse. Design/methodology/approach: The authors use the threshold GARCH (T-GARCH)-asymmetric dynamic conditional correlation (ADCC) model to evaluate the asymmetric dynamic conditional correlation between the return series and compare the diversifying, hedging and safe-haven ability of Bitcoin, gold and the two gold-backed cryptocurrencies (DGX and PAXG) against financial swings in the commodity market during the COVID-19 outbreak, the Russian–Ukrainian military conflict and SVB collapse. The authors also calculate the hedging ratios (HR) and hedging effectiveness index (HE). The authors finally use the wavelet coherence (WC) approach to check our results' robustness and further investigate the impact of the three crises on the relationship between Bitcoin, gold gold-backed cryptocurrencies and commodities. Findings: The results show that PAXG serves as a strong hedging instrument while gold, Bitcoin and DGX act as strong diversifiers during normal times. During crises, gold outperforms Bitcoin as a diversifier and a safe haven against commodities. Gold-backed cryptocurrencies also exhibit strong performance as diversifiers and safe havens. HR results indicate that Bitcoin and DGX are more cost-effective for commodities risk mitigation than gold and PAXG. In terms of hedging effectiveness, gold and PAXG emerge as the best hedging instruments for commodities, while DGX is considered the worst one. Bitcoin shows superior hedging against oil compared to wheat and gas risks. Moreover, the results of the WC approach confirm those of the T-GARCH-ADCC results in both the short and long run. Originality/value: This paper provides a comprehensive analysis of the diversification ability of gold, Bitcoin and gold-backed cryptocurrencies during different crises (the COVID-19 pandemic, the Russia–Ukraine conflict and the SVB collapse). By taking into consideration gold-backed cryptocurrencies, the authors expand the understanding of safe havens beyond conventional assets. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to investigate the effect of the economic policy uncertainty (EPU), geopolitical risk (GPR) and climate policy uncertainty (CPU) of USA on Bitcoin volatility from August 2010 to August 2022. Design/methodology/approach: In this paper, the authors have adopted the empirical strategy of Yen and Cheng (2021), who modified volatility model of Wang and Yen (2019), and the authors use an OLS regression with Newey-West error term. Findings: The results using OLS regression with Newey–West error term suggest that the cryptocurrency market could have hedge or safe-haven properties against EPU and geopolitical uncertainty. While the authors find that the CPU has a negative impact on the volatility of the bitcoin market. Hence, the authors expect climate and environmental changes, as well as indiscriminate energy consumption, to play a more important role in increasing Bitcoin price volatility, in the future. Originality/value: This study has two implications. First, to the best of the authors' knowledge, the study is the first to extend the discussion on the effect of dimensions of uncertainty on the volatility of Bitcoin. Second, in contrast to previous studies, this study can be considered as the first to examine the role of climate change in predicting the volatility of bitcoin. This paper contributes to the literature on volatility forecasting of cryptocurrency in two ways. First, the authors discuss volatility forecasting of Bitcoin using the effects of three dimensions of uncertainty of USA (EPU, GPR and CPU). Second, based on the empirical results, the authors show that cryptocurrency can be a good hedging tool against EPU and GPR risk. But the cryptocurrency cannot be a hedging tool against CPU risk, especially with the high risks and climatic changes that threaten the environment. [ABSTRACT FROM AUTHOR]

The security of any proof-of-work blockchain protocol is based upon the assumption that the probability of a miner finding the next valid block is proportional to that miner's hashing power and constant throughout the process of mining that block (i.e., that the mining process is a "memoryless" process). While the literature assumes that the mining process is indeed memoryless, in this paper we use deductive reasoning to show how, given the finiteness of hashing functions' domains, this is not the case. This implies that the Bitcoin protocol induces a centralization of miners' hashing power, which in turn threatens the long-term viability of Bitcoin and of other cryptocurrencies based on similar protocols. The novelty of this paper stems from our documenting of a previously unrecognized flaw in the incentive system sustaining Bitcoin's security. [ABSTRACT FROM AUTHOR]

Purpose: With the rise of cryptocurrency and its influence on the financial industry, this paper aims to explore cryptocurrency affordances that lead to approach–avoidance behavioral intentions for non-users (potential) and the intention to continue use for users (actual), drawing upon affordance theory and chasm theory. Design/methodology/approach: The authors collected data from 480 potential and actual users in Germany and used maximum likelihood structural equation modeling (ML-SEM) to analyze it. In particular, the data consisted of 301 cryptocurrency users in Germany\ the authors used ML-SEM to test the post-adoption model. Additionally, logistic regression was utilized to determine the dominant actual usage method (store of value or medium of exchange) for various cryptocurrency coins. Findings: According to the study's results, the perceived value benefits have a positive impact on the behavioral intention of potential users to adopt cryptocurrency, and they influence the intention of actual users to continue using it. However, both perceived volatility and financial risk tolerance are the most crucial factors hindering cryptocurrency adoption, whether in the pre-adoption or the post-adoption stage. Originality/value: This is the first study to reveal cryptocurrency affordances and examine their effect on behavioral intentions toward cryptocurrency adoption based on the differences between non-users (potential) and users (actual). Furthermore, the authors explore how cryptocurrency holders perceive and invest in different coins (e.g. NFTs), which sheds light on factors such as financial risk tolerance that affect their decision making. [ABSTRACT FROM AUTHOR]

Since its inception, Blockchain Technology, the underlying technology of Bitcoin has gained a lot of popularity and interest in various industries, varying from the healthcare industry, Internet-of-Things, smart grid, and supply chain management. Sports is one industry where blockchain can have significant breakthroughs where it can reshape the way the sports industry works by providing reliable athlete data management, secure mechanisms for data sharing, reforming the event management practices, collectibles traceability, and many more, but so far its impact is overshadowed by the hype surrounding the use of fan tokens and Non-Fungible-Tokens (NFT) collectibles. This paper aims to provide a systematic review of the ongoing research on the application of blockchain in the sports industry. this work shows that many studies have proposed different use cases for the application of blockchain in the sports industry on and off the field and targeting different stakeholders. However, we inferred a lack of adequate prototype implementations to evaluate the effectiveness of these proposed use cases. We further highlighted the technical details of the implementation of these blockchain-based applications, their limitations, and challenges, and proposed some solutions to support the integration of blockchain in this domain. The findings of this work will be beneficial for researchers, policymakers, and practitioners in the sports industry. [ABSTRACT FROM AUTHOR]

In recent years, blockchain technology has developed rapidly and has been widely used in medical, financial, energy and other fields. However, in the process of practical application, each blockchain is a small independent ecosystem, with all transactions and operations limited to the chain, resulting in a large number of mutually heterogeneous to independent blockchains. It presents challenges for cross-chain interactions, cross-organization data sharing, and cross-blockchain expansion, and hinders the wider application of blockchain technology. In addition, the traditional digital signature method based on elliptic curve cipher faces the threat of being cracked by quantum computing attacks. To solve the aforementioned problems, this paper proposed a blockchain smart contract technique based on quantum computing attack resistance(BSCTQCAT). The technique first introduces the digital signature of the lattice cipher into the blockchain to resist the quantum search algorithm attack. Then, based on the smart contract authentication scheme, the nodes on multiple heterogeneous chains are organized into an identity agent layer P2P network, through which transactions on the chain will establish a credible identity management and message authentication mechanism between different chains, solving the current problem that each chain is difficult to communicate with each other. In this paper, the performance of the algorithm is evaluated by simulating the Bitcoin transaction scenario and analyzing the experimental data. [ABSTRACT FROM AUTHOR]

The cryptocurrency market is characterized by extreme volatility, with events such as the Terra-LUNA crash of 2022 raising significant questions about the resilience of algorithmic stablecoins. This paper investigates the collapse of LUNA Classic during the USTC depeg, focusing on the role of trading volumes and collateral assets like Bitcoin in amplifying the price crash. Using a Vector Logistic Smooth Transition AutoRegressive (VLSTAR) model, we analyze daily data from October 2020 to November 2022 to uncover how exogenous volumes influenced LUNA's price trajectory during the crisis. Our findings reveal that high trading volumes, particularly during regime two (the post-depeg period), significantly exacerbated the price decline, validating the impact of large-scale liquidations on LUNA's price path. Additionally, Bitcoin volumes played a critical role in destabilizing the system, confirming that the liquidity of underlying collateral assets is pivotal in maintaining price stability. These insights contribute to understanding the systemic vulnerabilities in algorithmic stablecoins and offer implications for future stablecoin design and risk management strategies. They are relevant for investors, policymakers, and researchers seeking to be aware of market volatility and prevent future crises in stablecoin ecosystems. [ABSTRACT FROM AUTHOR]

Electra was developed by an unknown man, Electra01. Following its emergence in cryptocurrency markets in 2018, its market capitalization briefly reached 136 billion USD, exceeding Bitcoin in value. The project's community of 20,000 individuals, wrote its white papers, updated its blockchain, instituted a foundation, introduced a payment system, and voted to be the best crypto project in 2020 in the world by a global vote. Following a fundamental controversy in its community, Electra collapsed as the founder sold his hundreds of millions of Electras in November 2020, effectively killing the project. Within a short period, the community left the founder behind, and moved on to a new project, giving (re)birth to their community money, this time called Electra Protocol. Drawing on an empirical case study, this paper presents an analysis of how cryptocurrency communities emerge, mature and migrate as they make data monies. [ABSTRACT FROM AUTHOR]

As the popularity of Bitcoin among finance communities continues, research on the relationship between Bitcoin and conventional commodities is becoming increasingly important. However, to date the literature has not yet found clear evidence of the dynamics of informational linkages between Bitcoin and the crude oil markets. This paper considers both static and time-varying information transmission of volatility, skewness and kurtosis, between two popular Bitcoin markets, Bitstamp and ItBit, and two major crude oil markets, WTI and Brent crude oil. A two-state regime-switching model is employed to estimate higher order moments and Legendre polynomials are applied to specify the time variations of spillovers. In addition, the latent impacts of the recent US–China trade war on spillovers are also explored. We conclude that crude oil is an information transmitter while Bitcoin is an information receiver in terms of the static and time-varying between-market transmission of three types of risk. The effect is affected by the occurrence of the trade war such that the information content of Bitcoin is enhanced once it begins. Moreover, Bitcoin is a diversifier for oil risk and this effect becomes more pronounced in the post-trade war period. [ABSTRACT FROM AUTHOR]

This paper proposes a novel asymmetric jump model for modeling interactions in discontinuous movements in asset prices. Given the jump behavior and high volatility levels in cryptocurrency markets, we apply our model to cryptocurrencies to study the impact of various types of jumps occurring in one cryptocurrency's price process on the discontinuity component of the realized volatility of other cryptocurrencies. Our model also allows us to assess the impact of co-jumps. Using high-frequency data to compute the daily realized volatility, we show that downside, upside, and small jumps observed in cryptocurrencies negatively affect the jump component of other cryptocurrencies' realized volatility, while large jumps have the opposite effect. We further find significant asymmetric effects between small and large as well as between downside and upside jumps for several cryptocurrencies. Moreover, we find evidence of co-jumping behavior, which can trigger future jumps. The practical implications of our findings are also discussed. Finally, we extend our analysis to study the effects of jumps in mainstream financial assets on cryptocurrencies' jump behavior and find that upside and downside jumps observed in the S&P 500 index negatively impact cryptocurrency jumps. [ABSTRACT FROM AUTHOR]

This paper analyzes the stability of stablecoins and proposes a framework to test for absolute and relative stability of stablecoins. Based on high-frequency data, we find strong evidence of excess price variations. While Bitcoin is a likely source of this excess volatility because stablecoin returns, volatility and volumes are highly correlated with corresponding Bitcoin time-series, we also demonstrate through a quasi-natural experiment that stablecoins increase the trading volume of Bitcoin. The findings suggest stablecoins play a key role in cryptocurrency markets. [ABSTRACT FROM AUTHOR]

Cryptocurrencies are disrupting the finance industry and influencing monetary policies across various countries, leading to increased concerns for governments, central banks, and international institutions. This paper presents an original investigation into the macro determinants of cryptocurrency ownership, with a specific focus on Bitcoin. Employing panel data analysis, the study covers 84 countries from 2010 to 2020. The study tackles the challenge of data limitations stemming from the anonymity of blockchain technology by using Bitcoin Client Software Downloads as a proxy for cryptocurrency ownership. The findings reveal several key factors. First, Bitcoin's price evolution plays a crucial role in enhancing its appeal, highlighting its role as an attractive asset for high returns and profit-seeking. Second, monetary freedom, reflecting sound monetary policy, acts as a deterrent to the ownership of cryptocurrencies. Third, political stability emerges as a critical driver, with countries exhibiting greater stability more likely to engage in cryptocurrency ownership. Additionally, gross domestic product per capita shows a positive impact, suggesting that wealthier nations with greater financial resources are more inclined to own cryptocurrencies, indicating economic affluence as a facilitator of cryptocurrency engagement. [ABSTRACT FROM AUTHOR]

The gap between software design and implementation often results in a lack of clarity and precision. Formal languages, based on mathematical rules, logic, and symbols, are invaluable for specifying and verifying system designs. Various semi-formal and formal languages, such as JSON, XML, predicate logic, and regular expressions, along with formal models like Turing machines, serve specific domains. This paper introduces a new specification formal language, ObTFL (Object Transaction Formal Language), developed for general-purpose distributed systems, such as specifying the interactions between servers and IoT devices and their security protocols. The paper details the syntax and semantics of ObTFL and presents three real case studies—federated learning, blockchain for crypto and bitcoin networks, and the industrial PCB board with machine synchronization—to demonstrate its versatility and effectiveness in formally specifying the interactions and behaviors of distributed systems. [ABSTRACT FROM AUTHOR]

Bitcoin is the first and most popular decentralized cryptocurrency to date. In this work, we extract and analyze the core of the Bitcoin protocol, which we term the Bitcoin backbone, and prove three of its fundamental properties which we call Common Prefix, Chain Quality, and Chain Growth in the static setting where the number of players remains fixed. Our proofs hinge on appropriate and novel assumptions on the "hashing power" of the protocol participants and their interplay with the protocol parameters and the time needed for reliable message passing between honest parties in terms of computational steps. A takeaway from our analysis is that, all else being equal, the protocol's provable tolerance in terms of the number of adversarial parties (or, equivalently, their "hashing power" in our model) decreases as the duration of a message passing round increases. Next, we propose and analyze applications that can be built "on top" of the backbone protocol, specifically focusing on Byzantine agreement (BA) and on the notion of a public transaction ledger. Regarding BA, we observe that a proposal due to Nakamoto falls short of solving it, and present a simple alternative which works assuming that the adversary's hashing power is bounded by 1/3. The public transaction ledger captures the essence of Bitcoin's operation as a cryptocurrency, in the sense that it guarantees the liveness and persistence of committed transactions. Based on this notion, we describe and analyze the Bitcoin system as well as a more elaborate BA protocol and we prove them secure assuming the adversary's hashing power is strictly less than 1/2. Instrumental to this latter result is a technique we call 2-for-1 proof-of-work (PoW) that has proven to be useful in the design of other PoW-based protocols. [ABSTRACT FROM AUTHOR]

Block-chain technology has swiftly emerged as a transformative force across varied sectors, revolutionizing data storage, security, and sharing. Originally introduced as the spine of cryptocurrencies like Bitcoin, Block-chain has evolved into a multifaceted innovation with applications surpassing finance. As its potential gains recognition, research volume dedicated to understanding and enhancing its applications intensifies. Block-chain, a secure and decentralized data management system, offers anonymity, security, privacy, transparency, and authenticity. The rapid evolution of this technology, extending beyond its cryptocurrency origins, has spurred prolific research across industries, driving collaborative efforts among authors, institutions, and regions. Analysis of 3419 research publications from 2016 to 2022 reveals a steep growth rate of 149.99%, emphasizing the field's exponential expansion. The scholarly impact is evident, with an average of 11.73 citations per document and 118,957 references. Collaboration metrics, including the Collaboration Index, Degree of Collaboration, and Collaboration Coefficient, demonstrate a growing trend of cooperation, reflecting a collective endeavor to unravel the complexities of Block-chain. The Modified Collaboration Coefficient further highlights inclusivity, displaying diverse voices contributing to Block-chain research. This collaborative ethos underscores the promise of Blockchain technology, propelling us toward a future enriched by collective intellect and innovative advancements. [ABSTRACT FROM AUTHOR]

Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges, gambling, marketplaces, and also scams such as high-yield investment projects. Identifying the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model accordingly. Feature engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small way. In this paper, we address the problem of identifying multiple classes of Bitcoin services, and for the poor classification of individual addresses that do not have significant features, we propose a Bitcoin address identification scheme based on joint multi-model prediction using the mapping relationship between addresses and entities. The innovation of the method is to (1) Extract as many valuable features as possible when an address is given to facilitate the multi-class service identification task. (2) Unlike the general supervised model approach, this paper proposes a joint prediction scheme for multiple learners based on address-entity mapping relationships. Specifically, after obtaining the overall features, the address classification and entity clustering tasks are performed separately, and the results are subjected to graph-basedmaximization consensus. The final result ismade to baseline the individual address classification results while satisfying the constraint of having similarly behaving entities as far as possible. By testing and evaluating over 26,000 Bitcoin addresses, our feature extraction method captures more useful features. In addition, the combined multi-learner model obtained results that exceeded the baseline classifier reaching an accuracy of 77.4%. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to examine the currently known techniques to tackle money laundering in Bitcoin mixers, and examine what gaps exist that would allow a criminal to get away with laundering Bitcoin obtained through illicit activities. Design/methodology/approach: This paper first establishes the relevant properties of Bitcoin, how transactions occur over the Bitcoin network and then introduces the Bitcoin transaction graph as an important data structure for any analysis of Bitcoin transactions. Next, the paper outlines how Bitcoin mixing works, along with the relevant properties of mixers that would be relevant for money laundering. The paper then assesses the known methods for identifying mixed transactions within the Bitcoin network, followed by an assessment on identifying money laundering activities on known mixed transactions. Findings: This paper argues that there remains a gap for criminals to launder money through Bitcoin mixing services as known methods would unlikely be able to trace a tainted transaction that goes through a decentralized mixer that uses off-chain communication techniques to coordinate the mixing and charges randomized mixing fees. Research limitations/implications: The study of known methods is restricted to literature published in the public domain. There are private organizations that are tackling similar problems, but their methods are not published and therefore cannot be included in this paper. Originality/value: To best of the author's knowledge, this is the first paper that performs a contemporaneous review on anti-money laundering in the context of Bitcoin mixing. This paper could assist regulators and policymakers in their understanding of Bitcoin mixers and provide guidance on where they should focus their resources to address the money laundering problem of Bitcoin mixing. [ABSTRACT FROM AUTHOR]

High-frequency quantitative trading in the emerging digital currency market poses unique challenges due to the lack of established methods for extracting trading information. This paper proposes a deep evolutionary reinforcement learning (DERL) model that combines deep reinforcement learning with evolutionary strategies to address these challenges. Reinforcement learning is applied to data cleaning and factor extraction from a high-frequency, microscopic view-point to quantitatively explain the supply and demand imbalance and to create trading strategies. In order to determine whether the algorithm can successfully extract the significant hidden features in the factors when faced with large and complex high-frequency factors, this paper trains the agent in reinforcement learning using three different learning algorithms, including Q-learning, evolutionary strategies, and policy gradient. The experimental dataset, which contains data on sharp up, sharp down, and continuous oscillation situations, was chosen to test Bitcoin in January-February, September, and November of 2022. According to the experimental results, the evolutionary strategies algorithm achieved returns of 59.18%, 25.14%, and 22.72%, respectively. The results demonstrate that deep reinforcement learning based on the evolutionary strategies outperforms Q-learning and policy gradient concerning risk resistance and return capability. The proposed approach offers a robust and adaptive solution for high-frequency trading in the digital currency market, contributing to the development of effective quantitative trading strategies. [ABSTRACT FROM AUTHOR]

The cryptocurrency market has undergone significant turbulence, characterized by enormous volatility shifts, as recently experienced during the COVID-19 shock. Although there is an abundant literature dealing with various aspects of cryptocurrencies, little attention has been devoted to understanding the interconnectedness among different cryptocurrencies, particularly the role of abrupt changes. This paper aims to fill this gap by conducting an intraday analysis to assess the contagion hypothesis within the cryptocurrency markets, with particular focus on the aprubt changes and whether it is a driver of co-aprubt changes in other markets. Specfically, we investigate four major cryptocurrencies (Bitcoin, Ethereum, Ethereum Classic, and Ripple) both prior to and during the COVID-19 shock, April 2019 to September 2020. Using the Diebold and Yilmaz methodology, we decompose the realized volatility into continuous and jump components, and examine how these spillovers evolve across cryptocurrency markets before and during the COVID-19 crisis. Our findings reveal that while the volatility and returns spillovers across the cryptocurrency market escalate during the crisis, there is a notable decrease in the jumps and co-jumps between cryptocurrencies. This suggests that the heightened interdependency observed is not rooted in fundamental factors. Morever, our findings show that spillover is especially prominent in the continuous part of the realised volatility dynamic. Notably, XRP emerges as the predominant transmitter in the context of continuous part of the realized volatility. Our study contributes to the emerging literature on the interconnectedness of price movement/co-mouvement across cryptocurrencies, offers a novel adaptation of the Diebold and Yilmaz methodology to capture the unique features of cryptocurrency prices. [ABSTRACT FROM AUTHOR]

Popular methods to value Bitcoin include the stock-to-flow model, Metcalfe's Law, technical analysis, and sentiment-related measures. Within this paper, I test whether such models and variables are predictive of Bitcoin's returns. I find that the stock-to-flow model predictions and Metcalfe's Law help to explain Bitcoin's returns in-sample but have limited to no ability to predict Bitcoin's returns out-of-sample. In contrast, Bitcoin market sentiment and technical analysis measures are generally unrelated to Bitcoin's returns in-sample and are poor predictors of Bitcoin's returns out-of-sample. Despite the poor performance of Bitcoin return predictors within out-of-sample regressions, I demonstrate that a very successful out-of-sample Bitcoin tactical allocation or "market timing" strategy is formed via blending out-of-sample univariate model predictions. This OOS-blended model trading strategy, which algorithmically allocates between Bitcoin and cash (USD), significantly outperforms buying-and-holding or "HODL"ing Bitcoin, boosting CAPM alpha by almost 1300 basis points while also increasing portfolio Sharpe Ratio and Sortino Ratio and dramatically reducing portfolio maximum drawdown relative to buying-and-holding Bitcoin. [ABSTRACT FROM AUTHOR]

Cryptocurrencies have now become an emerging blockchain-based payment technology; among them, bitcoin is the best known and most widely used. Users on these networks are pseudo-anonymous, meaning that while all transactions from an address are transparent and searchable by anyone, the users' true identities are not directly revealed; to preserve their privacy, users often use many different addresses. In recent years, some studies have been conducted regarding analyzing clusters of bitcoin addresses that, according to certain heuristics, belong to the same entity. This capability provides law enforcement with valuable information for investigating illegal activities involving cryptocurrencies. Clustering methods that rely on a single heuristic often fail to accurately and comprehensively cluster multiple addresses. This paper proposes Bitcoin Address Clustering based on multiple Heuristics (BACH): a tool that uses three different clustering heuristics to identify clusters of bitcoin addresses, which are displayed through a three-dimensional graph. The results lead to several analyses, including a comparative evaluation of WalletExplorer, which is a similar address clustering tool. BACH introduces the innovative feature of visualizing the internal structure of clusters in a graphical format. The study also shows how the combined use of different heuristics provides better results and more complete clusters than those obtained from their individual use. [ABSTRACT FROM AUTHOR]

In this paper, we investigate interdependencies between cryptocurrencies and investor sentiment by introducing the asymmetric TVP-VAR frequency connectedness approach. Our empirical results provide evidence of pronounced and time-varying interconnectedness between sentiment and cryptocurrency. In addition, we find that negative short-term interconnectedness dominates positive short-term interconnectedness until mid-2020 when this effect was reversed, persisting until the end of the sample period. While Ripple and Bitcoin are both found to be the main net transmitters of shocks, Stellar, Ethereum, and NEM are considered net receivers of shocks. Overall, our findings suggest that market sentiment is mainly driven by cryptocurrency volatility in both the short and the long run. [ABSTRACT FROM AUTHOR]

Merkley et al. (2023) examine how cryptocurrency influencers recommend digital coins on Twitter (X) and the associated price effects. They report that influencers may exploit market investors via potential pump and dump schemes. While plausible, researchers may develop a broader understanding of influencers' incentives and their influence by considering how investors engage these markets for social identity needs that enhance utility. Social-psychological research indicates that someone's social identity strongly influences their behavior, even making the behavior maladaptive. This paper discusses how crypto influencers create social identity resonance. It then discusses how influencers can leverage this resonance for potentially lucrative financial opportunities, which might manifest in different expected crypto price patterns. The paper concludes by recommending more research on influencers' experience, networks, and communication choices; the effects of video relative to text communication; and implications of social identity cohorts that influence prices and undermine regulatory trust in traditional markets. [ABSTRACT FROM AUTHOR]

Bitcoin is a payment system that generates a decentralized digital currency without ensuring temporal constraints in its transactions; therefore, it is vulnerable to double-spending attacks. Karame has proposed a formalization for a successful double-spending attack based on meeting three requirements. This focuses on fast payment scenarios where the product is delivered immediately after the payment is announced in the mempool, without waiting for transaction confirmation. This scenario is key in Bitcoin to increase the probability of a successful double-spending attack. Different approaches have been proposed to mitigate these attacks by addressing one or more of Karame's three requirements. These include the following: flooding every transaction without restrictions, introducing listeners/observers, avoiding isolation by blocking incoming connections, penalizing malicious users by revealing their identity, and using machine learning and bio-inspired techniques. However, to our knowledge, no proposal deterministically avoids double-spending attacks in fast payment scenarios. In this paper, we introduce DiFastBit: a distributed transaction differentiation scheme that shields Bitcoin from double-spending attacks in fast payment scenarios. To achieve this, we modeled Bitcoin from a distributed perspective of events and processes, reformulated Karame's requirements based on Lamport's happened-before relation (HBR), and introduced a new theorem that consolidates the reformulated requirements and establishes the necessary conditions for a successful attack on fast Bitcoin payments. Finally, we introduce the specifications for DiFastBit, formally prove its correctness, and analyze DiFastBit's confirmation time. [ABSTRACT FROM AUTHOR]

This paper applies deep learning models to predict Bitcoin price directions and the subsequent profitability of trading strategies based on these predictions. The study compares the performance of the convolutional neural network–long short-term memory (CNN–LSTM), long- and short-term time-series network, temporal convolutional network, and ARIMA (benchmark) models for predicting Bitcoin prices using on-chain data. Feature-selection methods—i.e., Boruta, genetic algorithm, and light gradient boosting machine—are applied to address the curse of dimensionality that could result from a large feature set. Results indicate that combining Boruta feature selection with the CNN–LSTM model consistently outperforms other combinations, achieving an accuracy of 82.44%. Three trading strategies and three investment positions are examined through backtesting. The long-and-short buy-and-sell investment approach generated an extraordinary annual return of 6654% when informed by higher-accuracy price-direction predictions. This study provides evidence of the potential profitability of predictive models in Bitcoin trading. [ABSTRACT FROM AUTHOR]

Since the advent of Bitcoin, cryptocurrencies have gained substantial popularity, and crypto wallets have evolved into the predominant tool for safeguarding and managing cryptographic keys to access cryptocurrency funds. Deterministic wallets are proposed as an advanced wallet mechanism to provide benefits such as low-maintenance, easy backup and recovery, and support for functionalities required by cryptocurrencies. Alkeilani Alkadri et al. (ACM CCS'20) presented the first post-quantum secure deterministic wallet scheme, but it exhibits a gap to bridge before achieving practical applicability, as reflected in both their concrete parameters size and computational efficiency. In this paper, we propose an efficient post-quantum secure deterministic wallet scheme. In particular, we present a new construction method for deterministic wallets, prove the security in the quantum random oracle model, and provide an efficient instantiation. The comparison result, with the work of Alkeilani Alkadri et al. (ACM CCS'20), shows our work has a comprehensive improvement on efficiency, e.g., the pk size is ≈ 40.7 times shorter, sk is ≈ 9.2 times shorter, and the signing time is ≈ 3.1 times faster. [ABSTRACT FROM AUTHOR]

Bitcoin is the most successful cryptocurrency with the highest market capitalization of up to 53%. Due to its pseudonymous mechanism, bitcoin is being utilized in a variety of illicit activities. It is noticed that around US$72 billion of unlawful activities per year involve Bitcoin. In this study, a systematic literature review is conducted on the illicit use of Bitcoin, and the measures required to counter the illicit activities using Bitcoin. In this work, authors have managed to select 45 research articles published during 2018-2022. The synthesis of selected articles revealed that bitcoin is proliferating in darknet markets. It is used to make payments for criminal activities such as drug trafficking, money laundering, human trafficking, pornography, ransomware, and other criminal activities like contract killers, Ponzi schemes, and terrorism financing. According to the findings from this study, out of 45 research articles, 24.4% of articles claim that Bitcoin has been used in drug trafficking whereas 17.7% believe that people use Bitcoin for money laundering. Moreover, Blockchain identity flexibility, dissociative anonymity, and a lack of deterrence encourage users to perform illegal activities. At present, the research community is actively involved in proposing and designing innovative approaches to counter the illicit use of Bitcoin. However, these solutions are unable to stop the misuse of Bitcoin. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to examine the dynamic conditional correlation (DCC) and hedging ratios between Dow Jones markets and oil, gold and bitcoin. Using daily data, including the COVID-19 pandemic and the Russia–Ukraine war. We employ the DCC-generalized autoregressive conditional heteroskedasticity (GARCH) and asymmetric DCC (ADCC)-GARCH models. Design/methodology/approach: DCC-GARCH and ADCC-GARCH models. Findings: The most of DCCs among market pairs are positive during COVID-19 period, implying the existence of volatility spillovers (Contagion-effects). This implies the lack of additional economic gains of diversification. So, COVID-19 represents a systematic risk that resists diversification. However, during the Russia–Ukraine war the DCCs are negative for most pairs that include Oil and Gold, implying investors may benefit from portfolio-diversification. Our hedging analysis carries significant implications for investors seeking higher returns while hedging their Dow Jones portfolios: keeping their portfolios unhedged is better than hedging them. This is because Islamic stocks have the ability to mitigate risks. Originality/value: Our paper may make a valuable contribution to the existing literature by examining the hedging of financial assets, including both conventional and Islamic assets, during periods of stability and crisis, such as the COVID-19 pandemic and the Russia–Ukraine war. [ABSTRACT FROM AUTHOR]

Cryptocurrencies, such as Bitcoin, have garnered significant attention in scholarship and beyond. Geographical work on cryptocurrencies has focussed on how their energy demand interacts with local communities and economies. Less is said about the organization of cryptoasset firms and their associated demands. This paper illuminates the complex geographies of one such firm, Bitfury Group, to investigate the global and national forms and structures such companies take and the factors encouraging them to concentrate operations in certain areas. To investigate the latter, we adopt the case study of Bitfury's operations in Georgia, a South-Caucasian country where its presence is significant. We adapt Haberly et al.'s analytical framework to explore Bitfury's geographical dimensions. We highlight how cheap electricity, regulatory and taxation regimes, personal encounters and personalities, and the materialities of hardware and energy-saving technology define these geographies and illuminate how Bitfury actively curates advantageous regulatory spaces. We encourage future work exploring Blockchain and Bitcoin technologies to understand the companies involved as simultaneously material and virtual, and as centrepieces in global networks interweaving production and finance. [ABSTRACT FROM AUTHOR]

We use the internal trading records of a major Bitcoin exchange leaked by hackers to detect and characterize wash trading—a type of market manipulation in which a single trader clears the trader's own limit orders to "cook" transaction records. Our finding provides direct evidence for the widely suspected "fake volume" allegation against cryptocurrency exchanges, which has so far only been backed by indirect estimation. We then use our direct evidence to evaluate various indirect techniques for detecting the presence of wash trades and find measures based on Benford's law, trade size clustering, lognormal distributions, and structural breaks to be useful, whereas ones based on power law tail distributions to give opposite conclusions. We also provide suggestions to effectively apply various indirect estimation techniques. This paper was accepted by Professor Bruno Biais, finance. Funding: J. Li acknowledges support by the U.S. Department of Homeland Security [Grant 205187] through the Criminal Investigations and Network Analysis Center. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2021.01448. [ABSTRACT FROM AUTHOR]

Bitcoin has received a lot of attention from both investors and analysts, as it forms the highest market capitalization in the cryptocurrency market. The use of parametric GARCH models to characterize the volatility of Bitcoin returns is widely observed in the empirical literature. In this paper, we consider an alternative approach involving nonparametric method to model and forecast Bitcoin return volatility. We show that the out-of-sample volatility forecast of the nonparametric GARCH model yields superior performance relative to an extensive class of parametric GARCH models. The improvement in forecasting accuracy of Bitcoin return volatility based on the nonparametric GARCH model suggests that this method offers an attractive and viable alternative to the commonly used parametric GARCH models. [ABSTRACT FROM AUTHOR]

The introduction of regulated CME futures contracts on Bitcoin in 2017 raised an expectation that cryptocurrencies would become part of mainstream financial markets. This also heightened links between traditional markets and Bitcoin, implying that the cryptocurrency would be subject to systematic spillovers. This paper uses high‐frequency data to examine whether Bitcoin basis risk is linked to investor sentiment from established financial markets. Our findings indicate that extreme investor sentiment, as reflected by the tail risk in various volatility indices, including the VIX, consistently correlates with a negative Bitcoin basis, where Bitcoin futures prices are lower than spot prices. Fluctuations significantly influence this relationship in the trading volume of Bitcoin futures and are more pronounced during periods of substantial unexpected inflation and deflation. These results underline the complex dynamics between market sentiment and cryptocurrency pricing, offering insights with substantial implications for investors and policymakers. [ABSTRACT FROM AUTHOR]

Do behavioral factors mediate the relationship between industry returns and Bitcoin returns? We use four industry indices in technology, energy, clean energy, and banking, and the Sentiment index from Thomson Reuters Marketpsych Indices as a behavioral factor to investigate this question. We show that the sensitivities of technology and clean energy industry indices to Sentiment, positively and significantly, strengthen the relationship between sentiment and Bitcoin returns. By showing that behavioral factors mediate the association between the returns of industry indices and Bitcoin returns, we provide evidence that investors' Sentiment captures the association between Bitcoin and sectors related to cryptocurrencies. Our results, however, do not support prior studies' findings of a direct relationship between the industry indices and Bitcoin returns. [ABSTRACT FROM AUTHOR]

Bitcoin is a complex phenomenon, whether in terms of the macro factors affecting its price or its role in the global energy infrastructure. However, extant literature pays too little attention to exploring the internal mechanisms of the protocol to be able to link them to how they affect the visible characteristics of Bitcoin. This paper uses secondary data from highly reputable Bitcoin-focused sources to systematically map the processes that enable Bitcoin to function as a peer-to-peer cash system. Novelty is achieved by applying the established and versatile "4I" organisational learning framework to provide a new lens through which to understand how the processes within Bitcoin enable and facilitate different types of changes to the protocol. Further insights are provided to organisational learning from Bitcoin, in relation to managing mission-critical changes to organisational systems. In addition, it presents an option for dealing with irreconcilable internal differences to "hard-fork" part of the organisation. While the scope of this paper is limited to secondary data, opportunities for further research, including primary data collection, are outlined to explore how Bitcoin knowledge disseminates within communities or companies. [ABSTRACT FROM AUTHOR]

This paper is based upon a lightening talk that was given on February 14, 2023 at the 2023 NISO Plus conference. The presentation described the research for the creation of a white paper on the use of blockchain technology along the scientific research workflow that will be released in early 2024 by the International Union of Pure and Applied Chemistry (IUPAC). What the three-year study found is that the technology is indeed being used in almost all the steps in the scientific research workflow - from hypothesis development through to publication - by commercial organizations as well as by non-profits and across all market sectors, even governments. For example, the U.S Department of Health and Human Services (HHS) uses blockchain technology in a pilot program, the Grant-recipient Digital Dossier (GDD), to manage their grant program more efficiently. As of July 2021, GDD had reduced the time required to complete grant assessment tasks from four-plus-hours to a fifteen-minute process. This paper briefly summarizes the findings, discusses the pros/cons of the technology, and provides a glimpse of how the technology is impacting the future of scientific research. [ABSTRACT FROM AUTHOR]

This paper explores the predictive power of economic and energy policy uncertainty indices and geopolitical risks for bitcoin's energy consumption. Three machine learning tools, SVR (scikit-learn 1.5.0),CatBoost 1.2.5 and XGboost 2.1.0, are used to evaluate the complex relationship between uncertainty indices and bitcoin's energy consumption. Results reveal that the XGboost model outperforms both SVR and CatBoost in terms of accuracy and convergence. Furthermore, the feature importance analysis performed by the Shapley additive explanation (SHAP) method indicates that all uncertainty indices exhibit a significant capacity to predict bitcoin's future energy consumption. Moreover, SHAP values suggest that economic policy uncertainty captures valuable predictive information from the energy uncertainty indices and geopolitical risks that affect bitcoin's energy consumption. [ABSTRACT FROM AUTHOR]

Financial markets are increasingly interlinked. Therefore, this study explores the complex relationships between the Tadawul All Share Index (TASI), West Texas Intermediate (WTI) crude oil prices, and Bitcoin (BTC) returns, which are pivotal to informed investment and risk-management decisions. Using copula-based models, this study identified Student's t copula as the most appropriate one for encapsulating the dependencies between TASI and BTC and between TASI and WTI prices, highlighting significant tail dependencies. For the BTC–WTI relationship, the Frank copula was found to have the best fit, indicating nonlinear correlation without tail dependence. The predictive power of the identified copulas were compared to that of Long Short-Term Memory (LSTM) networks. The LSTM models demonstrated markedly lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Scaled Error (MASE) across all assets, indicating higher predictive accuracy. The empirical findings of this research provide valuable insights for financial market participants and contribute to the literature on asset relationship modeling. By revealing the most effective copulas for different asset pairs and establishing the robust forecasting capabilities of LSTM networks, this paper sets the stage for future investigations of the predictive modeling of financial time-series data. The study highlights the potential of integrating machine-learning techniques with traditional econometric models to improve investment strategies and risk-management practices. [ABSTRACT FROM AUTHOR]

Cryptocurrencies offer the opportunity to develop a more inclusive and efficient financial system. Nevertheless, they also pose notable challenges and risks, particularly stemming from the growing prevalence of cryptocurrency-related fraudulent activities and crimes. This note discusses the prominent role of Islamic law in Egypt on the prohibition of cryptocurrencies, as demonstrated by the issuance of the Bitcoin Fatwā (a religious ruling) by Dar Al-Ifta, which is Egypt's official Islamic authority. The Fatwā categorises Bitcoin trading as haram, meaning it is forbidden in Islam. This religious ruling significantly influenced Egypt's legal position regarding cryptocurrencies, as reflected in the enactment of the Central Bank and Banking Sector Law No. 194 of 2020 and amendments of other key legislation related to monetary offences. This note critically analyses Egypt's past and current regulatory framework for cryptocurrencies. It demonstrates that the current system is practically ineffective and contradicts the objective of both the Fatwā and the law. The note recommends a reassessment of the Fatwā and the existing regulatory framework, advocating for the adoption of a regulation that permits cryptocurrency-related activities while prioritising the strengthening of penalties for cryptocurrency offenses. [ABSTRACT FROM AUTHOR]