Bitcoin's block reward is scheduled to decline to zero, raising concerns about whether the network can remain secure once miners rely solely on transaction fees. This paper seeks to identify the conditions under which large-scale and persistent deviation from honest mining can arise. We analyze and compare the payoffs of honest and deviating miners in a sequential decision model, and identify a deviation threshold $G_t$ at which honest mining ceases to be privately optimal. Around the 2024 Bitcoin halving, we show that current mining behavior does not exhibit large-scale or structural deviation. However, when the block reward is removed, the $G_t$ criterion implies that deviation can arise even with a very small fraction of transaction fees. Finally, we evaluate three protocol-level mechanisms: Base Fee, Fee Floor, and an adaptive maximum block size rule, and show that their combination raises the deviation threshold and mitigates incentive breakdown in a fee-only regime. These results provide a practical benchmark for assessing Bitcoin's security as block rewards disappear.
This study introduces a novel approach to walk-forward optimization by parameterizing the lengths of training and testing windows. We demonstrate that the performance of a trading strategy using the Exponential Moving Average (EMA) evaluated within a walk-forward procedure based on the Robust Sharpe Ratio is highly dependent on the chosen window size. We investigated the strategy on intraday Bitcoin data at six frequencies (1 minute to 60 minutes) using 81 combinations of walk-forward window lengths (1 day to 28 days) over a 19-month training period. The two best-performing parameter sets from the training data were applied to a 21-month out-of-sample testing period to ensure data independence. The strategy was only executed once during the testing period. To further validate the framework, strategy parameters estimated on Bitcoin were applied to Binance Coin and Ethereum. Our results suggest the robustness of our custom approach. In the training period for Bitcoin, all combinations of walk-forward windows outperformed a Buy-and-Hold strategy. During the testing period, the strategy performed similarly to Buy-and-Hold but with lower drawdown and a higher Information Ratio. Similar results were observed for Binance Coin and Ethereum. The real strength was demonstrated when a portfolio combining Buy-and-Hold with our strategies outperformed all individual strategies and Buy-and-Hold alone, achieving the highest overall performance and a 50 percent reduction in drawdown. A conservative fee of 0.1 percent per transaction was included in all calculations. A cost sensitivity analysis was performed as a sanity check, revealing that the strategy's break-even point was around 0.4 percent per transaction. This research highlights the importance of optimizing walk-forward window lengths and emphasizing the value of single-time out-of-sample testing for reliable strategy evaluation.
Abstract Around three-quarters of Bitcoin transactions occur off-chain. While most empirical studies focus exclusively on on-chain transactions, only few papers analyse off-chain transactions. The empirical evidence of Bitcoin market considering both types of trading strategies remains limited. This paper is one of the first to present an empirical analysis of both on- and off-chain demand and supply-side factors and their short- and long-run relationship with the Bitcoin price. Employing the ARDL approach with daily data from 2019 to 2024, we demonstrate a differentiated contribution of on-chain and off-chain drivers to the Bitcoin price. In the long-run, off-chain demand pressures have a significant relationship with the Bitcoin price. In the short-run, both off-chain demand and supply factors are statistically significantly related to the Bitcoin price. The relationship between blockchain transactions and the Bitcoin price is also present, albeit likely operating through a different channel than off-chain trades. These findings confirm the dual nature of the Bitcoin market, in which price movements are related to both market fundamentals and speculative considerations captured by on- and off-chain trades, respectively.
Bitcoin's limited programmability and transaction throughput have historically prevented native Bitcoin from participating in decentralized finance (DeFi) applications. Existing solutions depend on honest-majority thresholds, or centralized custodial entities that introduce significant trust requirements. This paper introduces Bitcoin Smart Accounts (BSA), a novel protocol that enables native Bitcoin to access DeFi through trust-minimized infrastructure while maintaining self-custody of funds. BSA achieves this through a combination of emulated Bitcoin covenants using Partially Signed Bitcoin Transactions (PSBTs) and Taproot scripts, a Trusted Execution Environment (TEE)-based arbitration system, and destination chain smart contracts that enable DeFi platforms to accept self-custodial Bitcoin as collateral without necessitating protocol-level modifications. The setup leverages liquidity secured by the Lombard Security Consortium which provides a twofold advantage: for a DeFi protocol, liquidators rely on fungible assets with deep liquidity to quickly exit positions, while for a depositor, the general trust assumptions of honest majority (m-of-n) are reduced to existential honesty (1-of-k). We present the complete protocol design, including the Bitcoin architecture, the TEE-based arbitration mechanism, and the Smart Account Registry for protocol management. We provide a security analysis that demonstrates the correctness, safety, and availability properties under our trust model. Our design enables native Bitcoin to serve as collateral in lending markets and other DeFi protocols without requiring users to relinquish custody of funds.
The objectives of this paper are to provide a methodology for applying the DeTEcT framework to modelling token economies, to formalise the configuration of the simulation environment, and to introduce an event analysis framework. A token economy is an economic system that has a unique mechanism for controlling its monetary supply, and a medium, in the form of a token or currency, for the valuation of goods and services, the settlement of transactions, and the storage of value. We show the key decisions that must be made when modelling an economy with the DeTEcT framework and showcase some numerical methods that can be used in conjunction with the framework to perform economic simulations. We also propose a framework for analysing and measuring the impacts of events on an economy, while also developing a procedure to measure the significance of these impacts. Throughout the paper, we use Bitcoin as a case study to demonstrate how to apply the frameworks and tools we proposed here. We show how a model of Bitcoin token economy can be set up, and how to measure the impacts of Bitcoin's endogenous policies (i.e., BIPs) on the wealth distribution of its economic participants.
Flexible demand is increasingly important in energy systems with high renewable penetration. Bitcoin mining is often cited as a large, theoretically flexible load. Despite electricity consumption rivaling medium-sized industrial economies, the energy market behavior and impacts of Bitcoin miners remain largely unexplored. We exploit the large-scale relocation of Bitcoin mining to Texas, which became the world's largest mining hub following China's 2021 ban, to estimate its effects on local wholesale electricity prices. Combining a novel, hand-collected dataset on mining facility locations with high-frequency wholesale price data, we identify price impacts using a DiD design. We find that miners select into renewable-rich, high-GDP per capita counties with initially lower electricity prices on average. Mining entry has no significant effect on daytime prices but increases nighttime prices by 19.9%, indicating that Bitcoin miners fail to exploit their operational flexibility. Instead they increase baseload demand and reinforce fossil generation during low-renewable periods.
We develop a model of aberrant behavior by Bitcoin miners and test it with a new 2017-2025 dataset.Miners' rewards, comprised partly of user fees, exhibit variability across blocks of transactions.When large reward disparities exist between adjacent blocks, miners have incentives to attempt alternative versions of prior blocks and claim other miners' rewards for themselves.Regression analysis shows that fee differentials are associated with these attacks and longer waiting times between blocks.These patterns imply potential destabilization of the Bitcoin blockchain as future mining rewards become more volatile due to gradual withdrawal of fixed block subsidies.
This paper studies the benefits of timing Bitcoin returns by upside and downside volatilities. Standard volatility management implicitly treats volatility spikes as signals of adverse states, reducing exposure when total volatility increases. However, in Bitcoin, volatility spikes are frequently due to price rallies, which typically indicate subsequent positive returns. We show that semivolatility timing rules that account for both downside and upside risk concerns yield substantially stronger risk-adjusted performance than buy-and-hold and volatility-managed strategies. This stems from the fact that high upside-driven volatility states in Bitcoin are disproportionately associated with positive returns in the next period.
Blockchain as a promising technology is gaining its popularity ever since proof-of-work based Bitcoin came to the world. Nevertheless, Bitcoin achieves consensus at an expensive cost of energy. Proof-of-stake is one of the solutions for such a problem. Participants of PoS protocols achieve dynamic-availability in permissionless settings. Parties can join and leave the protocol at their will without notifying others. However, such protocol relies heavily on a central clock, providing the function of synchrony by collecting the finish status of every honest participant. In our protocol, the global function maintains the round information for each participant no longer needed. We analyze and modify the round into real-time based round model. Message delivery delay is also taken into consideration of the round length. However, participant need the connection of a real-world time global clock which is crucial to calculate the current round. And round length also is adjusted due to the changing network situation at the start of every new epoch.
Jan 1, 2026·Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
Blockchain, originally developed to solve the double-spending problem in digital currencies like Bitcoin, has evolved into a foundational technology with broad applications across public and private sectors.Its key features-immutability, decentralized trust, and cryptographic security-enable authenticated data sharing without the need for a central authority.This is particularly valuable in systems like supply chains, where participants may not know or trust each other.Smart contracts further enhance blockchain's utility by automating agreements through code, reducing uncertainty and fostering trust among stakeholders.The rise of the decentralized web, combined with emerging technologies like IoT, AI, and AR/VR, signals a wave of disruptive innovation whose full impact is yet to be seen.Given the rapid pace of development, academic research is essential to understand and guide blockchain's evolution.Conferences are especially important for timely knowledge dissemination, as they can keep up with the fast-moving nature of the field better than traditional journals.This mini-track builds on a series of successful sessions from HICSS conferences (HICSS-51 through HICSS-58), which have focused on blockchain's impact in areas such as fintech, transformation, and innovation.Over the years, it has served as a valuable forum for exploring blockchain technology and its implications for process improvement and innovation.For the current edition, six accepted papers contribute to expanding the academic understanding and supporting broader adoption of blockchain solutions.The first paper, "Playing Strategic Games in The Open Network (TON): Analyzing the Robustness of Proof-of-Stake Slashing Incentives", by Sascha Hgele, analyzes how rational validators in the TON blockchain respond to slashing penalties in a proof-of-stake system.Using a game-theoretic model, it reveals that when penalty enforcement is uncertain, validators strategically weigh risks and rewards, which impacts
The growth of the crypto markets has changed the investment environment in a profound manner by elevating cryptocurrencies from purely speculative assets to institutional-grade investments. The current paper evaluates the investment characteristics of Bitcoin and Ethereum, the most popular cryptocurrencies, based on the modern portfolio theory framework. According to the analysis carried out for 2020-2025, the Bitcoin asset demonstrates an impressive Sharpe ratio of 1.7, substantially exceeding that of the S&P 500 (0.54) and gold (0.48-0.54). In favorable market conditions, Ethereum outperforms Bitcoin in terms of risk-adjusted returns, exhibiting even better characteristics. The study highlights a change in the mechanism of price fluctuations in the market from the \\\"four-year cycle\\\" to the flow of institutional capital. At the same time, correlation analysis shows that despite the absence of high correlation of these assets with other asset classes over the long term, the correlation between the two increases under market pressure. As a result, 1-4% of portfolio weight can be safely allocated to each asset, depending on the investment strategy.
Abstract Originally designed to support cryptocurrencies like Bitcoin, blockchain technology has evolved into a powerful tool with applications far beyond digital currency. This paper explores how blockchain is transforming software development by enabling decentralized, secure, and transparent systems. Key areas of focus include digital identity verification, smart contract automation, supply chain tracking, decentralized data storage, and secure e-governance solutions such as digital voting. The study outlines fundamental blockchain components—such as distributed ledgers, consensus mechanisms, and tokenization—and explains how they contribute to building tamper-resistant applications. It also examines blockchain’s role in powering Web3 technologies, decentralized finance (DeFi), and cross-chain interoperability. Through real-world case studies in healthcare, logistics, and digital governance, the paper highlights the tangible benefits of blockchain-based solutions while acknowledging current limitations like scalability, energy use, and regulatory issues. The analysis offers a forward-looking perspective on how software developers and organizations can harness blockchain to create resilient, next-generation applications.
A infraestrutura das redes de registro distribuído (DLT) atravessa uma fase de escrutínio rigoroso quanto à sua viabilidade ambiental e eficiência operacional. Este relatório técnico analisa exaustivamente os três principais paradigmas de consenso contemporâneos: Proof of Work (PoW), Proof of Stake (PoS) e Proof of History (PoH), sob a ótica do custo-benefício energético e da segurança sistêmica. O Proof of Work, embora detentor de uma robustez histórica inigualável, apresenta um consumo elétrico de proporções nacionais, demandando cerca de 1.375 kWh por transação na rede Bitcoin. O Proof of Stake, consolidado pela transição do Ethereum, reduziu o dispêndio energético em 99,95%, operando com uma média de 0,0026 kWh por transação através da substituição da exaustão computacional pelo compromisso de capital. O Proof of History, atuando como um relógio criptográfico integrado ao PoS na rede Solana, otimiza a ordenação temporal e a escalabilidade, resultando em um consumo marginal de 0,00051 kWh por transação, o mais eficiente entre os protocolos de alta performance. O estudo conclui que a migração para modelos de baixo consumo e alta vazão (throughput) é impulsionada não apenas por avanços técnicos, mas por marcos regulatórios como o MiCA da União Europeia, que exige transparência absoluta sobre o impacto climático dos ativos digitais.<br>
For most of the last decade, Environmental, Social, and Governance (ESG) principles and the world of Bitcoin and Web3 have existed on opposite sides of the corporate conversation. ESG represented the rise of responsible capitalism — a commitment to sustainability, stakeholder accountability, and ethical governance that traditional institutions were being pressured to adopt. Bitcoin and Web3, meanwhile, were seen by many critics as antithetical to those ideals: energy-hungry, unregulated, ungoverned, and speculative. That characterisation is now rapidly becoming outdated.
Дмитро Люшенко, Нодарі Горгіладзе, Олександр Туголуков, Михайло Шептун · 6 authors
Висока волатильність криптовалют та швидке поширення технологій штучного інтелекту (ШІ) у фінансовому секторі визначають необхідність точного прогнозування ризиків і поведінки інвесторів у процесі цифрової трансформації фінансових ринків. Метою дослідження є розробка системи економетричних моделей для оцінки прибутковості, волатильності, ліквідності та ризику падіння основних криптоактивів із використанням методів на основі ШІ. Методологічна структура включає моделі специфікацій ARDL-MIDAS, GARCH-MIDAS, PMG та logit, які поєднують високочастотні ринкові дані, макроекономічні індикатори, он-чейнгові метрики та індекси настроїв інвесторів. Вибірка охоплює вторинні дані за 2018-2025 роки для п'яти провідних активів — Bitcoin, Ethereum, BNB, XRP та Solana. Результати моделі ARDL-MIDAS показали, що збільшення обсягів торгівлі на 1% збільшує короткострокову прибутковість на 0,012 пункту, водночас зростання індексу VIX зменшує їх на 0,014 пункту. У моделі GARCH-MIDAS коефіцієнти α=0.085 та β=0.900 підтверджували високу інерцію волатильності біткоїна, а компонент MIDAS у VIX мав значний вплив 0.27. Модель панелі PMG виявила негативний довгостроковий вплив волатильності на ліквідність (−0,27) і позитивний ефект надпливу стейблкоїнів (−0,12), що вказує на функцію стабілізації. Логіт-модель довела, що збільшення на стандартне відхилення індексу VIX збільшує ризик краху на 52%. Отримані результати підтверджують ефективність поєднання економетричних методів і ШІ для аналізу цифрових фінансових ринків і технологій ШІ для аналізу цифрових фінансових ринків. Висновки підкреслюють можливість практичного застосування запропонованих моделей у фінансовому прогнозуванні, управлінні ризиками та політиці стабілізації цифрових активів у контексті розробки інтелектуальних фінансових систем на основі ШІ.
Bitcoin derives a verifiable temporal order from probabilistic block discovery and cumulative proof-of-work rather than from a trusted global clock. We show that block arrivals exhibit stable exponential behavior across difficulty epochs, and that the proof-of-work process maintains a high-entropy search state that collapses discretely upon the discovery of a valid block. This entropy-based interpretation provides a mechanistic account of Bitcoin's non-continuous temporal structure. In a distributed network, however, entropy collapse is not completed instantaneously across all participants. Using empirical observations of temporary forks, we show that collapse completion unfolds over a finite propagation-bounded interval, while remaining rapid in practice.
Bitcoin’s price dynamics are influenced by both internal factors (e.g., supply shocks, investor sentiment) and external drivers, among which the stability of stablecoins has attracted increasing academic and regulatory attention. This paper investigates the effect of stablecoin peg deviations (USDT and USDC) on Bitcoin returns using daily data from January 2020 to August 2025. Based on a vector autoregression (VAR) framework, we conduct unit root tests, lag order selection, model estimation, Granger causality tests, and impulse response analysis. Results show that both Bitcoin returns and stablecoin deviations exhibit strong short-term inertia. USDT and USDC deviations significantly Granger-cause Bitcoin returns, whereas the reverse causality is weaker. Impulse responses indicate that stablecoin deviations first produce positive shocks to Bitcoin returns, followed by negative corrections that gradually stabilize. The effect of USDT is more pronounced and persistent, underscoring its central role in cryptocurrency markets. These findings highlight the importance of monitoring stablecoin market stability, especially USDT, for investors and regulators seeking to manage systemic risks in crypto markets.
The global expansion of Bitcoin and cryptocurrencies brings unanswered questions of the Islamic finance that are legal in nature. The existing research is divided into two camps, namely, total prohibition, or conditional acceptance. It is a thematic analysis of 32 public fatwas (2014-2024) of 12 Islamic jurisdictions in the first systematic analysis. The application of cryptocurrencies and their Shariah acceptability are analyzed. This paper applies the six-stage model offered by Braun and Clarke and it establishes five key jurist themes. The former theme is the ambiguity of the issue of whether cryptocurrencies are to be treated as mal (property) or thamaniyyah (money). The second theme talks about gharar, i.e., excessive uncertainty that is caused by volatility, lack of transparency and regulatory instability. The third theme concerns speculation by trading which is similar to maysir (gambling). The fourth theme is about mafsadah, which is harm to society and includes illicit use, environmental costs and inequality. Lastly, the fifth theme is on interpretations and deviations which form conditional permissibility in the presence of regulation and transparency, which minimises the risks of jurisprudence. The findings indicate that juristic disagreement is not an issue of inconsistency but the use of the various kinds of reasoning on novel financial technologies. The study paves the way in the study of Islamic-finance, by transforming the disjointed textual load of fatwa into a juristic map, which articulates the reasons behind the variance of rulings, as opposed to how they vary. This paper can be used by Shariah boards, regulators, and developers of digital assets to take action on implementing maqasid al-Shari, in the regulation of digital assets.
This study aims to clarify the concept and types of digital assets within a comparative analytical framework, enriched by an examination of a number of English judicial precedents specifically addressing the precautionary attachment of digital assets. The study begins by defining digital assets, their types, and their legal nature in this context. It then addresses the concept of precautionary attachment, its conditions, and its scope. Furthermore, the study examines the extent to which digital assets may be subject to precautionary attachment through an analysis of judicial precedents under English common law, while also highlighting key features of the Singaporean approach in this regard. This is intended to contribute to the Saudi legal framework in light of the absence of explicit legislative regulation of digital assets within the Saudi Enforcement Law. The study also seeks to clarify the extent of judicial authority in issuing precautionary judicial orders relating to digital assets, while highlighting the practical and legal challenges associated with their intangible nature and cross-border character. The study reaches several findings, most notably that the Saudi regulator has not yet provided an explicit statutory definition of digital assets in any of the applicable laws or regulations, nor has it permitted dealing in or trading such assets within the Kingdom, as confirmed by official statements issued by regulatory authorities. Nevertheless, in contrast, digital assets have occupied a significant place in comparative legal scholarship, particularly within comparative legal systems. Digital assets are defined as data recorded on the blockchain that confer specific rights such as ownership, access, representation, voting, or practical use. The scope of digital assets extends to include a wide range of digitally stored content and rights, including cryptocurrencies, non-fungible tokens (NFTs), and Bitcoin. The study also proposes several recommendations, most importantly that the Saudi regulator should introduce an explicit statutory provision defining digital assets within one of the applicable laws or regulations, whether within the framework of the Capital Market Law, commercial transaction laws, or monetary regulations. This would ensure clarity regarding the legal nature of such assets, define their scope, and enhance the ability to regulate and deal with them in accordance with statutory rules and specific controls. The study further recommends amending Article (24/3) of the Implementing Regulations of the Enforcement Law to expressly include digital assets among the assets subject to precautionary attachment. Following such amendment, the provision would read as follows: All assets of the debtor shall be subject to precautionary attachment, whether movable, immovable, or digital assets in all their forms.
Cryptocurrencies have upended the financial industry since they provide decentralized and peer-to-peer transactions. However, due to market volatility and the numerous non-linear relationships between price dynamics and human mood, forecasting Bitcoin values is a difficult task. The deep learning architecture shown in this work combines sentiment confidence scores derived from cryptocurrency-related tweets utilizing Transformer-based natural language processing with historical price indicators. The model incorporates Convolutional Neural Networks (CNN) to detect local time-series patterns and Long Short-Term Memory (LSTM) networks to produce long-term dependencies. We apply this architecture, involving sequence-based preprocessing and normalization, to Bitcoin and Ethereum to ensure robustness. Evaluations in comparison to baseline models Sentiment fusion dramatically increases predicting accuracy, especially during times of market turbulence, according to CNN-LSTM without sentiment, vanilla LSTM, and ARIMA. Our research helps develop scalable, sentiment-aware financial forecasting algorithms that better reflect the behavior of real markets.
This study systematically reviews scientific research on predicting cryptocurrency markets. A total of 790 articles obtained from the Web of Science database were included in the analysis, and the structure of the literature was evaluated using bibliometric methods. The preliminary investigation indicated that studies examining the prediction of cryptocurrencies have undergone a substantial increase since 2016. While a significant proportion of the extant literature pertains to Bitcoin, the first cryptocurrency, it is evident that other cryptocurrencies, such as Ethereum, have also attracted the attention of researchers over the years. The analysis yielded four primary categories: machine learning-based prediction methods, financial risk and volatility analyses, behavioral and technical determinants, and finally, advanced deep learning methods. In the context of cryptocurrency prediction, studies have underscored the significance of attributes, emphasizing their role in enhancing the efficacy of prediction models. These studies have also highlighted the impact of integrating machine learning and deep learning-based models with conventional methods in enhancing the performance of established models. The study emphasizes the necessity to direct future research towards the integration of behavioral indicators and the examination of multiple market relationships.
This study aims to analyze the volatility spillovers between Bitcoin and Ethereum, the two main actors in the cryptocurrency market, and altcoins across sectoral and financial groups. Using data from January 1, 2021, to March 6, 2023, the study applied the VAR-based method developed by Diebold and Yılmaz (2012) and measured both directional and total volatility spillovers. The findings show that Bitcoin's volatility largely stems from internal dynamics and spreads to other cryptocurrencies to a limited extent. In contrast, Ethereum is more affected by external shocks and exhibits a stronger volatility spillover across the market. Among altcoin categories, Gaming, Analytics, and DeFi groups were found to be the most influential in volatility transmission, while thematic tokens such as NFT, Web3, and Metaverse were more sensitive to external volatility. In contrast, stablecoins and tokens in the identity and healthcare sectors were found to have relatively low volatility and a more stable structure. These results offer important insights for investors and regulators regarding risk management strategies and portfolio diversification. The study provides a valuable framework for understanding the systematic volatility dynamics within the cryptocurrency ecosystem