Keir Finlow-Bates, Markus Jakobsson, Hossein Siadati
The transition to post-quantum cryptography in blockchain systems such as Bitcoin and Ethereum is often framed as a purely cryptographic problem. In practice, it also presents significant economic and infrastructural challenges: in globally replicated networks, increases in transaction size and verification cost are multiplied across all participating nodes. Existing post-quantum signature schemes, including lattice-based constructions such as CRYSTALS-Dilithium and stateless hash-based schemes such as SPHINCS+, introduce substantial increases in signature size. At blockchain scale, these increases translate into higher storage, bandwidth, and validation requirements, potentially requiring multiple generations of hardware improvement to become operationally routine. Historical experience suggests that even moderate increases in data footprint can be contentious, as illustrated by the Bitcoin block size debates (2015--2017). We propose a hash-based commit--reveal construction that replaces a single signature-bearing transaction with two lightweight transactions, each containing a fixed-size (32-byte) hash output derived from well-established primitives such as SHA-256, BLAKE, or Keccak. This approach achieves post-quantum security under standard hash assumptions while increasing the effective transaction footprint by only approximately 1.5$\times$ to 2$\times$ per authorization event. These results indicate that practical post-quantum migration may benefit from rethinking transaction semantics rather than directly adopting larger signature schemes, and that viable designs for decentralized systems must account for system-wide cost amplification.
In this paper, we examine the patterns and determinants of cross-border cryptocurrency flows. While our analysis focuses primarily on Bitcoin flows, the cryptocurrency with the largest market capitalization, we show that our key results also extend to four major stablecoins. After documenting global patterns of cross-border Bitcoin flows and contrasting them with those of traditional capital flows, we employ a cross-country panel approach to identify the key drivers of cross-border crypto flows for up to 162 countries. Our results provide evidence for the presence of multiple coexisting motives. The most significant motives comprise strategies to adjust to unfavorable macro and financial developments, as well as the need to conduct international payment and remittance transfers. Moreover, by conducting a case study of cross-border Bitcoin flows after the COVID-19 shock, we find that these motives were particularly relevant at a time when economic conditions were weak and the need for remittances appeared high. Gaining a better understanding of the motives behind cross-border cryptocurrency transactions is crucial for informing the public debate on cryptocurrencies and their potential use cases.
A non-custodial threshold instrument for Bitcoin would allow value to transfer between parties without network connectivity, fees, or custodial dependency. Digital signatures and multisignature scripts provide part of the solution, but the core benefit is lost if the issuer retains a key capable of unilateral redemption. All prior multisignature schemes have positioned the issuer at or above the spending threshold. We propose a system that inverts this: the holder receives the two keys constituting the spending threshold of a 2-of-3 multisignature script, and the issuer holds one key arithmetically below it.
Ulugmurodov Farkhod Fakhriddinovich, Hasanov Anvar Erkinovich, Abduvakhobov Feruzbek Abdurakhmonovich
This article comprehensively analyzes the formation, stages of development and the impact of cryptocurrencies on the modern economy. In particular, the transformation processes that have occurred in the financial system with the emergence of digital assets such as Bitcoin and Ethereum are studied. The study highlights the role of blockchain technology in transparency, security and reducing transaction costs. It also assesses the role of cryptocurrencies as an investment tool, their impact on monetary policy, and their impact on stability and risk factors in global financial markets. The article also examines the mechanisms for regulating cryptocurrencies based on the experience of different countries, and substantiates their positive and negative effects on economic development. The results of the study serve to draw scientific conclusions on the effective use of cryptocurrencies in the digital economy.
Paul Gerhart, Jay Taylor, Sri Aravinda Krishnan Thyagarajan
Atomic swaps are a fundamental primitive for the trustless exchange of digital assets across blockchains: they guarantee that either both parties receive the agreed assets or neither party transfers. While this all-or-nothing guarantee is powerful, it also imposes an inherent determinism that rules out exchanges whose intended outcome is probabilistic. As a result, existing atomic swaps cannot realize trustless exchanges in which one party pays for a fixed chance of receiving a larger asset or reward, as in lotteries, randomized allocation mechanisms, and probabilistic cross-chain trades. We introduce probabilistic swaps, a new cryptographic primitive that extends atomic swaps to the probabilistic setting. In a probabilistic swap, one party's transfer is executed with a fixed, publicly specified probability embedded in the protocol and cannot be biased by either party. This yields a trustless mechanism for randomized exchange with verifiable odds and no trusted intermediary. Our construction combines adaptor signatures with oblivious pseudorandom functions (OPRFs) to realize the desired probabilistic outcome while ensuring that neither party can predict or bias it in advance. Along the way, we introduce a new mechanism for the atomic exchange of OPRF evaluations for payments, which may be of independent interest. A key feature of our approach is that it preserves the minimal on-chain footprint of modern atomic-swap protocols. The protocol relies only on standard Bitcoin scripts, such as digital signatures and timelocks, and is deployable on any blockchain that already supports atomic swaps. Consequently, probabilistic swaps are indistinguishable from ordinary on-chain transactions, which helps preserve privacy and fungibility. We provide formal security foundations and demonstrate practicality through a probabilistic swap in the Bitcoin testnet and in the Lightning Network.
Strategic competitions in the real world, from wars to geopolitical rivalries, often involve coalitions competing against rival groups. These contests are not simple interactions between unified entities, but multilayered processes in which coalitions face external competition while dealing with internal conflicts over resources and strategy. Existing game-theoretic models typically treat inter-coalition rivalry and intra-coalition competition separately. This paper introduces the Compound Coalition-Attrition Game (CCAG), a unified framework that integrates a war of attrition between coalitions with a simultaneous war of attrition within each coalition. In this model, the endurance of a coalition in external competition is determined by the strategic choices of its members, who compete internally for shares of the outcome. We prove the nonexistence of pure-strategy equilibria and characterize the unique mixed-strategy Nash equilibrium. The analysis reveals feedback effects: external competition intensifies internal conflict, while internal discord weakens external performance. A case study compares traditional commodity markets, including gold, copper, and silver, with cryptocurrency markets, including Bitcoin, Ethereum, and Solana, using data from 2018 to 2023 in a simulation framework. The results demonstrate applicability in industrial strategy, corporate decision-making, and geopolitical competition. The CCAG framework provides a tool for analysing complex strategic environments.
Cryptocurrency market infrastructure—public blockchains and cross-chain bridges supporting tens of billions in liquidity—is monitored as a systemic-risk surface by the Financial Stability Board and equivalent bodies, with defensive posture calibrated against human-level adversaries. Anthropic’s April 2026 release of Claude Mythos Preview has prompted institutional response across financial regulation but no blockchain-specific analytical framework. This paper develops one by defining Mythos-class as a vendor-neutral capability profile: a set of frontier autonomous offensive capabilities specified independently of any single model or vendor (defined by five constituent capability primitives). The central analytical claim is friction inversion: the patch primitives, segmentation, vendor-coordinated disclosure, and credential rotation that constrain Mythos-class capability in conventional IT environments are structurally absent on-chain. This makes blockchain exposure positioned differently in kind, not degree, from enterprise IT. The paper instantiates this finding against Bitcoin and Ethereum/L2 architectures through analysis of four major bridge exploits totaling over $1.74 billion in losses. Vendor-neutral defensive and governance frameworks defined against the capability profile rather than any specific model release are the correct unit of analysis. On this basis the paper offers general recommendations for protocol governance, audit and verification cadence, and regulatory posture, developed as an analytical framework rather than as empirically validated risk estimates.
У статті досліджено економічний потенціал блокчейн-технологій як інструменту протидії глобальним змінам клімату. Проаналізовано реальний екологічний вплив криптовалют, зокрема порівняно енергоспоживання мереж Bitcoin та Ethereum після переходу на Proof-of-Stake. Розглянуто механізми токенізації вуглецевих кредитів, роль децентралізованих фінансів (DeFi) та децентралізованих автономних організацій (DAO) у кліматичному фінансуванні. Висвітлено практичні кейси застосування блокчейну в секторі відновлюваної енергетики та ризики грінвошингу. Окремо проаналізовано внесок вітчизняних науковців у дослідження впливу блокчейну на екологічну стійкість та формування «зеленої» цифрової економіки в Україні. Визначено перспективи інтеграції штучного інтелекту та Web3-технологій у кліматичні ініціативи до 2030 року.
This paper develops a deep reinforcement learning framework for cryptocurrency portfolio management in which transaction costs are derived from the Riemannian geometry of the underlying volatility model rather than assumed constant. A Proximal Policy Optimisation agent is trained on a reward function grounded in non-equilibrium thermodynamics: we use the free-energy Bellman equation, in which transaction costs are the geodesic slippage on the Fisher information manifold of a maximum-entropy Markov-switching GARCH model, and regime-transition costs are the Wasserstein-2 distance between the calm and turbulent return distributions. A thermodynamic Carnot bound on portfolio efficiency is established and empirically validated. Five hypotheses are tested across Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash over January 2017 to March 2026. The geometric-cost agent achieves statistically superior Sharpe ratios relative to flat-fee baselines on four of five assets; portfolio turnover is reduced by 56 to 83 percent relative to signal-following; the thermodynamic friction point at which the agent prefers no-trade is asset-specific and ordered by turbulent half-life; a joint topological and geometric circuit breaker reduces Maximum Drawdown by 28 to 38 percent; and ablation confirms that every component of the observation vector contributes a statistically significant performance gain. The framework requires liquid cryptocurrency markets with validated parametric volatility models; transferability to other asset classes requires upstream recalibration.
Mohammad Y. Allaho, Mehmet H. Karaata, Israa A. Elgemiei
The distributed ledger systems rely heavily on miners, who are a vital component of the cryptocurrency ecosystem. Most cryptocurrencies cease to exist within five years of operation [1] due to churning. Most current cryptocurrency analyses in the literature focus on mining pools and ignore the individual miners’ perspective and in-depth analysis of the churning phenomenon and its possible reasons. In this study, we conducted a longitudinal and overall study on two of the most growing cryptocurrency networks, namely Bitcoin and Ethereum. The Bitcoin dataset used spans over 12 years (2009-2021). Whereas the Ethereum dataset spans over 8 years (2015-2023), including the two versions of Ethereum (before and after the merge). Our goal is to uncover the factors that drive miners’ churning and reveal essential characteristics of cryptocurrency mining, such as network fairness and centrality. Generally, both networks experience a decline in active miners over time. Our results confirm the centrality of the Bitcoin and Ethereum networks, whereas Bitcoin is found to be more distributed and fairer than Ethereum in both versions. Also, in Bitcoin, solo miners are less centralized and experience a fairer distribution of blocks formation than pool miners, however, pool miners have more mining rewards on average. Also, pools are found to decrease churning for pool miners compared to solo miners. Moreover, it is found that miners’ waiting time is a significant factor in miners’ churning. The existing protocols used require improvements to increase network decentralization and fairness, as well as reduce miners’ churn.
AbstractBlockchain technology has transformed digital transactions by providing a secure, decentralized way to record data. Its best-known use is Bitcoin, the first cryptocurrency to work without a central authority. This article examines a Bitcoin transaction to show how decentralization, transparency, and cryptographic security enable digital payments without third parties.
Este boletim quinzenal gratuito visa analisar o comportamento do Bitcoin, um ativo financeiro digital, oferecendo notícias, análises gráficas e informações sobre as mais recentes novidades, softwares e aplicativos relacionados a essa criptomoeda. Nosso objetivo é enriquecer as discussões em torno da cultura do Bitcoin, colaborando com a Amauta, uma instituição de economia criativa que busca disseminar conhecimento sobre inovação, educação e finanças na comunidade acadêmica e empresarial. Esperamos que este trabalho represente uma contribuição valiosa para o debate. Reconhecemos a importância do Bitcoin e seu impacto na economia global, motivo pelo qual nos dedicamos a fornecer informações atualizadas aos nossos leitores. Acreditamos que ao promover discussões e compreensão sobre o Bitcoin, podemos incentivar a adoção e o uso responsável dessa tecnologia disruptiva. Para além das análises e informações sobre o Bitcoin, incentivamos ativamente nossos leitores a se educarem sobre finanças pessoais e investimentos. Acreditamos que, munidos do conhecimento adequado, todos podem tomar decisões financeiras inteligentes e bem informadas. Comprometemo-nos a fornecer informações de alta qualidade e precisas, esforçando-nos para manter nossos leitores atualizados sobre as últimas tendências e desenvolvimentos no mundo do Bitcoin. Esperamos que este relatório seja do seu agrado e contribua para uma compreensão mais aprofundada do Bitcoin e das finanças pessoais em geral.
This paper argues that trust scores — from credit ratings and ESG scores to AI-generated trust metrics — fail not because of poor implementation, but because trust itself is the wrong abstraction. Trust is not a scalar quantity but a contextual, relational, and topological phenomenon. Any attempt to reduce it to a universal numerical score leads to fragility, manipulation, exclusion, and systemic failure. We identify five structural failure modes (context collapse, Goodhart's Law, epistemic centralization, irreversibility, and metric substitution for truth), supported by historical case studies (Enron, Wirecard, Volkswagen Dieselgate, the 2008 subprime crisis, ESG rating failures). A formal impossibility argument demonstrates that no universal trust score can simultaneously satisfy context independence, temporal stability, observer neutrality, and manipulation resistance. We propose proof-based systems as the alternative paradigm, where trust is not measured but rendered unnecessary through local, irreversible verification. Examples include Bitcoin Proof-of-Work, zero-knowledge proofs, and blockchain-based supply chain traceability.
The Landauer principle motivates the definition of economic temperature as the monetary price of processing a bit irreversibly. No empirical test of this definition exists in transparent fee markets. This paper fills that gap using daily Bitcoin and Ethereum data, constructing canonical thermodynamic state variables and evaluating five diagnostic layers: state variable behavior, Maxwell-type integrability, Carnot-style efficiency bounds, nonlinear regime separation, and structural break sensitivity to protocol events. Bitcoin's log-temperature behaves as a persistent mean-reverting process with an AR(1) coefficient of 0.97 and a half-life of 21 days; Ethereum is highly persistent, with weaker formal evidence of stationarity than Bitcoin. Maxwell integrability is frequency-dependent: Bitcoin passes all four relations at monthly frequency, whereas Ethereum passes two of four. Carnot-style evidence is the strongest: realized fee extraction efficiency stays well below the implied bound, with daily compliance exceeding 97% on both chains. Structural breaks around Bitcoin ordinals, EIP-1559, the merge, and Shanghai confirm that protocol changes reorganize the temperature relation. The thermodynamic framework provides structure that standard fee market analysis does not, including a first principles efficiency bound and a state space coherence test. The findings provide partial, frequency-dependent, and chain-specific empirical support for a Landauer-based thermodynamic description of blockspace markets.
Renewed attention to the identity of Bitcoin's pseudonymous creator has revived an old worry: that the roughly 1.148 million BTC mined by Satoshi and never moved represent a major tail risk for bitcoin. This paper argues that the worry is overstated. The mechanical downside of selling the position is bounded well below the feared collapse, and the outcomes most consistent with sixteen years of observed behavior are not bearish for bitcoin's effective supply. We analyze the position in two ways. First, we model the case of a purely financial holder. Multiple sale scenarios, checked against both a square-root-law estimate and the historical record of large sales, suggest that bitcoin's current market liquidity could absorb a patient multi-year sale with a cumulative price impact centered around 10 to 13 percent relative to a no-sale case. The same arithmetic also links the downside from a surprise sale to the upside from a confirmed burn: both are bounded by the same effective-supply adjustment, so the doom case and the burn-rally case cannot both be large. Second, we consider the preferences implied by the sixteen-year record. Ideological restraint, privacy, already having enough, and preserving the myth all point toward further dormancy, permanent loss of access, or a deliberate burn. A sale or an act of sabotage remains possible, but the record supports it less strongly. Under both approaches, the mechanical bear case is bounded, and the likeliest outcomes are neutral to mildly positive for bitcoin's effective supply. The argument does not rule out transient overshoot or leverage-driven amplification; it bounds the durable repricing the coins themselves can cause.
Piotr Fiszeder, Witold Orzeszko, Radosław Pietrzyk
Abstract This study investigates the use of ChatGPT as an automated tool for extracting and labeling Bitcoin-related news sentiment and examines how the resulting sentiment indicators affect Bitcoin returns and volatility. A large dataset of news headlines is processed via an API-based workflow, and the ChatGPT-derived sentiment indicators are subsequently incorporated as explanatory variables into selected statistical and machine learning models, including autoregressive (AR), heterogeneous autoregressive (HAR), Bayesian model averaging (BMA), least absolute shrinkage and selection operator (LASSO), and support vector regression (SVR). We find that while the sentiment indicators significantly improve in-sample estimation accuracy for returns and volatility, they do not lead to statistically significant gains in out-of-sample forecasting performance. This result suggests that ChatGPT-based sentiment measures primarily capture contemporaneous market-relevant information rather than persistent predictive signals, consistent with semi-strong market efficiency.
Стаття присвячена створенню мультимодальної системи прогнозування Bitcoin, яка об’єднує традицiйнi ринковi показники з аналiзом новин через нейромережi LSTM та GRU. Завдяки використанню GDELT та моделi FinBERT авторам вдалося видiлити вплив геополiтики й фiнансiв на крипторинок, що пiдняло точнiсть прогнозiв на 15-хвилинних iнтервалах з 53,2% до вражаючих 77,8%. Головна особливiсть пiдходу — механiзм щотижневого адаптивного донавчання, який рятує модель вiд застарiвання, та виявлення 30-хвилинної затримки, з якою макроекономiчнi новини реально вiдображаються на цiнi. Наукова новизна зосереджена на алгоритмi автоматичного коригування ваг мережi, що дозволяє системi самостiйно пiдтримувати актуальнiсть в умовах хаотичного ринку.
The aim of this study is to assess the viability and valuation of Bitcoin as a strategic corporate asset, evaluating its role as a store of value and its implications for financial management. Data were collected from specialized platforms such as investing.com, coinmetrics.io, gold.org, blockchain.com, fredf.org, and coinmarketcap.com as of July 23, 2025, complemented with institutional reports and recent academic literature. The methodology relies on three main approaches: a) gold market–adjusted valuation, b) Hayes’ cost-of-production model, and c) statistical analysis through Pearson’s correlation coefficient, alongside the Stock-to-Flow (SF) model. Results indicate that, assuming a 10% penetration of the investment gold market, Bitcoin’s value would be around USD 114,398, reaching up to USD 560,551 with 49% penetration. The cost-of-production model yielded an estimated value of USD 97,167, consistent with the range reported by the University of Cambridge and close to its market price (USD 118,175). Correlation analysis reveals a strong positive association with the Nasdaq (+0.81) and a negative one with the VIX (–0.61), positioning Bitcoin as a risk asset rather than a safe haven. The SF model highlights Bitcoin’s relative scarcity, with a ratio of 121.14 compared to gold’s 67.58, reinforcing its narrative as 'digital gold.' Overall, the findings suggest that Bitcoin’s strategic potential rests on its programmed scarcity and growing institutional adoption, though its consolidation will depend on sustained demand, lower volatility, and a clearer regulatory framework.
The increasing complexity of global financial systems has necessitated the adoption of more efficient and transparent mechanisms for combating money laundering (AML). Blockchain technology, with its decentralized, immutable, and transparent characteristics, presents a promising solution to address the limitations of traditional AML systems. This paper represents a review, exploring the potential applications of AI and blockchain in enhancing financial control systems, in particular, within AML compliance, focusing on key areas such as transaction monitoring, cross-institutional data sharing, and regulatory reporting. The integration of blockchain can streamline AML processes, reduce operational costs, and increase the effectiveness of detecting illicit financial activity. The combination of blockchain technologies and artificial intelligence algorithms in financial control is considered. It is shown how automation of transaction analysis can strengthen the stability of the banking system and prevent financial crimes. It is demonstrated that the convergence of Artificial Intelligence and blockchain technologies presents a transformative opportunity to strengthen AML frameworks, particularly in the face of rising crypto-enabled financial crimes. This research offers several important contributions to the academic literature. First, it presents a synthesis of the current status of artificial intelligence approaches used for compliance in detecting fraud in Bitcoin transactions. This review discusses the essential methodologies and tactics in a particular area that intersects finance and compliance but falls under the broader disciplines of AI-driven finance and decentralized finance (DeFi). The incorporation of AI into financial control marks a tremendous technological revolution that is affecting industries across the board. Second, the study assesses the current state of the publications, major trends, and research gaps, emphasizing areas that deserve additional investigation.
Bitcoin's security posture depends on more than consensus rules and proposal-based governance. This study proposes a formal cybersecurity framework for Bitcoin that integrates automated vulnerability monitoring, dependency inventory control, exploitability analysis, relational graph analytics, and funded bounty incentives. The framework is intended to reduce vulnerability exposure in Bitcoin Core and adjacent open-source dependencies through Dependabot for automated security pull requests, FOSSA for supply chain visibility, CycloneDX for exploitability classification, and Neo4j for relational tracking of vulnerability scope. The study argues that Bitcoin Improvement Proposals are insufficient for vulnerability management because they do not provide rapid remediation workflows or structured researcher incentives. A formal responsibility model is also proposed to assign accountability across maintainers, contributors, bounty hunters, financers, and policy functions. The resulting framework advances a more responsive, measurable, and auditable security posture for the Bitcoin ecosystem than existing frameworks.
The distributional specification in Markov-switching GARCH models has historically been driven by empirical convention rather than statistical theory. This paper derives the two-regime MS-GARCH specification from the Maximum Entropy Principle, providing an information-theoretic motivation for Student-t regime-conditional innovations in cryptocurrency volatility modelling. The framework is applied to five major cryptocurrencies, Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash, over the period January 2017 to March 2026, comprising 15,834 daily observations spanning six complete market cycles. Three principal findings emerge. First, a Calm-Phase Fragility pattern is identified: four of five assets exhibit calm-regime half-lives below one trading day (0.48 to 1.16 days), with turbulence the dominant long-run state (stationary turbulent probability in [0.451, 0.771] across all assets), establishing turbulence rather than calm as the structural baseline of the cryptocurrency ecosystem. Second, the Maximum Entropy derivation yields endogenous Student-t degrees of freedom, with heavy-tailed turbulent innovations (degrees of freedom approximately 4.5) confirmed across all assets, validating the MaxEnt constraint framework empirically. Third, near-unity turbulent GARCH persistence drives MS-GARCH point forecasts toward the persistence ceiling, consistent with an information-theoretic bound on predictability when the calm half-life collapses below one trading day; HAR-RV achieves the lowest QLIKE loss for three of five assets under these near-critical conditions. Cross-asset consistency is confirmed across seven statistical indicators including Hill tail exponents in [2.31, 3.26], Hurst exponents in [0.543, 0.577], and Wald tests rejecting parameter homogeneity at p < 0.001 for all assets. The framework is formalised as a deployable expert system for real-time regime monitoring and risk management.
Considerando a crescente expansão do mercado de criptoativos e a recorrente associação do bitcoin a práticas de ocultação patrimonial, torna-se relevante examinar sua utilização no delito de lavagem de dinheiro, especialmente diante dos riscos de responsabilização penal indevida de agentes que atuam licitamente nesse setor. Objetiva-se analisar a utilização do bitcoin no delito de lavagem de dinheiro, a partir do exame de sua definição, de suas formas de circulação e de sua possível inserção em dinâmicas de ocultação e dissimulação de valores de origem ilícita. Para tanto, proceder-se-á a uma pesquisa de abordagem qualitativa, com emprego do método jurídico-dogmático e da linha crítico-metodológica, mediante revisão bibliográfica e documental da legislação e da doutrina especializada. Desse modo, observa-se que o bitcoin, embora apresente características que podem favorecer sua utilização em esquemas de lavagem de capitais, como pseudoanonimato, descentralização, mobilidade transnacional e inexistência física, não constitui, por si só, instrumento ilícito. O que permite concluir que sua relevância penal depende da demonstração concreta de vínculo com infração penal antecedente e da prática de atos voltados à ocultação ou dissimulação da origem ilícita dos valores.