This study presents a comprehensive analysis of the cryptocurrency market through the lens of classical and modern economic schools, focusing on key regulatory mechanisms: staking, halving, token burning, and asset locking. The relevance of the research stems from the need to develop a theoretical framework for managing the stability and liquidity of decentralized financial systems amid high volatility and technological transformation. The hypothesis posits that integrating principles from economic schools (classical, Keynesian, monetarist, Austrian, institutional) with algorithmic cryptocurrency mechanisms can create a hybrid model of market resilience. Using an interdisciplinary approach, including mathematical modeling, regression, and correlation analysis of data on Bitcoin, Ethereum, XRP, and BNB, the study confirmed Bitcoin’s dominant role as a systemic asset through token burning and vesting. The practical implications include recommendations for optimizing regulatory mechanisms, diversifying investment portfolios, and designing stress tests to mitigate systemic risks.
This study investigates directional causality between Bitcoin and gold across different market conditions. Rather than relying on mean-based dependence, we examine how causal effects vary across return quantiles, investment horizons, and market regimes. To address this question, we apply a Causal–Frequency–Quantile–Regime (CFQR) framework. The approach combines frequency-domain Granger causality, quantile-based non-causality tests, and endogenous regime classification within a unified setting. Macroeconomic controls are included to reduce omitted variable bias. Statistical inference relies on bootstrap procedures with false discovery rate correction to account for multiple testing. Using daily data from 2013 to 2025, we find that the full-sample directional dominance between Bitcoin and gold is generally weak after multiple testing adjustments. However, under stress regimes, the causal relationship of gold to Bitcoin becomes more pronounced at longer investment horizons. Under normal conditions, causal effects remain unstable and fragmented. Economic effects are modest. Variance-based hedging gains are limited, while downside risk measures show moderate improvement during stress periods. Overall, the evidence suggests that gold does not serve as a universal hedge for Bitcoin, but may exert conditional informational influence during high-uncertainty states. The CFQR framework provides a structured way to identify such state-dependent causal patterns.
Understanding how Bitcoin mining is distributed across countries is important for evaluating both the sustainability and resilience of the network. In this study, we examine the evolution of total Bitcoin electricity consumption alongside the geographic distribution of Bitcoin mining. Data are provided by the Cambridge Centre for Alternative Finance (Licensed under CC BY–NC–SA 4.0): Annual data from the Cambridge Bitcoin Electricity Consumption Index (2010–2025) and a monthly panel of country-level Bitcoin hashrate shares for 105 countries (September 2019–January 2022). To assess the degree of decentralization in the global mining network, we employ entropy-based measures, inequality indices, and panel convergence tests. The results indicate that total electricity consumption grew exponentially during the early years of Bitcoin, but later transitioned to a more stable and approximately linear path. Country-level permutation entropy reveals highly volatile and dynamic mining trajectories. The Theil index shows that cross-sectional inequality declines over time, while increasing symbolic entropy reflects a progressively more even cross-country distribution of mining activity. Further evidence from σ-convergence supports a statistically significant reduction in cross-country dispersion of mining shares. Dynamic panel fixed-effects estimates reveal mean-reverting behavior in relative country shares, consistent with stochastic convergence. Finally, Phillips–Sul analysis points to heterogeneous early transition paths but ultimately supports convergence toward a single global club. The gradual geographical decentralization occurs alongside persistent core–periphery asymmetries in long-run mining shares. Overall, our findings suggest that Bitcoin mining behaves as a globally integrated industry in which computational capacity reallocates rapidly across countries in response to economic and regulatory conditions.
Abstract This study examines bubble dynamics in the S&P 500 Index and Bitcoin, with particular emphasis on the role of gold as a proxy for market-wide stress. We apply the GSADF bubble test, time-varying Granger causality, and multifractal detrended fluctuation analysis to both original and gold-filtered price series. The results reveal a pronounced asymmetry between equity and cryptocurrency markets. Bitcoin exhibits statistically significant and persistent bubble behavior in both raw and filtered data, accompanied by multifractal persistence consistent with self-reinforcing speculative dynamics. In contrast, the S&P 500 shows no consistent evidence of sustained bubble behavior, and its multifractal properties remain aligned with short memory and rapid information absorption. The causality analysis indicates a stable, state-dependent predictive relationship from gold to equity prices, suggesting sensitivity to global risk sentiment, while no comparable persistent linkage is observed for Bitcoin. Overall, the findings suggest that equity price dynamics remain connected to market-wide stress conditions, whereas Bitcoin’s behavior appears to be driven primarily by asset-specific speculative forces.
Since the launch of Bitcoin in 2008, social scientists have sought to clarify the relationship between cryptocurrency and money. A dominant conclusion – particularly among approaches employing a commodity theory of money – has been that cryptocurrency is not money but an instrument of speculation whose activities are confined to circulation. This paper deepens the analysis of cryptocurrency and money by drawing on shifts in Marxist theory from the 1970s in the work of Diane Elson and Suzanne de Brunhoff. These developments enable a conceptualisation of money in capitalism not simply as the sum of its functions, but as taking on a particular social form. Building upon this, I develop a novel conceptualisation of cryptocurrency as ‘fictitious money’, a social form anchored in the general equivalent that facilitates the circulation and valorisation of new digital assets in the total circuit of money capital. Situating cryptocurrency in a value-form approach attendant to the unities of the functions of money and of production and circulation reveals productive entanglements and trajectories for it that must be taken seriously within IPE.
Crypto currency has emerged as one of the most disruptive innovations in modern financial history. Beginning with the introduction of Bitcoin in 2009, decentralized digital currencies have challenged traditional financial systems by enabling peer-to-peer transactions without centralized intermediaries. This paper examines the impact of cryptocurrency on global financial systems, including banking, monetary policy, financial inclusion, cross-border payments, and regulatory structures. It explores both opportunities—such as decentralization, efficiency, and innovation—and risks, including volatility, regulatory uncertainty, financial crime, and systemic threats. The study also analyses the rise of decentralized finance (DeFi) and Central Bank Digital Currencies (CBDCs) as responses to the growing influence of blockchain-based financial models. The research concludes that while cryptocurrencies present transformative potential, their long-term integration into financial systems will depend on regulatory clarity, technological scalability, and macroeconomic stability.
This study provides an econometric investigation of Bitcoin’s return dynamics using daily data over 5.5 years from January 2020 to September 2025. This research deeply analyses the market behaviour of Bitcoin over other assets like Gold, Silver, Ethereum, Tether, Nifth50, BankNifty. In this analysis we used advanced time series and statistical models such as ARIMA, GARCH(1,1), Rolling GARCH, Half-Life estimation, and EGARCH models to evaluate conditional mean behavior, volatility clustering, persistence, asymmetric shock effects, and regime-dependent risk transmission. With the use of this models, rolling Garch reveals structural instability with persistence decline in later periods. EGARCH results asymmetric shock effects, where negative shocks increases volatility more than positive shocks. Forecasting models suggests that volatility will eventually return to its long term average, but risk is still expected to remain high for some time before normalizing. The analysis reveals strong conditional heteroskedasticity and near-integrated volatility persistence during crisis periods specific around the COVID-19 market collapse (2020), the FTX bankruptcy shock (2022), the April 2024 Bitcoin halving, and the 2025 Bybit exchange hack. Using various data visualizations, the analysis reveals high risky nature of Bitcoin trade with high returns compared to other assets. Deep learning model LSTM reveals the nature that closing price of next day is unpredictable as obvious in case of such high volatile nature of Bitcoin. These findings underline the importance and nature of trading in Bitcoin for individuals who are thinking to invest.
Riyan Yusuf Octafia, Amalia Nur Chasanah, Usman Usman, Bara Zaretta
This study aims to analyze the influence of the Bitcoin economy on the money supply (M1) in Indonesia, with Bitcoin volatility as an intervening variable. Using a quantitative approach, the data consists of 36 monthly time-series observations from 2022 to 2024. Data analysis techniques include regression analysis adjusted with the Prais-Winsten method to address autocorrelation issues, the Sobel test for mediation analysis, and path analysis. The results indicate that the Bitcoin economy has a direct, positive, and significant effect on the money supply (M1) in Indonesia. However, the Bitcoin economy was found to have a negative and non-significant effect on Bitcoin volatility. Similarly, Bitcoin volatility exerts a negative but non-significant influence on the money supply (M1). The Sobel test results prove that Bitcoin volatility does not function as an intervening variable mediating the relationship between the Bitcoin economy and the money supply (M1). These findings suggest that while the expansion of the Bitcoin ecosystem encourages an increase in domestic monetary liquidity, the price fluctuations of digital assets have not yet become a transmission channel that significantly disrupts the stability of national monetary aggregates.
The housing market is of great significance to the development and advancement of cities, but customary forms of property valuation are frequently biased, time-consuming, and not always effective. This paper focuses on the city of Irbid in Jordan, aiming to collect all the information on apartments and houses, predict the prices of properties, and clarify the key factors influencing the prices. Following the comprehensive cleaning process of the data and exploratory analysis, three ensemble machine learning models were trained and optimized to achieve accurate price predictions. The performance of all three models demonstrated excellent and consistent predictions, highlighting the efficiency of ensemble methods in predicting property prices. SHAP analysis indicated that the size of the house, the number of bedrooms, the number of lounges as well as the location are the most significant factors influencing the prices in Irbid. This reflects the functioning of the local market.
The article provides a comprehensive study of the systemic transformation of corporate governance in the context of global digitalization, characterized by the transition from hierarchical models to decentralized structures. It is substantiated that blockchain technology emerges as a new institutional foundation, where traditional bureaucratic verification mechanisms are replaced by algorithms based on cryptographic protocols. A particular emphasis is placed on the distinctions between public (permissionless) and private (permissioned) blockchain networks regarding the immutability of records. The study examines the concept of decentralized governance and the functional specifics of Decentralized Autonomous Organizations (DAOs), where operational logic and management regulations are implemented directly into the software code of smart contracts. This minimizes the influence of traditional administrative management and mitigates "single point of failure" risks. The theoretical framework of the work builds upon classical theories, such as Oliver Williamson’s "Transaction Cost Theory," Michael Jensen and William Meckling’s "Principal-Agent Theory," and the scholarly works of Harold Demsetz. Blockchain is analyzed as a tool that renders market exchange more economically viable than hierarchy. The author proposes an original interpretation of a multi-tier blockchain model for enterprise management, encompassing the infrastructure, network, consensus, data, and application layers. The essence of consensus algorithms (PoW, PoS, DPoS) is disclosed through the prism of management. Special attention is devoted to international experience in legal regulation and the processes of implementing these standards within the legislative framework of Ukraine. The economic effect and practical aspects of the study are analyzed through successful case studies of global corporations (IBM, Amazon, Oracle, Walmart, Nestlé) and Ukrainian business initiatives (TASCOMBANK, SETAM, Agroxy, Softengi). These cases demonstrate a significant reduction in verification costs, lower operating expenses, and increased transparency in supply chains. The transition to an innovative "Management-as-a-Service" paradigm is justified, where blockchain serves not merely as software but as a new firm architecture. Conclusions are drawn regarding a shift in the management ontology – moving from "governance by humans" to algorithmic "governance by code," which ensures data immutability, cyber resilience, and the possibility of real-time preventive risk monitoring. References: 1. Kuzmina, T. O., Berezovskyi, Yu., Kalinskyi, Ye., Arliukova, Yu., & Trofymchuk, A. (2024). 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We study the deployment performance of machine learning based enforcement systems used in cryptocurrency anti money laundering (AML). Using forward looking and rolling evaluations on Bitcoin transaction data, we show that strong static classification metrics substantially overstate real world regulatory effectiveness. Temporal nonstationarity induces pronounced instability in cost sensitive enforcement thresholds, generating large and persistent excess regulatory losses relative to dynamically optimal benchmarks. The core failure arises from miscalibration of decision rules rather than from declining predictive accuracy per se. These findings underscore the fragility of fixed AML enforcement policies in evolving digital asset markets and motivate loss-based evaluation frameworks for regulatory oversight.
Antonio Pérez de Juan, Íñigo Martín Melero, Raúl Gómez-Martínez, María Luisa Medrano-García
This study investigates the relationship between public attention to the Sustainable Development Goals (SDGs) and cryptocurrency demand, specifically for Bitcoin (BTC) and Cardano (ADA). Given the environmental concerns associated with Proof-of-Work (PoW) and the sustainability benefits of Proof-of-Stake (PoS), we hypothesize that increased SDG attention leads to higher demand for green cryptocurrencies like Cardano and lower demand for non-green cryptocurrencies like Bitcoin. Using Ordinary Least Squares (OLS) regression and supervised machine learning algorithms, we analyze weekly cryptocurrency returns and Google Trends data from 2020 to 2025. The findings suggest that SDG attention has a statistically significant but weak negative impact on Bitcoin returns, while no significant effect is observed for Cardano. Machine learning models fail to predict cryptocurrency demand effectively. These results indicate that sustainability awareness alone is not a primary driver of cryptocurrency investment behavior.
Time series forecasting enables early warning and has driven asset performance management from traditional planned maintenance to predictive maintenance. However, the lack of interpretability in forecasting methods undermines users' trust and complicates debugging for developers. Consequently, interpretable time-series forecasting has attracted increasing research attention. Nevertheless, existing methods suffer from several limitations, including insufficient modeling of temporal dependencies, lack of feature-level interpretability to support early warning, and difficulty in simultaneously achieving the accuracy and interpretability. This paper proposes the interpretable polynomial learning (IPL) method, which integrates interpretability into the model structure by explicitly modeling original features and their interactions of arbitrary order through polynomial representations. This design preserves temporal dependencies, provides feature-level interpretability, and offers a flexible trade-off between prediction accuracy and interpretability by adjusting the polynomial degree. We evaluate IPL on simulated and Bitcoin price data, showing that it achieves high prediction accuracy with superior interpretability compared with widely used explainability methods. Experiments on field-collected antenna data further demonstrate that IPL yields simpler and more efficient early warning mechanisms.
Aleksandar Šević, Željko Šević, Athanasios Fassas, Panayiotis Tzeremes
There is a strong impetus to make cryptocurrencies more environmentally friendly, and in our study it is has been analyzed whether commodity price shocks have varying impacts on clean and dirty cryptocurrency interconnectedness before, during and after the COVID-19 pandemic. Using the decomposed and partial connectedness measure we evaluate the connectedness of oil price shocks, demand, supply and risk, as well as five clean and five dirty cryptocurrencies from October 2017 until April 2024. The spikes in demand and disruptions in oil supply lead to price increases. Oil shocks have the largest impact on sampled crypto products during the COVID-19 period, as opposed to pre- and post-pandemic years, and they demonstrate a stronger influence on selected cryptocurrencies than internal crypto-to-crypto dynamics. During the crisis, the difference between clean and dirty cryptocurrencies becomes less relevant when compared to no-crisis periods. We also find that clean cryptocurrencies are net recipients of shocks, while dirty counterparts, dominated by Bitcoin and Ethereum, are net transmitters, especially during the recovery phase. Our findings are relevant for supporting the transition to clean cryptocurrencies and contribute to a better understanding of dynamic interconnectedness. • Examines the decomposed and partial connectedness • Uses time-varying parameter vector autoregression (TVP-VAR) models • Highlights the heterogeneity in cryptos’ responses to oil price fluctuations • Total Connectedness Index peaks during the COVID-19 pandemic • The distinctions between clean and dirty cryptocurrencies reemerged post-COVID
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.
As cryptocurrencies evolve from niche assets to systemic financial components, the banking sector faces a strategic dilemma: displacement or adaptation. Using 27,510 bank–year observations from 2014 to 2023 across thirty-two economies, predominantly within the European banking sector, this study isolates the technological prerequisites for this adaptation. We employ a continuous interaction model with robust controls to test how national digital infrastructure moderates bank responses to valuation cycles in the four dominant cryptocurrencies by market capitalization (Bitcoin, Ethereum, Ripple, and Binance Coin). The results document a robust lagged complementarity effect: in digitally advanced economies, cryptocurrency booms significantly increase bank non-interest income in the subsequent year, while lending portfolios remain unaffected. A one-standard-deviation increase in crypto returns interacts with digital capacity to boost fee revenue by approximately 0.7 percentage points (0.20 standard deviations). Crucially, this effect persists after controlling for GDP and equity market interactions, confirming that technological capacity, rather than general economic wealth, acts as the binding constraint. These findings refine FinTech adaptation research by demonstrating that high-bandwidth infrastructure enables banks to monetize external volatility via service deployment and custody, transforming a potential threat into a structural revenue stream.m.
Yue Li, Lei Wang, Kaixuan Wang, Zhiqiang Yang · 7 authors
The rapid proliferation of autonomous AI agents is driving a shift toward agentic commerce, where agents are expected to autonomously invoke and pay for services. While blockchain-based payments offer a programmable foundation for such interactions, the recently proposed x402 standard fails to enforce end-to-end atomicity across service execution, payment, and result delivery. In this paper, we present A402, a trust-minimized payment architecture that securely binds cryptocurrency payments to service execution. A402 introduces Atomic Service Channels (ASCs), a new channel protocol that integrates service execution into payment channels, enabling real-time, high-frequency micropayments for agentic commerce. Within each ASC, A402 employs an atomic exchange protocol based on TEE-assisted adaptor signatures, ensuring that payments are finalized if and only if the requested service is correctly executed and the corresponding result is delivered. To further ensure privacy, A402 incorporates a TEE-based Liquidity Vault that privately manages the lifecycle of ASCs and aggregates their settlements into a single on-chain transaction, revealing only aggregated balances. We implement A402 and evaluate it against x402 with integrations on both Bitcoin and Ethereum. Our results show that A402 delivers orders-of-magnitude performance and on-chain cost improvements over x402 while providing trust-minimized security guarantees.
Abstract This paper investigates whether Bitcoin serves as a safe haven and a diversification tool for both developed and emerging stock markets during the COVID-19 crisis, in comparison with gold. The analysis covers daily data from June 18, 2012, to May 25, 2020, across a representative set of developed (S&P500, FTSE100, DAX, CAC40, Nikkei225, Ibex35) and emerging (Shanghai, Nifty50, Ibovespa, MOEX) equity markets, providing a comprehensive view of asset interactions in different financial environments. Methodologically, we employ a two-step approach: an EGARCH model to estimate time-varying volatility, followed by a copula-based framework to capture nonlinear and asymmetric dependence structures. This combination allows for a nuanced assessment of asset behavior under both tranquil and crisis conditions. The results show that Bitcoin maintains weak dependence on developed equity markets during the COVID-19 period but fails to display consistent safe-haven characteristics under extreme stress. Gold, by contrast, continues to act as a reliable hedge, confirming its traditional role in protecting portfolios against market downturns. Overall, these findings suggest that while Bitcoin may provide diversification benefits under normal circumstances, it cannot yet replace gold as a robust safe-haven asset. For portfolio managers, this highlights the importance of gold in risk management, while underscoring Bitcoin’s evolving yet still uncertain role in global financial markets.
Yaser Sadati-Keneti, Mohammad Vahid Sebt, Reza R. Tavakkoli-Moghaddam, Orod Ahmadi
The aim of this research is to employ improved machine learning techniques to determine the best Bitcoin trading positions in response to sudden price changes caused by global emergencies such as pandemics, conflicts, and economic disputes. Specifically, this study examines price fluctuations during the COVID pandemic as a case study to evaluate the performance of the algorithms investigated. We present a novel hybrid approach that merges Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Decision Tree (DT) classification to effectively eliminate noisy data and extract pertinent information for accurate position forecasting. The DBSCAN algorithm organizes the data to reveal important patterns, while the DT classifier sorts the trading signals. The performance of the proposed DBSCAN-DT model is rigorously compared with established alternatives, including the Multi-Layer Perceptron (MLP), Support Vector Classifier (SVC), and traditional Decision Trees. Findings from the experiments show that the DBSCAN-DT hybrid consistently outperforms these benchmarks during the outbreak, epidemic, and pandemic phases of COVID, attaining greater accuracy in forecasting both trading positions and market trends. These findings emphasize the essential importance of incorporating pandemic-related disruptions into cryptocurrency price prediction models and showcase the flexibility of our method in addressing sudden market changes.
Nach dem anfänglichen Hype um die Blockchain-Technologie, die erstmals durch Satoshi Nakamotos Bitcoin bekannt wurde, hat sich der Bereich in Richtung der Entwicklung ausgereifter Blockchain-basierter Systeme und Anwendungen weiterentwickelt. In dieser weitläufigen Landschaft fungieren die einzelnen Blockchain-Plattformen und Ökosysteme häufig als isolierte Silos, die strikt von anderen Plattformen getrennt sind und über keine inhärenten Interoperabilitätsfunktionen verfügen. Trotz der Existenz etablierter Mechanismen für den Austausch von Coins und Tokens über heterogene Blockchains hinweg müssen Entwickler von Web3-Anwendungen, die aus Smart Contracts bestehen, möglicherweise auf individuelle Anpassungen zurückgreifen, um Blockchain-übergreifende Anwendungen zu ermöglichen. In vielen Fällen sind diese Ansätze nicht ausreichend skalierbar, wenn die Anwendung auf zusätzlichen Blockchain-Plattformen verteilt werden muss. Folglich sind weitere Anpassungen erforderlich. Darüber hinaus stellt sich die Frage der Speicherung gemeinsamer Anwendungsdaten, die mit Smart Contracts kompatibel und für das dezentrale Konzept der Blockchain geeignet sein muss. Diese Arbeit präsentiert einen Vorschlag für eine Blockchain-übergreifende Datenspeicherlösung, die das InterPlanetary File System (IPFS) als dezentrale Off-Chain-Persistenzschicht und Blockchain-Oracles nutzt, um Lese- und Schreibvorgänge zu ermöglichen. Der Einsatz von incentivierten Vermittlern in Verbindung mit einem neuartigen Oracle-Verifizierungsmechanismus für Schreibzugriffe erlaubt die Formulierung eines Lösungsentwurfs für ein vollständig dezentrales System. Dieser Ansatz ermöglicht die lose gekoppelte Verbindung von Blockchain-übergreifenden Anwendungen, wobei die einzelnen Blockchain-Plattformen nicht direkt aufeinander zugreifen müssen. Wir präsentieren eine prototypische Implementierung des Lösungsentwurfs und bewerten anschließend den Prototyp hinsichtlich Kosten, Leistung und Sicherheit. Im Vergleich zu einer hypothetischen Referenzlösung, die eine zentralisierte Persistenzschicht verwendet, zeigen wir, dass vollständige Dezentralisierung die Betriebskosten und die Leistung sowie die Integrität der gemeinsam genutzten Daten erheblich negativ beeinträchtigt.
Decentralized financial platforms rely heavily on Web of Trust reputation systems to mitigate counterparty risk in the absence of centralized identity verification. However, these pseudonymous networks are inherently vulnerable to adversarial behaviors, such as Sybil attacks and camouflaged fraud, where malicious actors cultivate artificial reputations before executing exit scams. Traditional anomaly detection in this domain faces two critical limitations. First, reliance on naive statistical heuristics (e.g., flagging the lowest 5% of rated users) fails to distinguish between victims of bad-mouthing attacks and actual fraudsters. Second, standard Graph Neural Networks (GNNs) operate on the assumption of homophily and cannot effectively process the semantic inversion inherent in signed (trust vs. distrust) and directed (status) edges. We propose TAS-GNN (Topology-Aware Signed Graph Neural Network), a novel framework designed for feature-sparse signed networks like Bitcoin-Alpha. TAS-GNN integrates recursive Web-of-Trust labeling and a dual-channel message-passing architecture that separately models trust and distrust signals, fused through a Status-Aware Attention mechanism. Experiments demonstrate that TAS-GNN achieves state-of-the-art performance, significantly outperforming existing signed GNN baselines.
Abstract The rapid expansion of cryptocurrency markets has significantly transformed global financial systems through the adoption of decentralized, blockchain-based transaction mechanisms. Digital assets such as Bitcoin and Ethereum operate on distributed ledger technology, which enhances transparency, immutability, and peer-to-peer verification without reliance on traditional financial intermediaries. Despite these technological advancements, the cryptocurrency ecosystem faces escalating cybersecurity risks that threaten the integrity of financial data and reporting systems. Cryptocurrency exchanges, digital wallets, custodial services, and decentralized finance (DeFi) platforms are increasingly targeted by cybercriminals through hacking, phishing schemes, ransomware attacks, private key theft, and smart contract vulnerabilities. These cybersecurity incidents have profound implications for financial record integrity, including unauthorized transactions, asset misappropriation, valuation distortions, and inaccuracies in financial statements. Unlike conventional banking systems, cryptocurrency transactions are often irreversible, amplifying the financial and accounting consequences of cyber breaches. Furthermore, the pseudonymous nature of blockchain transactions complicates audit verification, regulatory compliance, and internal control processes. As organizations integrate digital assets into their financial reporting frameworks, weaknesses in cybersecurity governance may undermine stakeholder confidence and market stability. This paper critically examines the major cybersecurity threats present in cryptocurrency markets and evaluates their direct and indirect impact on the reliability, accuracy, and auditability of financial records. It also analyzes existing risk mitigation strategies, including multi-factor authentication, cold storage solutions, encryption protocols, smart contract audits, and regulatory oversight mechanisms. The study concludes that while blockchain technology inherently promotes data immutability and transparency, systemic vulnerabilities at exchange, platform, and user levels continue to pose substantial risks. Strengthened cybersecurity governance frameworks, standardized accounting treatments for digital assets, and coordinated global regulatory efforts are essential to ensuring the long-term integrity and sustainability of cryptocurrency-based financial systems.
We document the first systematic evidence of negative spillover effects in crypto asset returns across blockchains. Using on-chain data from Ethereum, Solana, Binance Smart Chain, Arbitrum, and Avalanche (2022-2025), we show that surges on one chain often coincide with declines on others, in contrast to the positive co-movements typical of equity markets. These spillovers intensify during attention shocks, proxied by chain activity and extreme return events, and persist after controlling for global equity returns, interest rates, and Bitcoin. Nonlinear factor models reveal that attention-driven capital reallocation, rather than common information, underlies these dynamics. Our findings introduce a new form of cross-market linkage, attention-induced substitution, that shapes risk transmission in crypto markets. The results carry implications for portfolio diversification, systemic risk measurement, and regulation of token launches that may trigger cross-chain capital flight.
Dang Sy Duy, Nguyen Duy Chien, Kapil Dev, Jeff Nijsse
Graph neural networks (GNNs) offer a principled approach to financial fraud detection by jointly learning from node features and transaction graph topology. However, their effectiveness on real-world anti-money laundering (AML) benchmarks depends critically on training practices such as specifically weight initialisation and normalisation that remain underexplored. We present a systematic ablation of initialisation and normalisation strategies across three GNN architectures (GCN, GAT, and GraphSAGE) on the Elliptic Bitcoin dataset. Our experiments reveal that initialisation and normalisation are architecture-dependent: GraphSAGE achieves the strongest performance with Xavier initialisation alone, GAT benefits most from combining GraphNorm with Xavier initialisation, while GCN shows limited sensitivity to these modifications. These findings offer practical, architecture-specific guidance for deploying GNNs in AML pipelines for datasets with severe class imbalance. We release a reproducible experimental framework with temporal data splits, seeded runs, and full ablation results.