Mrs. A. Asrin Mahmootha, B. Aysha Banu, Mohammed Muhajir S, Mohamed Anas A Ā· 5 authors
Voting is a central component of a countryās political life cycle. Privacy, authentication, and integrity of citizensā votes are essential requirements of any electronic voting programme. To address these concerns, this paper proposes a hybrid e-voting system that integrates personal and public blockchain with a machine learningābased intrusion detection mechanism. The personal blockchain governs voter registration and vote casting, while the public blockchain stores the Merkle root hash for result integrity verification. An ML-based intrusion detection system monitors voting data centres and e-voting stations for anomalous behaviour. Homomorphic encryption and zero-knowledge proofs preserve voter anonymity. Experimental evaluation demonstrates that the proposed framework achieves an accuracy of 97.4%, precision of 96.8%, recall of 97.1%, and F1-score of 96.9% in detecting intrusion attempts. The system also reduces transaction latency by 34% compared to conventional blockchain voting systems. Results confirm that the framework delivers transparency, tamper-resistance, and strong security guarantees, making it a viable solution for modern democratic elections
Mr. Harshal Kadam, Mr. Mayur Prajapati, Mr. Amit Yadav, Prof. Sonali Karthik
ConQuote Connect is a smart digital platform designed to solve common problems in the construction industry, such as unclear project details, payment delays, miscommunication and the difficulty of finding trustworthy contractors. It creates a single, streamlined space where builders can post their construction projects and contractors can submit structured and easy to compare quotations. A key part of the system is the use of Building Information Modeling (BIM), which allows builders to upload 3D models of their projects. These models help both parties clearly understand the scope of work and visually track progress through milestones, such as marking when the foundation, floors, or roofing are completed. To make payments more secure, transparent, and fair, ConQuote Connect uses blockchain-powered smart contracts. These contracts safely hold project funds and only release payments when a builder confirms that a milestone has been completed through the BIM model. The platform also includes AI tools that assist in comparing contractor quotes and helping builders make faster, more informed and data backed decisions. When a contractor successfully completes a project, they receive a digital certificate in the form of an NFT, which becomes part of their verifiable reputation and track record on the platform. Both builders and contractors have their own personalized dashboards to manage tasks, communicate updates, track progress and approve or verify completed work. By combining BIM, blockchain and AI in one easy to use system, ConQuote Connect offers a modern, transparent and trustworthy way to manage construction projects reducing disputes, saving time and improving industry collaboration.
Adaeze Linus Miracle, Moniaye Ayadi, Dr. J. O. Omokehinde
Blockchain technology has emerged as one of the most transformative digital innovations with the potential to improve transparency, security, and operational efficiency in accounting and auditing. Despite these potential benefits, adoption among accounting firms in developing economies remains limited due to concerns regarding implementation costs, technological complexity, and regulatory uncertainty. This study examines the costābenefit dynamics of blockchain adoption among accounting firms in Nigeria by investigating the influence of implementation costs and operational benefits on blockchain adoption and evaluating the effect of blockchain adoption on operational performance. A cross-sectional survey research design was adopted, and primary data were obtained from 220 accounting professionals drawn from accounting and auditing firms in Lagos State, Nigeria. Descriptive statistics, Pearson correlation, and linear regression techniques were employed to analyse the data. The findings indicate that implementation costs significantly reduce the likelihood of blockchain adoption, whereas perceived operational benefits significantly enhance adoption. Furthermore, blockchain adoption exerts a positive and statistically significant effect on the operational performance of accounting firms through improvements in reporting accuracy, transparency, operational efficiency, and client confidence. The findings support the Technology Acceptance Model and Transaction Cost Economics by demonstrating that organisations evaluate emerging technologies based on the balance between expected benefits and associated implementation costs. The study concludes that although blockchain implementation requires substantial initial investment in infrastructure, integration, training, and regulatory compliance, its long-term operational benefits outweigh these costs. The study recommends phased implementation strategies, investment in digital competencies, and the development of supportive regulatory frameworks to accelerate blockchain adoption within the Nigerian accounting profession.
The Moroccan Ministry of Justice aims to enhance the process of the judicial system. Through digitalization, given the sensitive information and the complexity of managing this volume of data, along with the multiple electronic materials exchanged, several challenges regarding the security, integrity, and confidentiality of personal data are presented that indicate difficulties in confirming authenticity. Using blockchain technology, the Ministry of Justice can exchange data and knowledge in a secure and transparent way. The goal of the proposed method is to automate the procedure for generating birth certificates to strengthen trust, security, and operational efficiency within the Moroccan judicial system.
Background. Technical analysis remains among the most accessible forms of market decision support, but its incremental predictive value depends on design choices, and repeated model selection can create spurious backtest performance. Objective. This study evaluates whether widely used technical indicators, reconstructed from public formulas, can predict the next daily price direction of Bitcoin, the S&P 500, and gold. Methods. Daily data from 1 January 2015 to 14 July 2026 are examined using a chronological walk-forward design. Every signal observed at the close of day t is matched only with the sign of the close-to-close return from t to t+1. The main out-of-sample period begins in 2019, with 2019ā2022 used for model selection and 2023ā2026 reserved for confirmation. Performance is measured primarily by balanced accuracy and supplemented by accuracy, directional recall, stationary-bootstrap confidence intervals, and after-cost trading outcomes. Results. The best single indicator, Ichimoku 9/26/52, produced a macro balanced accuracy of 50.9%, while the best predetermined combination, Volume Confirmed, reached 50.8%. A new ridge-logistic hybrid indicator, SPAH-1, achieved 51.9% in validation and 51.3% in confirmation. Its confirmation balanced accuracy was 49.8% for Bitcoin, 49.2% for the S&P 500 proxy, and 55.0% for gold; only gold's bootstrap interval excluded 50%, but its predictions were strongly biased toward the upward class. After trading costs, Volume Confirmed underperformed buy-and-hold for all three assets. Additional selective experiments did not support an 80% daily prediction target. Conclusion. Technical indicators may assist regime description and decision confirmation, but they do not provide a robust universal next-day forecasting edge.
Cryptocurrency began as a marginal experiment among cypherpunk technologists and has since become an asset class worth trillions of dollars globally, and its rise has been matched by a parallel rise in criminal activity that feeds off the pseudonymity, decentralisation and borderless movement these assets allow. India counts among the countries with the largest populations of crypto holders anywhere, yet Parliament has still not enacted any statute dedicated to virtual digital assets; oversight instead comes piecemeal, through tax provisions, anti-money-laundering notifications and rulings handed down case by case. This article sets out the scale of crypto-related crime worldwide, reviews the current statistics, and examines the jurisdictional and enforcement difficulties that follow from them, with particular attention to how those difficulties play out inside India. India's liability framework is then measured against a handful of foreign models, the Indian judiciary's engagement with the resulting legal gaps is assessed against international standards and model policy approaches, and the article closes by setting out a proposed regulatory framework.
Harini D., S. Aruna, Santhanalakshmi V., D. P. Sivasakti Balan Ā· 5 authors
Cryptocurrencies have emerged as a significant component of the global financial systems, attracting considerable attentions from investors, researchers and policymakers. Simultaneously, Indian sectoral stock market plays a crucial role in the countryās economic development and investment landscape. Due to their high volatility, crypto currency markets may influence traditional financial markets and investment decisions .This study investigates the impact of crypto currency market movements from on selected Indian sectoral stock markets, including the information Technology, Banking, Pharmaceutical sectors. Historical data from major cryptocurrencies and sectoral stock indices like Bitcoin, Ethereum, Nifty IT, Nifty Bank, and Nifty Pharma indices which was collected and analyzed using explainable regression techniques. Based on the given datasets, the findings provide valuable insights into the relationship between cryptocurrency markets and selected Indian sectoral stock market indices, particularly in understanding how crypto currency market fluctuations during major global events, such as pandemics, geopolitical conflicts, and economic uncertainty, may influence investor behavior and sectoral performance across the Indian economy.
The development of smart contracts on distributed ledger technology has created very real doctrinal and evidentiary problems for the classical consent theory-based legal system. This article conducts a thorough comparative study on the legal regimes of defects of consent error, fraud, duress and misrepresentation regarding smart contracts in the light of the international conventions adopted by the United Nations Commission on International Trade Law (UNCITRAL) and the United Nations Principles of International Commercial Contracts (UNCPC). The study highlights key gaps in legislation and clear issues of evidence that hinder claimants from establishing vitiated consent in algorithmically executed contracts, grounded in primary legislative sources, such as the UAE Federal Law No. 5 of 1985 (Civil Transactions Law), UAE Electronic Commerce Law No. 1 of 2006, the regulatory frameworks of the Dubai International Financial Centre (DIFC) and the Abu Dhabi Global Market (ADGM), and Jordanian Civil Code No. 43 of 1976. The analysis demonstrates that, while automated self-executing code involves one-to-one interaction between a digital entity and a human user, both jurisdictions are poorly suited to deal with these types of interactions, as the record cannot be altered and the party deploying the code can be anonymous, and the 'agreement' can be either ambiguous or impossible in practice. The article suggests a three-part reform agenda ā (i) technology-neutral statutory amendments to explicitly apply the doctrine of ādefect of consentā to algorithmic agents; (ii) forensic evidentiary rules for the authentication of blockchain data and expert testimony; and (iii) a specialised dispute resolution mechanism based on the ADGM's current smart contract recognition framework. The findings add to the still emerging literature on smart contract legality in the Arab world, and provide practical suggestions for legislative reform.
Ruziev Ulugbek Shukhrat Ugli, Seok-Yoon Kim, Youngmo Kim, Sun-Jib Kim
In OTT (Over-The-Top) service environments, reliable recording and verification of content usage history are becoming increasingly important for copyright protection, royalty settlement, and dispute resolution. However, conventional platform-specific log management methods have limitations in interoperability, integrity assurance, and external verifiability. In addition, simple indicators such as the number of views or total playback time are insufficient to accurately reflect actual OTT viewing behavior, which often includes nonlinear interactions such as pause, seek, replay, and early stop. This paper proposes a platform-independent event-based verification method for OTT content usage history. The proposed method defines usage history metadata consisting of content information, session information, playback event information, and usage result information, and derives valid usage intervals from playback events such as play, pause, seek, replay, and stop. In addition, hash-based verification information and linked recording structures are applied to the generated usage history to enhance integrity and traceability. To examine the applicability of the proposed method, processing latency in the usage history aggregation stage was measured according to changes in the number of sessions and platforms. The experimental results show that the processing time increased in a stable manner as the session scale increased, indicating that the proposed method can be applied to large-scale OTT usage environments. This study is meaningful in that it presents a platform-independent and verifiable method for organizing OTT content usage history, and it can be further extended through future performance evaluation of blockchain recording and verification processes in diverse service environments.
The metaverse is emerging as a persistent and immersive digital environment that combines extended reality, cloud edge computing, artificial intelligence, big data analytics, digital twins, blockchain, and future wireless connectivity. However, real time metaverse services require ultra low latency, high data rates, context aware intelligence, scalable cloud infrastructure, and trustworthy data governance. This paper presents a PRISMA informed systematic review of AI driven cloud and 6G enabled metaverse research with special attention to big data analytics, security, privacy, and digital trust. A structured search strategy was designed across major scholarly databases and citation snowballing sources, and 45 studies were selected for qualitative synthesis. The review classifies the literature into six themes: AI and real time analytics, cloud edge device orchestration, 6G connectivity, metaverse security, privacy preserving mechanisms, and digital trust governance. The findings show that 6G and edge intelligence can support immersive metaverse services through sub millisecond interaction, distributed rendering, semantic communication, integrated sensing, and adaptive resource allocation. At the same time, the literature reveals open challenges involving identity management, biometric privacy, adversarial AI, cross platform interoperability, data provenance, and user trust. The paper concludes with a conference oriented research agenda for trustworthy AI cloud 6G metaverse systems.
Cryptocurrencies originate from critical reflections on the traditional monetary system. Supported by blockchain technology, they break the shackles of the traditional financial system via decentralization. While cryptocurrencies bring prospects for financial innovation, they also induce latent risks including market volatility and cross-border regulatory conflicts. Taking cryptocurrencies as the research object, this paper elaborates on their basic theories, clarifies their asset attribute rather than legal tender, and identifies decentralization as their core feature. It sorts out Chinaās existing financial supervision practices and the prominent dilemmas of legal governance triggered by cryptocurrencies, and compares differentiated regulatory systems of the European Union, Japan and the United States. Finally, this paper puts forward targeted legal governance paths compatible with Chinaās national conditions from three dimensions: clarifying supervision responsibilities, optimizing tax collection and administration, and tackling stablecoin risks, so as to provide theoretical support and practical references for promoting law-based governance of the socialist financial system with Chinese characteristics.
Blockchain platforms enable transparent and immutable solutions for verification of proofs of digital claims, permissions, states, and events. However, numerous decentralized applications still force their users to operate with wallets, native tokens, and transaction costs, even though they have nothing to do with their verification purpose. This issue makes decentralized systems less user-friendly and constrains their adoption among not technically proficient Web3 users. This paper explores different patterns for gasless verification of proofs on decentralized platforms. The research focuses on five architectural patterns of such decentralized proof systems, including read-only blockchain verification, off-chain signature verification, relayer-based meta-transactions, account abstraction with paymasters, and hybrid on-chain/off-chain proof anchoring. Each architecture is analyzed qualitatively considering its usability, cost-effectiveness, decentralization, security, scalability, and implementation complexity. The results show that gasless verification enhances the usability of such systems, but at the same time transfers responsibility of trust assumptions to relayers, paymasters, backend servers, signature protocols, and off-chain data availability mechanisms. In this regard, potential risks, which include replay attacks, centralization of relayers, malicious paymaster activities, uncertainty of signersā identity, and dependence on backend servers, are discussed. Besides, the paper offers a decision-making framework for choosing one of gasless verification architectures depending on the presence/absence of state change, authority of proofs, required verification frequency, degree of decentralization needed, and level of technical maturity of system users.
Decentralized Autonomous Organizations (DAO) are an emerging blockchain-based paradigm for decentralized governance. Despite growing interest, their conceptualization remains fragmented. This paper introduces DAO-Ontology, a domain ontology formalizing DAO concepts-including perspectives, characteristics , solutions, evaluation methods, application domains, and challenges. Developed via the OntoView methodology from a systematic mapping of 47 studies, it is implemented in OWL and validated with a Java application using the OWL API. The ontology provides a standardized vocabulary, supports semantic integration, and enhances understanding of DAO as sociotechnical systems.
The advancement in smart mobility communication technology enables intelligent vehicles to interconnect and communicate with each other, improving traffic safety and efficiency. However, the highly dynamic nature of the smart mobility network and vehicle behaviour creates the requirement for effective authentication systems to establish secure and reliable communication between vehicles. The implementation of a reputation system has been proposed to establish trust among untrusted vehicles, where the reliability of a propagated message is assessed based on the reputation of the sender vehicle. Thus, it prevents malicious vehicles from potentially broadcasting misleading messages that can cause accidents or disrupt the network. This paper proposes a novel decentralised and dynamic reputation management and computation model based on a consortium blockchain and a multi-signature smart contract. The implementation of blockchain and a smart contract provides a secure and transparent framework for registering vehicles, submitting events, voting feedback, evaluating reputation, and blocking malicious vehicles. To demonstrate the feasibility of the proposed model, we conducted security and performance analyses. The results show how our model can provide resistance against various system and reputation manipulation attacks, such as data tampering, message forging, self-promotion, vote duplication, bad-mouth, on-off, time-dependent, and collusion attacks. Additionally, the smart contract performance illustrates execution time within a millisecond or less, which demonstrates the practicality of our design for the smart mobility applications.
This paper explores the deployment of a blockchain supported land-registry system in rural Bihar. In light of transparency, fraud mitigation, governance efficiency and digital inclusion, this paper refutes the common assumption that immutability of data results in accurate title. This study employs validated secondary data, from the years 2020-2025, such as the Bihar National Family Health Survey 2019-2021, various official sources of the Digital India Land Records Modernization Programme, Bihar land-service portals, and peer-reviewed literature on the intersection of blockchain and land governance. According to the National Family Health Survey (NFHS)-5, approximately 84 percent of surveyed households in Bihar were classified as rural, and a majority of the respondents, 79.4 percent of women and 56.4 percent of men, had never used the Internet. The widening of the access gap was examined in the context of the mobile phone ownership and usage, the financial inclusion of women, as well as the self-reported ownership of a house or land. The evidence-weighted readiness assessment determined that the level of digitization was relatively better, but the level of coordination of institutions, governance of cybersecurity, design of correction mechanisms, and design of user participation mechanisms were relatively poor. This paper proposes a permissioned industry consortium ledger, where sensitive data and documents remain off-chain, and the only data recorded on-chain are the hashes, identifiers, approvals, timestamps and version references of the land parcels. Smart contracts are used to manage the workflows from registration to mutation, but are not used to resolve the issues of contested titles, inheritance, or boundaries. This paper proposes an assisted-access model with a phased implementation approach, a multilingual interface, an appeal mechanism, and gender-disaggregated analysis and evaluation. Rather than fabricating field surveys and administrative performance data, this paper presents a complete primary data collection framework with a detailed statistical analysis plan for empirical assessment.
This dissertation explores the impacts of blockchain-based audit trails and stakeholder feedback loops on ethical conduct in U.S. federal and state public administration. The study addresses ongoing ethical problems in government, such as low levels of accountability, inadequate procedures, policy manipulation, and a waning public confidence in government, despite the existence of government ethics codes and oversight bodies. Ethical adherence is considered using two complementary theories: the virtue theory (Honesty, Fairness, Integrity, Responsiveness, Trust) and the deontological theory (Duty fulfillment, Rule following, Documentation, Accountability, Procedural compliance). The study was conducted with closed and open-ended answers from public administration participants who have experience in ethics, compliance, audit, procurement, oversight, stakeholder engagement, or other related areas using an anonymous online survey via Google Forms. Ten responses were received and nine valid responses were analyzed. The findings revealed that audit trails linked to blockchain were seen as the most beneficial for holding accountable, tracing, ensuring record integrity, reviewing and ensuring duty-based compliance. Stakeholder feedback loops were seen as most beneficial for fairness, honesty, telling the truth, responsiveness and public trust. The two tools were perceived as complementary tools and not as competing tools. The results, however, also showed that transparency instruments are not necessarily the drivers of ethical behavior. They rely on leadership, staff development, correct data entry, active data review, meaningful follow-up to feedback, and follow-through on actions. The study brings to the public administration literature a connection between mechanisms of transparency and ethical adherence in both virtue-based and duty-based approaches. It provides pragmatic advice to agencies that want to build ethical governance through technology, participation, and organizational culture in complex federal, state, and government-adjacent administrative contexts, in everyday practice.
A multi-agent transaction optimization decision-making method based on blockchain Berge-NS equilibrium is proposed, aiming to protect user interests and achieve carbon reduction objectives. Firstly, by improving the utility function of electricity users and quantifying the impact of blockchain technology on the electricity utility of market entities, a blockchain based P2P electricity trading architecture for microgrids is constructed, and a blockchain network and utility function for electricity users are designed; Secondly, the Evolutionary Game Theory based on bounded rationality decision-making is introduced to construct a Berge-NS game model on both sides of electricity supply and demand. The distributed iterative algorithm and step size control method are used to solve the Nash equilibrium, and the strategy evolution of demand side subjects in the game process is studied through dynamic processes; Finally, numerical simulations were conducted to analyze the trading strategies of bilateral contract markets, centralized trading markets, and dual layer decision-making models for electricity sellers, verifying the feasibility and effectiveness of the models and algorithms. The experimental results show that the proposed multi-agent trading Berge-NS decision-making method for microgrid electricity market exhibits certain performance advantages in reducing carbon emissions, lowering user electricity costs, and improving user satisfaction.
This research presents a novel decentralized identity management system leveraging Ethereum smart contracts and cryptographic protocols to enable self-sovereign digital identities. Traditional centralized identity solutions pose risks related to data privacy, security, and user autonomy. To address these limitations, we propose a blockchain-based architecture integrating smart contracts for identity governance, IPFS for decentralized attribute storage, and threshold cryptography for private data recovery. Our dual-contract model (Identity Contract and Recovery Contract) facilitates secure identity creation, verifiable attribute attestation, and robust key recovery through social consensus. We introduce a privacy-preserving mechanism using encryption-key splitting among trusted peers to enable recovery of encrypted off-chain data. Implementation uses Web3.js, Solidity, and QR-code-based communication, abstracting cryptographic complexities from end-users. Security analysis addresses replay, man-in-the-middle, Sybil, and multi-user compromise attacks with mitigations including challenge-response authentication and time-delayed contract execution. This work contributes a cryptographically secure, user-centric identity framework ensuring data sovereignty, recoverability, and interoperability within the Ethereum ecosystem.
Across Bitcoin and the blockchain-art discourse, four recurring claims can be distinguished: trust minimization, authenticity, scarcity, and preservation. A deliberately critical corpus documents each as displaced rather than delivered: trust is relocated, authenticity is located in a registry, scarcity is manufactured, and preservation is asserted rather than achieved. This article reads these displacements against an analogue rule system running since 1 January 1993, in which each calendar day constitutes one work whose Authentic Number and price are fixed by formula, and which remains in suspension until a collector realizes it as a print. The comparison is structural: not protocols versus persons, but a centrally authored, personally warranted rule against a permissionless state maintained through distributed consensus. The formula makes each day's identity and price independently computable; a public realization record publishes which days have been realized; and two named authors ā Christopher Temt (concept and text) and Eugen Kment (drawing) ā sign the physical work and answer for the remaining claim. Accountable means exactly this: the non-computable claims have identifiable bearers and are publicly contestable. The system does not replace trust with proof. It separates what can be computed from what must be answered for in person.
The paper presents a new hybrid approach (LSTM-RF-SLSQP) that acts as an advanced decision-support tool for institutional crypto portfolio management. The methodology includes the use of LSTM neural networks to detect non-linear temporal patterns and RF algorithms to detect structural market noise. The ability to predict future prices based on LSTM-RF model has been extensively verified out-of-sample using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), and shown to have smaller prediction error than standalone algorithms on very volatile assets. Then, based on robust return and risk forecasts, SLSQP optimization algorithm allocates asset weights aiming at maximizing Sharpe ratio under specific institutional constraints, applying buy-and-hold approach with quarterly rebalance. The empirical study is performed for a portfolio of ten major cryptocurrencies (BTC, ETH, SOL, BNB, XRP, ADA, DOGE, TRX, LINK, DOT) using data provided by KuCoin exchange from January 2024 to January 2026. The numerical experiments demonstrated the significant superiority of the suggested framework compared to all benchmarks. Namely, the LSTM-RF-SLSQP approach provides the impressive annual return of 107.40%, Sharpe ratio of 2.04, with maximum drawdown equal to -3.80%.
This study examines how fairness perceptions influence cryptocurrency investors and market behavior, using the Ultimatum Game as the conceptual anchor. We specify a framework that links distributive, procedural, and informational justice to buy and sell intentions, holding horizons, and community participation, with emotions such as anger and trust acting as mediators. To address the distinction between investor-level perceptions and market-level volatility, the revised design combines online survey responses with token-day trading data, event indicators, on-chain concentration measures, disclosure records, and community-activity proxies. Survey data are used to test individual behavioral and emotional channels, whereas market data are used to estimate abnormal returns, realized volatility, and difference-in-differences effects around fairness shocks. The revised analysis therefore avoids inferring market volatility directly from respondent opinions. Findings indicate that perceived unfairness raises selling intention and shortens holding horizons, while objectively observed fairness shocks are associated with higher realized volatility. Heterogeneity checks show stronger effects for assets with high decentralization and active communities. The study offers design levers for transparent rules, governance safeguards, and clear disclosure cadence.
Fernando Henrique Antunes de Araujo, Milena KojiÄ, Petar MitiÄ, Kerolly Kedma Felix do Nascimento Ā· 5 authors
This study applies a prespecified dynamic MFDFA workflow across pandemic, geopolitical-conflict, and tariff-policy regimes for ten non-stable, long-history cryptoassets selected ex post from the 23 July 2026 market-cap ranking. The common sample comprises 3155 daily log returns per asset from 2 December 2017 to 22 July 2026; the final endpoint regime includes the U.S.āIran conflict. The estimator uses q=ā10,ā8,ā¦,10, linear detrending, 22 scales from 16 to N10, adjacent-secant Legendre transformation, a cubic spectrum peak, and 500-day windows stepped by 21 days plus a terminal endpoint. Full-sample IE=|Ī±0ā0.5| ranges from 0.01397 (LINK) to 0.08706 (BNB), and observed width ranges from 0.32625 to 0.74678. The controlled incremental U.S.āIran endpoint coefficient is ā0.02734 (two-way clustered SE 0.02369; p=0.2489), with asset-cluster t(9) interval [ā0.08226, 0.02759]. Quantile estimates range from +0.00206 at the 0.10 quantile to ā0.04769 at the 0.90 quantile; all five 999-replication asset-cluster bootstrap percentile intervals include zero. Exact rolling sensitivities are negative for the 500/14, 500/30, and 730/30 designs but positive for the 250/21 design, and every small-cluster interval includes zero. Direct spectrum-width contrasts also remain nonsignificant after Holm adjustment. None of ten observed widths survives BH correction in 200 shuffled-return surrogates per asset (2000 fits in total). The results document heterogeneous and specification-sensitive dynamic multifractal patterns, not isolated or causal crisis effects.
A fixed-odds contract on short-horizon price direction has a positive expected value only when its win probability exceeds the break-even rate implied by the payout ratio. A deployable predictor must also produce signals at a sufficiently stable rate. We formulate this setting as selective prediction with a coverage constraint and combine a five-seed gradient-boosting ensemble over a 90-dimensional causal feature panel with daily adaptive quantile thresholds, each estimated from the preceding 14 to 28 days of model scores, with parameters selected on training data alone. Configurations are frozen after three chronological pseudo-out-of-sample folds and evaluated on a held-out period from 1 January to 10 June 2026, and the whole procedure is then repeated on a quarterly re-freezing cadence over seven successive windows. Across BTC and ETH at 5- and 10-min horizons, with a payout of 0.8 and a 55.56% break-even rate, the models execute 10.4 to 11.0 trades per day, and all four selective win rates exceed break-even. Under a dependence-aware block bootstrap, three of four remain significant, and within a 32-test confirmatory family, two survive HolmāBonferroni correction. Coverage stays inside the operational band in 26 of 28 re-frozen windows. Compared under one execution protocol, a fixed calibration slice drifts out of band while a trailing window does not, and adaptive conformal inference (ACI) matches the proposed rule on coverage when its step size is tuned but not otherwise, whereas an outcome-driven conformal controller reduces coverage by more than an order of magnitude. The expected value is insensitive to exchange fees, which consume under 5% of the measured edge, and sensitive to the payout term. Under matched feature sets, training pools, and coverage, most of the apparent cross-asset difference does not persist. This paper presents a proof of concept for the framework rather than making any claim about cryptocurrency predictability.
This study examines whether major cryptocurrency returns respond systematically to scheduled Federal Reserve (Fed) interest rate announcements and whether FOMC-window movements are explained more by realised policy decisions or by broader risk-sentiment conditions. Using daily data for Bitcoin, Ethereum, XRP, Dogecoin, Solana, the U.S. Dollar Index, and VIX, the analysis covers 43 scheduled FOMC announcements between 2021 and 2026. Six cumulative event-window returns are evaluated through parametric mean tests, Wilcoxon signed-rank tests, and panel event-study regressions with crypto fixed effects and FOMC-event-clustered standard errors. Because the available surprise measure contains only two nonzero observations, the study focuses on realised rate changes, hike/cut/hold categories, asymmetric rate-change magnitudes, VIX changes, and DXY returns rather than formal monetary policy shocks. The results provide little evidence that cryptocurrency returns differ systematically from zero around FOMC announcements. Actual rate changes, policy-direction categories, and asymmetric hike/cut magnitudes do not robustly explain event-window returns, and crypto-specific interaction models provide no stable evidence of heterogeneous sensitivity across assets. By contrast, VIX changes are negatively and significantly associated with cryptocurrency returns in several windows, while DXY effects are weak and unstable. The study contributes by showing that FOMC-window cryptocurrency performance is better explained by risk-sentiment conditions than by the realised size or direction of Fed rate decisions.