While deep learning models have demonstrated superior performance in cryptocurrency forecasting, their deployment is often hindered by a lack of interpretability and trustworthiness. To bridge this gap, this paper proposes the Cryptocurrency Counterfactual Explanation (CryptoForecastCF) model. Recognizing the inherent volatility and complex non-linear dynamics of cryptocurrency markets, we argue that understanding the sensitivity of model outputs to slight variations in historical conditions is fundamental to robust risk management. CryptoForecastCF employs a gradient-based optimization strategy to generate meaningful counterfactual explanations. Specifically, it identifies minimal modifications, defined as the optimal perturbations to historical market features such as price constrained by â1 or â2 norms, that are sufficient to steer the model's future predictions into user-specified target intervals. This approach not only elucidates the key driving factors and decision boundaries of opaque models but also equips traders and risk managers with actionable insights, enabling them to identify the specific market shifts required to navigate high-stakes scenarios and mitigate unfavorable predictive outcomes.
Meme coins have become extremely popular in the cryptocurrency market, but they also carry a high level of risk. Many of these projects rely on social media hype and community excitement, yet a large number eventually turn out to be scams where developers steal investor funds and abandon the project, commonly known as rug pulls. This paper presents a smart analysis tool designed to help investors identify such risky meme coin projects before financial loss occurs. The proposed system examines both smart contract behavior and market-related factors, including ownership control, liquidity locking, token distribution, and developer wallet activity. The tool was tested on real-world meme coins, including well-known legitimate projects as well as confirmed scam tokens. The results show that the system is able to accurately distinguish between safe and high-risk projects. This approach provides a practical and effective way to improve investor safety in the rapidly evolving decentralized finance ecosystem
Cryptocurrency price forecasting has gained increasing attention due to the marketâs high volatility and structural complexity. While many recent studies have explored deep learning architectures, including attention- and transformer-based models, existing research still faces notable limitations: (i) inconsistent feature engineering choices, (ii) limited examination of hybrid machine-learning models, and (iii) a lack of transparent trading evaluation using realistic backtesting assumptions. To address these gaps, this study develops a hybrid forecasting and trading framework based on Support Vector Regression (SVR) combined with a set of rule-based technical strategies. Using four major cryptocurrencies â BTC, ETH, XRP, and LTC â from 2018 to 2020, the proposed framework integrates thirteen technical indicators with a sliding-window scheme and compares SVR against Random Forest (RF) and Long Short-Term Memory (LSTM) benchmarks. Empirical results show that SVR offers a competitive balance between predictive accuracy and computational efficiency, particularly in moderate-volatility regimes. The strategy backtesting further demonstrates that SVR-driven signals can outperform traditional technical rules under certain market conditions, although limitations remain for highly volatile assets such as Bitcoin. The study contributes to the literature by clarifying feature-design choices, evaluating SVR within a multi-asset setting, and providing reproducible code and datasets through an open-access repository.
Bitcoin Ordinals and inscriptions facilitate the on-chain storage of arbitrary data on the Bitcoin blockchain. In this study, we analyse the impact of inscriptions on the Bitcoin network. We find that inscriptions have significantly increased network activity, created additional demand for blockspace, and influenced Bitcoinâs fee market dynamics. Furthermore, we find that the rise of inscriptions coincided with an increased utilisation of Taproot, a notable increase in block size, and the longest sustained period of high blockspace utilisation in Bitcoinâs history. Our study shows that inscriptions have reshaped how Bitcoinâs blockchain is utilised and underscores the growing number of use cases beyond its original function as a peer-to-peer financial network.
We introduce the first fully operational Islamic Credit Default Swap (iCDS) on public blockchain infrastructure, combining a closed-form riba-free pricing formula with a deployed smart contract on Arbitrum. Building on the Îș-rate framework of Ackerer, Hugonnier & Jermann [1] and the credit-equivalence theorem of Ahmed, Bhuyan & Islam [3], the fair iCDS spread is s * = Îș(1-ÎŽ) at Îč = 0, where Îș is the convergence intensity and ÎŽ is the recovery rate. Conventional CDS, discounted at the risk-free rate Îč, prices at s conv = Îș(1-ÎŽ) âą Îč/(Îș+Îč)
We assess the cross-sectional relation between media coverage and cryptocurrency returns using 7.6 million news articles from a large-scale web corpus. We find that cryptocurrencies with no coverage earn higher risk-adjusted returns than those with high coverage. By decomposing coverage intensity into coverage breadth and novelty, we separate the dissemination of existing information from the arrival of new information. We show that media coverage combines two offsetting channels: breadth captures an attention-driven channel that predicts lower future returns, while novelty captures an information channel that predicts higher future returns. Our findings highlight the role of information diffusion in cryptocurrency returns.
Michael Neubert, Wolfgang Rams, Patrick Gruhn, Marcel Lötscher
Perpetual futures (often called perpetual swaps) are the dominant crypto-derivatives instrument. They replicate the economic exposure of a futures contract without an expiry date. They replace maturity-based convergence with a funding mechanism that transfers cash flows between longs and shorts, typically every eight hours. This paper explains how perpetuals evolved from early proposals for non-maturing futures into a standardized crypto market instrument, and why key design choices changed over time. It synthesizes recent theoretical and empirical research on funding design, pricing, and arbitrage intuition, market microstructure, liquidation risk, and regulation. Finally, this study proposes a research agenda organized around funding design, constrained arbitrage, transparency, decentralized exchange design, policy, and legal classification, because recent U.S. and EU developments show that the same economic structure may be characterized as a futures contract, swap, CFD-type instrument, or other derivative depending on statutory definitions, venue design, and supervisory interpretation. This paper proposes the following definition: a cryptocurrency perpetual is an open-ended, margin-based derivative that gives synthetic long or short exposure to an underlying crypto asset and replaces expiry-based settlement with periodic funding payments that anchor the contract price to a reference spot price.
Bitcoin derivatives trading regularly exceeds $200 billion daily, yet participants must trust centralized exchanges-the same exchanges that have repeatedly failed, from BitMEX's regulatory crisis in 2020 to FTX's collapse in 2022. This paper provides the first comparative analysis of three approaches that enable long/short Bitcoin exposure without exchange custody: Discreet Log Contracts (DLCs), Stable Channels, and Stablesats (included as a custodial comparison). Each mechanism allows two parties to take opposite sides of BTC/USD price movements-one hedging (short), one speculating (leveraged long)-settled entirely in Bitcoin. We analyze the mechanism design, trust assumptions, and trade-offs of each approach. These are not stablecoins; they are bilateral derivatives contracts. They sacrifice liquidity and convenience-the cost of trustlessness-serving participants unwilling to accept exchange counterparty risk.
Francis Chigozie Emmanuel, Ogaziechi Tobechi Anold, Obidinma Christian Alozie, Ikenna Tonna Adiele
The global freelance economy has experienced rapid growth, yet existing payment and escrow systems remain constrained by structural inefficiencies inherent in both centralized fiat-based and decentralized cryptocurrency-based models. Centralized escrow systems, while widely adopted due to their regulatory compliance and usability, suffer from custodial opacity, information asymmetry, high transaction costs, and limited verifiability. Conversely, purely decentralized blockchain-based escrow systems offer transparency and trust-minimized execution through smart contracts but face barriers including cryptocurrency price volatility, limited fiat integration, steep technical learning curves, and inadequate dispute resolution mechanisms for subjective deliverables. This article, a hybrid escrow system integrates traditional fiat payment infrastructure with decentralized Ethereum-compatible smart contract execution. The system adopts a three-layer architecture comprising a centralized service layer, a middleware synchronization layer, and a decentralized execution layer. A Finite State Machine (FSM) model governs escrow state transitions across both fiat-funded and cryptocurrency-funded transactions, ensuring determinism, auditability, and consistency. The system further incorporates a human-in-the-loop dispute resolution framework anchored to blockchain execution, enabling fair and transparent adjudication of subjective conflicts. Evaluation results demonstrate that the proposed hybrid architecture successfully bridges the gap between traditional finance and decentralized systems. The system achieved 100% correct FSM state enforcement with zero unauthorized fund releases across all test scenarios. Fiat-funded contracts were synchronized to the blockchain with an average latency of 8.4 seconds, while cryptocurrency-funded contracts confirmed on-chain within a median of 3.2 seconds on the Polygon testnet. All three dispute resolution outcomes were correctly enforced on-chain within an average of 5.1 seconds following adjudication, and API response times remained below 420 milliseconds under concurrent user loads. An ablation study further confirmed that all three architectural layers are individually necessary, as removing any single layer degraded transparency, payment flexibility, dispute resolution capability, or user accessibility. This research contributes a scalable and adaptable hybrid escrow blueprint applicable to fintech development, digital labour platforms, and cross-border payment systems.
This paper presents a comprehensive structural analysis of cryptocurrency derivative markets spanning January 2019 to December 2024, covering Bitcoin (BTC), Ethereum (ETH), and six additional tokens across over 2.83 billion high-frequency transactions on eight major centralized exchanges and three decentralized finance (DeFi) derivative protocols. Using a theoretically grounded multi-method frameworkâcomprising Vector Error Correction Models (VECM), Hasbrouck (1995) and Gonzalo-Granger (1995) information share decompositions, Heston (1993) and rough volatility (Gatheral et al., 2018) stochastic models, DCC-GARCH(1,1) augmented with realized kernel estimators, MIDAS regressions linking high-frequency derivative signals to lowerfrequency on-chain variables, and panel quantile regressions for cross-sectional volatility riskâwe deliver six primary empirical contributions. First, perpetual swap markets consistently dominate spot markets in price discovery, contributing 63.4% (BTC) and 58.7% (ETH) of price-efficient information on average, rising to 72.1% and 68.4%, respectively, during the top quartile of volatility daysâconsistent with informed-agent migration to leveraged venues. Second, the Heston leverage correlation estimate Ï = â0.61 for BTC and Ï = â0.73 for ETH reflects asymmetric tail risk demand rather than balance-sheet leverage, with the implied volatility smirk's left-tail slope strongly cointegrated with funding-rate deviations (r = â0.54, p < 0.001). Third, we estimate a time-varying variance risk premium averaging 14.8 (BTC) and 19.3 (ETH) annualized variance percentage points; panel regressions reveal that on-chain network congestion fees retain significant incremental explanatory power after controlling for VIX, DXY, and credit spreadsâa novel identification of a blockchain-specific volatility channel. Fourth, rough volatility models (Hurst exponent H â 0.08 for BTC) significantly outperform classical Heston specifications in fitting near-term implied volatility smiles, with RMSPE reductions of 31.7% for one-week expiry options. Fifth, CME Bitcoin Futures introduction produced a structural break in arbitrage efficiency, reducing basis mean-reversion halflives by 41.2% and lowering adverse-selection costs by 18.6 basis points. Sixth, on-chain DeFi perpetual protocols (GMX v2, dYdX v4) exhibit significantly higher adverse selection costs and lower price discovery shares (mean IS = 0.24) relative to centralized counterparts, but display timevarying convergence during U.S. regulatory uncertainty episodes. Our findings deliver unified implications for derivative pricing theory, risk management, and the architectural design of regulated cryptocurrency derivative markets.
We analyse returns on cryptocurrency perpetual futures by first developing a cost-of-carry model tailored to digital assets. The model captures the link between spot and perpetual futures prices, implying a positive convenience yield and negligible off-chain storage costs. Furthermore, we employ a log-linear approximation to demonstrate that expected return of holding perpetual futures derive from the current log basis, misperception of forward-looking spot price, and expected futures-spot spreads over the âmaturityâ of futures contract. We then assess a comprehensive set of 170 return predictors, classified into categories of basis, momentum, liquidity, size, and volatility. Sorting based on these predictors yields 63 statistically significant total returns (i.e. price movement plus funding fee yields, with each exceeding the 5 significance level). Finally, we demonstrate that a two-factor model, based on the log-basis and a price-volume relevant factor, effectively explains all 63 strategies, highlighting the role of systematic drivers in perpetual futures markets.
Traditional Purchase Order (PO)-to-invoice reconciliation processes in infrastructure finance are often fragmented, opaque, and vulnerable to error or manipulation, especially within public-sector grant-funded projects. Manual validation and spreadsheet-based tracking make it difficult to maintain transparency, traceability, and compliance across multiple 6stakeholders. This paper proposes a blockchain-enabled framework for automating audit trails in PO/invoice reconciliation, ensuring data integrity, accountability, and real-time verification of financial transactions. The study explores how distributed ledger technology (DLT) can integrate with existing enterprise resource planning (ERP) systems to record procurement eventsâpurchase orders, goods receipts, and invoicesâon an immutable, time-stamped ledger. Smart contracts are introduced to automatically validate invoiceâPO matches and flag anomalies in payment amounts, vendor identities, or project milestones. Using simulated public infrastructure grant data, the proposed framework compares blockchain-assisted reconciliation to traditional FP&A workflows on metrics such as accuracy, processing time, and audit readiness. The results demonstrate that blockchain-based reconciliation significantly enhances financial transparency, reduces manual effort, and mitigates fraud and double billing. Furthermore, integration with business intelligence dashboards enables continuous monitoring of fund utilization across projects. This research contributes to the emerging domain of financial technology in infrastructure governance by showing how blockchain can transform reconciliation from a reactive accounting process into a proactive, automated compliance mechanism.
The 2026 cryptocurrency market cycle has witnessed the emergence of a novel asset class that defies traditional financial categorization: the AI-Integrated Meme Asset (AIMA). This report provides an exhaustive analysis of this phenomenon, utilizing the trajectory of Act I: The AI Prophecy ($ACT) as a primary case study. We posit that the convergence of large language models (LLMs) and decentralized community coordination has created a new "meta" for liquidity formation, characterized by the transition from static meme imagery to dynamic, agentic interaction. Central to this analysis are two theoretical frameworks proposed herein: the "Spring Effect," a market mechanics model describing the kinetic release of accumulated volatility following suppression events, and "Cognitive HODLing," a behavioral finance concept drawing on Social Identity Theory and Kahnemanâs Prospect Theory to explain the rigidity of social consensus in the face of founder betrayal. Through a synthesis of on-chain data, behavioral analysis, and the philosophical frameworks of Vitalik Buterin and Satoshi Nakamoto, this report argues that $ACT represents the pioneer of a "Decentralized Agentic Economy," where value is derived not from revenue, but from the resilience of the human-AI social fabric.
Why does a market structure built on radical transparency paradoxically foster the proliferation of low-quality assets? Open-source crypto markets make information public but not necessarily usable. We develop a model in which investors allocate scarce attention before deciding whether to verify project quality. Technical complexity reduces the informativeness of processed public disclosure, while narrative assets can build salience through attention feedback. As a result, complex projects may fail to enter the verification set even when they would be valuable conditional on evaluation. Financing then falls because visibility expands market reach but only screened projects convert attention into capital. The model delivers a transparency paradox: more public information need not improve allocation when investors cannot process it at scale. Low-dimensional narrative assets can crowd out high-quality innovation, generating a complexity trap. The results imply that disclosure policy may be ineffective when it increases information volume without improving processability. Market regulation requires disclosure to be standardized, machine-readable, and certifiable, so that public information can be converted into valuation-relevant signals.
This paper empirically examines the relationship between Polymarket prediction market odds for the passage of the Digital Asset Market Clarity Act (CLARITY Act) and the price of Bitcoin, while controlling for broader equity market movements. Using daily data from January 1 to April 4, 2026 (94 observations), the analysis first confirms that Bitcoin log-returns and changes in Polymarket odds are stationary (Augmented Dickey-Fuller p
This study examines whether Bitcoin-collateralised lending can operate as a form of risk-disciplined leverage within decentralised finance (DeFi). A stylised framework is developed to characterise how over-collateralisation, automated liquidation rules, and interest-rate formation determine balance-sheet risk and portfolio efficiency. Particular attention is given to loan-to-value (LTV) constraints, custody structures, and liquidity buffers in identifying the conditions under which collateralised Bitcoin borrowing improves capital allocation without generating destabilising leverage cycles. The findings indicate that conservative collateralisation combined with active liquidity management mitigates insolvency risk even under high asset volatility. The analysis provides a formal characterisation of leverage constraints in decentralised lending and extends the literature on risk allocation and capital structure in digital asset markets.
The introduction of block chain-supported investment tools like cryptocurrencies, DeFi platforms and tokenized assets has brought new decentralized, clear and exciting choices to the world of finance. As the use of impact investing expands all over the world, learning how investors view these projects matters for their continued success. This study investigates the motivations, risk perceptions, and decision-making processes of investors engaging with blockchain-based financial products. Drawing on behavioral finance theories and existing literature, it explores how psychological biases, technological literacy, and external influences such as social media and regulatory shifts shape investor actions. The research identifies key gaps, including the limited focus on non-cryptocurrency products, underdeveloped behavioral models, and insufficient attention to demographic and longitudinal factors. By addressing these gaps, this study aims to provide actionable insights for policymakers, financial institutions, and technology developers, contributing to a deeper understanding of investor dynamics in the blockchain era.
The proliferation of agentic artificial intelligence systemsâcharacterized by autonomous goal-seeking, tool use, and multi-agent coordinationâpresents unprecedented challenges to existing legal and financial regulatory frameworks. While traditional AI governance has focused on model-level alignment through training-time interventions such as Reinforcement Learning from Human Feedback (RLHF), the deployment of large language models (LLMs) as persistent agents embedded within socio-technical systems necessitates a paradigm shift toward institutional governance structures. This paper examines the intersection of agentic AI, Retrieval-Augmented Generation (RAG), and their implications for legal accountability and financial market integrity. Through a comprehensive analysis of the Institutional AI framework proposed by Pierucci et al. [1], we argue that alignment must be reconceptualized as a mechanism design problem involving runtime governance graphs, sanction functions, and observable behavioral constraints rather than internalized constitutional values. We address the critical deficit identified by LeCun regarding the absence of world models in current agents, demonstrating how RAG architectures function as externalized epistemic infrastructure that grounds agentic cognition in verifiable data repositories. The paper subsequently interrogates the legal implications of these systems under the European Union's Artificial Intelligence Act (EU AI Act) and the regulatory thresholds established by the Financial Conduct Authority (FCA) and European Central Bank (ECB), proposing justified compliance boundaries for high-risk financial applications. Furthermore, we acknowledge significant governance gaps within Decentralized Finance (DeFi) protocols where institutional oversight mechanisms face structural limitations. By synthesizing technical insights from multi-agent systems, constitutional AI limitations, and offensive security frameworks, this work advances a jurisprudential foundation for agentic AI that prioritizes defensible audit trails, incentive-compatible compliance, and systemic stability over opaque internal alignment guarantees. The analysis concludes that the future of AI governance lies not in perfecting isolated model behavior, but in architecting institutional environments where compliant behavior emerges as the dominant strategy through carefully calibrated payoff landscapes.
The emergence of cryptocurrencies has introduced significant shifts in the global financial landscape, and India is no exception. This research paper examines the impact of cryptocurrency adoption on the Indian economy, focusing on three primary dimensions: economic growth, financial inclusion, and regulatory challenges. Through a comprehensive analysis of market trends, policy developments, and case studies, the paper reveals cryptocurrencies have the potential to stimulate economic growth by fostering is dual-faceted, as they also pose risks related to market volatility, financial stability, and regulatory uncertainty. The study further explores how cryptocurrencies can enhance financial inclusion by providing alternative financial services to underserved populations but also highlights the challenges in integrating these digital assets into the existing financial system. By evaluating both the opportunities and risks associated with cryptocurrency adoption, the paper offers policy recommendations aimed at harnessing the benefits while mitigating potential downsides. The findings underscore the need for a balanced approach in formulating regulations that support innovation while ensuring economic stability and investor protection. DOI - https://doi.org/10.65525/SVUP.9788199651593.2025.90-105