Bambang Leo Handoko, Arta Moro Sundjaja, Evelyn Hendriana
The rapid rise in cryptocurrency presents both opportunities and challenges for retail investors due to its volatility and technological complexity. Research on investment decisions has primarily focused on behavioural finance, often overlooking how learning and literacy shape investor actions. This study addresses this gap by examining how herding behaviour, financial literacy, and digital literacy impact cryptocurrency investment decisions. Grounded in Social Learning Theory and supported by UTAUT to operationalise digital literacy, this study examines how herding behaviour, financial literacy, and digital literacy shape cryptocurrency investment decisions. We analyse survey data from 138 Indonesian retail investors through PLS-SEM. Key findings show that financial literacy (β = 0.443, t = 5.041) and digital literacy (β = 0.495, t = 4.246) are primary determinants of investment decisions, while herding behaviour (β = 0.016, t = 0.628) does not directly influence them but does so indirectly by enhancing investor literacy. This demonstrates that social observation and learning can convert herd-driven impulses into rational choices when mediated by literacy. By extending Social Learning Theory into digital investment contexts, this study provides insights for investors and policymakers seeking to enhance financial and digital literacy.
Lina Bautista López, Edgar Esaul Vite Gómez, Lizet Manzo Martínez
This article offers a multidisciplinary approach to the study of cryptocurrencies through the analysis of different academic documents. Analysis is an effort to address the issue of such digital assets from an overview rather than a particular one. The objective is that cryptocurrencies are understood in their concept, origin and operation by those interested in the subject who are not immersed in it. Therefore, two theories that are the monetary theory and the economic theory of the law are considered to support the research in its several aspects such as the economic, legal, social, among others. The analysis makes it possible to identify common trends in the authors without departing from their own opinion of cryptocurrencies considering their discipline.
Purpose The absence of cryptocurrency (CC) tax regulations in many countries raises concerns about compliance and potential revenue losses. Understanding the factors that drive CC holders’ tax propensity is crucial for developing effective tax policies. Therefore, this research aims to explore the influence of contextual factors, e.g. CC legitimacy, CC investment risks, and social responsibility, and individual factors, e.g. attitude towards CC tax payment, technological competence and CC financial literacy, on CC tax payment propensity. Additionally, the study delves into the moderating role of financial literacy in the proposed model. Design/methodology/approach An integrated model of TPB-STC (theory of planned behaviour and social cognitive theory) was grounded in this study. Data were collected using a cross-sectional research approach through an online survey responded by CC investors. Findings The study found that attitude towards CC tax payment, social responsibility, legitimacy and CC financial literacy exerted a positive effect on the propensity to pay tax on CC. However, CC investment risks demonstrated a negative effect on propensity. Interestingly, the CC financial literacy-moderated interactions of crypto assets' legitimacy, technological competence and investment risks on CC tax payment propensity were significant. Practical implications The discoveries that emerged from this study contain practical and actionable insights for stakeholders, including regulators, tax authorities and investors. Educational programs focused on enhancing CC financial literacy should be integrated into public finance initiatives to improve taxpayers’ understanding of crypto taxation. Additionally, regulatory bodies can collaborate with crypto exchanges to implement transparent reporting mechanisms, making tax compliance more accessible and straightforward for investors. These actionable steps can help foster a proactive tax-paying culture, even in the absence of formal tax regulations. Originality/value This study provides a theoretical foundation of tax practices behaviour related to crypto in decentralized financial markets, paving the way for future research on self-regulating mechanisms within the present-day fast-moving crypto market.
Hisham Mohamed Hassan Al Hammadi, Muhammad Hafiz bin Badarulzaman, Abdulaziz Fahmi Omar Faqera
The regulatory architecture governing cryptocurrencies and virtual assets in the United Arab Emirates has expanded markedly through Federal Decree-Law No. 20 of 2018, Cabinet Decision No. 10 of 2019, Federal Decree-Law No. 46 of 2021, and Dubai Law No. 4 of 2022, reflecting the state’s ambition to position itself as a leading digital finance hub while addressing money laundering risks. Notwithstanding this legislative progress, significant challenges persist, stemming from the decentralized and pseudonymous nature of cryptocurrencies, fragmented institutional oversight across federal and emirate-level authorities, and constrained supervisory capacity for real-time monitoring. Existing scholarship has largely overlooked the interaction between legal design and institutional enforcement dynamics within the UAE’s cryptocurrency regime, creating a critical gap this study addresses. The study critically evaluates the legal and institutional frameworks governing cryptocurrencies, examines enforcement and compliance vulnerabilities within AML mechanisms, and assesses regulatory risks associated with cryptocurrency market adoption. Employing an exploratory qualitative doctrinal methodology, the analysis systematically examines primary legislation alongside secondary sources drawn from high-impact journals, authoritative monographs, and institutional reports, subjected to rigorous thematic analysis. Guided by Institutional Theory, the findings demonstrate that while the UAE’s framework is normatively comprehensive, enforcement effectiveness is undermined by coordination deficits and technological constraints. The study advances targeted recommendations to enhance regulatory coherence, institutional integration, and risk-based supervision, contributing to legal, financial regulation, international governance, and digital risk studies, while identifying directions for future comparative inquiry.
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 ℓ1 or ℓ2 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 market’s 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 – BTC, ETH, XRP, and LTC – 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 Bitcoin’s 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 Bitcoin’s history. Our study shows that inscriptions have reshaped how Bitcoin’s blockchain is utilised and underscores the growing number of use cases beyond its original function as a peer-to-peer financial network.
Token economies offer a transformative solution to the enduring challenges of funding and coordinating public goods. Through blockchain-enabled mechanisms such as smart contracts, decentralized governance, and incentive-aligned tokens, communities can address traditional market failures in the provision of non-rivalrous and non-excludable resources. This chapter explores the theoretical foundations of public goods economics, analyzes innovative funding models like quadratic funding, retroactive public goods funding, augmented bonding curves, and emerging regenerative finance systems, and examines how blockchain-based systems can support decentralized, transparent, and scalable public goods provisioning. Drawing on real-world case studies, the chapter evaluates the strengths and limitations of token economies in this domain, highlighting both the promises and the complexities of decentralized coordination.
Tokenized lending is a major application of decentralized finance (DeFi) that has evolved as an innovative platform for credit intermediation through blockchain-based smart contracts. Early token lending platforms such as MakerDAO (now Sky), Compound, and Aave were permissionless, built for decentralized retail lending. More recently, however, there has been an increase in the interest of tokenized lending by institutional investors and regulated entities looking for compliant, risk-managed solutions. This chapter explores the institutionalization of tokenized lending, including an overview and conceptual analysis of the growth, technology, and potential role in the financial system. Additionally, the chapter highlights the fragmented landscape of regulation on tokenized lending and more nuanced strategies taken by the United States, Canada, the European Union, and Singapore. Although this mixed picture provides the avenue for innovation, it also evinces risks of fragmentation and regulatory arbitrage. The chapter contextualizes the development, potential, and risks of the institutionalization of tokenized lending relative to the global and selected credit markets.
Detecting fraud in digital banking is a recognized challenge given the increasing sophistication of perpetrators as well as the limitations of traditional security models. Rules-based systems produce interpretability but cannot be adapted to emerging fraud threats. Advanced machine learning models require complex systems for training and serving, which are often not practical in lightweight and real-time environments. In this work, a Java-based Hybrid Framework for Fraud-Resilient Banking Systems is built that combines rule-based compliance, lightweight AI modeling, anomaly detection, and the application of cryptographic concept of Zero Knowledge Proof (ZKP) authentication in a decision layer. A synthetic dataset of 10,000 banking transactions representing realistic imbalances has been developed, with approximately 0.6% flagging transactions as fraudulent. The rules for interpretable transparency are applied in the event of high-value transactions or merchant transactions that are shown to be suspicious, the fraud detection is tackled through a logistic regression classifier to apply a probabilistic approach to fraud detection and z-scores have been used to identify anomalous outliers from an expected normal distribution as fraud. The framework included Schnorr’s ZKP protocol to authenticate the user without disclosing the secret credential. The consolidated scoring system incorporates the outputs of rules, AI probabilities, anomalies, and ZKP verification for sorting transactions into High, Medium, and Low risk. The experimental results on the Java implementation shows an achievable ROC AUC of 0.984. The system produces a balanced risk distribution, for 1.8% of transactions classified as high risk, 20% medium risk and 78% low risk. This research suggests that a lightweight Java-based fraud detection system can be made efficient, interpretable, and cryptographically augmented, and thus usable in practice for a banking platform where performance and security are key.
Decentralized Finance (DeFi) has emerged as a transformative force in the financial sector, leveraging blockchain technology to enable permissionless and automated financial services. A key component of DeFi's expansion is the rise of tokenized assets, which represent digital ownership of real-world and virtual assets. This chapter explores the various forms of tokenization, including cryptocurrencies, asset-backed tokens, and Central Bank Digital Currencies (CBDCs), and their integration into the DeFi ecosystem. It examines how tokenized assets enhance liquidity, facilitate financial inclusion, and create new investment opportunities. Additionally, the chapter discusses the interplay between decentralized and centralized financial models, regulatory challenges, and the risks associated with smart contracts, price volatility, and governance. By analyzing case studies and emerging trends, the chapter provides insights into the future of the tokenized economy and its potential to bridge traditional finance with decentralized innovations.
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 κ-rate framework of Ackerer, Hugonnier & Jermann [1] and the credit-equivalence theorem of Ahmed, Bhuyan & Islam [3], the fair iCDS spread is s * = κ(1-δ) at ι = 0, where κ is the convergence intensity and δ is the recovery rate. Conventional CDS, discounted at the risk-free rate ι, prices at s conv = κ(1-δ) • ι/(κ+ι)
This paper examines how banking compliance frameworks, particularly Italy’s Legislative Decree 231/2001 (MOG 231), are evolving in response to emerging technologies such as artificial intelligence (AI), blockchain, and smart contracts. Originally designed to regulate corporate criminal liability, MOG 231 must now address decentralised financial services, such as crypto wallets and tokenised payments, progressively integrated into traditional banking. This convergence of conventional banking and decentralised finance (DeFi) generates both opportunities and risks, demanding a reassessment of compliance, governance, and value creation models. While cryptocurrencies enable financial inclusion, microfinance, and operational efficiency, they also introduce vulnerabilities related to fraud, anonymity, and misuse by organised crime. These developments challenge legacy compliance systems to manage increasing technological and regulatory complexity. The study employs a conceptual methodology grounded in academic and regulatory literature, drawing on governance, financial regulation, and technology management. To capture the dynamic complexity of compliance adaptation, a system dynamics (SD) approach is used, with causal loop diagrams mapping interactions between compliance structures, technology adoption, performance, and risk exposure. Preliminary findings indicate that integrating AI and blockchain can enhance compliance capacity, regulatory responsiveness, and organisational resilience. However, persistent challenges, such as algorithmic accountability, smart contract enforceability, and integrating decentralised operations into centralised frameworks, suggest MOG 231 requires significant adaptation to effectively govern digitally enabled financial systems.
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 framework—comprising 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 risk—we 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 days—consistent with informed-agent migration to leveraged venues. Second, the Heston leverage correlation estimate ρ = −0.61 for BTC and ρ = −0.73 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 = −0.54, 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 spreads—a novel identification of a blockchain-specific volatility channel. Fourth, rough volatility models (Hurst exponent H ≈ 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 “maturity” 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.