This paper examines the transformative potential of Distributed Ledger Technology (âDLTâ) in bridging the massive infrastructure funding gap in emerging markets. Traditional project finance relies on complex, high-friction syndicated loan structures and equity distributions that often exclude smaller institutional investors and lack secondary market liquidity. The Bank for International Settlements defined tokenisation as the process of generating and recording a digital representation of traditional assets on a programmable platform. By tokenizing infrastructure assets, project sponsors can democratize access to capital and automate revenue distribution via Smart Contracts. However, this transition faces significant legal hurdles. This research provides a comprehensive analysis of the existing regulatory landscape, focusing on the Investments and Securities Act 2025 (âISA 2025â) and the Companies and Allied Matters Act 2020 (âCAMA 2020â) . It interrogates whether infrastructure tokens should be classified as securities, the enforceability of decentralized collateral registries, and the legal standing of automated waterfall payments in bankruptcy scenarios. The paper concludes by proposing a model regulatory sandbox framework designed to provide legal certainty for developers while maintaining robust investor protections.
This review synthesizes the emerging literature on behavioral finance in cryptocurrency perpetual futures and perpetual swaps. It uses a constrained systematic review of accessible repositories, publisher pages, and citation trails for studies published or posted from January 2021 to April 2026. The synthesis separates 13 direct perpetual-futures studies from 6 adjacent behavioral studies that inform interpretation. The central question is how behavioral mechanisms shape trading, pricing, and market quality in perpetual futures markets. The strongest direct evidence concerns speculative demand and basis risk, leverage choice and liquidation risk, funding-rate carry and arbitrage behavior, informed trading and market quality, and exchange-design effects across centralized and decentralized venues. Direct evidence on classic behavioral constructs such as fear of missing out, overconfidence, disposition effects, and learning remains sparse in perpetual-specific settings. Three conclusions stand out. First, perpetuals are behaviorally distinctive because funding fees, leverage, mark-to-market margining, and auto-liquidation create a high-frequency feedback system between prices and trader positions. Second, the strongest causal evidence indicates that perpetual contracts increase spot-market trading volume but worsen adverse-selection conditions when informed trading rises around funding windows. Third, exchange design matters because inverse, linear, quanto, oracle-priced, and VAMM-based contracts expose traders to different incentives and liquidation dynamics. The most important research gaps concern trader-level identification, CEX-DEX comparisons using comparable data, contract-type heterogeneity, and causal tests of leverage-rule changes.
Spot Bitcoin ETFs, approved in January 2024, trade only during NYSE hours but track an asset that trades around the clock. We study whether this mismatch affects Bitcoin's intraday risk profile using a symmetric one-year difference-in-differences design on hourly Coinbase data. The aggregate US-hour effect is null, but hour-specific and sub-hourly decomposition reveals a volatility spike concentrated in the first 30 minutes of ETF trading (9:30-10:00 ET), the only window surviving multiple testing correction. The pre-open half-hour (9:00-9:30) is insignificant, a pattern more consistent with order flow at the open than with anticipatory positioning. Quantile analysis shows left-tail deepening at the 5th and 10th percentiles of US-hour returns while the median is unaffected, and both tails widen at the opening window. Trading volume surges at both NYSE open and close, but only the open generates a volatility spike, and an ETH/USD comparison on the same exchange, which lacked comparable ETF exposure, shows no similar pattern, together supporting a BTC-ETF-specific interpretation. The findings suggest that clock-bound financial instruments can reshape when risk concentrates in continuous markets.
Decentralized finance liquidity providers (LPs) who use their Uniswap v3 positions as collateral on lending platforms such as Aave often face liquidations because these platforms rely on fixed Loan-to-Value (LTV) rules. These rules do not account for Impermanent Loss which can increase rapidly when asset prices move outside an LP's chosen price range. To address this, we simulated Uniswap v3 LP positions using historical ETH/USD price data and compared the standard fixed-LTV lending model with a hybrid risk-management framework that incorporates stress testing and dynamic exposure reduction. Under the conventional 65% LTV model, liquidations were frequent, with 39,649 liquidation events observed over roughly a decade of daily price data (2015-2025). The proposed framework reduced liquidations by 97.66% while maintaining healthier collateral positions, achieving this through adaptive reductions in effective leverage rather than full liquidation. These findings suggest that incorporating impermanent loss-aware risk controls into DeFi lending protocols could significantly reduce liquidations while keeping leveraged positions safer through periods of volatility. By combining the accessibility of DeFi with the risk management techniques used in traditional finance, lending platforms can become more stable and efficient, benefiting liquidity providers.
Abstract: Access to financial resources by individuals, corporations, and governments must undergo impact assessments concerning human rights. Public and private governance bodies exert influence over the global financial landscape, ensuring compliance with frameworks such as the UNâs 2030 SDGs through the "Equator Principles" and the "Principles for Responsible Investment." The article aims to analyze the effects of digital tools on responsible financing, such as through the decentralization of financial systems for credit access. It explores the use of artificial intelligence (AI) integrated into "smart contracts," the consumer credit market, especially on peer-to-peer lending platforms, and other fintech solutions for achieving ESG goals like poverty reduction. However, the use of AI and "smart contracts" may also pose risks to human rights. The primary approach involves reviewing international literature to identify emerging risks. The expected outcome is a comprehensive analysis of recent trends and challenges related to corporate social responsibility in the financial sector, particularly regarding human rights in the digital era.
Open access
FinTech, Crowdfunding, Digital Finance
Legal, Health, Environmental and COVID-19 Challenges
This undergraduate capstone thesis examines the challenges and opportunities associated with the regulation and adoption of Decentralized Finance (DeFi) in the United Arab Emirates. Drawing on a qualitative analysis of regulatory documents, academic literature, and 13 semi-structured interviews with DeFi practitionersâincluding smart contract developers, compliance/AML experts, product managers, and blockchain specialistsâthe study investigates how the UAEâs multi-jurisdictional framework (VARA, ADGM, CBUAE, and SCA) shapes institutional confidence and market participation. Key findings reveal structural challenges stemming from DeFiâs decentralized, borderless, and pseudonymous nature, such as the absence of a central âoff switch,â enforcement difficulties with KYC/AML and the FATF Travel Rule, consumer risks from smart-contract vulnerabilities and low financial literacy, and regulatory fragmentation across emirates. At the same time, experts identify substantial opportunities in cheaper cross-border remittances, real-world asset tokenization, SME financing through automated lending pools, and the UAEâs positioning as a global fintech hub. The research supports the thesis that greater regulatory clarity and enforcement coherence causally influence institutional confidence and the trajectory of DeFi adoption. It concludes with actionable policy recommendationsâincluding targeted regulatory sandboxes, RegTech investment, on-chain accountability mechanisms, innovation-linked incentives, and mutual recognition agreementsâto help the UAE balance innovation with consumer protection and financial stability.
This paper examines the transformation of the interest rate transmission mechanism under the conditions of rapid financial digitalization. The emergence of new financial intermediaries, decentralized finance (DeFi), digital lending platforms, and the growing role of big data and algorithmic pricing are reshaping how changes in the central bank policy rate affect the real economy. The research identifies novel transmission channels, including digital funding channels, crypto-asset price channels, and algorithmic expectation channels, while highlighting the risks of transmission fragmentation and uneven pass-through across sectors. The study concludes with policy recommendations for central banks to adapt their monetary policy strategies to the high-tech financial landscape.
Tokenized deposits settle continuously and nearly instantaneously, but faster withdrawal execution compresses the coordination game among depositors and increases funding fragility even for solvent banks. This paper examines how disclosure design interacts with settlement speed to determine run risk and welfare in tokenized banking environments. We compare conventional disclosure with verifiable compliance disclosure implemented via Zero-Knowledge Proofs (ZKPs), which allow banks to certify regulatory liquidity compliance without revealing precise balance sheet positions. ZKP disclosure eliminates coordination-driven runs on compliant banks by pooling institutions near the regulatory threshold, at the cost of weaker market discipline as sophisticated depositors acquire less private information. A calibrated simulation using FDIC call report data for large US commercial banks, including the five Cari Network members, quantifies run probabilities and welfare across settlement speeds and depositor compositions. Tokenization without enhanced disclosure amplifies fragility; combining fast settlement with verifiable compliance disclosure improves welfare, particularly for retail-oriented funding bases. Disclosure architecture is not ancillary to tokenized deposit regulation, it is central to it.
Over the past decade, decentralized digital currencies have gained prominence in finance and technology, but their growth has also drawn adversaries exploiting security vulnerabilities. This paper reviews the literature on cryptocurrency and security using bibliometric analysis of Web of Science and Scopus articles published between 2013 and May 2025. Tools such as Biblioshiny and VOSviewer were employed to explore key trends, influential contributors, collaborative networks, and emerging themes. A novel contribution of this study is the use of Large Language Models (LLMs) to address inconsistent affiliation formats, enabling accurate identification of leading academic organizations. The results demonstrate that LLM-based harmonization effectively prevents misrepresentation in bibliometric datasets. Overall, this study not only summarizes evolving research trends in cryptocurrency and security but also highlights the potential of LLMs to enhance bibliometric methods, suggesting broader applications for improving the accuracy and reliability of future scholarly analyses.
The tokenization of real-world assets (RWA) represents one of the most transformative applications of blockchain technology in modern financial markets. By converting tangible and intangible assets such as real estate, commodities, private equity, intellectual property, and infrastructure into blockchain-based digital tokens, tokenization enables fractional ownership, enhanced liquidity, programmability, and borderless capital formation. This paper examines how RWA tokenization is reshaping entrepreneurial finance by lowering barriers to entry for both founders and investors, expanding access to global capital pools, and fostering new hybrid models of decentralized and regulated finance. Drawing on developments within the broader blockchain ecosystem, decentralized finance (DeFi), and emerging regulatory frameworks, the study evaluates technological architecture, economic implications, governance mechanisms, risk considerations, and policy challenges. The paper argues that RWA tokenization has the potential to democratize entrepreneurial finance by enhancing inclusion and efficiency, while also introducing new systemic, legal, and ethical complexities that require coordinated regulatory innovation.
Anthony Chidi Nzomiwu, Francisca Uzooyibo Okoye, Benedict Iyke Okoronkwo
Small and Medium Enterprises (SMEs) face a persistent financing gap globally, estimated at significant portions of GDP in emerging markets like Nigeria, while facing different structural barriers in developed economies like Poland. Decentralized Finance (DeFi) offers theoretical solutions through peer-to-peer lending and tokenized assets, yet pure DeFi adoption remains low among SMEs due to regulatory uncertainty, technical complexity, and volatility. This paper employs Institutional Theory (North, 1990) and Ozili's (2023) tripartite framework of regulation, infrastructure, and capacity to compare the Nigerian and Polish contexts. Drawing on a synthesis of recent literature (2018-2026), the study argues that "pure" DeFi is ill-suited for immediate SME adoption in either context. Instead, a "Hybrid Finance" model where regulated fintech intermediaries bridge traditional banking and blockchain protocols offers the most viable pathway. The analysis highlights Nigeria's reactive regulatory stance (e.g., the 2021 ban and subsequent lifting) versus Poland's adaptive integration within the EU's Markets in Crypto-Assets (MiCA) framework. The paper concludes that institutional embedding, rather than technological disruption alone, is critical for closing the SME financing gap.
This study assesses whether Bitcoinâs linkage with AI equities remains robust after accounting for equity risk sentiment. To this end, the study employs the multiscale quantileâonâquantile correlation (MSQQC) and multiscale quantileâonâquantile partial correlation (MSQQPC) approaches, using daily data covering 02/01/2019â16/06/2025. The results indicate that BTCâAI comovement is strongly stateâ and frequencyâdependent rather than stable across the joint distribution or across horizons. In the highâfrequency band, dependence is weak and only intermittently significant, with localised negative regions around BTC â 0.20 with AI â 0.30â0.50 and BTC â 0.30 with AI â 0.70. In the midâfrequency band, significance concentrates in the tails, showing negative dependence under downside stress conditions such as BTC â 0.10â0.30 with AI â 0.10, alongside sign changes when BTC is in upperâtail states. In the lowâfrequency band, dependence becomes broadly positive and significant across most quantile combinations, with limited decoupling when AI is highly elevated (â 0.80â0.90) and BTC is also in upper quantiles (â 0.70â0.90). Importantly, conditioning on VIX and VVIX does not materially alter these patterns, suggesting that sentiment influences segments of shortârun dependence but does not overturn the longerârun BTCâAI linkage. The study derives policy recommendations from these findings.
This research assesses the key blockchain infrastructures utilized in decentralized finance, tokenized asset markets, institutional settlement systems, and the development of new digital financial ecosystems. The paper focuses on both Layer 1 and Layer 2 networks, emphasizing aspects such as scalability, institutional uptake, privacy frameworks, transaction traceability, and alignment with regulations. Data sourced from credible industry publications, official institutional announcements, and peer-reviewed research suggests that Ethereum remains the leading platform for decentralized finance and tokenized asset infrastructure. In contrast, Solana has shown significant institutional growth through stablecoin settlements, tokenized real-world assets, and enterprise collaborations. Chains that prioritize privacy are increasingly facing regulatory challenges due to anti-money laundering (AML) obligations. The study concludes that hybrid blockchain models, which integrate public settlement with compliance frameworks, are the primary pathway for adoption within the financial sector.
Sai Srikanth Madugula, jose Luis de la Rosa Esteva, Daya Shankar
This paper presents an integrated framework for decentralized invoice-backed loan underwriting combining interpretable machine learning, dynamic pricing algorithms, and on-chain trust infrastructure. We develop and validate SHAP-explainable ML models for real-time default probability assessment, design a Reverse Kelly AMM smart contract for optimal risk-adjusted loan pricing, integrate ERC-725 identity and on-chain reputation scoring with an automated insurance reserve, and deploy the system on Ethereum testnet with end-to-end functional and security testing. Stress testing across simulated default and fraud scenarios demonstrates the model achieves AUC-ROC of 0.89 on validation data, maintains LP yields of 12â18% under normal conditions while containing non-performing loan ratios below 3% under adverse scenarios, and sustains reserve solvency across 95th percentile stress events. The framework addresses critical gaps in DeFi lending by bridging regulatory interpretability requirements with decentralized credit assessment, demonstrating both technical feasibility and economic viability for permissionless SME financing at scale.
Tokenization of real-world assets (RWAs)âthe representation of off-chain assets on a digital ledgerâhas gained momentum across money market funds, government bonds, gold, and private credit. It bridges traditional finance and on-chain markets while continuing to rely on traditional infrastructure for custody, legal enforcement, and price discovery. Tokenization promises efficiency gains in issuance, trading, and settlement and may facilitate secondary-market liquidity by making claims on otherwise illiquid assets more transferable. We distinguish three categories: tokenized liquid assets (e.g., gold and equities), money-like claims (e.g., stablecoins and tokenized deposits), and tokenized illiquid assets (e.g., loans and private credit). Tokenization of liquid assets integrates traditional markets with decentralized finance and reallocates liquidity across venues. Tokenization of illiquid assets, by contrast, facilitates secondary-market trading opportunities, but whether it creates meaningful liquidity depends on market design, investor participation, valuation quality, and asset opacity. This liquidity transformation inherits incentive problems familiar from banking and securitization while introducing new economic and operational risks related to custody, redemption design, and oracles. We argue that the trading speed of a tokenized claim should match the speed at which the underlying asset can be traded, valued, or redeemedâthe speed-matching principle that organizes our policy framework. We propose a policy framework that ties regulatory requirements to the economic role tokens play and the speed and liquidity of their underlying markets rather than to the underlying technology, prioritizing clear legal foundations, credible redemption mechanisms, robust custody and audit standards, and sound oracle governance.
Smart contracts have revolutionized finance by enabling decentralized applications without intermediaries, yet their widespread adoption has exposed significant gaps in understanding their practical implications and unresolved challenges. Unlike existing works that primarily focus on theoretical overviews, this paper employs a rigorous empirical methodology to bridge the gap between research and real-world operations. By combining insights from user and developer communities, practical experiments on testnets and mainnets, and a comprehensive analysis of prior studies, this work uncovers underexplored applications, highlights discrepancies between theoretical models and actual behaviors, and identifies emerging security, privacy, and social challenges. The paper first provides structural insights into the surveyed contents and then introduces critical security, privacy, and social considerations. The first category of surveyed contents includes well-documented applications such as automated market makers (AMMs), non-fungible tokens (NFTs), and flash loans. Unlike existing works, this study offers unified explanations that integrate fragmented information while presenting experimental findings and practical proposals. The second category covers under-explored applications like NFT vouchers for real-world assets, wrapped NFTs, and reversible transactions. By offering actionable insights and usage guidelines, this study distinguishes itself by addressing the nuanced, practical realities of smart contract applications, equipping researchers, developers, and users with the knowledge needed to navigate the evolving world of smart contracts effectively.
Lending in decentralized finance (DeFi) relies on collateral and efficient liquidations to manage credit risk. The permissionless and pseudonymous nature of public blockchains precludes reputation-based lending in DeFi and renders liabilities effectively non-recourse. Frictions in collateral liquidations increase the risk of bad debt and may ultimately lead to protocol defaults and losses for liquidity providers. This paper studies liquidation dynamics in the Aave V2 Main Market on Ethereum using block-level data covering 46 months and more than 54 000 borrower positions. While most undercollateralized debt is liquidated almost instantaneously, a non-trivial share of positions remains open for extended periods. Using a state model to distinguish healthy, viable for liquidation, and stale borrower positions, this paper quantifies transition probabilities and identifies factors associated with liquidation success. Logistic regression results show that liquidation size, lower network transaction fees, and relative profitability are associated with the probability of liquidation success in the subsequent block. At the same time, oracle price distortions and asset price volatility are associated with lower liquidation likelihood, consistent with heightened execution risk. The findings provide new high-frequency evidence on liquidation frictions in a large and mature DeFi lending market. The results contribute to the understanding of the microstructure of DeFi liquidations and credit risk in decentralized lending protocols.
Ethereum is a decentralized blockchain platform that allows developers to deploy and run smart contracts, which are self-executing programs responsible for handling digital transactions without intermediaries. ERC standards define how these smart contracts are expected to behave in the Ethereum ecosystem. When these rules are not implemented correctly, contracts may contain security weaknesses that can lead to financial loss or unexpected behavior. For this reason, verifying whether a contract complies with ERC requirements is an important task during the development process. However, compliance verification is still often performed manually, which makes the process slow and dependent on expert knowledge. Most existing static analysis tools mainly detect predefined vulnerability patterns, but they may miss behavioral deviations from Ethereum Request for Comments (ERC) specifications that are not explicitly encoded as patterns. In this study, we present a multi-agent LLM framework designed to automate ERC compliance auditing. The system extracts contract-specific code fragments and evaluates them using multiple independent Large Language Model agents. Their outputs are aggregated through a confidence-weighted mechanism that aims to stabilize the final decision. Experiments on ERC-20, ERC721, and ERC-1155 contracts show that the multi-agent configuration improves recall and reduces false negatives compared to single-agent auditing.
This paper investigates the extent to which cognitive heuristics, social influence, and digitally-mediated sentiment drive the extreme volatility of cryptocurrency, Decentralised Finance (DeFi), and Non-Fungible Token (NFT) markets, and the degree to which these dynamics deviate from the Efficient Market Hypothesis. Using an integrative narrative review and a synthesis of empirical evidence from 2014â2025, the paper develops the Integrated Digital Asset Behavioural Model (IDABM), a four-variable framework relating market stability to social velocity (Sv ), heuristic load (Hl ), platform gamma (PÎł ), and liquidity leverage (Ll ). The analysis draws on demographic and sentiment data, case evidence from the 2022 Terra/ Luna and FTX collapses, and a comparative cross-asset bias taxonomy. The findings indicate that digital asset markets constitute a pure sentiment environment in which the absence of conventional valuation anchors produces heuristic dominance and structurally amplified herding behaviour. The paper concludes that effective regulation must shift from informational disclosure toward behavioural guardrails â including algorithmic accountability, regulation of gamified trading interfaces, and behavioural literacy requirements.
Aim: This study examines whether and how the disposition effect shapes Ethereum investorsâ selling decisions. It asks whether investors are more likely to realize gains than losses, whether this asymmetry strengthens during high-volatility periods, and whether it weakens around major protocol upgrades, including the Merge, Shapella, and Dencun. Methodology: The study builds a high-frequency address-day panel for 2020â2024 using public on-chain data and labeled centralized-exchange deposit clusters as conservative proxies for sell decisions. Rolling cost bases are reconstructed under FIFO and value-weighted rules, and unrealized gains and losses are linked to realized sales through discrete-time logit and Cox hazard models. The design also includes event windows and robustness checks. Findings: The framework is designed to identify three mechanisms: asymmetric realization of gains over losses, stronger gain realization under high volatility, and attenuation around major protocol-upgrade events. Implications: The study offers a transparent design for analyzing behavioral bias in crypto-asset markets with verifiable blockchain data. It is relevant to exchanges, regulators, and market designers concerned with investor behavior and risk management. Originality/value: The article extends behavioral finance to Ethereum by using public ledger data rather than brokerage records and by integrating behavioral bias, volatility regimes, and protocol events in one framework.
Jun Young Byun, Yosep Na, Daehyun Kim, Hyun Ho Jeon · 6 authors
This paper examines whether on-chain factors derived from Ethereum blockchain data contain pricing information beyond established cryptocurrency risk factors. We construct 27 on-chain factors across four dimensions (network activity, scale-adjusted activity, valuation ratios, and token distribution) for 122 Ethereum-based tokens from July 2020 to August 2025, and evaluate them against 26 benchmark factors spanning size, momentum, volume, and volatility using a double-selection LASSO framework, complemented by portfolio sorts and three-factor regressions. Eight on-chain factors are significant in the cross-sectional pricing test, with the strongest evidence concentrated in scale-adjusted activity and token distribution. Transaction count to network value is the only factor that remains significant in the cross-sectional pricing test, portfolio sorts, and three-factor regressions. In contrast, valuation ratios based on market-to-realized values do not survive as independent sources of abnormal return once momentum is taken into account, reflecting the mechanical overlap between recent price appreciation and slowly adjusting realized values. Token-distribution factors, particularly small-holder share and centralized exchange share, generate the most robust abnormal returns and remain economically meaningful under equal-weighted construction and conservative transaction-cost assumptions. Subperiod analysis further reveals a change in on-chain pricing power: scale-adjusted activity factors are stronger earlier in the sample, whereas distribution-based factors become more important over time. Overall, the results show that blockchain-native information, especially holder distribution, captures a distinct dimension of cryptocurrency asset pricing.
Digitalisation of finance led to the creation of a digital financial economy, where digital assets such as cryptocurrencies, decentralized financial assets, non-fungible tokens, stablecoins, etc. were traded. In this study, machine learning and deep learning techniques, including ARIMA, FB Prophet, LSTM, and BiLSTM, have been used to forecast the prices of digital assets. In this study, Bitcoin, Ethereum, Uniswap, Aave, ApeCoin, and Decentraland tokens have been categorized into three groups, and the prediction models have been trained using the tokens' closing prices. The authors find that NFTs have been underestimated and that DeFi assets have greater growth potential. Whereas cryptocurrencies have been traded more and shown greater volatility than other asset classes. BiLSTM achieves the best results, with higher accuracy in price prediction. Here, it has been seen that ApeCoin, Decentraland, and Bitcoin are more stable than other assets. Thus, for an optimised portfolio and additional savings, it is necessary to provide a proper asset mix.