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 events—purchase orders, goods receipts, and invoices—on an immutable, time-stamped ledger. Smart contracts are introduced to automatically validate invoice–PO 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 Kahneman’s 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
En menos de un año, Bolivia ha pasado de prohibir el uso de criptomonedas a incorporar su uso de manera frecuente. Esta investigación examina la dinámica de los retornos y el riesgo asociado al Bitcoin, la criptomoneda de mayor valor en el ecosistema cripto, mediante modelos diseñados para activos de alta volatilidad. El análisis se basa en una serie temporal de datos diarios recopilados durante doce años, con énfasis en la medición de retornos negativos. Los resultados muestran que la media de los retornos es positiva y estadísticamente significativa, aunque su capacidad explicativa sobre la variabilidad total es limitada, lo cual es consistente con el comportamiento típico de series financieras de alta frecuencia. En cuanto a la volatilidad, se confirma la presencia de heterocedasticidad condicional, con efectos ARCH y GARCH altamente significativos. La persistencia de la volatilidad, evidenciada por un coeficiente GARCH cercano a uno, indica que los episodios de alta o baja volatilidad tienden a mantenerse en el tiempo. Estos hallazgos destacan la relevancia de modelar adecuadamente la varianza condicional en el análisis de activos financieros como el Bitcoin. Adicionalmente, se identificó la necesidad de ajustar la escala de los datos, recomendándose una rescalación previa para mejorar la precisión en futuras estimaciones.
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.
Abstract. The global transition to renewable energy requires substantial capital mobilization beyond conventional banking channels. This systematic bibliometric investigation examines 1,245 scientific publications addressing innovative financing approaches through blockchain technology, crowdfunding platforms, and green fintech solutions. Using data from the Scopus database covering the period 2018-2025, we systematically assessed publication trends, international collaboration structures, and conceptual frameworks shaping this field. Our results demonstrate remarkable expansion, with publication output increasing sevenfold between 2018 and 2024. Chinese research institutions contribute to approximately 40,2% of global scientific output, while thematic clustering reveals five main research streams. By identifying leading organizations, influential researchers, and developing concepts including asset tokenization and decentralized energy trading, this investigation provides evidence-based guidance for advancing research agendas and informing climate finance policy development.
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 systems—characterized by autonomous goal-seeking, tool use, and multi-agent coordination—presents 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
In recent years, digital currencies have dominated headlines due to the fast growth in its nominal value. While it remains unclear whether mass adoption of digital currencies by consumers and businesses will occur, the increasing investments by households into cryptocurrencies as risky asset warrants research into the impact of digital currencies. Additionally, digital currencies have a significant impact on money supply and fiat currencies, banking and payments systems, and consequently monetary policy transmission. In recent years, banks have also started leveraging digital assets for crucial functions such as liquidity operations. Additionally, major central banks around the world have started projects to research into Central Bank Digital Currencies (CBDC), as a potential replacement for fiat currencies. This matters for small-open economies such as Singapore, which are highly dependent on open capital flows and trade to grow the economy. This paper, utilising a Structural VAR model, examines the impact of cryptocurrencies on the business cycle in Singapore and whether the adoption of digital currencies into financial markets in past years and in the years to come, could have an impact on the business cycle and monetary policy transmission. Our results show that cryptocurrency has some impact on output when compared to our counterfactual model; these results remain robust even when re-ordered.
On May 26, 2026, Strategy Inc. (MSTR) began selling Bitcoin for the first time since 2022, disposing of 32 BTC (approximately $2.5 million, or 0.0038% of its holdings) to fund preferred stock dividend payments. This transaction directly contradicted the company's long-standing public commitment to HODL, a foundational cryptocurrency acronym meaning "Hold On for Dear Life" that reflects an unyielding, long-term asset retention strategy. Using an event study methodology with the S&P 500 as a benchmark, we find a cumulative abnormal return (CAR) of-16.2% for Bitcoin over the subsequent six trading days (p = 0.012). MSTR stock experienced an even larger CAR of-20.3%, consistent with its beta of 3.02 relative to the market. Despite the trivial sale volume, the market interpreted this transaction as a negative signal about future treasury policy. Our results challenge the "inoculation" hypothesis suggested by Executive Chairman Michael Saylor, who stated that selling a small amount would "send the message" and prepare the market for potential future distributions. The magnitude of the market reaction suggests the inoculation was incomplete, demonstrating that breaking a core narrative carries outsized signaling value that significantly impacts asset valuations.