With the advancement of digital payment technologies, central banks worldwide have increasingly begun to explore the implementation of Central Bank Digital Currencies (CBDCs). This paper presents a comprehensive review of the latest developments in CBDC system design and implementation. By analyzing 135 research papers published between 2018 and 2025, the study provides an in-depth examination of CBDC design taxonomy and ecosystem frameworks. Grounded in the CBDC Design Pyramid, the paper refines and expands key architectural elements by thoroughly investigating innovations in ledger technologies, the selection of consensus mechanisms, and challenges associated with offline payments and digital wallet integration. Furthermore, it conceptualizes a CBDC ecosystem. A detailed comparative analysis of 26 existing CBDC systems is conducted across four dimensions: system architecture, ledger technology, access model, and application domain. The findings reveal that the most common configuration consists of a two-tier architecture, distributed ledger technology (DLT), and a token-based access model. However, no dominant trend has emerged regarding application domains. Notably, recent research shows a growing focus on leveraging CBDCs for cross-border payments to resolve inefficiencies and structural delays in current systems. Finally, the paper offers several forward-looking recommendations for future research.
The CAP theorem asserts a trilemma between consistency, availability, and partition tolerance. This paper introduces a rigorous automata-theoretic and economically grounded framework that reframes the CAP trade-off as a constraint optimization problem. We model distributed systems as partition-aware state machines and embed economic incentive layers to stabilize consensus behavior across adversarially partitioned networks. By incorporating game-theoretic mechanisms into the global transition semantics, we define provable bounds on convergence, liveness, and correctness. Our results demonstrate that availability and consistency can be simultaneously preserved within bounded epsilon margins, effectively extending the classical CAP limits through formal economic control.
We develop a monetary macro accounting theory (MoMaT) and its software specification for a consistent national accounting. In our money theory money functions primarily as a medium of payment for obligations and debts, not as a medium of exchange, originating from the temporal misalignment where producers pay suppliers before receiving revenue. MoMaT applies the legal principles of Separation and Abstraction to model debt, contracts, property rights, and money to understand their nature. Monetary systems according to our approach operate at three interconnected levels: micro (division of labor), meso (banking for risk-sharing), and macro (GDP sharing, money issuance). Critical to money theory are macro debt relations, hence the model focuses not on the circulation of money but on debt vortices: the ongoing creation and resolution of financial obligations. The Bill of Exchange (BoE) acts as a unifying contractual instrument, linking debt processes and monetary issuance across fiat and gold-based systems. A multi-level BoE framework enables liquidity exchange, investments, and endorsements, designed for potential implementation in blockchain smart contracts and AI automation to improve borrowing transparency. Mathematical rigor can be ensured through category theory and sheaf theory for invariances between economic levels and homology theory for monetary policy foundations. Open Games can structure macroeconomic analysis with multi-agent models, making MoMaT applicable to blockchain economic theory, monetary policy, and supply chain finance.
This paper develops a formal game-theoretic model to examine how protocol mutability disrupts cooperative mining behaviour in blockchain systems. Using a repeated game framework with stochastic rule shocks, we show that even minor uncertainty in institutional rules increases time preference and induces strategic deviation. Fixed-rule environments support long-term investment and stable equilibrium strategies; in contrast, mutable protocols lead to short-termism, higher discounting, and collapse of coordinated engagement. Simulation results identify instability zones in the parameter space where rational mining gives way to extractive or arbitrage conduct. These findings support an Austrian economic interpretation: calculability requires rule stability. Institutional noise undermines the informational basis for productive action. We conclude that protocol design must be treated as a constitutional economic constraint, not a discretionary variable, if sustainable cooperation is to emerge in decentralised systems.
This paper integrates Austrian capital theory with repeated game theory to examine strategic miner behaviour under different institutional conditions in blockchain systems. It shows that when protocol rules are mutable, effective time preference rises, undermining rational long-term planning and cooperative equilibria. Using formal game-theoretic analysis and Austrian economic principles, the paper demonstrates how mutable protocols shift miner incentives from productive investment to political rent-seeking and influence games. The original Bitcoin protocol is interpreted as an institutional anchor: a fixed rule-set enabling calculability and low time preference. Drawing on the work of Bohm-Bawerk, Mises, and Hayek, the argument is made that protocol immutability is essential for restoring strategic coherence, entrepreneurial confidence, and sustainable network equilibrium.
This paper presents a formal analysis of the Lightning Network as a monetary system structurally diverging from Bitcoin's base-layer settlement model. We demonstrate that under increasing transaction demand, BTC transaction fees rise superlinearly due to throughput constraints, while Lightning Network routing costs approach a bounded asymptote. Using mathematical modeling, game-theoretic proofs, and complexity analysis, we show that Lightning enables indefinite off-chain operation via the emergence of liquidity hub oligopolies. These hubs exhibit properties of unregulated financial intermediaries, including rent extraction, opacity, and systemic fragility. Strategic agent models show that channel closure becomes economically infeasible, and routing problems approach hardness limits in P-Space complexity. We conclude that Lightning does not merely extend Bitcoin, but constitutes a synthetic financial system with shadowbank characteristics, lacking reserve discipline, transparency, or enforceable settlement guarantees.
Blockchain's economic value lies in enabling financial and economic transactions without relying on trusted, centralized intermediaries. In practice, however, transactions pass through a fragmented chain of intermediaries before being included on-chain. Because standard blockchain data reveal only the winning block, this process is largely unobservable. We address this limitation by constructing a novel dataset of 15,097 non-winning Ethereum blocks, that is, blocks proposed but not selected for inclusion. We show that 21% of user transactions are delayed: they appear in candidate blocks but not in the winning block, implying that fragmented routing materially affects inclusion time. We further show that execution quality varies substantially across candidate blocks: for the same swap, both execution probability and execution price differ across proposed blocks. To study these differences, we examine competition between two arbitrage bots trading between decentralized and centralized exchanges. We find that, conditional on inclusion in a block that also contains transactions from these bots, user swaps in the same (opposite) direction are less likely (more likely) to execute and receive worse (better) prices. These results show that routing and block composition are central determinants of execution quality and market quality in on-chain markets.
A common assumption in cryptocurrency markets is a positive relationship between total-value-locked (TVL) and cryptocurrency returns. To test this hypothesis we examine whether the returns of TVL-sorted portfolios can be explained by common cryptocurrency factors. We find evidence that portfolios formed on TVL exhibit returns that are linear functions of aggregate crypto market returns, that is they can be replicated with appropriate weights on the crypto market portfolio. Thus, strategies based on TVL can be priced with standard asset pricing tools. This result holds true both for total TVL and a simple TVL measure that removes a number of ways TVL may be overstated.
Gordon Y. Liao, Ziming Zeng, Mira Belenkiy, Jacob Hirshman
This paper introduces a fraud-deterrent access validation system for public blockchains, leveraging two complementary concepts: "Transaction Proximity", which measures the distance between wallets in the transaction graph, and "Easily Attainable Identities (EAIs)", wallets with direct transaction connections to centralized exchanges. Recognizing the limitations of traditional approaches like blocklisting (reactive, slow) and strict allow listing (privacy-invasive, adoption barriers), we propose a system that analyzes transaction patterns to identify wallets with close connections to centralized exchanges. Our directed graph analysis of the Ethereum blockchain reveals that 56% of large USDC wallets (with a lifetime maximum balance greater than \$10,000) are EAI and 88% are within one transaction hop of an EAI. For transactions exceeding \$2,000, 91% involve at least one EAI. Crucially, an analysis of past exploits shows that 83% of the known exploiter addresses are not EAIs, with 21% being more than five hops away from any regulated exchange. We present three implementation approaches with varying gas cost and privacy tradeoffs, demonstrating that EAI-based access control can potentially prevent most of these incidents while preserving blockchain openness. Importantly, our approach does not restrict access or share personally identifiable information, but it provides information for protocols to implement their own validation or risk scoring systems based on specific needs. This middle-ground solution enables programmatic compliance while maintaining the core values of open blockchain.
Ayush Jha, Abootaleb Shirvani, Ali Jaffri, Svetlozar T. Rachev · 5 authors
This paper introduces a state-dependent momentum framework that integrates ESG regime switching with tail-risk-aware reward-risk metrics. Using a dynamic programming approach and solving a finite-horizon Bellman equation, we construct long-short momentum portfolios that adjust to changing ESG sentiment regimes. Unlike traditional momentum strategies based on historical returns, our approach incorporates the Stable Tail Adjusted Return ratio and Rachev ratio to better capture downside risk in turbulent markets. We apply this framework across three asset classes, Russell 3000 equities, Dow Jones~30 stocks, and cryptocurrencies, under both pro- and anti-ESG market regimes. We find that ESG-loser portfolios significantly outperform ESG-winner portfolios in pro-ESG regimes, a counterintuitive result suggesting that market overreaction to ESG sentiment creates short-term pricing inefficiencies. This pattern is robust across tail-sensitive performance metrics and is most pronounced under a two-week formation and holding period. Our framework highlights how ESG considerations and sentiment regimes alter return dynamics, offering practical guidance for investors seeking to implement responsive momentum strategies under sustainability constraints. These findings challenge conventional assumptions about ESG investing and underscore the importance of dynamic, regime-aware portfolio construction in environments shaped by regulatory signals, investor flows, and behavioral biases.
Decentralized Finance (DeFi) on Ethereum has undergone significant transformations since its emergence during the DeFi summer of 2020. With the introduction of Proof of Stake (PoS) and Proposer-Builder Separation (PBS), the transaction supply chain on Ethereum has shifted from relying entirely on the public mempool for DeFi interactions to an astonishing 80% usage of private RPCs. These private RPCs submit transactions directly to builders, skipping the public mempool, while conducting Order Flow Auctions (OFAs) to capture MEV backrun rebates and gas rebates. Our findings reveal that not all RPCs OFAs produce the same outcomes. These insights underscore the significant implications of OFA design choices on transaction efficiency and execution quality, and thus why an order flow originators should pay close attention to which OFA they use.
Alessandro Longo, Teodoro Criscione, Julio Linares, Sowelu Avanzo
Circles UBI is a blockchain-based Community Currency System (CCS) that has been active in Berlin (Germany) since October 2021. The Circles Coop, which launched the project in 2021, was shut down in December 2023. In this paper, we show the results of a survey carried out between October and November 2023. The respondents were twenty-five individuals involved in various ways in the Circles' network. The main emerging narrative points out how their participation was deeply motivated by their identification with the values and ideals of the Circles community. Among them, we selected five profiles that stood for their difference in type and degree of involvement. Finally, we report some stories of economic linkages that suggest a positive externality in adopting a local community currency. To our knowledge, this is the first qualitative study of a universal basic income designed as a community currency and adopting blockchain technology. This pilot project was a remarkable experiment for its adopted advanced technological and social innovations. In fact, as far as we know, the integration of basic income and local currency features has been experimented with only in two other cases (Maricá, Brazil and Barcelona, Spain) and none of them adopted a decentralized ledger system. In this work, we try to outline strengths and weaknesses that emerged after about two years of activity. For this reason, future researchers and activists interested in this field will find valuable information.
The U.S. dollar's status as the global reserve currency faces growing challenges from a 36 trillion dollar national debt, geopolitical shifts, and the emergence of digital currencies. This paper introduces the Quantum Reserve Token (QRT), a decentralized digital currency backed by quantum computational capacity - a scarce, productive resource projected to add over 1 trillion dollars to global GDP by 2035. Unlike Bitcoin's fixed-supply volatility, stablecoins' dependence on fiat trust, or central bank digital currencies' jurisdictional limits, QRT uses quantum computing power as a novel value anchor. This study develops a monetary theory-based framework for QRT, compares it to existing digital currency models, and evaluates its feasibility across technological, economic, geopolitical, and adoption dimensions. QRT offers a stable, neutral, and scalable reserve currency alternative, potentially reshaping the global monetary system.
Can we create binding agreements between nations? Recently, scholars have argued that blockchain technology enables us to do so. Given that this could greatly affect the anarchical world order implied by state sovereignty, this remarkable claim is investigated thoroughly. By focusing on the technical implementation of smart contracts between nations, this article finds that the potential to create binding agreements using blockchain technology is far more limited than recently suggested.
Blockchain is a technological innovation that has the potential to radically change our financial markets by providing an alternative management approach to the "promise market", which is the foundation of our financial systems. Its disruptive potential also extends to corporate finance, where blockchain is beginning to influence valuation methods and capital allocation strategies, offering new perspectives on how companies are assessed and financed. However, for a new financial architecture based on blockchain and advancements in technology -- what is commonly referred to as Fintech -- to replace, in whole or in part, traditional finance, it will need to overcome significant challenges such as regulation, environmental sustainability, its association with illegal activities, and achieving greater efficiency in cryptocurrency markets. For this reason, the future of Fintech is likely to be more conventional -- yet also more transparent, efficient, and regulated -- ultimately evolving to resemble the traditional finance we know.
Juan Beccuti, Thunj Chantramonklasri, Matthias Hafner, Nicolas Oderbolz
This paper examines how various categories of Ethereum stakers respond to changes in the consensus issuance schedule, and the potential impact of such changes on the composition of the staking market. To this end, we have develop and calibrate a game-theoretic model of the Ethereum staking market, incorporating strategic interactions between various staking agents. Our findings suggest that solo stakers may be more sensitive to variations in staking rewards than ETH holders using centralized exchanges or liquid staking providers. This increased sensitivity is driven not only by the cost structure of solo staking, but also by the competitive dynamics between different staking solutions in the market. When faced with a downward-sloping issuance schedule, staking agents compete for limited staking yields, and their choice of staking supply affects the revenues of other stakers. Therefore, the presence of other staking methods with access to MEV revenues and other DeFi yield sources when staking, as well as inattentive stakers, puts competitive pressure on solo stakers. Consequently, our model predicts that a reduction in issuance is likely to crowd out solo stakers. We present preliminary empirical evidence to support this result, using an instrumental variable estimation approach to estimate the yield elasticity of staking supply for different staking categories.
Ethereum has adopted a rollup-centric roadmap to scale by making rollups (layer 2 scaling solutions) the primary method for handling transactions. The first significant step towards this goal was EIP-4844, which introduced blob transactions that are designed to meet the data availability needs of layer 2 protocols. This work constitutes the first rigorous and comprehensive empirical analysis of transaction- and mempool-level data since the institution of blobs on Ethereum on March 13, 2024. We perform a longitudinal study of the early days of the blob fee market analyzing the landscape and the behaviors of its participants. We identify and measure the inefficiencies arising out of suboptimal block packing, showing that at times it has resulted in up to 70% relative fee loss. We hone in and give further insight into two (congested) peak demand periods for blobs. Finally, we document a market design issue relating to subset bidding due to the inflexibility of the transaction structure on packing data as blobs and suggest possible ways to fix it. The latter market structure issue also applies more generally for any discrete objects included within transactions.
David Lee Kuo Chuen, Yang Li, Weibiao Xu, Willy Zhao
Proponents of blockchains believe that this technology will revolutionize e-commerce. To evaluate this belief, we invite several groups of students to transact on a decentralized peer-to-peer marketplace built on the platform provided by Origin Protocol Inc., and then we conduct a survey about their experience of usage. Based on our survey results, we find that 33% of respondents play tricks on others, which implies that this undesirable result may hinder the widespread adoption of blockchain technologies. We also attempt to propose a conceptual mechanism to mitigate fraudulent behaviors. In the event of disputation, a trusted authority is entitled to the right to downgrade the fraudulent side's credit record, which is stored by a permissioned blockchain accessed only by the authority. Such a punishment can effectively decrease agents' incentives to sell counterfeits and leave fake ratings. In sum, we must distinguish what we proposed blockchains will do and what blockchains can do before enabling this technology in e-commerce.
With the growing popularity and rising value of cryptocurrencies, skepticism surrounding this groundbreaking innovation persists. Many financial and business experts argue that the value created in the cryptocurrency realm resembles the generation of currency from thin air. However, a historical analysis of the fundamental concepts that have shaped money reveals striking parallels with past transformations in human society. This study extends these historical insights to the present era, demonstrating how enduring monetary concepts are once again redefining our understanding of money and reshaping its form. Additionally, we offer novel interpretations of cryptocurrency by linking the intrinsic nature of money, the communities it fosters, and the cryptographic technologies that have provided the infrastructure for this transformative shift.
Ignacy Nieweglowski, Aviv Yaish, Fahad Saleh, Fan Zhang
Cryptoassets launched by political figures, e.g., political finance (PoliFi) tokens, have recently attracted attention. Chief among them are the eponymous tokens backed by the 47th president and first lady of the United States, TRUMPandMELANIA. We empirically analyze both, and study their impact on the broad decentralized finance (DeFi) ecosystem. Via a comparative longitudinal study, we uncover a "Trump Effect": the behavior of these tokens correlates positively with presidential approval ratings, whereas the same tight coupling does not extend to other cryptoassets and administrations. We additionally quantify the ecosystemic impact, finding that the fervor surrounding the two assets was accompanied by capital flows towards associated platforms like the Solana blockchain, which also enjoyed record volumes and fee expenditure.
Blockchain technology, though conceptualized in the early 1990s, only gained practical relevance with Bitcoin's launch in 2009. Recent advancements have demonstrated its transformative potential, particularly in the digital art and global payment sectors. Non-fungible tokens (NFTs) have redefined digital ownership, while financial institutions use blockchain to enhance cross-border transactions, reducing costs and settlement times. Using the Diamond-Mortensen-Pissarides (DMP) model, this paper examines blockchain's impact on labor markets by improving job-matching efficiency, thereby reducing unemployment. However, high research costs and competition with incumbent technologies hinder early-stage blockchain adoption. We extend the DMP model to analyze the role of government intervention through tax and wage policies in mitigating these barriers. Our findings suggest that lowering firm tax rates can accelerate blockchain innovation, enhance labor market efficiency, and promote employment growth, highlighting the critical balance between technological progress and economic policy in fostering blockchain-driven economic transformation.
Stefano Balietti, Pietro Saggese, Stefan Kitzler, Bernhard Haslhofer
This chapter explores how Decentralized Autonomous Organizations (DAOs), a novel institutional form based on blockchain technology, challenge traditional centralized governance structures. DAOs govern projects ranging from finance to science and digital communities. They aim to redistribute decision- making power through programmable, transparent, and participatory mechanisms. This chapter outlines both the opportunities DAOs present, such as incentive alignment, rapid coordination, and censorship resistance, and the challenges they face, including token concentration, low participation, and the risk of de facto centralization. It further discusses the emerging intersection of DAOs and artificial intelligence, highlighting the potential for increased automation alongside the dangers of diminished human oversight and algorithmic opacity. Ultimately, we discuss under what circumstances DAOs can fulfill their democratic promise or risk replicating the very power asymmetries they seek to overcome.
The Bitcoin Network is a sophisticated accounting system that allows its underlying cryptocurrency to be trusted even in the absence of a reliable financial authority. Given its undeniable success, the technology, generally referred to as blockchain, has also been proposed as a means to improve legacy accounting systems. Accounting for real-world data, however, requires the intervention of a third party known as an Oracle, which, having not the same characteristics as a blockchain, could potentially reduce the expected integration benefit. Through a systematic review of the literature, this study aims to investigate whether the papers concerning blockchain integration in accounting consider and address the limitations posed by oracles. A broad overview of the limitations that emerged in the literature is provided and distinguished according to the specific accounting integration. Results support the view that although research on the subject counts numerous articles, actual studies considering oracle limitations are lacking. Interestingly, despite the scarce production of papers addressing oracles in various accounting sectors, reporting for ESG already shows interesting workarounds for oracle limitations, with permissioned chains envisioned as a valid support for the safe storage of sustainability data.
Machine learning is critical for innovation and efficiency in financial markets, offering predictive models and data-driven decision-making. However, challenges such as missing data, lack of transparency, untimely updates, insecurity, and incompatible data sources limit its effectiveness. Blockchain technology, with its transparency, immutability, and real-time updates, addresses these challenges. We present a framework for integrating high-frequency on-chain data with low-frequency off-chain data, providing a benchmark for addressing novel research questions in economic mechanism design. This framework generates modular, extensible datasets for analyzing economic mechanisms such as the Transaction Fee Mechanism, enabling multi-modal insights and fairness-driven evaluations. Using four machine learning techniques, including linear regression, deep neural networks, XGBoost, and LSTM models, we demonstrate the framework's ability to produce datasets that advance financial research and improve understanding of blockchain-driven systems. Our contributions include: (1) proposing a research scenario for the Transaction Fee Mechanism and demonstrating how the framework addresses previously unexplored questions in economic mechanism design; (2) providing a benchmark for financial machine learning by open-sourcing a sample dataset generated by the framework and the code for the pipeline, enabling continuous dataset expansion; and (3) promoting reproducibility, transparency, and collaboration by fully open-sourcing the framework and its outputs. This initiative supports researchers in extending our work and developing innovative financial machine-learning models, fostering advancements at the intersection of machine learning, blockchain, and economics.