Yihan Hong, Hengxiang Feng, Yinghan Wang, Boxuan Li
The approval of the Bitcoin Spot ETF in January 2024 marked a transformative event in cryptocurrency markets, signaling increased institutional adoption and integration into traditional finance. This study examines Bitcoin's changing relationships with traditional assets, including equities, gold, and fiat currencies, following this milestone. Using rolling correlation analysis, Chow tests, and DCC-GARCH models, we found that Bitcoin's correlation with the S\&P 500 increased significantly post-ETF approval, indicating stronger alignment with equities. Its relationship with gold stabilized near zero, while its correlation with the U.S. Dollar Index remained consistently negative, reflecting its continued independence from fiat currencies. These findings offer insights into Bitcoin's evolving role in portfolios, implications for market stability, and future research opportunities on cryptocurrency integration into traditional financial systems.
This paper analyzes the intersection of presidential authority and cryptocurrency markets during Donald J. Trump's second term (2025-2029). We examine developments from 2024 through October 2025, focusing on how executive influence, family business ventures, and digital assets became intertwined in ways that blurred boundaries between public office and private profit. Using a mixed-methods approach that combines quantitative market data with qualitative institutional assessment, we identify politically linked digital assets as a distinct class characterized by reflexive valuations, asymmetric risk distribution, and systemic vulnerabilities. The Trump family's integrated cryptocurrency ecosystem reached peak valuations exceeding eleven billion dollars before collapsing by more than one trillion in market capitalization following a tariff announcement in October 2025. Results highlight conflicts of interest, failures in market microstructure, and the emergence of political finance as a monetizable phenomenon in the digital age. The study contributes to understanding how presidential signaling reshapes capital flows, how politically branded tokens function as quasi-currencies, and how sudden policy actions can trigger cascading liquidations across global digital asset systems.
Bitcoin operates as a macroeconomic paradox: it combines a strictly predetermined, inelastic monetary issuance schedule with a stochastic, highly elastic demand for scarce block space. This paper empirically validates the Endogenous Constraint Hypothesis, positing that protocol-level throughput limits generate a non-linear negative feedback loop between network friction and base-layer monetary velocity. Using a verified Transaction Cost Index (TCI) derived from Blockchain.com on-chain data and Hansen's (2000) threshold regression, we identify a definitive structural break at the 90th percentile of friction (TCI ~ 1.63). The analysis reveals a bifurcation in network utility: while the network exhibits robust velocity growth of +15.44% during normal regimes, this collapses to +6.06% during shock regimes, yielding a statistically significant Net Utility Contraction of -9.39% (p = 0.012). Crucially, Instrumental Variable (IV) tests utilizing Hashrate Variation as a supply-side instrument fail to detect a significant relationship in a linear specification (p=0.196), confirming that the velocity constraint is strictly a regime-switching phenomenon rather than a continuous linear function. Furthermore, we document a "Crypto Multiplier" inversion: high friction correlates with a +8.03% increase in capital concentration per entity, suggesting that congestion forces a substitution from active velocity to speculative hoarding.
This research introduces the Decentralized Finance (DeFi) TrustBoost Framework, which combines blockchain technology and Explainable AI to address challenges faced by lenders underwriting small business loan applications from low-wealth households. The framework is designed with a strong emphasis on fulfilling four crucial requirements of blockchain and AI systems: confidentiality, compliance with data protection laws, resistance to adversarial attacks, and compliance with regulatory audits. It presents a technique for tamper-proof auditing of automated AI decisions and a strategy for on-chain (inside-blockchain) and off-chain data storage to facilitate collaboration within and across financial organizations.
Crypto enthusiasts claim that buying and holding crypto assets yields high returns, often citing Bitcoin's past performance to promote other tokens and fuel fear of missing out. However, understanding the real risk-return trade-off and what factors affect future crypto returns is crucial as crypto becomes increasingly accessible to retail investors through major brokerages. We examine the HODL strategy through two independent analyses. First, we implement 480 million Monte Carlo simulations across 378 non-stablecoin crypto assets, net of trading fees and the opportunity cost of 1-month Treasury bills, and find strong evidence of survivorship bias and extreme downside concentration. At the 2-3 year horizon, the median excess return is -28.4 percent, the 1 percent conditional value at risk indicates that tail scenarios wipe out principal after all costs, and only the top quartile achieves very large gains, with a mean excess return of 1,326.7 percent. These results challenge the HODL narrative: across a broad set of assets, simple buy-and-hold loads extreme downside risk onto most investors, and the miracles mostly belong to the luckiest quarter. Second, using a Bayesian multi-horizon local projection framework, we find that endogenous predictors based on realized risk-return metrics have economically negligible and unstable effects, while macro-finance factors, especially the 24-week exponential moving average of the Fear and Greed Index, display persistent long-horizon impacts and high cross-basket stability. Where significant, a one-standard-deviation sentiment shock reduces forward top-quartile mean excess returns by 15-22 percentage points and median returns by 6-10 percentage points over 1-3 year horizons, suggesting that macro-sentiment conditions, rather than realized return histories, are the dominant indicators for future outcomes.
As Decentralized Finance (DeFi) develops, understanding user intent behind DeFi transactions is crucial yet challenging due to complex smart contract interactions, multifaceted on-/off-chain factors, and opaque hex logs. Existing methods lack deep semantic insight. To address this, we propose the Transaction Intent Mining (TIM) framework. TIM leverages a DeFi intent taxonomy built on grounded theory and a multi-agent Large Language Model (LLM) system to robustly infer user intents. A Meta-Level Planner dynamically coordinates domain experts to decompose multiple perspective-specific intent analyses into solvable subtasks. Question Solvers handle the tasks with multi-modal on/off-chain data. While a Cognitive Evaluator mitigates LLM hallucinations and ensures verifiability. Experiments show that TIM significantly outperforms machine learning models, single LLMs, and single Agent baselines. We also analyze core challenges in intent inference. This work helps provide a more reliable understanding of user motivations in DeFi, offering context-aware explanations for complex blockchain activity.
Digital Asset Treasury (DAT) companies, public firms that hold large crypto reserves as a core strategy, deliver levered exposure to digital assets but face acute downside risk when equity premia over net asset value multiples (mNAV) compress in bear markets. This paper develops a survival framework that couples conservative treasury policy with an operating line that monetizes holdings independent of mark-to-market gains. Using Strategy (formerly MicroStrategy) as a case, we propose a "BTC-to-sats" payments rail that allocates a small, risk-capped liquidity sleeve of the treasury to Lightning Network channels, generating price-agnostic fee revenue (acquiring bps, routing, hedge/FX spread) while keeping settlement exposure near zero beta to BTC. We formalize a no-forced-sale condition and show how disclosed KPIs allow investors to test whether operating cash flows can bridge an 18 to 24-month bear without liquidations. The feasibility of the rail is supported by Strategy's Lightning initiative and empirical Lightning performance. Our model generalizes across DAT types and provides implementable disclosures that can sustain an mNAV premium through cycles.
Jinho Cha, Young‐Chul Kim, Junyeol Ryu, Sangjun Park · 6 authors
This study develops a strategic procurement framework integrating blockchain-based smart contracts with bounded demand variability modeled through a truncated normal distribution. While existing research emphasizes the technical feasibility of smart contracts, the operational and economic implications of adoption under moderate uncertainty remain underexplored. We propose a multi-supplier model in which a centralized retailer jointly determines the optimal smart contract adoption intensity and supplier allocation decisions. The formulation endogenizes adoption costs, supplier digital readiness, and inventory penalties to capture realistic trade-offs among efficiency, sustainability, and profitability. Analytical results establish concavity and provide closed-form comparative statics for adoption thresholds and procurement quantities. Extensive numerical experiments demonstrate that moderate demand variability supports partial adoption strategies, whereas excessive investment in digital infrastructure can reduce overall profitability. Dynamic simulations further reveal how adaptive learning and declining implementation costs progressively enhance adoption intensity and supply chain performance. The findings provide theoretical and managerial insights for balancing digital transformation, resilience, and sustainability objectives in smart contract-enabled procurement.
Cha, Jinho, Long Hoang Pham, Thi Quynh Trang Vo, Jaeyoung Cho · 5 authors
This study develops and analyzes an optimization model of smart contract adoption under bounded risk, linking structural theory with simulation and real-world validation. We examine how adoption intensity alpha is structurally pinned at a boundary solution, invariant to variance and heterogeneity, while profitability and service outcomes are variance-fragile, eroding under volatility and heavy-tailed demand. A sharp threshold in the fixed cost parameter A3 triggers discontinuous adoption collapse (H1), variance shocks reduce profits monotonically but not adoption (H2), and additional results on readiness heterogeneity (H3), profit-service co-benefits (H4), and distributional robustness (H5) confirm the duality between stable adoption and fragile payoffs. External validity checks further establish convergence of sample average approximation at the canonical O(1/sqrt(N)) rate (H6). Empirical validation using S&P 500 returns and the MovieLens100K dataset corroborates the theoretical structure: bounded and heavy-tailed distributions fit better than Gaussian models, and profits diverge across volatility regimes even as adoption remains stable. Taken together, the results demonstrate that adoption choices are robust to uncertainty, but their financial consequences are highly fragile. For operations and finance, this duality underscores the need for risk-adjusted performance evaluation, option-theoretic modeling, and distributional stress testing in strategic investment and supply chain design.
Decentralized coordination and digital contracting are becoming critical in complex industrial ecosystems, yet existing approaches often rely on ad hoc heuristics or purely technical blockchain implementations without a rigorous economic foundation. This study develops a mechanism design framework for smart contract-based resource allocation that explicitly embeds efficiency and fairness in decentralized coordination. We establish the existence and uniqueness of contract equilibria, extending classical results in mechanism design, and introduce a decentralized price adjustment algorithm with provable convergence guarantees that can be implemented in real time. To evaluate performance, we combine extensive synthetic benchmarks with a proof-of-concept real-world dataset (MovieLens). The synthetic tests probe robustness under fee volatility, participation shocks, and dynamic demand, while the MovieLens case study illustrates how the mechanism can balance efficiency and fairness in realistic allocation environments. Results demonstrate that the proposed mechanism achieves substantial improvements in both efficiency and equity while remaining resilient to abrupt perturbations, confirming its stability beyond steady state analysis. The findings highlight broad managerial and policy relevance for supply chains, logistics, energy markets, healthcare resource allocation, and public infrastructure, where transparent and auditable coordination is increasingly critical. By combining theoretical rigor with empirical validation, the study shows how digital contracts can serve not only as technical artifacts but also as institutional instruments for transparency, accountability, and resilience in high-stakes resource allocation.
Philippe Bergault, Sébastien Bieber, Olivier Guéant, Wenkai Zhang
In traditional financial markets, yield curves are widely available for countries (and, by extension, currencies), financial institutions, and large corporates. These curves are used to calibrate stochastic interest rate models, discount future cash flows, and price financial products. Yield curves, however, can be readily computed only because of the current size and structure of bond markets. In cryptocurrency markets, where fixed-rate lending and bonds are almost nonexistent as of early 2025, the yield curve associated with each currency must be estimated by other means. In this paper, we show how mathematical tools can be used to construct yield curves for cryptocurrencies by leveraging data from the highly developed markets for cryptocurrency derivatives.
Sabrina Aufiero, Silvia Bartolucci, Fabio Caccioli, Pierpaolo Vivo
This work explores the formation and propagation of systemic risks across traditional finance (TradFi) and decentralized finance (DeFi), offering a comparative framework that bridges these two increasingly interconnected ecosystems. We propose a conceptual model for systemic risk formation in TradFi, grounded in well-established mechanisms such as leverage cycles, liquidity crises, and interconnected institutional exposures. Extending this analysis to DeFi, we identify unique structural and technological characteristics - such as composability, smart contract vulnerabilities, and algorithm-driven mechanisms - that shape the emergence and transmission of risks within decentralized systems. Through a conceptual mapping, we highlight risks with similar foundations (e.g., trading vulnerabilities, liquidity shocks), while emphasizing how these risks manifest and propagate differently due to the contrasting architectures of TradFi and DeFi. Furthermore, we introduce the concept of crosstagion, a bidirectional process where instability in DeFi can spill over into TradFi, and vice versa. We illustrate how disruptions such as liquidity crises, regulatory actions, or political developments can cascade across these systems, leveraging their growing interdependence. By analyzing this mutual dynamics, we highlight the importance of understanding systemic risks not only within TradFi and DeFi individually, but also at their intersection. Our findings contribute to the evolving discourse on risk management in a hybrid financial ecosystem, offering insights for policymakers, regulators, and financial stakeholders navigating this complex landscape.
Straddle Option is a financial trading tool that explores volatility premiums in high-volatility markets without predicting price direction. Although deep reinforcement learning has emerged as a powerful approach to trading automation in financial markets, existing work mostly focused on predicting price trends and making trading decisions by combining multi-dimensional datasets like blogs and videos, which led to high computational costs and unstable performance in high-volatility markets. To tackle this challenge, we develop automated straddle option trading based on reinforcement learning and attention mechanisms to handle unpredictability in high-volatility markets. Firstly, we leverage the attention mechanisms in Transformer-DDQN through both self-attention with time series data and channel attention with multi-cycle information. Secondly, a novel reward function considering excess earnings is designed to focus on long-term profits and neglect short-term losses over a stop line. Thirdly, we identify the resistance levels to provide reference information when great uncertainty in price movements occurs with intensified battle between the buyers and sellers. Through extensive experiments on the Chinese stock, Brent crude oil, and Bitcoin markets, our attention-based Transformer-DDQN model exhibits the lowest maximum drawdown across all markets, and outperforms other models by 92.5\% in terms of the average return excluding the crude oil market due to relatively low fluctuation.
As decentralized finance (DeFi) evolves, distinguishing between user behaviors - liquidity provision versus active trading - has become vital for risk modeling and on-chain reputation. We propose a behavioral scoring framework for Uniswap that assigns two complementary scores: a Liquidity Provision Score that assesses strategic liquidity contributions, and a Swap Behavior Score that reflects trading intent, volatility exposure, and discipline. The scores are constructed using rule-based blueprints that decompose behavior into volume, frequency, holding time, and withdrawal patterns. To handle edge cases and learn feature interactions, we introduce a deep residual neural network with densely connected skip blocks inspired by the U-Net architecture. We also incorporate pool-level context such as total value locked (TVL), fee tiers, and pool size, allowing the system to differentiate similar user behaviors across pools with varying characteristics. Our framework enables context-aware and scalable DeFi user scoring, supporting improved risk assessment and incentive design. Experiments on Uniswap v3 data show its usefulness for user segmentation and protocol-aligned reputation systems. Although we refer to our metric as zScore, it is independently developed and methodologically different from the cross-protocol system proposed by Udupi et al. Our focus is on role-specific behavioral modeling within Uniswap using blueprint logic and supervised learning.
We introduce a decentralised, algorithmic framework for permissionless, multi-strategy capital allocation via tokenised, automated vaults. The system is designed to function analogously to a multi-strategy asset management company, but implemented entirely on-chain through a modular architecture comprising four interacting layers. The first, the capitalisation layer, composed of vaults that facilitate multi-asset deposits, tokenises investor participation, and specifies high level risk limits and admissible venues for deployment. The second, the strategy layer, enables the submission of strategies by human developers or autonomous agents, creating a decentralised marketplace governed by a validation mechanism incorporating adversarial and gamified elements. The third, the execution layer, operationalises strategy deployment using the host blockchain network's services. The fourth layer, the validated allocation layer, assesses and allocates capital among validated strategies, dynamically rebalancing toward those exhibiting superior risk-adjusted performance. In the framework, each admitted strategy acts as a manager for the "fund", encapsulated in a smart contract vault that issues transferable V-Tokens, conveying fractional ownership of the real-time portfolio operated by the vault. The system is designed to be open to participation by both human and AI agents, who collectively perform the roles of capital allocators, strategy developers, and validated allocators. The resulting structure is a self-regulating asset management ecosystem capable of decentralised, cooperative optimisation across traditional and digital financial domains. This framework is facilitated by a host chain network, which offers native automation and data oracle services enabling vault entities to autonomously operate on-chain, paving the way for being self sufficient in dynamic allocation of capital.
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.
We propose \textit{OpenAlpha}, a community-led strategy validation framework for decentralised capital management on a host blockchain network, which integrates game-theoretic validation, adversarial auditing, and market-based belief aggregation. This work formulates treasury deployment as a capital optimisation problem under verification costs and strategic misreporting, and operationalises it through a decision waterfall that sequences intention declaration, strategy proposal, prediction-market validation, dispute resolution, and capital allocation. Each phase of this framework's validation process embeds economic incentives to align proposer, verifier, and auditor behaviour, producing confidence scores that may feed into a capital allocation rule. While OpenAlpha is designed for capital strategy assessment, its validation mechanisms are composable and extend naturally to evaluating external decentralised applications (DApps), enabling on-chain scrutiny of DApp performance, reliability, and integration risk. This architecture allows for adaptive, trust-minimised capital deployment without reliance on centralised governance or static audits.
This paper investigates the evolving link between cryptocurrency and equity markets in the context of the recent wave of corporate Bitcoin (BTC) treasury strategies. We assemble a dataset of 39 publicly listed firms holding BTC, from their first acquisition through April 2025. Using daily logarithmic returns, we first document significant positive co-movements via Pearson correlations and single factor model regressions, discovering an average BTC beta of 0.62, and isolating 12 companies, including Strategy (formerly MicroStrategy, MSTR), exhibiting a beta exceeding 1. We then classify firms into three groups reflecting their exposure to BTC, liquidity, and return co-movements. We use transfer entropy (TE) to capture the direction of information flow over time. Transfer entropy analysis consistently identifies BTC as the dominant information driver, with brief, announcement-driven feedback from stocks to BTC during major financial events. Our results highlight the critical need for dynamic hedging ratios that adapt to shifting information flows. These findings provide important insights for investors and managers regarding risk management and portfolio diversification in a period of growing integration of digital assets into corporate treasuries.
Pietro Saggese, Michael Fröwis, Stefan Kitzler, Bernhard Haslhofer · 5 authors
Total Value Locked (TVL) aims to measure the aggregate value of cryptoassets deposited in Decentralized Finance (DeFi) protocols. Although blockchain data is public, the way TVL is computed is not well understood. In practice, its calculation on major TVL aggregators relies on self-reports from community members and lacks standardization, making it difficult to verify published figures independently. We thus conduct a systematic study on 939 DeFi projects deployed in Ethereum. We study the methodologies used to compute TVL, examine factors hindering verifiability, and ultimately propose standardization attempts in the field. We find that 10.5% of the protocols rely on external servers; 68 methods alternative to standard balance queries exist, although their use decreased over time; and 240 equal balance queries are repeated on multiple protocols. These findings indicate limits to verifiability and transparency. We thus introduce ``verifiable Total Value Locked'' (vTVL), a metric measuring the TVL that can be verified relying solely on on-chain data and standard balance queries. A case study on 400 protocols shows that our estimations align with published figures for 46.5% of protocols. Informed by these findings, we discuss design guidelines that could facilitate a more verifiable, standardized, and explainable TVL computation.
Stablecoins represent a critical bridge between cryptocurrency and traditional finance, with Tether (USDT) dominating the sector as the largest stablecoin by market capitalization. By Q1 2025, Tether directly held approximately $98.5 billion in U.S. Treasury bills, representing 1.6% of all outstanding Treasury bills, making it one of the largest non-sovereign buyers in this crucial asset class, on par with nation-state-level investors. This paper investigates how Tether's market share of U.S. Treasury bills influences corresponding yields. The baseline semi-log time trend model finds that a 1% increase in Tether's market share is associated with a 1-month yield reduction of 3.8%, corresponding to 14-16 basis points. However, threshold regression analysis reveals a critical market share threshold of 0.973%, above which the yield impact intensifies significantly. In this high regime, a 1% market share increase reduces 1-month yields by 6.3%. At the end of Q1 2025, Tether's market share placed it firmly within this high-impact regime, reducing 1-month yields by around 24 basis points relative to a counterfactual. In absolute terms, Tether's demand for Treasury Bills equates to roughly $15 billion in annual interest savings for the U.S. government. Aligning with theories of liquidity saturation and nonlinear price impact, these results highlight that stablecoin demand can reduce sovereign funding costs and provide a potential buffer against market shocks.
Hongzhe Wen, Songbai Li, Ronald Siu Man Lau, Jamie Zhang
With market capitalization exceeding USD250 billion by mid-2025, stablecoins have evolved from a crypto-focused innovation into a vital component of the global monetary structure. This paper identifies the characteristics of stablecoins from an analytical perspective and investigates the role of stablecoins in forming a hybrid monetary ecosystem where public (fiat, CBDC) and private (USDC, USDT, DAI) monies coexist. Through a number of econometric analysis models, we find that stablecoins maintain strong peg stability, while each type exhibiting distinctive responses to market variables such as trading volume and capitalization depending on the mechanisms behind. We also introduce a hybrid system design that proposes a two-layer structure, which private stablecoin issuers are backed by central bank reserves, ensuring uniformity, security, and programmability. This model takes advantages of both decentralized finance and payment innovation, while utilizing the Federal Reserve's institutional trust. A case study on the SVB-USDC de-peg event in 2023 illustrates how such a hybrid system could have prevented panic-induced instability through transparent reserves, secured liquidity, and interoperable assets. Through examination of the Dybvig model and simulation, we conclude that a hybrid monetary model not only enhances financial inclusivity, scalability, and dollar utility in digital ecosystems, but it also strengthens systemic resilience, offering a credible blueprint for future digital dollar architectures.
Sara A. Safari, Maximilian Janisch, Thomas Lehéricy
In the theory of financial markets, a stylized fact is a qualitative summary of a pattern in financial market data that is observed across multiple assets, asset classes and time horizons. In this article, we test a set of eleven stylized facts for financial market data. Our main contribution is to consider a broad range of geographical regions across Asia, continental Europe, and the US over a time period of 150 years, as well as two of the most traded cryptocurrencies, thus providing insights into the robustness and generalizability of commonly known stylized facts.
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.
We propose a mechanism embedded into the foundational infrastructure of a blockchain network, designed to improve the utility of idle network resources, whilst enhancing market microstructure efficiency during block production by leveraging both network-owned and external capital. By systematically seeking to use idle network resources for internally capture arbitrageable inefficiencies, the mechanism mitigates extractable value leakage, reduces execution frictions, and improves price formation across venues. This framework optimises resource allocation by incentivising an ordered set of transactions to be identified and automatically executed at the end of each block, redirecting any realised arbitrage income - to marketplaces operating on the host blockchain network (and other stakeholders), which may have otherwise been extracted as rent by external actors. Crucially, this process operates without introducing additional inventory risk, ensuring that the network remains a neutral facilitator of price discovery. While the systematic framework governing the distribution of these internally captured returns is beyond the scope of this work, reinvesting them to support the ecosystem deployed on the host blockchain network is envisioned to endogenously enhance liquidity, strengthen transactional efficiency, and promote the organic adoption of the blockchain for end users. This mechanism is designed specifically for Supra's blockchain and seeks to maximally utilise its highly efficient automation framework to enhance the blockchain network's efficiency.