Jeong-Hyuck Park, Chanhee Park, Claudio J. Tessone, Yu Zhang
Digitalisation transforms money from distinguishable physical objects into fungible informational units. A recent theoretical framework predicts that such indistinguishable wealth obeys bosonic occupancy statistics, leading to geometric ownership distributions and enhanced inequality. Using Bitcoin blockchain data, we test this prediction on 63 UTXO denominations across 72 monthly snapshots (2018--2023). A one-parameter geometric model describes the ownership distributions, reproducing both mean holdings and their temporal evolution; Jensen--Shannon divergence values lie below $0.08$ in $99.74\%$ of cases. The inferred inverse-temperature parameter satisfies the analytic mean--temperature relation to better than $0.1\%$ in every sample -- a self-consistency test that two-parameter alternatives cannot pass -- and remains within a narrow band across eight orders of magnitude in denomination and over six years. Bitcoin UTXO ownership statistics are therefore consistent with bosonic occupancy laws, suggesting that the informational nature of electronic money may act as a structural driver of inequality in digital economies.
The Landauer principle motivates the definition of economic temperature as the monetary price of processing a bit irreversibly. No empirical test of this definition exists in transparent fee markets. This paper fills that gap using daily Bitcoin and Ethereum data, constructing canonical thermodynamic state variables and evaluating five diagnostic layers: state variable behavior, Maxwell-type integrability, Carnot-style efficiency bounds, nonlinear regime separation, and structural break sensitivity to protocol events. Bitcoin's log-temperature behaves as a persistent mean-reverting process with an AR(1) coefficient of 0.97 and a half-life of 21 days; Ethereum is highly persistent, with weaker formal evidence of stationarity than Bitcoin. Maxwell integrability is frequency-dependent: Bitcoin passes all four relations at monthly frequency, whereas Ethereum passes two of four. Carnot-style evidence is the strongest: realized fee extraction efficiency stays well below the implied bound, with daily compliance exceeding 97% on both chains. Structural breaks around Bitcoin ordinals, EIP-1559, the merge, and Shanghai confirm that protocol changes reorganize the temperature relation. The thermodynamic framework provides structure that standard fee market analysis does not, including a first principles efficiency bound and a state space coherence test. The findings provide partial, frequency-dependent, and chain-specific empirical support for a Landauer-based thermodynamic description of blockspace markets.
This paper introduces a heterogeneous macroeconomic model of a Proof-of-Stake (PoS) network to analyze the long-term centralizing effects of external traditional finance (TradFi) yields. We model a continuum of rational actors divided into two distinct classes: investors, who optimize portfolios between staking and external variance-dominated investments, and consumers, who balance staking yields against the transactional utility of holding liquid assets. By employing a quasi-linear utility function to model consumer behavior, we derive a cubic polynomial that strictly defines the unique macroeconomic equilibrium of the coupled network. The model demonstrates that, at scale, external macroeconomic factors force the complete institutional capture of the PoS consensus layer. Because investors have access to external risk premiums, their wealth compounds exponentially, leading to massive capital inflows that crush the protocol's internal staking yield to effectively zero. We show that as the yield is crushed, consumer wealth becomes strictly upper-bounded. Ultimately, consumers are forced to cease staking entirely and hold all remaining wealth in liquid form to satisfy their transactional constraints.
Automated Market Makers (AMMs), as a core infrastructure of decentralized finance (DeFi), uniquely drive on-chain asset pricing through a deterministic reserve ratio mechanism. Unlike traditional markets, AMM price dynamics is triggered largely by on-chain events (e.g., swap) that change the reserve ratio, rather than by continuous responses to off-chain information. This makes event-level analysis crucial for understanding price formation mechanisms in AMMs. However, existing research generally neglects the micro-structural dynamics at the AMMs level, lacking both a comprehensive dataset covering multiple protocols with fine-grained event classification and an effective framework for event-aware modeling. To fill this gap, we construct a dataset containing 8.9 million on-chain event records from four representative AMMs protocols: Pendle, Uniswap v3, Aave and Morpho, with precise annotations of transaction type and block height timestamps. Furthermore, we propose an Uncertainty Weighted Mean Squared Error (UWM) loss function, which incorporates the block interval regression term into the traditional Temporal Point Process (TPP) objective function by weighting the uncertainty with homoscedasticity. Extensive experiments on eight advanced TPP architectures across four representative DeFi protocols demonstrate that this loss function reduces the time prediction error by an average of 31.17% while maintaining the accuracy of event (transaction) type prediction, establishing a robust benchmark for event-aware prediction in the AMMs ecosystem. This work provides the necessary data foundation and methodological framework for modeling the discreteness and event-driven characteristics of on-chain price discovery. All datasets and source code are publicly available. https://github.com/finbrain-lab-hkustgz/Deep-AMM-Events
A central question of the Ethereum ecosystem is where Maximal Extractable Value (MEV)revenue originates and to what extent it stems from harming unsuspecting users. It is acceptable if MEV arises from arbitrages between centralised and decentralised exchanges (CEX-DEX). Yet theoretical models have significantly underestimated the scale of these arbitrages, while empirical studies have highlighted their importance - though these remain conservative estimates, constrained by numerous debatable heuristic assumptions. Revisiting the theoretical model, we found that CEX-DEX arbitrages require trading volumes on the order of the total activity of major liquidity pools and yield profits comparable to MEV. Most prior AMM models utilised the Black-Scholes (BS) stochastic differential equation (SDE) - i.e., geometric Brownian motion - and assumed continuous price trajectories where asset prices move in small increments only.We argue that BS underestimates arbitrage profits by ignoring price jumps, which are precisely the points at which arbitrage opportunities tend to arise. To address this gap, we present an extended discrete-time AMM model in which the price process is the sum of a diffusive component and stochastic jumps that can have arbitrary noise distributions. Although mathematically more involved this framework allows us to employ a general discrete-time SDE and compute the stationary probability distribution via function iteration with geometric convergence. We further prove that the resulting mispricing process is an ergodic Markov chain. We implement our model in C++, collect spot prices and AMM exchange data from the Ethereum blockchain and fit the model parameters to the observed prices. The estimates derived from our model closely match empirical observations and provide a natural theoretical explanation for several fundamental questions in the blockchain ecosystem.
Version: v1.6.4 (June 2026) Major additions in this version: phased migration protocol with cryptographic quarantine (Section 6.4.4), sensitivity boundaries delineating the statistical decoupling threshold up to mu = 1.9% (Section 6.7), and integration of recent empirical MEV findings (Mancino & Rezzoli, 2025). Abstract Contemporary blockchain architectures face a critical impasse defined herein as the "Tetra-Lemma"—a four-dimensional optimization problem encompassing decentralization, security, scalability, and thermodynamic sustainability. Legacy Proof-of-Work networks confront diminishing security budgets due to the exhaustion of block subsidies, while Proof-of-Stake systems inherently risk oligarchic centralization. This paper establishes a Unified Monetary-Supply Framework that resolves these structural conflicts by synthesizing the deterministic Customized Halving schedule with the probabilistic regeneration logic of the Proof of Rinne (PoR). We demonstrate that by enforcing a "Thermodynamic Statute of Limitations" on dormant assets, the protocol functions as a Non-Equilibrium Thermodynamic Engine. This architecture transforms entropic asset attrition—traditionally viewed as systemic loss—into a regenerative security budget. The remainder of the abstract, covering the SDE and Fokker-Planck validation, the ZKP owner recovery model, and the resulting equilibrium, is in the manuscript. Data & Code AvailabilityThe mathematical models and high-precision stochastic simulations (e.g., Monte Carlo paths, SDE convergence, and Fokker-Planck distributions) presented in this manuscript are fully reproducible. The corresponding Python simulation suite and open-source models are made available at the author's GitHub repository (rincoin-regenerative-simulations) to ensure scientific transparency. Integrity & Provenance This document is anchored to the Bitcoin blockchain via OpenTimestamps. The proof file verification_data_v1.6.4.ots, included in the files below, covers the SHA-256 digest of Tokino_Rincoin_v1.6.4.pdf: 5269207ea7e363e8df312ed50c00afc119b43e6fa5d3c717e6a7d8fc9863147b The archived proof is in its as-submitted form: it commits the digest to the public OpenTimestamps calendars and does not itself embed the Bitcoin attestations. Completing it against those calendars — which both verification paths below do automatically — yields three Bitcoin attestations, the earliest in block 952366. An OpenTimestamps proof carries no wall-clock time of its own — any date reported for it is read from a Bitcoin block header. To verify, upload the PDF and the .ots file to opentimestamps.org, or with a Bitcoin node: ots verify -f Tokino_Rincoin_v1.6.4.pdf verification_data_v1.6.4.ots — the -f flag is required because the proof's filename differs from the document's. The provenance of this document is recorded in a separate signed artifact, the Rincoin Provenance Certificate (10.5281/zenodo.21415730), which binds this whitepaper to the digest above and is the reference for the full anchoring detail. That certificate carries its own OpenPGP signature, Bitcoin anchor, and PAdES signature; this whitepaper itself carries the OpenTimestamps proof only. Zenodo archival gives this record a persistent identifier and an independent retrieval path; it is not itself a cryptographic control. Validation_Scientific_Provenance_v1.6.4.pdf in the files below is an earlier certificate edition, retained as evidence. It is superseded by the record cited above. Correspondence & AffiliationPrimary Author: Tokino, Michiru (時乃 満)Affiliation: Rincoin Core Research Academic Inquiries: edu@aevust.org Community Governance: @aevustus (Discord) / @aevust (X/Telegram) Keywords: Rincoin, Proof of Rinne (PoR), regenerative crypto-economics, non-equilibrium thermodynamics, non-equilibrium steady state (NESS), stochastic differential equations (SDE), Fokker-Planck equation, recirculation incentive mechanism, macroeconomic homeostasis, Nash equilibrium, cryptographic vault, zero-knowledge proofs (ZKP), modular blockchain architecture, account abstraction, blockchain tetra-lemma, MEV mitigation, sandwich attack resistance, sensitivity analysis, statistical decoupling threshold, phased migration protocol
Decentralized finance (DeFi) systems currently rely on static parameters and reactive mechanisms that fail to adapt to rapidly changing market conditions. These limitations contribute to systemic inefficiencies including yield instability, capital fragmentation, and the extraction of value through adversarial mechanisms such as maximal extractable value (MEV). This paper introduces The Aeon Protocol, a control-theoretic framework for adaptive financial infrastructure. The protocol models decentralized liquidity management as a closed-loop control system in which economic variables are continuously monitored, predicted, and regulated through feedback mechanisms derived from classical control theory. The Aeon architecture integrates four primary system layers: • KENDRA — predictive forecasting and regime detection from on-chain data streams• NOEMA — model predictive control for economic orchestration• AURA — ethical routing layer that captures and redistributes MEV through sealed-bid auctions• LEIA — liquidity management engine governing protocol-owned liquidity across decentralized markets At the core of the system is a PID-controlled adaptive yield mechanism designed to regulate total value locked (TVL) and stabilize protocol yield within bounded ranges. A complementary Burn-and-Mint Equilibrium (BME) mechanism dynamically adjusts token supply to maintain long-term economic balance. A central implication of the Aeon architecture is the emergence of a self-reinforcing liquidity ecosystem. By integrating predictive forecasting, control optimization, and ethical MEV capture into a closed-loop economic system, the protocol continuously identifies inefficiencies in decentralized markets and redirects the associated value back into the protocol’s liquidity layer. This process transforms otherwise extractive market dynamics into a productive feedback cycle, where captured value is redistributed through liquidity provisioning, treasury reserves, and reflection mechanisms. Empirical simulations and historical replay experiments demonstrate that this feedback architecture materially increases capital utilization across the system. In controlled Monte Carlo simulations spanning 10,000 market scenarios, the protocol achieved improvements of 50–180% in capital efficiency, while redirecting approximately 68% of extractable value to protocol participants rather than external arbitrage actors. These results suggest that adaptive control systems can convert structural market inefficiencies into a persistent source of liquidity and yield generation, enabling decentralized financial networks to operate as self-regulating economic environments rather than static rule-based infrastructures. Formal analysis establishes asymptotic stability conditions for the controller using the Routh–Hurwitz criterion and Lyapunov stability methods, providing theoretical guarantees that the system converges toward equilibrium under defined parameter constraints. Collectively, the results demonstrate that control-theoretic economic architectures can provide a principled foundation for designing stable, transparent, and adaptive decentralized financial infrastructure. The Aeon Protocol represents a broader research direction toward autonomous economic systems, where financial networks operate as self-regulating feedback environments capable of maintaining equilibrium under dynamic market conditions.
Abstract This study examines bubble dynamics in the S&P 500 Index and Bitcoin, with particular emphasis on the role of gold as a proxy for market-wide stress. We apply the GSADF bubble test, time-varying Granger causality, and multifractal detrended fluctuation analysis to both original and gold-filtered price series. The results reveal a pronounced asymmetry between equity and cryptocurrency markets. Bitcoin exhibits statistically significant and persistent bubble behavior in both raw and filtered data, accompanied by multifractal persistence consistent with self-reinforcing speculative dynamics. In contrast, the S&P 500 shows no consistent evidence of sustained bubble behavior, and its multifractal properties remain aligned with short memory and rapid information absorption. The causality analysis indicates a stable, state-dependent predictive relationship from gold to equity prices, suggesting sensitivity to global risk sentiment, while no comparable persistent linkage is observed for Bitcoin. Overall, the findings suggest that equity price dynamics remain connected to market-wide stress conditions, whereas Bitcoin’s behavior appears to be driven primarily by asset-specific speculative forces.
O presente artigo formaliza o <i>Economic Centrifugal Dispersion Model</i> (ECDM) como uma estrutura analítica de alta fidelidade para a compreensão da propagação de capital e incentivos em ecossistemas de Web3 e finanças descentralizadas (DeFi). Fundamentado em uma convergência interdisciplinar entre a praxeologia da escola austríaca, a física estatística e a dinâmica de sistemas complexos, o modelo propõe que a injeção monetária em sistemas baseados em <i>blockchain</i> gera forças dispersivas análogas às forças centrífugas. A pesquisa detalha a formulação matemática do modelo, integrando equações diferenciais não lineares para descrever o comportamento de variáveis como o influxo de capital, a velocidade de circulação e a resistência institucional. Adicionalmente, o trabalho explora a aplicação da Lei de Benford como ferramenta de auditoria estatística para detecção de anomalias em transações <i>on-chain</i> e propõe o Índice de Fragilidade Tokenômica (FTF) como métrica de risco sistêmico. Através da análise de expoentes de Lyapunov e diagramas de bifurcação, demonstra-se como pequenas flutuações paramétricas em Organizações Autônomas Descentralizadas (DAOs) podem induzir regimes de caos determinístico. O estudo conclui que a sustentabilidade de protocolos descentralizados depende de um equilíbrio crítico entre a dispersão centrífuga e a coesão institucional, oferecendo um arcabouço para o <i>design</i> de sistemas econômicos resilientes.<br>
The stability of global financial markets is increasingly threatened by rapid liquidity cascades, manifesting empirically as instantaneous "Flash Crashes." Current quantitative risk models, such as Value-at-Risk (VaR) and the Efficient Market Hypothesis (EMH), assume continuous liquidity and treat extreme volatility as probabilistic statistical anomalies based on historical distributions. These models fundamentally lack a deterministic, geometric boundary for limit-order book coherence. This paper introduces a strict topo-dynamical framework for financial network scaling. By modeling the market structure as a spatial competition between the geometric propagation of liquidity and localized volatility shocks, we derive a universal square-root geometric invariant (ℓmarket). We provide an intuitive translation of this threshold, explicitly dissect the failure of VaR during the May 2010 Flash Crash, and map the invariant across both traditional equities and Decentralized Finance (DeFi) Automated Market Makers (AMMs). Finally, we present a hardware-aware (FPGA) blueprint for Active Liquidity Throttling (ALT), acknowledging systemic implementation risks and regulatory hurdles.
FUNDAMENTAL LAW OF REALITY: TERNARY SYNTHESIS OF MATHEMATICS, PHYSICS, AND HISTORY Version 11.0 (Complete Synthesis with Structural Proof of Fermat's Last Theorem) This paper presents an algorithmic system discovered by the author during many years of analyzing price movements in financial markets. Four software modules written in MQL4 revealed a universal ternary hierarchical structure possessing Z₃-symmetry. From the code analysis, the fundamental group Z₃ × Z₃, generating 9 basic relations, and the formula for the number of intersection points in the hierarchy, P = N – 2K, were derived. The discovered structure has proven to be universal across various fields of knowledge: Mathematics: Z₃ × Z₃ is isomorphic to a subgroup of SU(3) and the nilpotent ring ℂ[x,y]/(x³, y³); the system's fractal dimension is D = log 3 / log 2 ≈ 1.585. Number Theory: The synchronization parameter ρ = 0 at the non-trivial zeros of the Riemann zeta function is equivalent to the Riemann Hypothesis, numerically confirmed on 4153 zeros (100% match). Physics: Z₃ × Z₃ ⊂ SU(3) describes the color symmetry of Quantum Chromodynamics; the 9 compactification moduli of string theory correspond to the 9 system relations; the ρ = 0 state is interpreted as a transition to 11-dimensional M-Theory. History: Using an inverse problem method on 251 key dates, the reference points T₀ = –5502, T₁ = –5501, T₂ = –5500 were determined. The formula D = Tₛ + 3k + s describes all key historical events. Four epochal points (–5502, –3315, –1128, 1059) mark shifts in civilizational cycles. Verification on over 12,000 dates and a blind test of 20 dates yielded 100% accuracy. Markets: On BRENT oil data (1998–2026), 4 convergence points (2005, 2011, 2018, 2025) were found with an 81-month interval, corresponding to the historical epochal points. Geopolitics: 20 key events of 2025 correspond 100% to the model's predictions for zones s=0,1,2. Fermat's Last Theorem: A structural explanation is derived through the formula P = N - 2K: for n > 2, the hierarchy depth K ≥ 2 leads to a critical shortage of intersection points for synchronizing three independent circuits (x, y, z). A physical analogy is drawn with quark confinement in quantum chromodynamics. The cumulative statistical significance of all confirmations is p < 10⁻⁹³⁵, which excludes random coincidence. The system is fractally invariant and works identically at any time scale (from minute charts to millennia). The source code (4 MQL4 modules + Python implementation) is available upon request for non-commercial research under the CC BY-NC-ND 4.0 license. Keywords: ternary hierarchy, Z₃ × Z₃, intersection points, Riemann Hypothesis, Fermat's Last Theorem, SU(3), string theory, M-theory, historical periodization, fractals, algorithmic realism, power law distribution, confinement.
The rapid growth of cryptocurrency markets has created new challenges in understanding and predicting the structural dynamics of digital asset prices. Bitcoin, as the most traded blockchain-based currency, exhibits extreme volatility, nonlinear patterns, and complex regime shifts that traditional financial models cannot adequately capture. This study proposes a hybrid analytical framework that integrates K Means clustering with the Hidden Markov Model to identify and model multiple market regimes in Bitcoin time series data. The Bitcoin dataset used in this research contains minute-level records that were preprocessed to extract key indicators, namely logarithmic returns and rolling volatility, which represent the short-term dynamics of market behavior. The K Means algorithm was first employed to segment the data into three distinct clusters that correspond to bullish, bearish, and sideways regimes, followed by the application of the Hidden Markov Model to estimate probabilistic transitions between these regimes over time. The results reveal that the hybrid K Means and Hidden Markov Model approach achieves superior performance compared to a standalone model, as indicated by a higher log likelihood and a lower Bayesian Information Criterion value. The transition probability matrix shows that bullish and bearish regimes are highly persistent, while the sideways regime acts as a transitional buffer that connects both market extremes. The empirical findings confirm that Bitcoin prices evolve through persistent and probabilistically determined regimes rather than random fluctuations. The proposed framework provides a more comprehensive understanding of cryptocurrency market dynamics and offers practical value for investors, risk analysts, and policymakers in designing adaptive trading and risk management strategies within blockchain-based financial ecosystems.
Abstract Bitcoin's price history follows an approximate power law in time, with \((R^2 = 0.947)\) over 2011--2026. We show that replacing uniform calendar time with activity-warped time ---where time advances faster during high-activity periods---improves both in-sample fit and out-of-sample prediction. Two warping signals are evaluated: price volatility (absolute daily log-returns) and on-chain transaction volume (daily USD value transacted). Both benefit from a power transform \((w_t^\gamma)\) that reshapes the weight distribution: \((\gamma = 2.41)\) for volatility (amplifying large-move days) and \((\gamma = 0.56)\) for transaction volume (compressing extreme spikes). Transaction volume emerges as the stronger signal, achieving \((R^2 = 0.958)\) in-sample and winning 8 of 9 walk-forward splits (mean \((\Delta R^2 = +0.414)\)). Volatility wins 5 of 9 splits but requires no external data. Transaction volume selects \((\alpha = 0)\) (pure warped time), while volatility retains a calendar component (\((\alpha \approx 0.4)\)). Neither signal benefits from smoothing. Despite being nearly uncorrelated (\((r = -0.007)\)), combining the two signals does not improve out-of-sample performance---each captures complementary but individually sufficient information about Bitcoin's growth dynamics.
Recent developments in distributed ledger technology, artificial intelligence, and decision-making agents hold the promise of radically transforming market infrastructures. Indeed, the emergence of Autonomous Market Infrastructure (AMI)—an open, fully automated, and decentralized set of market-related functionalities—is widely anticipated. Such infrastructures, serving agents capable of fully autonomous behavior, would enable fully automated trading strategies. Moreover, as AMI-based solutions require minimal human intervention, they could be implemented at a fraction of existing costs. This should bolster competition and democratization, as AMI is accessible to everyone and establishes a level playing field.
The decentralized finance market exhibits extreme volatility and complex nonlinear dynamics that pose significant challenges for accurate price prediction and risk management. Traditional time series models, including Long Short-Term Memory networks and Transformer architectures, struggle with either computational inefficiency in capturing long-rangedependencies or inadequate context retention across extended sequences. This research investigates the application of Structured State Space Models, particularly the Mamba architecture with selective state spaces, for modeling temporal dependencies in DeFi markets. The proposed framework addresses the limitations of conventional approaches by leveraging SSMs' linear-time complexity while maintaining superior long-sequence modeling capabilities through context-aware selective mechanisms. Our methodology integrates SSM architectures with DeFispecific features including on-chain transaction volumes, liquidity metrics, and market microstructure indicators. Experimental validation across multiple cryptocurrency pairs demonstrates that SSM-based models achieve competitive performance compared to attentionbaseTransformers while offering substantial computational advantages. The results indicate that selective state space mechanisms enable effective capture of both short-term volatility patterns and long-horizon price trends in decentralized markets. This work contributes to the emerginintersection of advanced sequence modeling techniques and blockchain-based financial systems, providing insights for algorithmic trading strategies and risk assessment frameworks in the rapidly evolving DeFi ecosystem.
Abstract Automatic trading systems cope with the needs of put out emotional biases from the trading operation of public assets. These systems place orders based on a price model that forecasts the future price of an asset. Those systems, developed by edge funds and institutional investors, are not available to the public, and extensive research in this field is worth the effort. In this research, we developed a short-term price model based on a neural network and used it to forecast the near-future price direction. More in depth, we introduced the feature extraction process and parametric labeling strategy to build an ML ready dataset that includes more than 400 cryptocurrencies. The model is then validated by building a trading strategy on the two most capitalized cryptos at the time of writing: Bitcoin and Ethereum. The validation uses a trading simulation that spans six years of historical data for Bitcoin and Ethereum, including both retrospective (backtest) and prospective (forward test) evaluations. The results demonstrate that the neural network-based model exhibits a very good generalization to patterns found in historical data, enabling predictions in future data within the trading simulation. In addition, a comprehensive analysis of the importance of features was conducted to enhance the interpretability and performance of the model. Finally, we test our model in a simulated trading session; it shows that, with a simple buy-only strategy plus a stop loss, the trading system limits the draw dawn during bear markets.
Shiyu Zhang, Zining Wang, Jin Zheng, John Cartlidge
Systemic risk refers to the overall vulnerability arising from the high degree of interconnectedness and interdependence within the financial system. In the rapidly developing decentralized finance (DeFi) ecosystem, numerous studies have analyzed systemic risk through specific channels such as liquidity pressures, leverage mechanisms, smart contract risks, and historical risk events. However, these studies are mostly event-driven or focused on isolated risk channels, paying limited attention to the structural dimension of systemic risk. Overall, this study provides a unified quantitative framework for ecosystem-level analysis and continuous monitoring of systemic risk in DeFi. From a network-based perspective, this paper proposes the DeFi Correlation Fragility Indicator (CFI), constructed from time-varying correlation networks at the protocol category level. The CFI captures ecosystem-wide structural fragility associated with correlation concentration and increasing synchronicity. Furthermore, we define a Risk Contribution Score (RCS) to quantify the marginal contribution of different protocol types to overall systemic risk. By combining the CFI and RCS, the framework enables both the tracking of time-varying systemic risk and identification of structurally important functional modules in risk accumulation and amplification.
This research establishes a formal topological framework for managing non- stationary market assets in portfolios by synthesizing high-dimensional chaotic dy- namics with industrial quality control and cryptographic verification. We introduce the Hala Operator as a state-dependent regulator capable of inducing Successive Controlled Collapse (SCC)—a process that maps continuous chaotic flows onto discrete, stable fixed-point constellations. By utilizing Taguchi Design of Experiments (DoE) for off-market robustness and Zero-Knowledge SNARKs for execution privacy, we provide a mathematically rigorous solution to the "Newtonian Trap" of market unpredictability. Formal proofs of global stability, dimension collapse via divergence analysis, and the uniqueness of the discrete constellation are presented.
The association between cryptocurrency and sustainability is a complex and growing topic. Given that such linkage requires a continuous investigation, this empirical research, unlike the existing literature, explores if the volatility dynamics of digital assets are driven by the changes in sustainability uncertainty. In doing so, we use a recently developed ESG-based sustainability uncertainty index (ESGUI) and examine its effect on the volatility dynamics of Bitcoin and Ethereum ETFs. Employing the mixed data sampling (MIDAS) approach shows that ESGUI exerts a negative effect on the realized volatility of cryptocurrency markets. One possible explanation for this linkage is that as sustainability-related uncertainty rises, investors tend to adopt sustainability practices and initiatives. This shift towards sustainable practices can result in more consistent and foreseeable long-term economic conditions, thereby reducing the volatility of financial markets including the digital asset class. Our analysis offers key implications to cryptocurrency investors.
Aleksei Adadurov, S. Barseghyan, Anton Chtepine, Antero Eloranta · 6 authors
This paper examines the impact of reducing Ethereum slot time on decentralized exchange activity, with a focus on CEX-DEX arbitrage behavior. We develop a trading model where the agent's DEX transaction is not guaranteed to land, and the agent explicitly accounts for this execution risk when deciding whether to pursue arbitrage opportunities. We compare agent behavior under Ethereum's default 12-second slot time environment with a faster regime that offers 1-second subslot execution. The simulations, calibrated to Binance and Uniswap v3 data from July to September 2025, show that faster slot times increase arbitrage transaction count by 535% and trading volume by 203% on average. The increase in CEX-DEX arbitrage activity under 1-second subslots is driven by the reduction in variance of both successful and failed trade outcomes, increasing the risk-adjusted returns and making CEX-DEX arbitrage more appealing.
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.
We develop an agent-based model in which inflation emerges from decentralized price-setting and credit-financed production in an endogenous-money economy. Firms operate under working-capital constraints, form market-based price expectations through heterogeneous adaptive learning, and set prices via cost-plus rules with endogenous mark-ups. Bank lending simultaneously creates deposits, while heterogeneous lending rates and credit rationing shape firms' financing costs and, through unit costs, their pricing decisions. The economy features interacting production and credit networks: intermediate-input linkages propagate cost shocks across supply chains, while bank--firm relationships transmit financial conditions across firms. The interaction of network-based pass-through, state-dependent pricing incentives, and evolving credit conditions generates inflationary regimes, including episodes driven by pricing cascades and feedback loops.
Spot trading on decentralized exchanges (DEXs) remains materially inferior to centralized exchanges (CEXs) in throughput, latency, and market-maker tooling, ceding global spot liquidity to opaque, non-custodial intermediaries. We present Vela, a spot exchange engine designed from first principles to recover CEX-grade performance while preserving the verifiability and self-custody properties of a DEX. The core engine is an optimized Rust state machine running entirely in memory, achieving a median per-operation latency of 1.08 microseconds (p50) — 4.7 times faster than the prior state of the art — and 57,300 operations per second under a realistic mixed market-making simulation across ten simultaneous markets. Exchange state is maintained in a Merkle Patricia Trie whose root is periodically committed to an underlying blockchain, anchoring state integrity to an external consensus mechanism. Verifiability is achieved through an optimistic zero-knowledge proving scheme: state updates are assumed valid by default, with a seven-day challenge window during which any party may submit a proof of incorrect execution, and an on-demand fast-finality path for users requiring immediate settlement. We introduce two features novel to DEX design: (1) a market-maker credit system enabling capital-efficient cross-market quoting analogous to CEX credit lines, implemented natively within the matching engine's state transition function with atomic collateral enforcement; and (2) private L3 market data feeds authenticated via server-issued nonce challenges and wallet signatures, substantially reducing market-maker exposure to adverse selection. We describe the full architecture, five performance optimizations including a Delta elimination that reduces p99.9 tail latency by 73%, flamegraph profiling findings, and decentralization mechanisms including forced inclusion via a delayed inbox. The Vela engine is released as open-source software under the MIT license at github.com/arpjw/vela.
The rapid expansion of the digital economy has exposed significant limitations in traditional economic frameworks, which struggle to explain phenomena such as algorithmic decisionmaking, data-driven value creation, and platform-based concentration. Existing approachesranging from production function extensions to platform models-remain fragmented and lack a unified micro-foundation. This paper proposes a behavior-centered framework to characterize economic forms and introduces the concept of economic morphology defined along four dimensions: agent structure, factor composition, behavioral pathways, and spatial distribution. Building on this framework, we define the Information Process Ratio (IPR) as a measurable indicator capturing the proportion of information-processing activities within economic behavior. Using IPR as a discriminant variable, we identify four major economic forms in human historyagricultural (IPR 10-20%), industrial (30-40%), service (50-60%), and digital (75-90%+). We show that the digital economy represents a distinct morphology, not a continuation of the industrial paradigm. Contemporary financial technology (FinTech) systems-high-frequency trading (HFT), decentralized finance (DeFi), and automated market makers (AMMs)represent extreme high-IPR regimes (95-99%), making them natural laboratories for testing the framework's predictions. We operationalize IPR using transaction-level proxies such as order-to-trade ratios (OTR), cancellation rates, and algorithmic trading share, enabling empirical application in financial markets. The framework generates testable implications linking IPR to transaction intensity, market concentration, returns to scale, algorithmic mediation, and high-frequency volatility. We further introduce the concept of IPR arbitrage, whereby economic activity flows toward higher-IPR systems, and propose a Financial Tension Index (FTI) to capture systemic strain in high-IPR environments. By shifting the analytical focus from agents to behaviors, this paper provides a unifying perspective for understanding the structural transformation of the digital economy and offers concrete implications for financial technology regulation, algorithmic market design, and systemic-risk monitoring.