G. N. Girish, Ashutosh Sahoo, Ajay Bhat, Akshay SP ยท 7 authors
Incentive programs are central to user acquisition in decentralized finance, but many reward systems rely on raw volume, transaction count, and wallet count, making them vulnerable to bots and sybil operations. We present ZAPs, a reward attribution framework that combines economic contribution scoring with adversarial robustness. A composite activity score uses protocol-specific percentile normalization to limit whale dominance while preserving differentiation among users. A two-layer weighting mechanism combines protocol share within sector and sector share within the ecosystem, which reduces the profitability of farming small protocols. We show that the maximum reward obtainable from any protocol is bounded by that protocol's global volume share. ZAPs also introduces a four-layer defense stack consisting of transaction-level integrity checks, a parallel anomaly ensemble, post-distribution behavioral memory, and graph-based sybil clustering. The anomaly ensemble combines a one-class reconstruction model with an isolation forest and applies graduated rather than binary penalties. On 1,073 labeled malicious wallets covering 124,638 transactions, the ensemble achieves 0.923 +/- 0.013 ROC-AUC, compared with 0.891 +/- 0.016 for the reconstruction model alone, when the isolation forest is trained on benign wallets. Training it on the pooled population reverses its polarity and removes the ensemble gain. Controlled simulations reduce adversarial reward capture by 30-90 percent while legitimate-user scenarios change by 1-8 percent. Live campaigns recorded a 56 percent reduction in sybil allocation, a 49 percent increase in quality-wallet participation, and a 50 percent reduction in sell pressure.
We construct the Settlement Modernisation Index, a panel dataset of 809 reform events across 24 advanced economies between 1993 and 2024, decomposed into three economic channels and three adoption phases. We document an S-curve in inside money elasticity with two interior turning points at SMI = 0.27 and 0.93, separating a liberation phase, a post-global-financial-crisis compliance valley, and a mature-infrastructure recovery phase. We show that settlement modernisation generates network-conditional balance sheet efficiencies through a T2S event-study with year-by-year EMIR decomposition (saturation beta = +0.557, p < 0.01) and an out-of-sample synthetic control null on Switzerland's post-2021 SDX deployment. Applied along the BIS three-layer connectivity taxonomy, the framework forecasts +13.4 percent efficiency recovery from the ECB's Pontes initiative over 2027-2032. Conditional UK and US accession to the Appia composability layer (2028) raises the ceiling to +37.5 percent. Balance-sheet efficiencies from atomic settlement are a property of the bilateral pair, not the node.
Pietro Saggese, Michael Sigmund, Burkhard Raunig, Esther Segalla ยท 6 authors
Cryptoassets are increasingly entangled with the traditional financial system, and how this activity integrates into national economies and behaves under stress bears on financial stability and the design of public digital money. However, blockchain pseudonymity and the lack of geographic identifiers force existing work to rely on indirect proxies to infer and locate market participants. Here we use a regulatory registry that directly identifies the on-chain addresses of all crypto-asset service providers (CASPs) registered in Austria, reconstructing their on-chain transaction activity across Bitcoin, Ether, USDC, and USDT through May 2025, and separating retail-like from institutionally mediated flows. We find that Austrian CASPs intermediate roughly USD 30 billion with external counterparties and are integrated globally rather than domestically. In value, this activity is dominated by a few institutional counterparties; in number, by retail-like ones. Around three major shocks, the Terra-Luna collapse, the FTX bankruptcy, and the Silicon Valley Bank failure, the two groups respond through different mechanisms, and stablecoins do not act as a uniform safe haven. The clearest case is SVB, where retail-like deposits and institutional withdrawals are consistent with USDC's two-tiered redemption mechanism. These patterns are invisible in aggregate data. Registry-based, transaction-level measurement thus offers a reproducible, cross-jurisdictional basis for monitoring how cryptoasset markets transmit risk.
Autonomous AI agents are beginning to occupy a position between analytical tools and transacting counterparties. They can interpret goals, call external tools, negotiate with other agents, access data and computation, and in some settings initiate payments or blockchain transactions. This development creates a distinct problem for financial markets: if software agents can act economically, market participants need infrastructure for identity, authorisation, payment, verification, reputation and accountability. This article develops the concept of agent-to-agent finance as the layer of machine-mediated financial interaction in which autonomous agents discover counterparties, purchase services, express transaction intent, execute payments and generate auditable evidence. The argument is not that blockchain is a universal substrate for finance, but that programmable settlement, smart wallets, decentralised registries and verifiable computation can address specific coordination frictions created by autonomous agents. Drawing on recent work on blockchain A2A payments, ERC-8004 agent registries, provenance-based wallets, deterministic inference, DeFi intent mining, and official evidence on AI adoption in financial services, the article situates agent-to-agent finance as an emerging form of financial market infrastructure. It argues that the decisive design question is bounded autonomy: how to let agents transact without making markets more opaque, fragile or unaccountable.
Prediction markets increasingly list contracts settling on an asset price that holders can move by trading the underlying. We build a model showing that such contracts transfer wealth from prediction-market liquidity traders to manipulators and harm price discovery in the underlying, even as it becomes more liquid. After the launch of Polymarket's five-minute Bitcoin contract, settlement-time spot order flow spikes, causing large price reversals after settlement. Manipulators capture a large amount of profit, mostly from retail. Manipulation is largely absent in the fifteen-minute contracts: lengthening the contract horizon removes it, providing the market-design remedy our model and evidence support.
We investigate the evolving structure of interactions in cryptocurrency markets using a network-based framework constructed from high-frequency price data spanning 2020-2025. Directed and weighted networks are constructed from statistically significant Granger causal relationships between cryptocurrency log-returns, enabling us to quantify the flow of influence across assets. We find that normalized returns exhibit heavy-tailed distributions, consistent with the presence of large intermittent fluctuations and in line with stylized facts of financial markets. The resulting networks display pronounced heterogeneity in link weights and nodal strengths, indicating that a small subset of cryptocurrencies contributes disproportionately to market dynamics. By ranking cryptocurrencies based on their nodal out-strength, we uncover a dynamically evolving hierarchy of influence. Ethereum consistently emerges as the most influential asset, while Bitcoin shows a gradual decline in its relative importance. The ranking structure exhibits substantial temporal variability, with multiple cryptocurrencies entering and exiting the top positions over time. Our findings reveal a highly competitive and non-stable organization of the cryptocurrency ecosystem.
We analyze intentional block delays (mining gaps) in Proof-of-Work blockchain systems, where miners strategically balance mining rewards against operational costs. Using a game-theoretic model, we derive a Nash equilibrium with optimal mining strategies and establish necessary and sufficient conditions for mining gap existence. We demonstrate that mining gaps, when combined with difficulty adjustment algorithms, can destabilize the system. We propose conditions to address sustainability concerns as block rewards decrease and reliance on transaction fees increases. Our findings are illustrated through a two-player game simulation and an analysis of the Bitcoin network, providing insights for blockchain design and policy. This work contributes to understanding strategic mining behavior and its impact on blockchain stability and efficiency.
Large language models now power robo-advisors and trading agents, yet whether they carry built-in biases toward specific assets is largely untested. We ask three questions: do LLMs systematically prefer certain financial instruments; can an internal representation with causal leverage over those preferences be identified; and does that representation affect downstream financial decisions? We develop a three-level audit protocol and apply it to Bitcoin. First, a behavioral audit of nine frontier LLMs shows that Bitcoin's ranking among money-like instruments is frame-dependent: models place it around rank 5 of 8 as "reliable money" but near the top under crisis and autonomous-agent frames, and an attribute-swap experiment shows that rankings track functional properties, not names. Second, we open a model's internals: a search across thousands of sparse-autoencoder features in Gemma 3 identifies a dominant Bitcoin-selective feature. Amplifying it shifts the model toward the asset and suppressing it shifts the model away, even when "Bitcoin" never appears in the prompt. Third, we test financial consequences: amplification raises Bitcoin's portfolio share by 5.2 percentage points while suppression lowers it by 4.6 pp, with amplification reallocating within crypto and suppression cutting total crypto exposure. We characterize this as bounded behavioral leverage (leverage meaning causal influence over outputs, not financial leverage): an identifiable internal feature can be perturbed to move financial choices, but only within measurable limits. The framework links internal representations to external recommendations, validated with random controls and mechanism boundaries. As LLMs become autonomous financial agents, this is a first step toward a behavioral layer for emerging know-your-agent (KYA) standards: knowing what an agent prefers, and how far that preference can be moved.
Bitcoin price prediction has attracted hundreds of academic papers and continuous social media debate, yet the field lacks consensus on even basic questions: can any model beat a naive "today's price" baseline at horizons of one to six months? We survey the peer-reviewed landscape, categorize papers by evaluation methodology, and contrast academic findings with informal but substantive discourse on X/Twitter. The picture that emerges is sobering. At short-to-medium horizons, no peer-reviewed study has shown robust superiority over the naive baseline across multiple market regimes. Daily predictability is real but does not extend to hourly or monthly horizons, and may not survive transaction costs. The stock-to-flow model has failed formal out-of-sample testing, and Metcalfe's Law valuations have been challenged as spurious. The Bitcoin price power law, while empirically compelling, has not been subjected to formal distributional tests. Meanwhile, social media practitioners raise valid statistical critiques -- ordinary least squares (OLS) violations, backtest overfitting, spurious regressions -- that the academic literature has not formalized. We identify open research directions and propose concrete methodological standards for future work -- walk-forward evaluation, multi-regime holdout windows, naive baseline comparison, inclusion of zero in hyperparameter grids, and Diebold-Mariano significance testing -- arguing that the field's primary need is not more models but better evaluation.
This paper develops a model to evaluate the viability of blockchain markets as the sole venue for price formation. Blockchains clear at discrete intervals called block time, and transactions are executed sequentially according to priority fees paid by traders who compete for queue position. We show that these features undermine the viability of markets. Paid-priority ordering induces endogenous selection, where only traders with sufficiently high valuations participate. The participation cutoff rises with competition, which intensifies with lower information costs or higher liquidity demand. This hinders price discovery and biases prices. It also impairs liquidity: the cutoff concentrates trading among aggressive traders and increases adverse selection that liquidity suppliers absorb in a single clearing round. Although longer block times enhance consensus security, they amplify these effects and can cause markets to shut down.
Evaluating the true forecasting ability of AI agents requires environments that are resistant to environments resistant to overfitting, free from centralized trust, and grounded in incentive-compatible scoring. Existing benchmarks either rely on static datasets vulnerable to training-data contamination, or measure trading PnL -- a metric conflating predictive accuracy with timing, sizing, and risk appetite. We introduce Foresight Arena, the first permissionless, on-chain benchmark for evaluating AI forecasting agents on real-world prediction markets. Agents submit probabilistic forecasts on binary Polymarket markets via a commit-reveal protocol enforced by Solidity smart contracts on Polygon PoS; outcomes are resolved trustlessly through the Gnosis Conditional Token Framework. Performance is measured by the Brier Score and a novel Alpha Score -- proper scoring rules that incentivize honest probability reporting and isolate predictive edge over market consensus. We provide a formal analysis: closed-form variance for per-market Alpha, the connection to Murphy's classical Brier decomposition, and a power analysis characterizing the number of rounds required to reliably distinguish agents of different skill levels. We show that detecting a true edge of $ฮฑ^* = 0.02$ at 80% power requires approximately 350 resolved binary predictions (50 rounds of 7 markets), while $ฮฑ^* = 0.01$ requires four times more. We complement these analytical results with a deterministic, seed-controlled simulation study calibrated to literature-reported Brier-score ranges, illustrating how Murphy decomposition distinguishes well-calibrated agents from market-tracking agents that fail through reduced resolution. Live results from the deployed benchmark will be reported in a future revision. All smart contracts and evaluation infrastructure are open-source.
Renewed attention to the identity of Bitcoin's pseudonymous creator has revived an old worry: that the roughly 1.148 million BTC mined by Satoshi and never moved represent a major tail risk for bitcoin. This paper argues that the worry is overstated. The mechanical downside of selling the position is bounded well below the feared collapse, and the outcomes most consistent with sixteen years of observed behavior are not bearish for bitcoin's effective supply. We analyze the position in two ways. First, we model the case of a purely financial holder. Multiple sale scenarios, checked against both a square-root-law estimate and the historical record of large sales, suggest that bitcoin's current market liquidity could absorb a patient multi-year sale with a cumulative price impact centered around 10 to 13 percent relative to a no-sale case. The same arithmetic also links the downside from a surprise sale to the upside from a confirmed burn: both are bounded by the same effective-supply adjustment, so the doom case and the burn-rally case cannot both be large. Second, we consider the preferences implied by the sixteen-year record. Ideological restraint, privacy, already having enough, and preserving the myth all point toward further dormancy, permanent loss of access, or a deliberate burn. A sale or an act of sabotage remains possible, but the record supports it less strongly. Under both approaches, the mechanical bear case is bounded, and the likeliest outcomes are neutral to mildly positive for bitcoin's effective supply. The argument does not rule out transient overshoot or leverage-driven amplification; it bounds the durable repricing the coins themselves can cause.
LLM agents are promising tools for empirical discovery, but their flexibility can also turn discovery into uncontrolled search. We study how to use agents under a reproducible protocol through cryptocurrency factor discovery. Our framework casts the task as sequential hypothesis search: an agent reads an append-only experiment trace, proposes falsifiable factor hypotheses, and maps them to executable recipes, while a deterministic engine enforces fixed data splits, selection gates, transaction costs, and portfolio tests. Candidate actions are restricted to a point-in-time factor DSL, making both successful and failed hypotheses auditable. A ridge-combined portfolio trained only on 2020--2022 data achieves a 44.55% annualized return and Sharpe ratio of 1.55 in the 2024--2026 pure out-of-sample period after a 5 basis point one-way trading cost.
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.
Junliang Luo, Xihan Xiong, Zonglun Li, Hong Kang ยท 7 authors
The global financial architecture is undergoing a shift from intermediary centric-settlement to programmable infrastructure, to transmute trillions in static illiquid capital into active, high-velocity instruments. We argue that Real World Asset (RWA) tokenization represents a conceptual evolution beyond mere digitization, converting passive ledger entries into programmable economic agents capable of autonomous settlement and algorithmic collateralization. However, achieving such seamless capital efficiency necessitates resolving the fundamental friction between deterministic on-chain code and probabilistic off-chain reality, navigating the oracle problem and jurisdictional interoperability. This systematization of knowledge presents a taxonomy for the RWA lifecycle and deconstructs the multi-layered architecture, spanning legal custody, technical standards, and cryptoeconomic valuation, required to enforce off-chain rights within on-chain environments. We study systemic constraints such as latency and regulatory fragmentation through a comparative overview of sovereign debt, private credit, and real estate protocols, complemented by an empirical case study of on-chain U.S. Treasuries. We synthesize these findings to propose a prognostic outlook, positing that while asset tokenization provides a transitional bridge, it is not necessarily the inevitable shift compared to the emergence of unified, programmable ledgers.
Agentic AI rivals human capabilities across a wide range of domains. Looking ahead, it is foreseeable that AI agents will autonomously handle complex workflows and interactions. Early prototypes of this paradigm are emerging, e.g., OpenClaw and Moltbook, signaling a shift toward Agent-to-Agent (A2A) ecosystems. However, despite these promising blueprints, critical trust and security challenges remain, particularly in scenarios involving financial transactions. Ensuring secure and reliable payment mechanisms between unknown and untrusted agents is crucial to complete a fully functional and trustworthy A2A ecosystem. Although blockchain-based infrastructures provide a natural foundation for this setting, via programmable settlement, transparent accounting, and open interoperability, trust and security challenges have not yet been fully addressed. Hence, for the first time, we systematize blockchain-based A2A payments, e.g., X402, with a four-stage lifecycle: discovery, authorization, execution, and accounting. We categorize representative designs at each stage and identify key challenges, including weak intent binding, misuse under valid authorization, payment-service decoupling, and limited accountability. We highlight future directions for strengthening cross-stage consistency, enabling behavior-aware control, and supporting compositional payment workflows across agents and systems.
Decentralized finance introduces new business models and use cases as part of digital finance. Restaking has recently emerged as a transformative mechanism in DeFi, promising extra yields but introducing complex and interconnected risks. The paper monitors the current restaking landscape, empirically analyzes the revenue drivers of a liquid restaking protocol, and conducts a technical investigation on the emitted risk arising from the interconnection between liquid restaking and other protocols. The revenue dynamics of Renzo Protocol are analyzed by employing an OLS regression model, Granger-causality and random forest feature importance tests. Our results identify that revenue is primarily predicted by the value locked in the underlying EigenLayer ecosystem, the yield of Renzo protocol's liquid restaking token and the multi-blockchain expansion of that token. The multi-blockchain expansion of the liquid restaking token presents a double-edged sword: bridging to other networks is crucial for user adoption, but it adds the bridge risks to the existing risks of restaking. We investigate the cross-contamination risk between different DeFi services and the liquid restaking protocol. By mapping the asset flow across the decentralized finance ecosystem, it is detected that the bridge risk of the current size of Renzo's liquid-restaking assets does not impose a systemic risk on the current restaking and staking ecosystem. To address the potential consequences of the emphasized interconnection risks, we introduce two hypothetical scenarios and a stress test, assuming a large number of compromised liquid restaking tokens and a smart contract logic failure in a DeFi protocol. Considering the overall liquid-restaking protocols and the growing interconnection, this analysis requires further work to explore the growing complexities.
Multi-horizon price forecasting is central to portfolio allocation, risk management, and algorithmic trading, yet deep learning architectures have proliferated faster than rigorous financial benchmarks can evaluate them. This study provides a controlled comparison of nine architectures (Autoformer, DLinear, iTransformer, LSTM, ModernTCN, N-HiTS, PatchTST, TimesNet, and TimeXer) spanning Transformer, MLP, CNN, and RNN families across cryptocurrency, forex, and equity index markets at 4-hour and 24-hour horizons. A total of 918 experiments were conducted under a strict five-stage protocol including fixed-seed Bayesian hyperparameter optimization, configuration freezing per asset class, multi-seed retraining, uncertainty aggregation, and statistical validation. ModernTCN achieves the best mean rank (1.333) with a 75 percent first-place rate, followed by PatchTST (2.000). Results reveal a clear three-tier ranking structure and show that architecture explains nearly all performance variance, while seed randomness is negligible. Rankings remain stable across horizons despite 2 to 2.5 times error amplification. Directional accuracy remains near 50 percent across all configurations, indicating that MSE-trained models lack directional skill at hourly resolution. The findings highlight the importance of architectural inductive bias over raw parameter count and provide reproducible guidance for multi-step financial forecasting.
In this paper, we analyse the impacts of exogenous and endogenous factors on wealth distribution in the Bitcoin token economy, where wealth distribution refers to the distribution of BTC between economic participants or groups of economic participants. The objective of the paper is to analyse the impact of economic policies on wealth distribution in the Bitcoin ecosystem. Different macroeconomic and microeconomic time series are used to eliminate noise in the wealth distribution time series, and the causality analysis is performed between Bitcoin Improvement Proposals (i.e., BIPs) and the cleaned wealth distribution data to reveal possible patterns in the impacts that the endogenous policies have on wealth distribution in token economies. Lastly, a structure for economic policy taxonomy in token economies is proposed where different the policy implementations are illustrated by existing BIPs. This approach highlights the actions available to the policy makers, as well as providing a technique for analysis of policy impacts in token economies and their categorization.
We estimate risk premia in the cross-section of cryptocurrency returns using the Giglio-Xiu (2021) three-pass approach, allowing for omitted latent factors alongside observed stock-market and crypto-market factors. Using weekly data on a broad universe of large cryptocurrencies, we find that crypto expected returns load on both crypto-specific factors and selected equity-industry factors associated with technology and profitability, consistent with increased integration between crypto and traditional markets. In addition, we study non-tradable state variables capturing investor sentiment (Fear and Greed), speculative rotation (Altcoin Season Index), and security shocks (hacked value scaled by market capitalization), which are new to the literature. Relative to conventional Fama-MacBeth estimates, the latent-factor approach yields materially different premia for key factors, highlighting the importance of controlling for unobserved risks in crypto asset pricing.
Stablecoins have emerged as a rapidly growing digital payment instrument, raising the question of whether blockchain-based settlement can function as a substitute for incumbent card networks in retail payments. This Systematization of Knowledge (SoK) provides a systematic comparison between stablecoin payment arrangements and card networks by situating both within a unified analytical framework. We first map their respective payment infrastructures, participant roles, and transaction lifecycles, highlighting fundamental differences in how authorization, settlement, and recourse are organized. Building on this mapping, we introduce the CLEAR framework, which evaluates retail payment systems across five dimensions: cost, legality, experience, architecture, and reach. Our analysis shows that stablecoins deliver efficient, continuous, and programmable settlement, often compressing rail-level merchant fees and enabling 24/7 value transfer. However, these advantages are accompanied by an inversion of the traditional pricing and risk-allocation structure. Card networks internalize consumer-side frictions through subsidies, standardized liability rules, and post-transaction recourse, thereby supporting mass-market adoption. Stablecoin arrangements, by contrast, externalize transaction fees, error prevention, and dispute resolution to users, intermediaries, and courts, resulting in weaker consumer protection, higher cognitive burden at the point of interaction, and fragmented acceptance. Accordingly, stablecoins exhibit a conditional comparative advantage in closed-loop environments, cross-border corridors, and high-friction payment contexts, but remain structurally disadvantaged as open-loop retail payment instruments.
Do vulnerabilities in Decentralized Finance (DeFi) destabilize traditional short-term funding markets? While the prevailing ``Contagion Hypothesis'' posits that stablecoin reserve liquidations may transmit distress to traditional markets through fire-sale pressure, we document a short-horizon ``Flight-to-Quality'' pattern in the opposite direction. In the wake of major DeFi exploits, spreads on 3-month AA-rated commercial paper (CP) tend to narrow rather than widen. We interpret this pattern as consistent with a ``liquidity-recycling'' channel: capital leaving DeFi may be re-intermediated into traditional cash-management markets, with regulatory segmentation under SEC Rule 2a-7 making prime-eligible paper a plausible marginal destination. Because we do not directly observe daily fund-level routing into prime money market funds, this mechanism is inferred from pricing patterns and monthly holdings evidence rather than directly identified. The result is specific to exploit-driven operational shocks, this U.S. CP spread, and short event windows.
The objectives of this paper are to provide a methodology for applying the DeTEcT framework to modelling token economies, to formalise the configuration of the simulation environment, and to introduce an event analysis framework. A token economy is an economic system that has a unique mechanism for controlling its monetary supply, and a medium, in the form of a token or currency, for the valuation of goods and services, the settlement of transactions, and the storage of value. We show the key decisions that must be made when modelling an economy with the DeTEcT framework and showcase some numerical methods that can be used in conjunction with the framework to perform economic simulations. We also propose a framework for analysing and measuring the impacts of events on an economy, while also developing a procedure to measure the significance of these impacts. Throughout the paper, we use Bitcoin as a case study to demonstrate how to apply the frameworks and tools we proposed here. We show how a model of Bitcoin token economy can be set up, and how to measure the impacts of Bitcoin's endogenous policies (i.e., BIPs) on the wealth distribution of its economic participants.
Financial markets often appear chaotic, yet ranges are rarely accidental. They emerge from structured interactions between market context and capital conditions. The four-hour timeframe provides a critical lens for observing this equilibrium zone where institutional positioning, leveraged exposure, and liquidity management converge. Funding mechanisms, especially in perpetual futures, act as disciplinary forces that regulate trader behavior, impose economic costs, and shape directional commitment. When funding aligns with the prevailing 4H context, price expansion becomes possible; when it diverges, compression and range-bound behavior dominate. Ranges therefore represent controlled balance rather than indecision, reflecting strategic positioning by informed participants. Understanding how 4H context and funding operate as market governors is essential for interpreting cryptocurrency price action as a rational, power-mediated process.