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.
Ankenbrand, Thomas, Bieri, Denis, Ferrazzini, Stefano, Hoehener, Johannes
Tokenised money encompasses a broad range of digital monetary instruments issued on distributed ledger technology, including Central Bank Digital Currencys (CBDCs), deposit tokens, stablecoins, and decentralised protocol-based designs. Despite their shared monetary function, these instruments differ markedly in issuer structure, collateralisation, stability mechanisms, governance, and technological embedding, creating conceptual ambiguity. This paper proposes a concise taxonomy spanning twelve key design dimensions, offering a systematic framework for comparing heterogeneous forms of tokenised money. The taxonomy clarifies how different design choices shape monetary properties, risks, and policy implications, supporting clearer analysis and dialogue across academia, industry, and regulation.
Do Ethereum's Layer-2 (L2) rollups actually decongest the Layer-1 (L1) mainnet once protocol upgrades and demand are held constant? Using a 1245-day daily panel from August 5, 2021 to December 31, 2024 that spans the London, Merge, and Dencun upgrades, we link Ethereum fee and congestion metrics to L2 user activity, macro-demand proxies, and targeted event indicators. We estimate a regime-aware error-correction model that treats posting-clean L2 user share as a continuous treatment. Over the pre-Dencun (London+Merge) window, a 10 percentage point increase in L2 adoption lowers median base fees by about 13% -- roughly 5 Gwei at pre-Dencun levels -- and deviations from the long-run relation decay with an 11-day half-life. Block utilization and a scarcity index show similar congestion relief. After Dencun, L2 adoption is already high and treatment support narrows, so blob-era estimates are statistically imprecise and we treat them as exploratory. The pre-Dencun window therefore delivers the first cross-regime causal estimate of how aggregate L2 adoption decongests Ethereum, together with a reusable template for monitoring rollup-centric scaling strategies.
As data is an essential asset for any DeFi application, selecting an oracle is a critical decision for its success. To date, academic research has mainly focused on improving oracle technology and internal economics, while the drivers of oracle choice on the client side remain largely unexplored. This study addresses this gap by gathering insights from leading DeFi protocols, uncovering their rationale for oracle selection and their preferences regarding whether to outsource or internalize data-request mechanisms. Data are collected from founders, C-level executives, and oracle engineers of 32 DeFi protocols, whose combined total value locked (TVL) exceeds 55% of the oracle-using DeFi segment. The study leverages a one-time mixed-method survey, using tailored question paths for in-house versus third-party oracle users. Quantitative answers are summarized, compared across groups, and examined through Spearman rank-order correlations to explore pairwise associations among evaluation dimensions, while open-ended responses are inductively coded into keywords and broader themes to triangulate common selection motives and switching challenges. Insights support the view that protocol choices are tied to technological dependencies, in which the immutability of smart contracts amplifies lock-in, hindering agile switching among data providers. Furthermore, when viable third-party solutions exist, protocols generally prefer to outsource rather than build and maintain internal oracle mechanisms.
Wenbin Wu, Kejiang Qian, Alexis Lui, Christopher Jack · 8 authors
We curate the DeXposure dataset, the first large-scale dataset for inter-protocol credit exposure in decentralized financial networks, covering global markets of 43.7 million entries across 4.3 thousand protocols, 602 blockchains, and 24.3 thousand tokens, from 2020 to 2025. A new measure, value-linked credit exposure between protocols, is defined as the inferred financial dependency relationships derived from changes in Total Value Locked (TVL). We develop a token-to-protocol model using DefiLlama metadata to infer inter-protocol credit exposure from the token's stock dynamics, as reported by the protocols. Based on the curated dataset, we develop three benchmarks for machine learning research with financial applications: (1) graph clustering for global network measurement, tracking the structural evolution of credit exposure networks, (2) vector autoregression for sector-level credit exposure dynamics during major shocks (Terra and FTX), and (3) temporal graph neural networks for dynamic link prediction on temporal graphs. From the analysis, we observe (1) a rapid growth of network volume, (2) a trend of concentration to key protocols, (3) a decline of network density (the ratio of actual connections to possible connections), and (4) distinct shock propagation across sectors, such as lending platforms, trading exchanges, and asset management protocols. The DeXposure dataset and code have been released publicly. We envision they will help with research and practice in machine learning as well as financial risk monitoring, policy analysis, DeFi market modeling, amongst others. The dataset also contributes to machine learning research by offering benchmarks for graph clustering, vector autoregression, and temporal graph analysis.
Since its inception, Bitcoin has been positioned as a revolutionary alternative to national currencies, attracting immense public and academic interest. This paper presents a critical evaluation of this claim, suggesting that Bitcoin faces significant structural barriers to qualifying as money. It synthesizes critiques from two distinct schools of economic thought - Post-Keynesianism and the Austrian School - and validates their conclusions with rigorous technical analysis. From a Post-Keynesian perspective, it is argued that Bitcoin does not function as money because it is not a debt-based IOU and fails to exhibit the essential properties required for a stable monetary asset (Vianna, 2021). Concurrently, from an Austrian viewpoint, it is shown to be inconsistent with a strict interpretation of Mises's Regression Theorem, as it lacks prior non-monetary value and has not achieved the status of the most saleable commodity (Peniaz and Kavaliou, 2024). These theoretical arguments are then supported by an empirical analysis of Bitcoin's extreme volatility, hard-coded scalability limits, fragile market structure, and insecure long-term economic design. The paper concludes that Bitcoin is more accurately characterized as a novel speculative asset whose primary legacy may be the technological innovation it has spurred, rather than its viability as a monetary standard.
This study explores Bitcoin's value formation through the Granular Interaction Thinking Theory-Value Theory (GITT-VT). Rather than stemming from material utility or cash flows, Bitcoin's value arises from informational attributes and interactions of multiple factors, including cryptographic order, decentralization-enabled autonomy, trust embedded in the consensus mechanism, and socio-narrative coherence that reduce entropy within decentralized value-exchange processes. To empirically assess this perspective, a Bayesian linear model was estimated using daily data from 2022 to 2025, operationalizing four informational value dimensions: Store-of-Value (SOV), Autonomy (AUT), Social-Signal Value (SSV), and Hedonic-Sentiment Value (HSV). Results indicate that only SSV exerts a highly credible positive effect on next-day returns, highlighting the dominant role of high-entropy social information in short-term pricing dynamics. In contrast, SOV and AUT show moderately reliable positive associations, reflecting their roles as low-entropy structural anchors of long-term value. HSV displays no credible predictive effect. The study advances interdisciplinary value theory and demonstrates Bitcoin as a dual-layer entropy-regulating socio-technological ecosystem. The findings offer implications for digital asset valuation, investment education, and future research on entropy dynamics across non-cash-flow digital assets.
Regulatory technology (RegTech) is transforming financial compliance by integrating advanced information technologies to strengthen anti money laundering and countering the financing of terrorism (AML CFT) frameworks. Recent literature suggests that such technologies represent more than just an efficiency tool; they mark a paradigm shift in regulation and the evolution of financial oversight (Kurum, 2023). This paper aims to provide a narrative review of recent RegTech applications in financial crime prevention, with a focus on key compliance domains. A structured literature review was conducted to examine publications between 2020 and 2024 with a thematic synthesis of findings related to customer due diligence (CDD) and know your customer (KYC), transaction monitoring, regulatory reporting and compliance automation, information sharing and cross border cooperation, as well as cost efficiency. Findings reveal that RegTech solutions give financial institutions more responsibility for detecting and managing financial crime risks, making them more active players in compliance processes traditionally overseen by regulators. The combined use of technologies such as artificial intelligence (AI), blockchain, and big data also generates synergistic effects that improve compliance outcomes beyond what these technologies achieve individually. This demonstrates the strategic relevance of integrated RegTech approaches.
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.
The third Bitcoin halving that took place in May 2020 cut down the mining reward from 12.5 to 6.25 BTC per block and thus slowed down the rate of issuance of new Bitcoins, making it more scarce. The fourth and most recent halving happened in April 2024, cutting the block reward further to 3.125 BTC. If the demand did not decrease simultaneously after these halvings, then the neoclassical economic theory posits that the price of Bitcoin should have increased due to the halving. But did it, in fact, increase for that reason, or is this a post hoc fallacy? This paper uses synthetic control to construct a weighted Bitcoin that is different from its counterpart in one aspect - it did not undergo halving. Comparing the price trajectory of the actual and the simulated Bitcoins, I find evidence of a positive effect of the 2024 Bitcoin halving on its price three months later. The magnitude of this effect is one fifth of the total percentage change in the price of Bitcoin during the study period - from April 2, 2023, to July 21, 2024 (17 months). The second part of the study fails to obtain a statistically significant and robust causal estimate of the effect of the 2020 Bitcoin halving on Bitcoin's price. This is the first paper analyzing the effect of halving causally, building on the existing body of correlational research.
Carlo Brunetta, Amit Chaudhary, Stefano Galatolo, Massimiliano Sala
Dynamically distributed inflation is a common mechanism used to guide a blockchain's staking rate towards a desired equilibrium between network security and token liquidity. However, the high sensitivity of the annual percentage yield to changes in the staking rate, coupled with the inherent feedback delays in staker responses, can induce undesirable oscillations around this equilibrium. This paper investigates this instability phenomenon. We analyze the dynamics of inflation-based reward systems and propose a novel distribution model designed to stabilize the staking rate. Our solution effectively dampens oscillations, stabilizing the yield within a target staking range.
Pixels and market cycles both move NFT prices. Non-fungible tokens (NFTs) are unique digital assets, often used to represent ownership of digital art, collectibles, and other media, secured on blockchain networks like Ethereum. The rise of NFTs has led to the creation of a multi-billion-dollar market for digital art and collectibles, making it a key area of interest for researchers, artists, and investors. Using 94,039 transactions from 26 major generative Ethereum collections, this study extracts 196 machine-quantified image descriptors - color, composition, palette structure, geometry, texture, and deep-learning embeddings - and applies a three-stage filter to identify stable predictors for hedonic regression. A static mixed-effects model shows that market sentiment and transparent, interpretable image traits have significant and independent pricing power: higher focal saturation, tighter compositional concentration, and greater curvature are rewarded, while clutter, heavy line work, and dispersed palettes are discounted; deep embeddings add limited incremental value once explicit traits are included. To assess state dependence, a Bayesian dynamic mixed-effects panel with cycle effects is estimated, allowing Composition Focus - Saturation - the ratio of saturation in the central region to the whole image, capturing vividness and concentration at the focal area - to vary across market regimes. Collection-level heterogeneity (brand premia) is absorbed by random effects. The time-varying coefficients exhibit clear regime sensitivity, with stronger premia in expansionary phases and weaker or negative loadings in downturns, while the grand-mean effect is small on average. Overall, NFT prices reflect both observable digital product characteristics and market regimes, and the framework offers a cycle-aware tool for asset pricing, platform strategy, and market design in digital art markets.
We analyze 15,097 blocks proposed for inclusion in Ethereum's blockchain over an eight-minute window on December 3, 2024, during which 38 blocks were added to the chain. We classify transactions as exclusive -- appearing only in blocks from a single builder -- or private -- absent from the public mempool but included in blocks from multiple builders. We find that, depending on the methodology, exclusive transactions account for between 77.2% and 84% of the total fees paid by transactions in winning blocks. Moreover, we show that exclusivity cannot be fully attributed to persistent relationships between senders and builders: only between 7% and 8.4% of all on-chain exclusive transaction value originates from senders who route exclusively to one builder. Finally, we observe that transaction exclusivity is dynamic. Some transactions are exclusive at the start of a bidding cycle but later appear in blocks from multiple builders. Other transactions remain exclusive to a losing builder for two or three cycles before appearing in the public mempool. These transactions are therefore delayed and then exposed to potential attacks.
The Decentralized Physical Infrastructure (DePIN) market is revolutionizing the sharing economy through token-based economics and smart contracts that govern decentralized operations. By 2024, DePIN projects have exceeded \$10 billion in market capitalization, underscoring their rapid growth. However, the unregulated nature of these markets, coupled with the autonomous deployment of AI agents in smart contracts, introduces risks such as inefficiencies and potential misalignment with human values. To address these concerns, we introduce EconAgentic, a Large Language Model (LLM)-powered framework designed to mitigate these challenges. Our research focuses on three key areas: 1) modeling the dynamic evolution of DePIN markets, 2) evaluating stakeholders' actions and their economic impacts, and 3) analyzing macroeconomic indicators to align market outcomes with societal goals. Through EconAgentic, we simulate how AI agents respond to token incentives, invest in infrastructure, and adapt to market conditions, comparing AI-driven decisions with human heuristic benchmarks. Our results show that EconAgentic provides valuable insights into the efficiency, inclusion, and stability of DePIN markets, contributing to both academic understanding and practical improvements in the design and governance of decentralized, tokenized economies.
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.
The global financial system stands at an inflection point. Stablecoins represent the most significant evolution in banking since the abandonment of the gold standard, positioned to enable "Banking 2.0" by seamlessly integrating cryptocurrency innovation with traditional finance infrastructure. This transformation rivals artificial intelligence as the next major disruptor in the financial sector. Modern fiat currencies derive value entirely from institutional trust rather than physical backing, creating vulnerabilities that stablecoins address through enhanced stability, reduced fraud risk, and unified global transactions that transcend national boundaries. Recent developments demonstrate accelerating institutional adoption: landmark U.S. legislation including the GENIUS Act of 2025, strategic industry pivots from major players like JPMorgan's crypto-backed loan initiatives, and PayPal's comprehensive "Pay with Crypto" service. Widespread stablecoin implementation addresses critical macroeconomic imbalances, particularly the inflation-productivity gap plaguing modern monetary systems, through more robust and diversified backing mechanisms. Furthermore, stablecoins facilitate deregulation and efficiency gains, paving the way for a more interconnected international financial system. This whitepaper comprehensively explores how stablecoins are poised to reshape banking, supported by real-world examples, current market data, and analysis of their transformative potential.
This paper examines fiscal policy spillovers through informal international financial channels, using the US stimulus checks as a positive, sudden, and direct fiscal shock. I utilize granular, transaction-level cryptocurrency data combined with an algorithm to probabilistically identify cross-border "crypto vehicle" transactions to construct bilateral cryptocurrency flows between countries. Using a difference-in-differences strategy, I compare cryptocurrency outflows between the US and other high-income countries and find a sharp but temporary increase in cryptocurrency outflows as a result of the direct stimulus. I quantify the fiscal spillover relative to expenditure and place an upper bound of 2.52% through this channel. This implies that fiscal spillovers through remittance channels are likely modest in size.
The convergence of Web3 technologies and AI agents represents a rapidly evolving frontier poised to reshape decentralized ecosystems. This paper presents the first and most comprehensive analysis of the intersection between Web3 and AI agents, examining five critical dimensions: landscape, economics, governance, security, and trust mechanisms. Through an analysis of 133 existing projects, we first develop a taxonomy and systematically map the current market landscape (RQ1), identifying distinct patterns in project distribution and capitalization. Building upon these findings, we further investigate four key integrations: (1) the role of AI agents in participating in and optimizing decentralized finance (RQ2); (2) their contribution to enhancing Web3 governance mechanisms (RQ3); (3) their capacity to strengthen Web3 security via intelligent vulnerability detection and automated smart contract auditing (RQ4); and (4) the establishment of robust reliability frameworks for AI agent operations leveraging Web3's inherent trust infrastructure (RQ5). By synthesizing these dimensions, we identify key integration patterns, highlight foundational challenges related to scalability, security, and ethics, and outline critical considerations for future research toward building robust, intelligent, and trustworthy decentralized systems with effective AI agent interactions.
Stablecoins have become a foundational component of the digital asset ecosystem, with their market capitalization exceeding 230 billion USD as of May 2025. As fiat-referenced and programmable assets, stablecoins provide low-latency, globally interoperable infrastructure for payments, decentralized finance, DeFi, and tokenized commerce. Their accelerated adoption has prompted extensive regulatory engagement, exemplified by the European Union's Markets in Crypto-assets Regulation, MiCA, the US Guiding and Establishing National Innovation for US Stablecoins Act, GENIUS Act, and Hong Kong's Stablecoins Bill. Despite this momentum, academic research remains fragmented across economics, law, and computer science, lacking a unified framework for design, evaluation, and application. This study addresses that gap through a multi-method research design. First, it synthesizes cross-disciplinary literature to construct a taxonomy of stablecoin systems based on custodial structure, stabilization mechanism, and governance. Second, it develops a performance evaluation framework tailored to diverse stakeholder needs, supported by an open-source benchmarking pipeline to ensure transparency and reproducibility. Third, a case study on Real World Asset tokenization illustrates how stablecoins operate as programmable monetary infrastructure in cross-border digital systems. By integrating conceptual theory with empirical tools, the paper contributes: a unified taxonomy for stablecoin design; a stakeholder-oriented performance evaluation framework; an empirical case linking stablecoins to sectoral transformation; and reproducible methods and datasets to inform future research. These contributions support the development of trusted, inclusive, and transparent digital monetary infrastructure.
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.
This study proposes a novel methodological framework integrating a LightGBM regression model and genetic algorithm (GA) optimization to systematically evaluate the contribution of COVID-19-related indicators to Bitcoin return prediction. The primary objective was not merely to forecast Bitcoin returns but rather to determine whether including pandemic-related health data significantly enhances prediction accuracy. A comprehensive dataset comprising daily Bitcoin returns and COVID-19 metrics (vaccination rates, hospitalizations, testing statistics) was constructed. Predictive models, trained with and without COVID-19 features, were optimized using GA over 31 independent runs, allowing robust statistical assessment. Performance metrics (R2, RMSE, MAE) were statistically compared through distribution overlaps and Mann-Whitney U tests. Permutation Feature Importance (PFI) analysis quantified individual feature contributions. Results indicate that COVID-19 indicators significantly improved model performance, particularly in capturing extreme market fluctuations (R2 increased by 40%, RMSE decreased by 2%, both highly significant statistically). Among COVID-19 features, vaccination metrics, especially the 75th percentile of fully vaccinated individuals, emerged as dominant predictors. The proposed methodology extends existing financial analytics tools by incorporating public health signals, providing investors and policymakers with refined indicators to navigate market uncertainty during systemic crises.
This paper investigates the dynamics of risk transmission in cryptocurrency markets and proposes a novel framework for volatility forecasting. The framework uncovers two key empirical facts: the asymmetric amplification of volatility spillovers in both tails, and a structural decoupling between market size and systemic importance. Building on these insights, we develop a state-adaptive volatility forecasting model by extracting time-varying quantile spillover features across different volatility components. These features are embedded into an extended Log-HAR structure, resulting in the SA-Log-HAR model. Empirical results demonstrate that the proposed model outperforms benchmark alternatives in both in-sample fitting and out-of-sample forecasting, particularly in capturing extreme volatility and tail risks with greater robustness and explanatory power.
Persistent financial frictions - including price volatility, constrained credit access, and supply chain inefficiencies - have long hindered productivity and welfare in the global agricultural sector. This paper provides a theoretical and applied analysis of how fiat-collateralized stablecoins, a class of digital currency pegged to a stable asset like the U.S. dollar, can address these long-standing challenges. We develop a farm-level profit maximization model incorporating transaction costs and credit constraints to demonstrate how stablecoins can enhance economic outcomes by (1) reducing the costs and risks of cross-border trade, (2) improving the efficiency and transparency of supply chain finance through smart contracts, and (3) expanding access to credit for smallholder farmers. We analyze key use cases, including parametric insurance and trade finance, while also considering the significant hurdles to adoption, such as regulatory uncertainty and the digital divide. The paper concludes that while not a panacea, stablecoins represent a significant financial technology with the potential to catalyze a paradigm shift in agricultural economics, warranting further empirical investigation and policy support.
Lucas Barbosa, Sam Kirshner, Rob Kopel, Eric Tze Kuan Lim · 5 authors
This paper outlines an incentive-driven and decentralized approach to verifying the veracity of digital content at scale. Widespread misinformation, an explosion in AI-generated content and reduced reliance on traditional news sources demands a new approach for content authenticity and truth-seeking that is fit for a modern, digital world. By using smart contracts and digital identity to incorporate 'trust' into the reward function for published content, not just engagement, we believe that it could be possible to foster a self-propelling paradigm shift to combat misinformation through a community-based governance model. The approach described in this paper requires that content creators stake financial collateral on factual claims for an impartial jury to vet with a financial reward for contribution. We hypothesize that with the right financial and social incentive model users will be motivated to participate in crowdsourced fact-checking and content creators will place more care in their attestations. This is an exploratory paper and there are a number of open issues and questions that warrant further analysis and exploration.