243 curated articles, papers, and podcasts about prediction markets for builders, investors, and researchers.
Argues that prediction markets aimed at informing voters should operate as nonprofits rather than for-profit businesses. Points out that valuable political information rarely correlates with profitable trading opportunities, and charitable structures face less regulatory scrutiny.
Concepts: information aggregation, regulatory arbitrageProposes a mechanism for prediction markets where outcomes cannot be objectively verified. Uses the last reporter's prediction as a reference point, creating incentives for truthful reporting through negative cross-entropy payments. Proves truthful reporting is a perfect Bayesian equilibrium.
Concepts: self-resolving markets, proper scoring rules, incentive compatibility, information aggregationExplains how prediction markets work and debunks the common misconception that market prices equal probabilities. Breaks down why risk-free rates, opportunity costs, and spreads create systematic price deviations from true beliefs.
Concepts: event contracts, price discoveryIntroduction to decentralized prediction markets with a SWOT analysis of Polymarket. Covers how the platform works, its regulatory positioning, liquidity constraints, and growth opportunities.
Concepts: information aggregation, price discoveryArgues that prediction markets' unpopularity isn't due to regulation but fundamental demand-side issues. Markets need savers, gamblers, or sharps to function, but prediction markets attract none: they're zero-sum (no savers), have long resolution times (no gamblers), and are too small for professional traders (no sharps).
Concepts: liquidity provision, market making, hedging, forecasting accuracyCase study of Polymarket's Venezuelan election market, where UMA token-holders overrode the platform's own resolution rules to declare opposition candidate Gonzalez the winner despite official results. Highlights conflicts of interest when oracle voters can also bet on outcomes.
Concepts: oracle design, dispute resolution, UMA protocol, corruption value multipleAnalysis of Polymarket's growth trajectory and business model. Notes that 99.2% of trading volume concentrates in political markets and two-thirds of cumulative volume occurred in the last six months, raising questions about sustainability beyond election cycles.
Concepts: conditional tokens, network effects, election marketsTechnical explainer of how onchain prediction markets work, using Polymarket as the primary case study. Covers the Gnosis Conditional Token Framework, Central Limit Order Books vs AMMs, UMA Oracle dispute resolution mechanics, and liquidity incentive programs.
Concepts: conditional tokens, oracle design, dispute resolution, liquidity provision, order bookCompares prediction markets with traditional polls and expert commentary along two axes: grassroots vs top-down and expertise density. Uses the 2024 Biden-Trump race to show how Polymarket priced in Biden's withdrawal probability while polls measured only head-to-head support.
Concepts: information aggregation, wisdom of crowds, election markets, forecasting accuracyResearch report on Polymarket's growth (35x increase in weekly active users from May to September 2024) and competitive positioning. Covers technical infrastructure (Gnosis CTF, UMA oracles, PolyLend), participant biases, and oracle complications like the Venezuelan election dispute.
Concepts: conditional tokens, oracle design, dispute resolution, network effects, liquidity provisionSpeculates on how Polymarket's 2024 presidential election market could be manipulated through its oracle system. Argues that Fox News was chosen as an oracle despite being unlikely to call the election for a non-Trump candidate, and that UMA token holders could sway disputed resolution votes given UMA's small market cap.
Concepts: oracle design, dispute resolution, election markets, corruption value multipleCharacterizes Polymarket as a crypto media/creator economy platform rather than just an event-trading platform. Notes the 166:1 ratio of monthly visits to MAU suggests significant non-trading visitors, and that Polymarket users are older and less focused on maximizing risk-reward compared to typical crypto traders.
Concepts: network effects, price discovery, election marketsTechnical primer on prediction market design, from wisdom of crowds theory to decentralized oracle mechanisms. Argues prediction markets could systematize event probabilities to expand financial markets like derivatives did historically, but current implementations face challenges in liquidity fragmentation, oracle incentives, and complexity.
Concepts: wisdom of crowds, oracle design, information aggregation, liquidity provisionFrames prediction markets as predecessors to financialized social networks. Uses Polymarket (which briefly hit #1 in app stores) as evidence that linking user views to financial stakes creates engagement through capital formation rather than pure attention capture. Argues these platforms are stress-testing primitives for Web3 social infrastructure.
Concepts: network effects, info financeCritical analysis of prediction market reliability during the 2024 US election. Documents how four coordinated accounts controlled 23% of Polymarket's open interest, 41% of volume appeared to be wash trading, and argues current platforms lack the structural conditions for reliable forecasting.
Concepts: liquidity provision, corruption value multiple, election markets, oracle designArgues that prediction markets represent one application within a broader 'info finance' ecosystem. Proposes these mechanisms can improve governance, scientific research, journalism, and social media through information-pricing mechanisms that go beyond simple betting.
Concepts: info finance, information aggregation, futarchy, decision marketsPodcast discussion separating hype from reality in prediction markets. Covers foundational mechanics, comparative advantages over pollsters and experts, and future applications including corporate decision-making, scientific reproducibility, and governance innovations.
Concepts: information aggregation, futarchyArgues prediction markets face fundamental barriers to scaling beyond sports and elections due to misaligned incentives: speculators demand rapid resolutions (42% of election volume in final week), investors prefer wealth-building assets over locked capital, and market makers need consistent retail flow that doesn't exist for niche topics.
Concepts: liquidity provision, market makingPost-mortem of Polymarket's US government shutdown market, where the market resolved 'Yes' to a shutdown that never happened. Traces the failure to structural issues in oracle design: token holders who can trade and vote, retroactive rule changes, and a corruption cost lower than the value at stake.
Concepts: oracle design, dispute resolution, UMA protocol, corruption value multiple, conditional tokensOutlines 10 trends and feature ideas for prediction markets including shorter duration markets, modular dispute resolution, AI as arbiters and participants, market segmentation, yield-bearing stablecoin integration, and conditional markets that allow betting on if-then outcomes.
Concepts: oracle design, decision markets, conditional tokensExamines the paradox of a Polymarket on Jesus Christ's return trading at 3% with over $100k wagered. Identifies two mysteries: why no one arbitrages the mispricing (requires ~$1M lockup for minimal 1% return), and why anyone bets 'Yes' at all (true believers, resolution gaming, or novelty value).
Concepts: price discovery, liquidity provisionChallenges the conception that prediction markets serve as effective policy tools. Argues that conditional prediction markets proposing 'Will X lead to consequence Y?' fail to translate probability assessments into meaningful policy guidance despite being their central theoretical appeal.
Concepts: decision markets, forecasting accuracyExplains why apparent arbitrage opportunities between Polymarket and Kalshi often aren't risk-free. Different rule definitions, resolution criteria, and oracle systems mean seemingly identical markets can resolve differently. Argues prediction markets are fundamentally non-fungible due to differing referee systems, leading to permanent landscape fragmentation.
Concepts: arbitrage, arbitrage, oracle design, dispute resolutionProposes 'covariance markets' as a solution to offering joint probability bets (parlays) without fragmenting liquidity. Instead of creating separate markets for all AND/OR combinations, a single covariance market between two base markets enables all 8 joint combinations while maintaining concentrated liquidity.
Concepts: parlays, liquidity provision, covariance marketsArgues prediction markets should shift from edge-seeking tools for professionals to identity-signaling social platforms. Proposes a TikTok-like feed where bets become public expressions of belief, removing the friction of intent and transforming markets from information tools into social spaces.
Concepts: network effects, info financeArgues Polymarket has reached escape velocity in network effects. Notes that zero trading fees isn't a bug but a growth feature, and that with every news cycle the platform trojan-horses itself into the conversation. Sees Polymarket becoming vital infrastructure for future financial markets.
Concepts: network effects, platform competitionArgues the Oracle Problem for prediction markets is actually a definition problem, not a trustlessness problem. Lists 12+ Polymarket controversies where contention arose from ambiguous event definitions, not oracle failures. Proposes that proprietary centralized oracles with platform stake are preferable to decentralized mercenary capital.
Concepts: oracle design, resolution criteriaIdentifies three characteristics successful prediction markets will focus on: high leverage (parlays, perps, intraday events), highly frequent markets (for retention), and high market outcome values (for capital attraction). Notes the prediction market wars have only begun.
Concepts: platform competition, parlaysArgues prediction markets should adopt batched auction mechanisms instead of continuous limit order books. Claims no practical social benefit exists from sub-second reaction times, and batching would redirect trader effort toward meaningful questions while reducing zero-sum speed competition.
Concepts: batched auctions, order book, price discoveryVC landscape analysis covering incumbents (Polymarket vs Kalshi metrics) and emerging players (Limitless, Onit, Hedgehog, Inertia). Explores advanced concepts including futarchy (MetaDAO), conditional DeFi markets, and beauty contest games. Outlines investment criteria: prosumer appeal, category focus, permissionless market creation, and parlay support.
Concepts: futarchy, platform competition, oracle designComprehensive survey of 20+ prediction market projects organized by X follower count. Categorizes into real-money platforms (Polymarket, Kalshi, Augur), play-money (Manifold), defunct (Veil), and protocols (Azuro). Highlights novel mechanisms like quiz-based markets, perpetual info markets, and social betting.
Concepts: platform competitionSkeptical take arguing prediction markets are dangerous policy despite theoretical appeal. Claims they lack heterogeneous risk preferences necessary for efficiency, relying instead on continuous retail losses. Warns that large-scale prediction markets exhibit reflexivity, potentially incentivizing manipulation toward negative, sensational outcomes.
Concepts: reflexivity, hedging, market makingIntroduces 'opportunity markets' where prices remain hidden from everyone except the sponsor during an opportunity window. Designed to let institutions discover high-probability opportunities (talent scouting, research commercialization) before competitors can access the signal, solving the public goods problem of price discovery.
Concepts: decision markets, opportunity markets, liquidity provisionEducational thread on the game-theoretic foundations of prediction markets. Explains why truth-telling is the dominant strategy through incentive compatibility, details how LMSR works as a proper scoring rule, and argues prediction market builders need economists and game theory experts on their teams.
Concepts: LMSR (logarithmic market scoring rule), incentive compatibility, proper scoring rules, information aggregationComprehensive technical typology covering how prediction markets differ in outcome resolution (oracle-based vs self-resolving), trading mechanics, and design tradeoffs. Draws from established platforms and emerging ones to help users and developers navigate the ecosystem's diversity and associated risks.
Concepts: oracle design, resolution criteriaIntroduction to a series arguing prediction market mass adoption is threatened by structural barriers to market maker participation. Claims binary markets are frequently unhedgeable and suffer from adverse selection, making them a professional market maker's nightmare compared to options or crypto.
Concepts: liquidity provision, market making, adverse selectionEducational deep-dive on adverse selection and market making fundamentals. Uses the classic Jane Street coffee interview question to illustrate why conditional on someone trading with you, you should be less confident your trade was good. Explains how market makers profit from retail flow while avoiding toxic informed counterparties.
Concepts: adverse selection, market making, toxic flowApplies adverse selection concepts specifically to prediction markets. Compares market making difficulty across Indian options (easy), crypto (medium), and prediction markets (legendary). Argues gap risk is effectively worse than any other asset class because informed counterparties can have near-perfect information and take out entire order books.
Concepts: adverse selection, gap risk, market making, toxic flowArgues Polymarket and Kalshi have achieved product-market fit but remain stuck at a local maxima. Identifies three barriers: insufficient liquidity (small trades can materially reprice markets), lack of competitive parity with sportsbooks on parlays, and inability to resolve complex outcomes like the Time Person of the Year market resolving to 'other'.
Concepts: liquidity provision, parlays, oracle designAcademic survey of prediction mechanism design from a mechanism design perspective. Covers scoring rules, market scoring rules (LMSR), cost-function-based market makers, dynamic parimutuel markets, incentive compatibility, combinatorial markets, and peer prediction systems for subjective events where ground truth doesn't exist.
Concepts: LMSR (logarithmic market scoring rule), proper scoring rules, information aggregation, peer predictionComprehensive 57-page guide covering prediction market fundamentals, tech stack (blockchain, collateral, market engines, oracles), current state (Polymarket vs Kalshi regulatory and product divergence), emerging builders across market engines and consumer apps, and open questions including oracle collusion, long-dated capital costs, and leverage.
Concepts: information aggregation, oracle design, liquidity provision, platform competitionArgues prediction markets should be embedded in chat interfaces rather than standalone apps. Points to Base App, World Chat, and XMTP as examples of chat-native prediction experiences. Claims the competitive advantage belongs to whoever controls both the chat interface and the AI coaching intelligence.
Concepts: network effectsArgues prediction markets' next phase involves Impact Markets (pricing assets conditional on events, e.g., 'BTC price if Fed cuts 75bp') and Decision Markets (using conditional valuations to automate governance). Claims Impact Markets enable true economic hedging by collapsing multi-step inference into direct price discovery.
Concepts: decision markets, futarchy, impact markets, conditional tokensProposes using AI as the linchpin to scale prediction markets to billions of users. Envisions an AI quartet: content creators (generating markets), event recommenders (personalization), liquidity allocators, and information aggregators. Argues this enables prediction markets at microscopic scale, making them personally relevant.
Concepts: AI agents, liquidity provisionComprehensive podcast covering prediction market fundamentals: information aggregation via Hayek's price signals, thick vs thin markets, when markets work (elections, scientific replication) and when they struggle, the oracle problem, and applications to corporate forecasting and futarchy governance.
Concepts: information aggregation, wisdom of crowds, oracle designFirst-person account from a seven-figure prediction market trader. Covers strategy as a 'bond mule' (locking capital for small premiums on near-resolution markets), OSINT information sources (non-English media, Telegram), fractional Kelly sizing, and trading personality-driven markets. Notes edge has compressed as space matured.
Concepts: market making, Kelly criterion, adverse selectionArgues price prediction markets (short-term expiries like 'will BTC close above $100k?') represent a new asset class. Compares AMM vs CLOB mechanics, notes Limitless achieves 50-400bps spreads (better than onchain options at 1000+bps). Outlines how prediction markets enable synthetic covered calls, structured hedging, and volatility expression.
Concepts: liquidity provision, order book, market makingProposes a unified stochastic kernel (logit jump-diffusion) for prediction markets analogous to Black-Scholes for options. Treats traded probability as a risk-neutral martingale, exposing belief volatility, jump intensity, and correlation as quotable risk factors. Defines derivative instruments (variance swaps, correlation swaps, first-passage notes) for hedging belief risk.
Concepts: LMSR (logarithmic market scoring rule), market makingExplains the SKC (Srinivasan, Karger, Chen) mechanism for prediction markets on unverifiable outcomes. Markets resolve using crowd consensus as the outcome, with delta-based scoring rewarding participants for moving markets toward final consensus. Enables markets for subjective questions lacking ground truth.
Concepts: self-resolving markets, peer prediction, incentive compatibility, proper scoring rulesProposes Meta Pool, a cross-chain infrastructure to unify prediction market liquidity across fragmented platforms. Introduces resolution-aware meta-pools for swapping semantically similar tokens with different oracles, CredibilityTokens for trading oracle trustworthiness, and ConvergenceTokens for hedging divergence risk. Estimates $3.4-8.5M in annual efficiency losses from current fragmentation.
Concepts: liquidity provision, oracle design, arbitrage, conditional tokensExamines Trendle, a perpetual attention market that treats social engagement as a tradable index. Covers the two-layer architecture (attention index + market layer), anti-gaming mechanisms (normalization, deseasonalization, quantile clipping), and funding rates that penalize crowded positions. Frames attention as 'the only scarce resource in the digital age.'
Concepts: attention markets, info financeTaxonomy categorizing prediction markets by susceptibility to three manipulation vectors: information asymmetry (insiders know outcome), reflexivity (market signals influence outcome), and social coercion (participants can directly cause outcome). Argues a market's manipulation profile determines whether you're trading on information edge, narrative momentum, or ability to cause the outcome.
Concepts: reflexivity, information asymmetryComprehensive taxonomy of 14 prediction market mechanism types beyond standard binary markets. Covers bonding curve markets, opinion markets (beauty contests), opportunity markets (private prices), hyperstition markets (self-fulfilling coordination), futarchy (MetaDAO), perpetual markets, quantum markets (capital-efficient parallel conditionals), and no-loss PMs. Each design optimizes for different goals: accuracy, speed, coordination, or outcome manifestation.
Concepts: futarchy, decision markets, parlays, hyperstition markets, no-loss prediction marketsManifesto arguing binary yes/no prediction markets are incomplete—they flatten nuanced beliefs into coin flips and pay the same whether you were barely right or sharply right. Proposes distribution-native markets that reward precision: pay more for being closer to the actual outcome. Cites 130x volume growth from early 2024 to late 2025 as the category's credibility moment.
Concepts: forecasting accuracy, price discovery, information aggregation, distribution marketsArgues binary event contracts fragment liquidity and flatten beliefs into 1-bit structures—achieving 8-bit resolution requires 256 separate markets. Proposes treating beliefs as vectors over probability distributions on a shared liquidity surface. Traders express full distributions and are rewarded for variance compression (reducing entropy), not just final outcome correctness.
Concepts: information aggregation, liquidity provision, price discovery, distribution marketsArgues prediction markets treat reflexivity as a bug, but hyperstition markets weaponize it as a feature. Where prediction markets ask 'what will happen?', hyperstition markets ask 'what can we make happen?' Positions this as futarchy with execution built in—betting YES means coordinating action toward manifestation. The market discovers the price of coordination through dynamic subsidies.
Concepts: reflexivity, futarchy, decision markets, hyperstition marketsGalaxy research report on prediction markets evolving from niche speculation to mainstream financial infrastructure. Covers Polymarket ($9B valuation, 1.6M users) and Kalshi (top finance iOS app), emerging leverage mechanisms (Space, Gondor), AI as interface layer for fragmented venues, and convergence toward derivatives—event contracts as hedges, collateral, and composable financial primitives.
Concepts: AI agents, decision markets, liquidity provision, platform competitionArgues the hardest PM problem isn't pricing but deciding what actually happened. Proposes cryptographically-committed LLMs as resolution judges—trading human bias and conflicts of interest for more tractable technical vulnerabilities. Cites Polymarket disputes (Venezuela election, Ukraine map, government shutdown) as evidence current systems fail at scale.
Concepts: oracle design, dispute resolution, AI agents, resolution criteriaExamines mispricing inefficiencies on Polymarket, identifying two categories of arbitrage opportunities: those within single markets and those spanning multiple related markets. Using blockchain transaction analysis, the researchers estimate approximately $40 million in profits were extracted through exploitation of these pricing inconsistencies.
Concepts: arbitrage, cross-platform arbitrage, price discoveryLarge-scale field experiment testing prediction market manipulation across 817 markets. Randomly shocked prices and tracked effects over 60 days with hourly data. Finds markets can be manipulated with effects persisting for months, though they gradually fade. Markets with more traders, higher volume, and external probability estimates prove more resistant.
Concepts: market manipulation, price discovery, information aggregation, liquidity provisionQuestions whether prediction markets are capturing the right signal. Argues binary yes/no markets flatten complex beliefs into coin flips, losing the precision that separates superforecasters from average predictors. Uses the 2024 French trader whale ($30M moving election odds) and a Vanderbilt study (PredictIt's 93% accuracy vs 67% on high-volume platforms) to argue that more liquidity doesn't mean better signal.
Concepts: distribution markets, superforecasting, calibration, wisdom of crowds, information aggregationSummarizes research analyzing 72 million Kalshi trades. Identifies three persistent biases: longshot bias (5c contracts win only 4.18% of the time), maker-taker asymmetry (makers outperform at 80 of 99 price levels), and YES/NO asymmetry (YES buyers average -1.02% returns vs +0.83% for NO buyers). Finance markets are most efficient (0.17% spread) while crypto is least (2.69%).
Concepts: longshot bias, market making, price discovery, retail flowArgues prediction market builders face a binary choice: compete for venue liquidity against entrenched incumbents (Polymarket, Kalshi) or build decision-support tools for power users. Claims the most valuable layer is not the marketplace but analytics and tooling that helps traders surface mispriced probabilities, model correlated outcomes, and improve conviction sizing.
Concepts: network effects, platform competitionBuilds a formal framework to decompose why prediction markets have late volume: is it because information arrives late (hazard), or because early entry is punished by adverse selection (toxicity)? Introduces LOX, a metric computed from on-chain trades that measures whether new entrants hesitate more than volume alone would predict. Explains why boxing markets cluster with news markets despite being categorized as sports.
Concepts: adverse selection, liquidity provision, market making, information aggregation, LOX (log-odds excess lateness)Argues prediction markets are becoming a legitimate asset class with potentially the largest TAM, since anything with uncertainty and future resolution is tradable. Notes the category has reached an inflection point with professionals entering, but lacks proper tooling that mature asset classes have (Bloomberg for stocks, Axiom for memecoins). Makes the case for dedicated prediction market terminals.
Concepts: information aggregation, platform competitionCatalogs eight distinct arbitrage strategies available on prediction markets: classic YES+NO mispricing, cross-platform, range, conditional, time, hedged, resolution, and orderflow arbitrage. Each type includes concrete examples with dollar amounts and specific risk factors to watch for.
Concepts: time arbitrage, orderflow arbitrage, cross-platform arbitrage, arbitrage, resolution criteriaProvides a quantitative framework for distinguishing gambling from systematic trading on prediction markets, including a five-point diagnostic and three trader archetypes classified by profitability. Explains why Polymarket's CLOB creates renewable structural arbitrage by design, and covers Kelly position sizing, adverse selection measurement via fill quality, and probability term structure as tools for building a repeatable edge.
Concepts: Kelly criterion, arbitrage, adverse selection, forecasting accuracy, order bookCompares Kalshi and Polymarket's NFL game markets during the 2025 season. Finds Kalshi reprices faster (median 7-second lead) while Polymarket has deeper liquidity requiring 3-4x more volume to move prices comparably. Uses Kyle-style market impact analysis to quantify the price discovery vs. liquidity depth tradeoff between centralized and on-chain order book architectures.
Concepts: price discovery, liquidity provision, order book, market making, platform competitionBottoms-up TAM analysis arguing prediction markets can reach $85-200B annual volume by 2028 through sports betting capture (5-20%), event-driven financial hedging, and emerging categories. Covers five infrastructure challenges that must be solved: liquidity sustainability (subsidized MM transitioning to self-sustaining), discovery/UX, trade expressiveness (leverage faces gap risk unique to binary markets), permissionless market creation, and multi-tier oracle resolution. Identifies 2026 World Cup and midterms as critical stress tests.
Concepts: platform competition, liquidity provision, parlays, gap risk, oracle designArgues prediction markets are evolving a second layer analogous to derivatives built on stock exchanges. Covers three hedging use cases: crypto risk hedging via binary price markets, attention markets (Trendle) as sentiment hedges against binary positions, and cross-platform hedging enabled by DeFi composability (Gondor lending against PM positions, DFlow tokenizing Kalshi contracts as SPL tokens). Identifies liquidity fragmentation, execution risk, and UX as barriers to mainstream hedging adoption.
Concepts: hedging, attention markets, conditional tokens, cross-platform arbitrage, parlaysSix-month empirical analysis of political prediction market quality across Kalshi and Polymarket. Finds only 1.3% of political markets are liquid enough to be manipulation-resistant, bid-ask spreads exceed 20% on most contracts, and only 53% of resolved US elections appeared on both platforms. Proposes a four-part blueprint: stock relevant questions, cross-subsidize political liquidity from sports profits, deploy AI market makers where human interest is insufficient, and standardize contract definitions across platforms.
Concepts: liquidity provision, forecasting accuracy, platform competition, AI agents, hedgingArgues prediction markets are the natural marketplace for sovereign AI agents to trade their core commodity: information. Frames decentralized PMs as the 'bazaar' where agents monetize alpha through positions, market creators earn fees from surfacing unanswered questions, and reproducible computation enables incorruptible AI judges for dispute resolution. Positions this as an alternative to centralized AI lab alignment—market incentives align agents through financial participation rather than top-down instruction.
Concepts: AI agents, info finance, information aggregation, oracle designExamines a $10 million Polymarket dispute over whether events in Venezuela constitute an 'invasion,' exposing how semantic ambiguity in market resolution criteria can create massive financial consequences. Raises fundamental questions about who controls truth determination when contract language is open to interpretation.
Concepts: resolution criteria, dispute resolution, oracle design, corruption value multipleArgues that First-Come-First-Served order matching in prediction markets creates perverse incentives: latency wars between traders and defensive spread widening by market makers. Proposes priority batch auctions that process cancellations before maker orders before taker orders, allowing market makers to quote tighter spreads and improving price quality for all participants.
Concepts: batched auctions, market making, bid-ask spread, adverse selection, continuous double auctionFrames prediction markets as crypto's first truly native financial primitive, one that couldn't scale on traditional finance rails due to regulatory chokepoints. Traces the historical pattern where financial innovations move from 'gambling' to infrastructure, and argues that margin and derivatives layers are the missing pieces that will unlock institutional capital. Highlights the unique properties of prediction market positions: time-bounded decay and binary convergence to truth, which create a distinct trading mechanic the author calls temporal arbitrage.
Concepts: NEW: temporal arbitrage, gap risk, regulatory arbitrage, liquidity provisionExamines early results from the Prediction Arena experiment, where six AI models trade real money on Kalshi. Five of six are underwater after three weeks, suggesting that raw information processing isn't enough to generate edge. Argues that successful prediction market traders profit from embodied, local knowledge (monitoring flights, calling embassies) rather than synthesizing public information, a domain where AI remains fundamentally constrained.
Concepts: AI agents, information asymmetry, forecasting accuracy, price discoveryDraws a parallel between prediction markets and Nielsen ratings to argue that coordination value matters more than accuracy. Points to Polymarket's Golden Globes and WSJ partnerships and Kalshi's CNN deal as signs that prediction markets are shifting from external forecasting tools to embedded institutional infrastructure. Once adopted as the shared reference point, displacement becomes nearly impossible regardless of methodological superiority.
Concepts: network effects, platform competition, forecasting accuracy, information aggregationArgues that prediction markets face two structural problems preventing them from becoming transformative economic instruments: corporate hedging is impractical due to market fragmentation and basis risk, and insider trading undermines retail participation. Draws parallels to online poker and memecoins to suggest that without structural reforms, prediction markets will remain primarily a sports betting product.
Concepts: retail flow, adverse selection, information asymmetry, liquidity provision, hedgingCompares the traditional sportsbook house model with prediction market exchanges to explain why exchanges offer better odds. Uses data showing Betfair's ~3% overround versus bookmakers' ~12% to argue that peer-to-peer exchange models produce fairer pricing and welcome all winners, unlike sportsbooks that limit successful bettors.
Concepts: bid-ask spread, market making, order book, price discoveryCritiques Kalshi's 2025 measles cases market as an example of prediction markets being applied to inappropriate domains. Argues that turning a public health crisis into a speculative instrument is ethically questionable and reflects poorly on the industry's judgment about which events deserve tradeable contracts.
Concepts: event contracts, resolution criteriaArgues that prediction markets are financial instruments, not gambling, by examining Polymarket's architecture across multiple layers: peer-to-peer order book mechanics, information aggregation through skin-in-the-game pricing, hedging use cases, and UX design that suppresses gambling patterns. Contrasts the exchange model with the house-edge casino model to argue the gambling label stems from outdated legal frameworks.
Concepts: order book, information aggregation, price discovery, market manipulation, hedgingAnalysis of 149 CPI prediction markets on Kalshi from 2021 to 2026 finds that trading volume explains less than 1% of variance in forecast accuracy, challenging the assumption that more liquidity improves market quality. Introduces Minimum Viable Liquidity (Cost of Expertise divided by Price Gap) as a framework for determining the threshold of liquidity needed to attract informed traders. Argues platforms should prioritize breadth over depth, running many thin markets rather than concentrating volume in few contracts.
Concepts: NEW: minimum viable liquidity, liquidity provision, forecasting accuracy, market making, price discoveryResponds to Kyla Scanlon's New York Times op-ed claiming prediction markets create reflexive loops that alter outcomes. Argues that unlike stock markets, prediction markets lack causal mechanisms through which odds could influence the events they forecast, making them thermometers rather than thermostats. Attributes concerns about market influence to journalism failures in contextualizing odds, not structural flaws in market design.
Concepts: reflexivity, information aggregation, price discoveryFrames prediction markets as a real-time information layer that complements traditional journalism by aggregating probabilistic forecasts from financially-incentivized participants. Argues that skin-in-the-game accountability produces more accurate signals than commentary-based analysis, with price movements often anticipating news before official announcements. Uses Polymarket and Kalshi as examples and acknowledges COVID-19 as a case where markets underperformed.
Concepts: information aggregation, wisdom of crowds, price discovery, forecasting accuracy, incentive compatibilityExamines Clinton and Huang's research on 2024 election market accuracy, finding PredictIt at 93%, Kalshi at 78%, and Polymarket at 67%, while also documenting significant cross-platform price divergences for identical contracts near Election Day. Raises concerns about Kalshi's media partnerships with CNN and CNBC, arguing they create incentives for sensational coverage of market movements and potential manipulation of thin markets.
Concepts: election markets, forecasting accuracy, arbitrage, market manipulation, price discoveryTraces the quantitative finance toolkit from backtesting (Deflated Sharpe Ratio, combinatorial purged cross-validation) through factor models, Black-Litterman portfolio optimization, Bayesian regime detection, and machine learning, then argues each technique transfers directly to prediction markets. The core claim is that prediction markets are the purest testing environment for investment theory because binary resolution eliminates the unobservable noise that obscures strategy quality in traditional finance. Uses LMSR's mathematical identity with the softmax function to bridge quant finance and prediction market pricing.
Concepts: binary contracts, LMSR (logarithmic market scoring rule), Kelly criterion, market making, price discoveryArgues that asset futarchy solves trustless joint ownership by making treasury raids economically irrational: exploiting minority shareholders requires buying their tokens above fair value while simultaneously depressing conditional market prices, making the attack self-defeating by construction. Examines MetaDAO's implementation and Proposal 6, where an attempted governance attack was repelled through this mechanism. Also addresses limitations including soft rug pulls, settlement price complexity, and regulatory constraints around insider trading.
Concepts: futarchy, decision markets, conditional tokens, price discoveryInvestigates combinatorial prediction markets, which extend the standard model to support forecasts on conditional events (e.g., A given B) and Boolean combinations of events rather than only base events. Reports experimental results comparing combinatorial versus flat market structures on forecasting accuracy and calibration. Co-authored by Robin Hanson, whose LMSR underpins most automated prediction markets.
Concepts: NEW: combinatorial prediction markets, market scoring rules, LMSR (logarithmic market scoring rule), proper scoring rules, forecasting accuracyPresents ForecastBench, a benchmark tracking how well LLMs forecast real-world outcomes against superforecasters and crowd forecasters. The best LLM (GPT-4.5) achieves a Brier score of 0.101 versus superforecasters' 0.081, with LLMs improving roughly 0.016 Brier points per year, projecting parity by late 2026. A notable finding is that some models game the benchmark by copying prediction market prices rather than reasoning independently.
Concepts: Brier score, superforecasting, calibration, forecasting accuracyA Reforge co-founder's bearish case against prediction markets, structured as 23 distinct failure modes. Covers structural constraints across capital efficiency, liquidity mechanics, adverse selection, oracle governance, and regulatory fragmentation. Argues that prediction markets face fundamental limitations that perpetual futures markets do not, making institutional scaling unlikely under current designs.
Concepts: adverse selection, market making, liquidity provision, oracle design, reflexivityIdentifies seven axes on which new prediction market entrants can differentiate: product quality, asset variety, capital efficiency, oracle reliability, liquidity provision, regulatory compliance, and vertical vs. horizontal strategy. Draws on parallels with NFT and perps exchange competition to argue that incumbents' product debt creates openings for challengers. Contrasts Polymarket and Kalshi as examples of horizontal and vertical product strategies.
Concepts: platform competition, liquidity provision, oracle design, regulatory arbitrageArgues that insider trading in prediction markets is structurally different from traditional securities markets because prediction markets can make almost anything tradable, often in contexts where relevant confidentiality duties are unclear. Proposes solutions across three layers: platform-level detection and position limits scaled to account size, market design mechanisms like dynamic spread widening and market maker insurance pools, and legal frameworks from updated corporate compliance policies to CFTC guidance.
Concepts: NEW: insider trading, adverse selection, market making, liquidity provisionAnnual letter arguing that retail financial speculation has permanently shifted from investment to entertainment, driven by smartphone access and zero-commission trading. Documents the collapse of holding periods, zero-day options comprising 59% of options volume in 2025, and the proliferation of insider trading cases across sports and corporate prediction markets as event markets expand to cover everything. Frames this as a structural shift rather than a cyclical bubble.
Concepts: retail flow, info finance, platform competitionLegal analysis explaining that insider trading in prediction markets is governed by existing fraud law rather than a distinct insider trading statute. The key question is whether a trader has deceptively breached an implied or explicit promise about how confidential information may be used. Argues prediction markets complicate this analysis by expanding tradable events into contexts where no clear company-based duty exists, making insider trading liability increasingly difficult to determine.
Concepts: insider trading, adverse selection, information aggregationFT Alphaville piece documenting the practice of 'bonding' in prediction markets: treating near-certain outcomes as synthetic zero-coupon bonds to earn a small but near-guaranteed yield. Uses the Polymarket 'Will Jesus Christ return' market as the central example, where betting 'No' at 96% odds implies a 4.76% yield to maturity. Notes that bonding trades face two key constraints: illiquidity (only $150k executable at one time) and catastrophic tail risk if the near-impossible outcome occurs.
Concepts: NEW: bonding trades, gap risk, liquidity provisionArgues that prediction markets will replace traditional advertising by converting ad spend into liquidity that rewards deep attention rather than buying fleeting impressions. The proposed model: a sponsor seeds a market with $50k–$500k, traders discover it and research the topic to profit, then share analysis organically — creating sustained cognitive engagement at ~$20 per person-hour versus seconds of passive exposure from display ads. Cites Polymarket's $9B election volume and Substack partnership as early evidence, and frames the sponsor's outlay as venture capital for an attention engine rather than a media buy.
Concepts: network effects, liquidity provision, platform competitionArgues that nearly any prediction market tied to a public figure's actions, tenure, or appearances implicitly embeds assassination as a resolution path — what the authors call 'kinetic intervention.' Uses the Charlie Kirk assassination and subsequent Kalshi market voiding as the central case study. Warns that blanket void-on-death rules can backfire by incentivizing violence from losing bettors, and proposes that platforms hire geopolitical risk officers to evaluate resolution wording, monitor anomalous betting signals (BETINT), and build early-warning capacity before tragedy occurs.
Concepts: market manipulation, oracle design, incentive compatibilityTraces the history of prediction markets from 1419 Vatican papal elections through the Iowa Electronic Markets to the Polymarket era, arguing the sector is at an inflection point. Surveys the insider trading scandals (Musk tweets, French elections), moral hazard concerns (assassination markets), and the wave of new entrants (Robinhood, DraftKings, Crypto.com, FanDuel) that signal mainstream adoption. Concludes that prediction markets are evolving, not decaying, but need regulatory clarity and structural reform to mature.
Concepts: insider trading, market manipulation, platform competition, election markets, info financeWritten during the US-Israel strikes on Iran, examines whether prediction markets on armed conflicts are net informational goods or perverse incentive engines. Dissects the IDF insider trading case where soldiers traded Polymarket positions before strikes, the CFTC's regulatory stance, and the divergent approaches of Kalshi (regulated, avoids conflict markets) versus Polymarket (offshore, lists them freely). Argues the information value is real but the moral hazard is structurally underpriced, and proposes guardrails including delayed settlement and conflict-of-interest screens.
Concepts: insider trading, incentive compatibility, market manipulation, oracle design, decision marketsData-driven deep dive into Polymarket's order book structure using 600M+ raw datapoints filtered to a 343M research dataset. Categorizes order flow into soft (retail), hard (professional), and AI flow, revealing that Polymarket's liquidity is episodic and attention-driven: the p95 peak hour shows hundreds of millions in open interest while the p50 median is thin. Order book analysis shows surface symmetry at top-of-book but systematic ask-side skew at deeper levels, and market impact data confirms that medium-to-large orders hit liquidity cliffs. Argues the core problem is trapped capital — dollars reserved multiple times against mutually exclusive outcomes — and that better netting and capital efficiency, not more money, is the fix.
Concepts: order book, liquidity provision, market making, adverse selection, AI agentsIdentifies five MEV-style edges on Polymarket that most retail traders are unaware of: oracle latency arbitrage (trading on news before UMA oracle updates), resolution arbitrage (front-running outcome settlement), dispute sniping (gaming the UMA dispute process), orderbook imbalance exploitation, and conditional probability arbitrage across correlated markets. Frames Polymarket as a 'hidden MEV playground' where sophisticated actors extract value from structural inefficiencies rather than informational edges.
Concepts: orderflow arbitrage, oracle design, time arbitrage, order book, cross-platform arbitrageReviews Philip Tetlock's Superforecasting and draws a direct line from the book's core thesis — that forecasting skill is measurable, trainable, and outperforms expert punditry — to Polymarket's success during the 2024 US election. Explains Tetlock's key concepts (foxes vs hedgehogs, the Good Judgment Project, Brier scores, calibration) and argues that Polymarket effectively operationalized Tetlock's framework at scale by converting crowd forecasting into a liquid financial market.
Concepts: superforecasting, wisdom of crowds, calibration, forecasting accuracy, information aggregationComprehensive research report on AI agents for prediction markets, proposing a four-layer architecture (data, analysis, execution, learning) for autonomous trading systems. Maps the ecosystem of existing agents, compares Kelly criterion vs fixed-fraction bet sizing, surveys arbitrage strategies across platforms, and outlines business models (agent-as-a-service, liquidity mining, data sales). Argues that AI agents will become the dominant market participants within two years, transforming prediction markets from retail-driven speculation into infrastructure for probabilistic information.
Concepts: AI agents, Kelly criterion, arbitrage, liquidity provision, market makingData-driven analysis of Kalshi's business model using all 203 million trades across $41.7B in volume. Reveals that Kalshi functions more like a poker rake than a sportsbook, charging fees via the formula fee = 0.07 × C × P × (1-P), which incentivizes trading near 50% probability. Key finding: sports comprise 82% of total volume, making Kalshi functionally a sports betting platform despite its CFTC-regulated derivatives positioning. Includes clear explanations of order book mechanics, binary contract pricing, and the regulatory framework (clearinghouse structure, no-action letters) alongside original data visualizations of volume distribution and resolution patterns.
Concepts: order book, binary contracts, platform competition, bid-ask spreadChallenges the smart-versus-dumb money dichotomy in prediction markets by synthesizing research from Snowberg/Wolfers/Zitzewitz, INSEAD's BIN model, and Wharton's cognitive search framework. Argues that noisy traders fund the probability space rather than serve as exit liquidity, and compares how binary CLOBs versus continuous probability markets decompose and harness noise differently.
Concepts: NEW: noise decomposition, adverse selection, liquidity provision, calibration, market makingWalks through UMA's Optimistic Oracle pipeline as used by Polymarket: assertion, liveness period, dispute, and DVM token-holder voting. Uses the $240M Zelenskyy suit market as a case study where semantic ambiguity ('Is a militarized black outfit a suit?') triggered a disputed resolution ultimately decided by whale token holders, illustrating how the system's 'decentralized courtroom' handles edge cases.
Concepts: oracle design, UMA protocol, dispute resolution, resolution criteriaUses the case of a trader sniping Polymarket's geopolitical strike markets at 10 cents to argue that continuous order books are structurally broken for binary assets. In traditional markets, sniping costs basis points; in prediction markets, it costs 80 cents on the dollar because prices jump from 0.10 to 0.99 on a single tweet. Proposes frequent batch auctions (citing Budish, Cramton, and Shim) to shift competition from speed to price accuracy, and introduces the concept of a 'liquidity mirage' where the highest social-value markets are precisely those where passive liquidity is unsustainable.
Concepts: toxic flow, batched auctions, gap risk, market making, adverse selectionSets out to defend insider trading in prediction markets but arrives at a more conditional position. Introduces a 'discovery vs betrayal' framework: in distributed-truth markets like elections, informed traders sharpen the signal because no one holds the full answer; in concentrated-truth markets like earnings, insiders monetize sealed results rather than synthesize public fragments. Argues the real question is not whether insiders should be allowed but what kind of informational asymmetry a market can absorb without losing the participation and trust that make the signal useful.
Concepts: insider trading, information asymmetry, adverse selection, information aggregationWritten by a former CFTC General Counsel, argues that courts in sports event contract litigation are overlooking the strongest basis for CFTC jurisdiction: the Commodity Exchange Act's 'commonly known to the trade' catchall, which classifies any transaction the derivatives industry calls a swap as one. Since every exchange, broker, and clearinghouse involved treats sports event contracts as swaps, the test resolves the federal preemption question cleanly while preserving state authority over off-exchange sports betting.
Concepts: NEW: federal preemption, event contracts, regulatory arbitrageArgues that leverage solves, rather than amplifies, prediction market problems. In a 1x market, only whales can move prices because the barrier is capital, not insight; at 10x, thousands of smaller traders can collectively contest a mispricing. Addresses gap risk (binary resolution makes platform-native margin dangerous) by describing a temporal arbitrage approach where leveraged markets close before event resolution. Also proposes a vault-based yield layer where LPs earn returns from trading activity rather than directional outcome exposure.
Concepts: gap risk, market manipulation, liquidity provision, temporal arbitrageAnalyzes 600 million Polymarket orderbook datapoints, finding ~70% of one-cent price moves do not continue in the same direction. Coins 'semantic tick size' to describe how a prediction market's minimum price increment doubles as a narrative unit—each penny reads as a one-percentage-point probability change, creating overreactions that a contrarian fade strategy can profitably harvest. Frames this against Tetlock's TradeSports microstructure research, where passive limit order walls slow price discovery while impatient market orders amplify short-term noise.
Concepts: NEW: semantic tick size, bid-ask spread, price discoveryAsks whether large language models can outperform prediction market consensus prices and argues the more tractable framing is using LLMs as updaters rather than predictors. Distinguishes cold prediction (generating a probability estimate without prior context) from updating (revising an existing estimate as new information arrives), and considers what each role implies for AI tools deployed alongside human traders in live markets.
Concepts: AI agents, forecasting accuracy, information aggregationArgues that prediction market TAM should include the supply side: as the cost of producing real-time probability estimates collapses, the addressable market extends beyond trading volume to every decision that benefits from better forecasts. Presents an ordered liquidity formation path from entertainment to information to institutional demand, and contends that scaling to $1T requires massive breadth in long-tail markets rather than concentrated depth in a few high-volume categories.
Concepts: NEW: long-tail markets, minimum viable liquidity, information aggregation, network effects, platform competitionUses the March 2026 Strait of Hormuz crisis to argue that binary order-book prediction markets hit an architectural ceiling when pricing granular, multi-outcome risk. Compares how traditional options solve this for tradable assets, then explains how automated market scoring rules (LMSR/CLMSR) offer protocol-native liquidity, coherent pricing, and capital efficiency for events without underlying assets. Walks through a concrete WTI crude oil scenario showing how scoring-rule markets reward precise thesis expression over simple directional bets.
Concepts: NEW: multi-outcome markets, market scoring rules, liquidity provision, order book, binary contractsFrames prediction market prices as public goods whose benefits are non-excludable but whose liquidity costs fall on a narrow trader base. Proposes cross-subsidization as the growth mechanism: profitable markets fund socially valuable ones that can't sustain themselves, the same way newspaper ads funded investigative journalism. Also argues that accuracy isn't the only axis of value, showing how markets can serve risk transfer (hedging hurricane or policy exposure) and information accountability functions even when prices drift from pure probability.
Concepts: liquidity provision, adverse selection, hedgingIntroduces implied correlation as a trading opportunity in prediction markets. Polymarket currently prices a 60%+ chance of 5% unemployment this year alongside only a 10% chance of aggressive Fed rate cuts, even though every historical episode of that unemployment spike triggered an average of seven cuts. Argues that because prediction markets are still priced idiosyncratically, cross-market mispricings like this are common and create attractive relative value trades.
Concepts: NEW: implied correlation, arbitrageSurveys the landscape of teams trying to add leverage to prediction markets and explains why most are converging on 1x to 1.5x rather than the 10x or 20x they advertise. The core problem is gap risk: binary outcomes resolve instantly, skipping the intermediate prices that liquidation engines need to function. Uses dYdX's TRUMPWIN perp on election night 2024 as a case study where sophisticated safeguards still broke under real conditions, then categorizes current approaches into three camps: constrain leverage, engineer around it with dynamic fees and circuit breakers, or ship and iterate.
Concepts: gap risk, binary contracts, liquidity provision, adverse selectionAnalyzes tick-level order flow across Polymarket and Kalshi to decompose market bias by trader type. Finds that the classic favorite-longshot bias may be a statistical artifact masking a pervasive "yes bias" driven by temporal volatility and incomplete controls for contract lifecycle. Also shows that whales are not the sharpest participants: heavily capitalized traders systematically bleed expected value to small-order traders, likely driven by ideological conviction rather than informational edge.
Concepts: NEW: yes bias, longshot bias, adverse selection, retail flowScreens 93,000 Polymarket markets and flags traders with a 69.9% win rate, more than 60 standard deviations above chance, estimating $143 million in anomalous profits. Documents specific cases from geopolitical events to celebrity announcements where wallets appear to trade on material non-public information. Proposes a regulatory framework combining platform-level registration, contract-level restrictions on high-risk categories, and an extended misappropriation doctrine to close the legal gaps that leave prediction market insider trading largely unpoliced.
Concepts: NEW: market surveillance, insider trading, information asymmetry, adverse selection, regulatory arbitrageAnalyzes 7,292 resolved Polymarket markets and 28,793 on-chain trades to test whether yes bias exists in trade data. Finds that traders buy whichever token is cheaper, not whichever is labeled YES, and that the apparent bias is a compound effect of longshot preference channeled through Polymarket's "Will X happen?" question framing, which systematically assigns the longshot to the YES token.
Concepts: yes bias, longshot bias, retail flowProposes a DeFi primitive for borrowing against prediction market positions, arguing that collateralization solves the capital lock-up problem in long-dated markets. Walks through how position lending could improve liquidity, correct persistent mispricings like longshot mispricing, and open composability with the broader financial ecosystem, while flagging the liquidation risks unique to binary outcomes.
Concepts: NEW: position collateralization, liquidity provision, arbitrage, longshot biasCategorizes every active Polymarket wallet across trade frequency and volume tiers, producing a seven-persona map of platform participants. Finds that 2% of users (high-frequency, high-capital wallets) generate roughly 90% of all platform volume, with crypto markets dominated by algorithmic execution and politics markets driven by casual event-driven participants. Draws implications for fee design, category expansion, and why optimizing for user growth versus volume growth requires fundamentally different product decisions.
Concepts: retail flow, market making, platform competitionAnalyzes 72.1 million trades ($18.26B volume) on Kalshi and documents a systematic wealth transfer from takers to makers averaging 1.12% excess returns on each side. Takers disproportionately buy YES longshots, accepting returns 64 percentage points lower than equivalent NO positions. Shows this transfer only emerged after Kalshi's October 2024 legal victory attracted professional algorithmic market makers, and that market efficiency varies sharply by category: finance markets are near-efficient while entertainment and media markets show gaps exceeding 7 percentage points.
Concepts: longshot bias, yes bias, market making, retail flow, adverse selectionFits a Bayesian hierarchical model to 292 million trades across 327,000 contracts on Kalshi and Polymarket to decompose calibration errors into structured components: universal horizon effects, domain-specific biases, and trade-size scale effects, which together explain 87.3% of variance on Kalshi. Finds persistent underconfidence in political markets where prices compress toward 50%, and shows that large trades amplify this pattern on Kalshi but not on Polymarket, pointing to platform-specific microstructure differences.
Concepts: calibration, longshot bias, election markets, noise decompositionProposes a tiered framework for evaluating prediction market reliability, ranking financialized economic indicators highest and speculative prop bets lowest. Outlines three practical use cases: triangulating against traditional polls, nowcasting delayed economic data in real time, and hedging event risk. Draws on a Federal Reserve paper validating Kalshi's data quality and Tetlock's forecasting research to ground the argument.
Concepts: NEW: nowcasting, NEW: endogeneity, wisdom of crowds, information aggregation, insider tradingArgues that prediction markets are a proof of concept for a broader shift: probability as infrastructure. Proposes three 'probability layers' beyond trading: attention markets that price content virality forward, credibility markets that turn trust into a continuously updated score, and demand markets that capture consumer intent before production. Frames the endgame as probability signals embedded invisibly into every decision surface on the internet.
Concepts: NEW: probability infrastructure, NEW: credibility markets, NEW: demand markets, info finance, information aggregationCongressional Research Service legal sidebar analyzing whether and how insider trading law applies to prediction markets. Walks through SEC Rule 10b-5, CFTC Rule 180.1, the STOCK Act, and Title 18 criminal statutes, then examines the CFTC's February 2026 advisory on two Kalshi enforcement actions. Identifies the core gap: existing law requires breach of a duty, but many prediction market insiders (e.g., a political candidate betting on his own race) may not owe one. Surveys four pending bills in the 119th Congress that would close this gap in different ways.
Concepts: insider trading, event contracts, federal preemption, market surveillanceDocuments a pattern of insider trading on prediction markets, from wallets that profited $1.2 million on the timing of US strikes on Iran to trades linked to classified intelligence. Compares how Kalshi's KYC-based surveillance and Polymarket's pseudonymous blockchain create different enforcement challenges. Argues platforms should reconsider contract offerings before regulators act.
Concepts: insider trading, market surveillance, information asymmetry, market manipulationTraces how insider trades on Polymarket before Trump's March 2026 Iran announcement may have leaked into regulated oil and stock futures markets. Proposes that quant funds extracted the informational signal from pseudonymous crypto trades and acted on it in KYC-regulated venues, all without breaking existing laws. Highlights a structural gap where information flows freely across platforms even when regulatory frameworks differ.
Concepts: insider trading, information asymmetry, regulatory arbitrage, price discoveryExamines how to distinguish wash trading from legitimate market making on Polymarket using network analysis. Wash traders exhibit homophily, trading only within their collusive group, while market makers trade indiscriminately with diverse counterparties. Describes an algorithm developed by Columbia researchers that identified a cluster of 200 wallets generating $113 million in volume with just $57.86 in aggregate losses.
Concepts: NEW: wash trading, market surveillance, market manipulation, market makingEuropean regulators face a classification problem: prediction market contracts could be gambling, MiFID II derivatives, or something else entirely, and different Member States treat them differently. Proposes a structured 'Prediction Test' modeled on Malta's Financial Instrument Test for crypto-assets, which would systematically categorize contracts through exclusion to determine which regulatory regime applies.
Concepts: NEW: regulatory classification, regulatory arbitrage, event contractsAnalyzes 36,777 Polymarket events to understand what happens when continuous questions are split into dozens of independent binary contracts. Volume follows an extreme Pareto distribution: the top 3 markets capture over 75% of trading activity regardless of event size, leaving a large fraction as untradeable ghost markets. The $0.01 tick size compounds the problem, creating a rounding tax that makes low-probability contracts structurally imprecise.
Concepts: NEW: liquidity fragmentation, binary contracts, multi-outcome markets, semantic tick sizeWall Street equity research analysis of anonymized trading data comparing prediction market and sports betting returns. The median prediction market user has an ROI of -8%, worse than sports bettors at -5%, with only traders above $500K in volume achieving positive returns (+2.6%). Finds that prediction markets attract sharper competition than regulated sportsbooks, creating worse outcomes for casual retail participants.
Concepts: platform competition, retail flow, adverse selection, market makingDecomposes 222 million Polymarket trades into directional and execution components and finds that forecasting accuracy does not predict profitability. Traders who pick the right side still lose money because they arrive late and pay unfavorable prices, while automated traders with near-random directional skill profit by paying 2.52 cents less per contract.
Concepts: NEW: execution quality, retail flow, adverse selection, bid-ask spread, market makingTraces the parimutuel betting model from its 1867 origins in French horse racing to modern prediction markets. Argues that parimutuel pools solve the cold start problem by bootstrapping liquidity without market makers, but identifies three friction points that need upgrading: locked positions, timing constraints, and price readability.
Concepts: NEW: parimutuel markets, liquidity provision, market making, platform competitionIndustry analysis mapping $63.5 billion in 2025 prediction market volume and a $200 billion+ 2026 run rate. Identifies a structural tension: sports drive current revenue (83% of Kalshi volume), but valuations price in an information infrastructure future that hasn't arrived yet. Argues distribution platforms like Robinhood and Coinbase will capture most value as they vertically integrate into exchange infrastructure.
Concepts: platform competition, regulatory arbitrage, info finance, network effects, information aggregationBreaks down the legal fight over whether sports event contracts are "swaps" under the Commodity Exchange Act, which would give the CFTC exclusive jurisdiction and preempt state gambling laws. Courts are split across 19 pending federal lawsuits, with the Third Circuit ruling in Kalshi's favor. The case hinges on how broadly to read two phrases in Dodd-Frank's swap definition and will likely reach the Supreme Court within two years.
Concepts: federal preemption, event contracts, regulatory arbitrageUses Kalshi's "mentions markets" (contracts that pay off if a specific word is said at a press conference) to illustrate a structural problem: prediction markets require crisp binary boundaries, but reality rarely provides them. Disputes over whether Cardi B "performed" at the Super Bowl, whether Zelenskiy "wore a suit," and what counts as a "word" show that platforms need linguists and philosophers as much as traders.
Concepts: resolution criteria, event contracts, oracle designQuantifies when prediction markets become structurally cheaper than derivatives for pricing binary institutional risk. Analyzes 87 contracts across 11 categories and finds that high-VRP categories like Bitcoin (4.83%) and elections already cross the displacement threshold, while FOMC markets compressed from a 12 percentage point cost gap to under 2 points between 2024 and 2026. Frames the cost differential as "apparatus rent" paid for constructed dealer infrastructure that event contracts can bypass.
Concepts: event contracts, hedging, bid-ask spreadLandscape report comparing four models for adding leverage to prediction markets: lending pools (Gondor/Morpho-style), prime brokers (Ultramarkets), synthetic desks (CFD counterparties), and perpetual futures (dYdX TRUMPWIN). Sizes the fee revenue opportunity at $15M base case to $50.7M bull case, with 87% driven by financing revenue on open interest rather than trading fees. All four models share a structural dependency on CLOB venue architecture that degrades during jump events.
Concepts: position collateralization, gap risk, adverse selection, market makingApplies the sportsbook industry's square vs. sharp distinction to prediction markets. Argues Kalshi (3.5% take fee) and Polymarket (1.5%+) are building square prediction markets that monetize price-insensitive retail takers, while sharp prediction markets focused on trading efficiency will thrive outside the US. Notes that raising fees from 0.5% to 0.75% on 4casters had no material impact on volume, suggesting sports bettors are less price-sensitive than assumed.
Concepts: retail flow, platform competition, adverse selection, market makingBloomberg Businessweek feature on the regulatory and cultural collision as prediction markets blur the boundary between financial trading and sports gambling. Covers how billions in volume have attracted enforcement actions from multiple states, insider trading concerns, and partnerships with CNN, CNBC, and major sports leagues. The central question: are these platforms legitimate financial infrastructure or disguised betting operations?
Concepts: regulatory arbitrage, platform competition, insider tradingMarket sizing report on Brazil as a prediction market opportunity. The country is simultaneously the 5th largest betting market ($4.1B GGR, 25M bettors) and 5th in crypto adoption ($6-8B monthly, 90% stablecoins), creating a $100B+ serviceable market with no domestic platform offering event trading mechanics. Regulatory classification by the CVM as derivatives vs. gambling will determine whether institutional capital can participate.
Concepts: network effects, regulatory arbitrage, platform competitionProposes a workflow for using prediction market probabilities as inputs to equity valuation models. Walks through two case studies: translating Polymarket's 51% tariff refund probability into a 35% effective probability for Logitech's margin impact, and converting a 29.5% FDA approval probability into a $5.4B probability-weighted EV uplift for Eli Lilly. The key insight is that raw market probabilities must be adjusted for contract wording mismatches and economic relevance before they become useful for stock analysis.
Concepts: hedging, info finance, calibrationOn-chain analysis of Polymarket's fast crypto markets from September 2025 through March 2026. Five-minute contracts overtook 15-minute in weeks ($2.3B vs $795M in notional volume), bots control 55-62% of volume across fast markets, and Bitcoin drives 77% of turnover. The piece argues that with $23.7M in taker fees collected in 83 days through a maker-rebate fee model, Polymarket has structurally converged with a derivatives exchange.
Concepts: market making, liquidity provision, toxic flow, retail flow, regulatory classificationHistorical walkthrough of why LMSR-based automated market makers structurally failed for prediction markets. In a binary market that resolves to 0 or 1, impermanent loss becomes permanent: the pool inevitably holds worthless shares on the losing side, and trading fees cannot offset a guaranteed structural loss. The piece traces Polymarket's late-2022 migration from an LMSR AMM to a central limit order book as the moment the industry recognized that prediction market liquidity needs a different mechanism than token swaps.
Concepts: LMSR (logarithmic market scoring rule), liquidity provision, order book, price discoveryBuilder's bullish case for a prediction market supercycle, written from the perspective of the founder behind Kreo (a Polymarket automation product). Argues the space is still early based on the absence of cult-like community formation compared to NFTs or meme-coins, nascent builders programs, and upcoming permissionless markets. Covers the gambling-vs-trading distinction, insider trading as both a problem and a signal source, and Polymarket's pricing edge over traditional sportsbooks on most events.
Concepts: network effects, platform competition, regulatory classification, insider trading, parlaysCritique of the narrative that prediction markets are truth machines. Polymarket's headline Brier score of 0.047 masks category-specific failures like sports markets scoring 0.325 (worse than a coin flip), and 99% of volume concentrates in the final hours before resolution. The author argues prediction markets only work on roughly 2% of listed contracts (binary, high-profile, short-term events with millions at stake), and that when outlets like CNN and WSJ broadcast illiquid market odds as authoritative signal, whale trades on thin books get laundered through credible newsrooms.
Concepts: Brier score, calibration, forecasting accuracy, minimum viable liquidity, wisdom of crowdsa16z's overview of institutional adoption of prediction markets, centered on Kalshi. Outlines a three-stage framework: using markets as data sources, integrating them into compliance workflows, and finally actively hedging risk. Sports hit $3B weekly volume but reached an all-time low as a share of total volume, while entertainment, crypto, and culture categories show stronger retention. The main bottleneck for institutional participation is full notional collateral requirements, which Kalshi is addressing through margin trading licenses.
Concepts: network effects, platform competition, hedging, regulatory classificationTechnical research on adapting the Avellaneda-Stoikov market-making framework for prediction markets. Classical models fail because prediction market prices are bounded probabilities (0 to 1) rather than unbounded asset prices, creating non-constant volatility and guaranteed terminal convergence. After a logit-space transformation lost $1,114 in backtest, XO Labs iterated to a probability-space engine with inventory-skewed spreads, volatility regime detection, and multi-outcome coordination that turned profitable at $453. Includes the full mathematical framework and open problems.
Concepts: market making, adverse selection, bid-ask spread, market scoring rulesData-driven analysis benchmarking Kalshi's sports markets against traditional sportsbooks. Kalshi's monthly volume grew 80x to $14.4B in March 2026, with NCAA March Madness generating $3.3B in notional volume comparable to total US wagered on the tournament. In-game prices correlate at 0.99+ with FanDuel, but Kalshi's taker fees (up to 3.5% at midpoint) and thinner in-game liquidity (76% depth decline vs pre-game) currently limit institutional execution. Includes a valuation comparison showing Kalshi priced as an exchange ($20B) vs sportsbooks trading at 2-4x revenue.
Concepts: platform competition, calibration, execution quality, cross-platform arbitrageResponse to Axios and More Perfect Union coverage framing prediction markets as gambling. Park argues the investing/gambling line is about +EV, not the game itself, and that the flip side of speculation is always insurance. Contends prediction markets are structurally different from other derivatives because they are precise (binary payoffs create clean basis risk to truth) and have finite expiry, and that fears about insider trading are overblown since liquidity in obscure asymmetric markets will be negligible. Closes with a media-criticism argument that prestige outlets attack prediction markets because the markets threaten institutional control over truth.
Concepts: insider trading, information asymmetry, hedging, binary contracts, market makingHistory of U.S. prediction market regulation culminating in the February 2026 jurisdictional standoff between CFTC Chairman Mike Selig and Utah Governor Cox. Park traces the arc from the Iowa Electronic Markets' 1988 no-action letter, through Intrade's 2012 collapse and the binary options fraud era, to Kalshi's court win establishing that 'gaming' does not cover financial contracts on uncertain outcomes. Argues federal preemption under the Commodity Exchange Act will hold against state attorneys general, and that Bitwise's PredictionShares ETF launch marks the point where political event contracts become a mainstream financial product.
Concepts: federal preemption, regulatory classification, election markets, event contracts, information aggregationArgues that central limit order books fixed the capital destruction problem of early Polymarket AMMs but introduced a new pathology: passive participation is impossible, and only professional market makers can quote. Kalshi reportedly has 23 active market makers with the top three providing 70% of election-contract liquidity, meaning any market those firms ignore is dead on arrival. Positions this as the reason prediction markets remain concentrated in politics and sports while entertainment, science, and culture verticals stay empty, and makes the case for peer-to-peer architecture that lets the first participant seed liquidity for the second.
Concepts: order book, long-tail markets, market making, liquidity provision, minimum viable liquidityShort research-agenda framing from Daedalus Research announcing a paper on institutional liquidity in prediction markets. Argues that as institutional market makers shape these venues, the academic conversation must move beyond forecast accuracy to execution quality, market structure, and trader welfare. Useful as a pointer to shifts in how prediction market research is being scoped.
Concepts: execution quality, liquidity provision, retail flow, toxic flowPart 1 of a Ranger Global research series on onchain prediction market microstructure. Walks through why CLOBs beat constant-product AMMs for binary events, the YES/NO minting and merging invariant that lets depth expand whenever matched counterparties exist, and probability-scaled dynamic fees that shrink near 0 and 1. Closes with a regression of prediction market midpoints against BTC spot, finding PM traders systematically underreact to spot moves by 10-20% and that latency under 100ms now captures 73% of arbitrage profits.
Concepts: order book, conditional tokens, market making, price discovery, execution qualitySkeptical counter to the democratizing-finance framing around prediction markets. Cites Polymarket data showing 70% of 1.7 million addresses lost money and that the top 0.04% captured over 70% of the $3.7B in realized profits, and argues the structure predictably funnels retail into informed counterparties (including platform-operated market-making desks at Kalshi and Crypto.com). Concedes prediction markets are genuinely useful as an information layer while arguing they are a poor retail trading product.
Concepts: retail flow, toxic flow, adverse selection, information asymmetry, information aggregationSurvey of recent research on why conventional market making fails in prediction markets. Covers cross-venue fragmentation (the same contract at 58-67 cents on different platforms), the January 2026 Polymarket XRP exploit that paid $231K on thin weekend liquidity, Kalshi's structural longshot bias, and evidence from 150M Polymarket trades that the top 5% skilled traders earned $228M while spread capture barely moves P&L. Concludes passive LPs on these venues behave more like underwriters of terminal risk than classical market makers.
Concepts: liquidity fragmentation, adverse selection, longshot bias, cross-platform arbitrage, market makingReframes prediction markets as consumer-wrapped binary options, drawing on the author's OTC commodity derivatives background. Introduces Minsky's 'vega wedge' as the structural overcharge that binary hedgers pay when they replicate via vanilla options (around 4.8% for BTC binaries, 7-20% for gold), and argues prediction markets can undercut that tax in categories with deep volume. Diagnoses what still keeps institutional capital out: no shared Black-Scholes-equivalent pricing language, missing risk infrastructure, and liquidity that remains retail-dominated.
Concepts: binary contracts, hedging, event contracts, price discovery, information asymmetryCatalog of 14 resolution failures over 18 months at Polymarket and Kalshi affecting over $500M in volume, including the $242M Zelenskyy-suit market, the $120M TikTok ban, and the $47M Cardi B halftime market that resolved yes on Polymarket and no on Kalshi. Groups the failures into four patterns: vague criteria, decentralized oracle capture (UMA token cap below disputed volume), centralized operator discretion, and cross-platform divergence. Useful reference for anyone designing resolution mechanisms or underwriting oracle risk.
Concepts: dispute resolution, resolution criteria, UMA protocol, oracle design, market manipulationLaunch post for sdk.markets, a toolkit for creating parimutuel prediction markets on arbitrary questions, built on Base. Argues CLOB infrastructure does not fit thin community markets and that parimutuel pools are simpler and fairer when there is no natural counterparty. Details design choices that address parimutuel's classic 'wait and see' sniping problem (short answer windows, snapshot locking, DPM-style pricing) and three resolution modes: single admin, multi-admin consensus, and an AI oracle that resolves from arbitrary URLs.
Concepts: parimutuel markets, long-tail markets, oracle design, resolution criteria, self-resolving marketsBeginner-oriented primer from Blockchain at Berkeley covering what prediction markets are, how order books translate bids and asks into probabilities, why they matter for business, media, and policy, and the Polymarket vs Kalshi comparison (offshore crypto-native vs CFTC-regulated; public onchain trades vs private USD activity). Good starting point to share with people new to the category.
Concepts: binary contracts, order book, information aggregation, platform competition, decision marketsTeaser thread for a 50+ page Q1 2026 prediction markets report. Claims the space did more volume in one quarter than all of 2025 combined, and distills 11 insights on scale, concentration, monetization, and the attention the category is drawing from institutions and regulators.
Concepts: platform competition, network effects, regulatory classificationFrames insider trading as a structural feature of prediction markets, not a bug. Walks through the DOJ case against Master Sergeant Gannon Ken Van Dyke, who made $400k on Polymarket trading the Maduro raid, and the prior Israeli reservist arrests, then quotes Mansour, Coplan, Tenev, and Hanson on how insider flow is what makes prices accurate. Argues platforms face a calibration problem: too permissive and noise traders flee perceiving rigging, too strict and informed flow disappears and prices decay into sentiment. Predicts Polymarket fully drops pseudonymous trading and ramps surveillance over the next year.
Concepts: insider trading, market surveillance, adverse selection, information aggregationLong essay from the Bento.fun founder positioning prediction markets as the next stage in the history of expression: from print to radio to social media, each medium widened who could speak, but only markets demand that speakers bear consequence for being wrong. Pulls in Hayek on price as coordination, Taleb on skin in the game, and Hanson on futarchy to argue the same primitive now extends to politics, sports, journalism, and science. Useful as a values-level framing of why staked speech might out-trust cheap talk in an AI-saturated information environment.
Concepts: information aggregation, wisdom of crowds, futarchy, price discoveryEmpirical paper finding that prediction market accuracy is not the wisdom of crowds. Roughly 3% of accounts drive most price discovery: their trades anticipate future prices, respond to news immediately, and improve calibration across a market's lifecycle. The remaining accounts contribute volume and liquidity but minimal information, and their losses fund the informed minority. Reframes the standard story about why prediction markets work and has implications for platform design, surveillance, and how to credibly market accuracy.
Concepts: wisdom of crowds, information aggregation, price discovery, adverse selectionDefense of prediction markets that reframes the moral critique as a critique of capitalism itself. Litvin walks through the standard objections (gambling, insider trading, manipulation, slot-machine durations) and pairs each with a larger-scale analog in traditional finance: the $950M oil ceasefire trades on CME, LIBOR, accredited investor rules, dollar debasement. Argues that the legal line between gambling and investing collapses under scrutiny and that prediction markets are simply a more legible version of dynamics already accepted everywhere else.
Concepts: regulatory classification, insider trading, market manipulation, event contractsFollow-up to functionSPACE's V1 discretisation analysis, splitting Polymarket's 18,863 multi-market events into continuous (price brackets, weather ranges, margin percentages) versus categorical (teams, candidates) and re-running the pathology tests. Both types concentrate 90% of volume in the top 5-6 markets, but ghost markets turn out to be largely a categorical phenomenon: continuous events distribute volume more evenly across buckets and survive the liquidity cliff longer at high N. With continuous events overtaking categorical by event count in 2026Q1, the case for a continuous-distribution primitive applies to a growing share of the platform.
Concepts: liquidity fragmentation, long-tail markets, binary contracts, minimum viable liquidityPrediction markets make information legible in a way stocks and options do not. When someone bets big on an attack on Maduro, everyone immediately knows what the bet is about, unlike a stock price spike that could mean anything. Andrew Courtney argues this legibility is a feature even when it surfaces uncomfortable national security implications.
Concepts: legibility, insider trading, information asymmetry, price discoveryA trader who ran ~10,000 automated trades through Polymarket dissects why most prediction market terminals fail: execution mirages, non-synthesizing research layers, and strategy tabs that are deck screenshots disguised as features. Lays out the baseline infrastructure real traders need and argues only two terminal types survive the next 24 months: institutional API rails and a trader-native terminal built by someone who has actually lived the workflow.
Concepts: execution quality, cross-platform arbitrage, order book, market makingARK Invest sizes the prediction market opportunity at $1-5 trillion medium-term by benchmarking against global OTC derivative volumes. Argues that sports volumes are largely regulatory arbitrage from states without legal online sports betting and that the real disruption lies in event contracts unbundling risk from traditional derivatives, giving retail investors direct exposure to discrete outcomes.
Concepts: info finance, regulatory arbitrage, liquidity provision, event contractsDan Schwarz analyzes ~13,500 prediction market contracts from Polymarket to ask whether billion-dollar prediction markets deliver on their promise of informing decisions. Over 80% of volume goes to sports, crypto and elections, and accuracy on "useful" markets hasn't improved since early 2025. The piece argues AI chatbots may supersede prediction markets as the primary forecasting interface, leaving markets to serve an epistemic role as common knowledge infrastructure.
Concepts: info finance, forecasting accuracy, probability infrastructure, nowcastingIsaac Rose-Berman dissects Kalshi's claim that as an exchange rather than a bookmaker, it has no stake in whether users lose. Through a detailed walkthrough of maker-taker fees, adverse selection and the zero-sum structure of prediction markets, he argues the business depends on ordinary users systematically losing to sophisticated market makers just as surely as any sportsbook. A coin-flip bet that should be fair becomes a 3.4% expected loss after fees.
Concepts: execution quality, market making, adverse selection, retail flowRobin Hanson responds to a CFTC call for comments by arguing prediction markets deserve the same regulatory treatment as other information institutions like journalism and academia. Drawing parallels between six common harms shared across all information systems, from insider trading to manipulation, he contends markets should be approved by default and restricted only on clear evidence of specific harm. The piece makes the economist's case that the information value of prediction markets justifies a lighter regulatory touch than traditional gambling law.
Concepts: regulatory classification, insider trading, market manipulation, info financeDWF Ventures argues prediction markets are evolving into a financial derivatives asset class. The piece analyzes structural barriers to leverage and collateral lending: jump risk, binary valuation and regulatory uncertainty. It surveys emerging solutions including epoch-based fee models, perpetual futures on outcomes and tokenised positions on Solana.
Concepts: position collateralization, gap risk, regulatory classification, event contractsArgues forecasting accuracy has outpaced product design: a decade inside the US intelligence community produced zero complaints about forecast quality, yet forecasting firms remain niche while simulation startups Aaru and Simile raised nine-figure rounds. Diagnoses the gap as a failure to embed forecasts into institutional workflows, and recommends forecasting companies hire deployment managers who transform probabilities into artifacts clients can act on.
Concepts: decision markets, probability infrastructure, platform competitionProvides a framework for understanding which prediction market categories can win institutional capital, using variance risk premium analysis to determine where event contracts beat options replication. Situates the current benchmark-building race (election ETFs, corporate event notes, AI capability markets) within the historical pattern of credit and crypto infrastructure formation.
Concepts: event contracts, election markets, platform competitionMakes the case that prediction market perpetuals are structurally different from crypto perps because event contracts lack a tradeable underlying, making most implementations liquidation arcades. Argues the winning path is building perps as institutional hedging infrastructure for continuous event-risk management rather than leveraged betting.
Concepts: event contracts, liquidity provision, hedgingBreaks down Hyperliquid's HIP-4 outcome contracts as an onchain options layer rather than just another prediction market, comparing its unified margin engine, fee structure, and token value capture to Polymarket and Kalshi. Maps the design space for credit default swaps, parametric insurance, and futarchy that the primitive unlocks.
Concepts: event contracts, platform competition, conditional tokensAnalyzes three structural reasons prediction market prices diverge from true probabilities even with rational participants: favorite-longshot bias from Kelly betting, risk-premium distortion from market correlation, and risk-neutral forward pricing in long-dated contracts. Argues markets still outperform individuals because they weight capital-backed beliefs rather than equal-weighted opinions.
Concepts: longshot bias, price discovery, Kelly criterionArgues play-money prediction markets are accurate only because they operate in a low-manipulation regime, and this accuracy is self-undermining: the more important they become, the more valuable they are to manipulate. Contrasts the manipulation resistance of real-money markets against the economic noise they introduce, including opportunity cost, hedging distortion, and participation friction.
Concepts: market manipulation, information aggregationUses the $292M Kelp DAO rsETH bridge exploit to motivate why crypto-native parametric cover is needed, then compares HIP-4's binary event contract structure to CDS, catastrophe bonds, reinsurance sidecars, and weather derivatives. Argues that when outcome contracts share margin with underlying exposure on the same execution layer, HIP-4 unlocks a market two orders of magnitude larger than current DeFi insurance.
Concepts: event contracts, resolution criteria, hedging, oracle designA detailed analysis of how Robinhood's distribution advantage—27 million funded users and cross-selling across stocks, options, crypto, and futures—gives it an existential edge over standalone prediction market platforms like Kalshi and Polymarket. Walks through Robinhood's Rothera JV (buying the exchange infrastructure), the economics of vertical integration, and why regulatory threats to sports contracts hurt specialists far more than multi-asset brokers.
Concepts: platform competition, network effects, NEW: distribution moatA clear-eyed diagnosis of why decision markets (futarchy) haven’t taken off despite the elegant theory. Identifies two structural problems: thin markets that can’t attract enough informed traders for idiosyncratic decisions, and a conditional-futures architecture where token-price-as-KPI is too noisy to incentivize rational trading. Covers MetaDAO and Combinator as current implementations grappling with these issues.
Concepts: decision markets, futarchy, minimum viable liquidityA formal comment to the CFTC's proposed rulemaking on prediction markets, submitted by an HKS researcher. Proposes a four-dimensional framework for classifying event contracts (information structure, manipulation economics, social utility, repugnance), reframes insider trading into three distinct patterns (outcome influence, duty breach, information advantage), and analyzes resolution integrity through three documented Polymarket/Kalshi case studies.
Concepts: regulatory classification, event contracts, insider trading, resolution criteriaCompares spreads across DraftKings, FanDuel, Polymarket, and Kalshi on identical sports markets to isolate how counterparty identification versus maker competition affects pricing. Finds PM prices are 100-300 bps better on liquid markets but long-tail markets on prediction markets have 10-50% spreads. Argues sportsbooks tighten spreads through counterparty-aware pricing while PMs compensate through maker competition and order book transparency.
Concepts: adverse selection, market making, toxic flow, liquidity provision, long-tail marketsAnalyzes Polymarket's short-duration crypto up/down markets, which now account for 16% of monthly volume but generate ~40% of platform fees. Finds professional algo traders (19 addresses) extract consistent profits through paired trading and maker execution while 69% of retail traders lose money. Argues prediction markets are becoming a game of volatility rather than forecasting.
Concepts: market making, execution quality, retail flow, liquidity provision, temporal arbitrageAnalyzes Trepa's high-frequency forecasting contest on Solana and shows its current design (median error cutoff, steep accuracy weights) induces a Keynesian beauty contest equilibrium that underweights private information. Introduces the orthogonal precision multiplier, a mechanism that rewards accurate forecasts decorrelated from the consensus, transforming Trepa into a tunable second-order oracle. Proves equilibrium existence via potential game theory, quantifies information gain through mutual information, and addresses practical vulnerabilities including oracle latency, median instability, and collusion.
Concepts: NEW: Keynesian Beauty Contest, oracle design, information aggregation, reflexivity, proper scoring rulesMaps the prediction market landscape as a stack war between crypto rails (Polymarket), regulated rails (Kalshi), and execution wrappers (Coinbase, Robinhood). Argues the sector is stratifying into product archetypes rather than converging on a winner-takes-all outcome, with TradFi incumbents pushing standardized binaries that fit existing market structure.
Concepts: market structure, platform competition, event contracts, distribution moat, regulatory classificationTests whether Polymarket forecasts for influenza hospitalizations and measles cases can beat established benchmarks. The markets fail to outperform the CDC FluSight ensemble for flu or simple ARIMA baselines for measles. Two specific failure modes identified: traders placed probability mass on impossible cumulative outcomes, and low trading volume undermined price discovery.
Concepts: polymarket, forecasting accuracy, information aggregationDustin Gouker draws structural parallels between the rise of prediction markets today and the daily fantasy sports boom of 2013-2015. He maps four similarities: duopoly dynamics, state regulatory pushback, industry self-regulation attempts, and casino industry opposition. Written by someone who covered both industries, it provides a grounded perspective on where the prediction market story arc might lead.
Concepts: polymarket, platform competition, regulatory classificationSeva Gunitsky argues that prediction markets create three distinct dangers for global conflict: military insider trading at scale, manipulation of outcome resolution, and propaganda advantages for autocratic regimes. The article walks through real cases including the Israeli Air Force betting scandal and the 'hair dryer problem' — cases that challenge the idealistic framing of prediction markets as truth machines.
Concepts: insider trading, market manipulation, reflexivity, self-resolving marketsXO Labs publishes a framework for thinking about prediction market resolution as a spectrum rather than a single mechanism. The paper identifies the resolver trilemma between efficiency, decentralization, and security, using the Ukraine minerals market whale tax incident on Polymarket as a case study in token-weighted governance failure. They propose a layered architecture (XO Oracle) that routes each market to the level of security it actually requires.
Concepts: oracle design, dispute resolution, corruption value multiple, resolution criteriaMO compares three market architectures for expressing shaped beliefs: binary prediction markets, options structures, and continuous prediction markets. The article traces the distribution gap from the Black-Scholes era through modern crypto markets and argues that continuous payout curves replace the workarounds traders currently use.
Concepts: NEW: continuous prediction markets, binary contracts, market structure, price discoveryThis paper studies how to design input context for LLMs to forecast mention market outcomes, where contracts resolve based on whether a company mentions a specified keyword during its earnings call. The authors introduce Market-Conditioned Prompting (MCP), which treats the market price as a Bayesian prior and instructs the LLM to update it using textual evidence from news and prior transcripts. A mixture of the market price and MCP (MixMCP) outperforms the market baseline alone, suggesting LLMs can serve as complementary forecasters alongside live market prices.
Concepts: AI agents, forecasting accuracy, information aggregationAn on-chain investigation revealing how a single entity extracted over $5m from Polymarket's crypto markets using 350+ accounts. The piece walks through the manipulation mechanism — accumulating positions on Polymarket then moving Binance's BTC price during settlement windows — and traces the fund flow through Hyperliquid wallets to Binance. Includes specific suggestions for TWAP-based settlement and account-level enforcement.
Concepts: market manipulation, market surveillance, resolution criteriataetaehoho runs an empirical analysis of Polymarket's liquidity rewards and sponsorships, finding they can tighten spreads only when daily spend exceeds roughly 1% of the existing book. Even above that threshold, incentive size barely predicts which markets respond; pre-existing liquidity conditions and pre-trends are far stronger determinants of uplift than reward intensity.
Concepts: liquidity provision, market making, market structure, incentive compatibilityThe first comprehensive empirical look at who actually profits on Polymarket and who doesn’t. Analyzing 588 million trades and $67 billion in volume, the paper finds the top 1% of users capture 76.5% of profits through disciplined limit order strategies, while the bottom 90% lose money taking liquidity with market orders. The authors also examine and ultimately rule out insider trading as an explanation for the largest winners’ performance.
Concepts: liquidity provision, insider trading, market structureAelix diagnoses why prediction markets remain stuck on sports gambling — roughly 65% of Polymarket and Kalshi volume goes to sports, with another 12% each on crypto and politics, leaving useful markets like STEM at 1.2%. The bottleneck isn't technology but market structure: prediction markets are zero-sum, so savers don't participate; gamblers drive volume toward short-term entertainment; sharps follow the gamblers; and useful markets starve. AI agents break this cycle by acting as cheap, forced-participation sharps — they're cloneable, parallelizable, can be compelled to trade on any question, and dramatically lower the minimum viable liquidity threshold for niche markets. The piece also revisits the history of corporate internal prediction markets (HP BRAIN, Eli Lilly, Google) and argues that AI sidesteps the organizational failures that killed them, reviving the futarchy and info-finance vision on more tractable terms.
Concepts: AI agents, info finance, minimum viable liquidity, decision marketsDan Schwarz, who built both of Google's internal prediction markets across two decades, tells the inside story of why they struggled and what it means for the future. Prophit (2005–2011) attracted 20% of Google employees but died when regulatory approval for an external launch stalled. Gleangen (2020–2022) reached over 10,000 users but faced a deeper problem: managers valued transparency and adjustability over raw accuracy, and the forecasts were asking internal questions when executives needed competitor intelligence on OpenAI and Microsoft. The piece closes with AI agents as the cost-reducing wedge that might finally make corporate prediction markets viable.
Concepts: decision markets, futarchy, regulatory classification, AI agentsEric Liu (co-founder @totalistrading) explains how prediction market market makers can reduce required collateral for parlay tickets by computing worst-case portfolio exposure with integer linear programming instead of collateralizing each ticket in isolation. He walks through the ILP formulation, shows how it avoids the infeasible 2^n enumeration, and reports 10-80% collateral reduction from backtests against Totali's markets using HiGHS/scipy. The post argues this enables tighter quotes, deeper liquidity, and more expressive combo bets without sacrificing fully collateralized peer-to-peer settlement.
Concepts: parlaysSpartan Labs explores what happens when you give Andrej Karpathy's LLM Council design actual stakes. Their Simmer prototype runs 30 AI agents across 6 models and 5 reasoning personas in an LMSR market, producing a capital-weighted forecast without requiring human liquidity. Early observations reveal coordination cascades when agents share identical search results, a finding with implications for any multi-agent prediction system.
Concepts: AI agents, decision marketsProposes a two-stage screener that uses Granger causality to find lead-lag pairs across Kalshi Economics markets, then passes candidates through an LLM that checks whether the proposed direction has a plausible economic transmission mechanism based on event descriptions. The LLM re-ranker barely moves the win rate (51.4% to 54.5%), but it dramatically shrinks the downside — average losing trade drops from $649 to $347 by filtering out statistically fragile links that look good in backtests but break in practice.
Concepts: AI agents, temporal arbitrage, noise decompositionKaleb (Polychain) breaks down why leverage is deceptively hard in prediction markets. The core problem is jump risk — binary markets can resolve instantly to $0 with no liquidation window, meaning financiers must pass expected jump losses back to traders as upfront fees. But a fairly priced upfront fee cancels out the leverage benefit entirely. His solution: short epoch-based pricing (like perps funding) where the financier only prices gap risk over a few hours at a time, rolling forward.
Concepts: gap risk, liquidity provisionBinary prediction markets struggle with continuous outcomes like oil prices because capital fragments across individual strike prices. This piece uses Polymarket's crude oil dataset to illustrate the staircase hedging problem. It then shows how continuous probability markets solve this with a single density-based pricing mechanism.
Concepts: continuous prediction markets, binary contracts, liquidity fragmentation, hedgingBinary prediction markets compress continuous probability distributions into yes/no outcomes, discarding variance, skew, and tail information that forecasters naturally produce. functionSPACE analyzes 622 mutually exclusive bracket events on Polymarket and finds that less than half price a coherent probability distribution. The piece argues for continuous market design as the primitive that finally matches how both institutional analysts and prediction market traders actually reason about uncertainty.
Concepts: binary contracts, distribution markets, market structure, proper scoring rulesNot all historic data is equally useful for prediction. Eric Menser argues that treating all past games as equally relevant misses the higher-signal comparisons buried in specific contexts - team matchups, announcer crews, and game types. Trade Butter enriches Kalshi word market resolution history with broadcast metadata to surface sharper signals.
Concepts: calibration, forecasting accuracyGrace Deng from SevenX Ventures compares prediction markets against sports betting incumbents like DraftKings across product, business model, and legal dimensions. Parlay complexity gives traditional sportsbooks a structural advantage that orderbook-based platforms cannot easily replicate. The article argues that convergence rather than replacement will define the next phase—hybrid models where PMs and sportsbooks interoperate.
Concepts: platform competition, parlays, market structure, federal preemption, retail flowEvaluates whether multi-agent LLM architectures can resolve prediction market outcomes more accurately than single-model baselines. Tests independent aggregation and deliberative consensus against GPT-5 Nano, DeepSeek V3, and Llama-3.3-70B on 1,189 resolved questions from KalshiBench. Finds that confidence-weighted voting across agents edges past single models, while deliberation degrades accuracy — and proposes a hybrid system that auto-resolves unanimous high-confidence questions while flagging disagreements for human review.
Concepts: AI agents, oracle design, forecasting accuracy, resolution criteriaAn accessible explainer from Scott Duke Kominers on how prediction markets aggregate information through market prices. The piece covers the core mechanism, benefits over polls, and key design challenges around participation, insider trading, and manipulation.
Concepts: information aggregation, incentive compatibility, market manipulationNic Carter examines the MicroStrategy Bitcoin sale market dispute and argues that Polymarket's entire market resolution system is structurally broken. He shows how outsourcing resolution to UMA creates a fundamental economic security failure: the UMA token market cap ($37M) is far smaller than the disputed market volume ($317M), making manipulation profitable and nearly undetectable. Carter compares Polymarket's approach to regulated venues like Kalshi and CME, which specify sources upfront, allow deferred settlement, and settle centrally without passing responsibility to anonymous token holders.
Concepts: dispute resolution, resolution criteria, UMA protocol, oracle designThis is the most formal treatment of futarchy's microstructural challenges to date, modeling how selection bias and equilibrium multiplicity emerge from the joint information structure of action and outcome beliefs. The paper's central insight — that on-chain wallet transparency creates a cross-market welfare channel with a narrow interior optimum — has direct implications for how conditional prediction markets should be designed on blockchain venues.
Concepts: futarchy, market structure, information aggregation, decision marketsXO Labs publishes the first in a planned series on XO Vaults, a passive liquidity design that bridges the gap between CLOB platforms and pooled liquidity in prediction markets. It analyses three existing liquidity models and identifies a structural gap: platforms with best execution have no passive product, while platforms with passive deposits lack order-book execution. The article proposes a five-factor vault allocation algorithm with circuit breakers designed to protect against informed trading.
Concepts: liquidity provision, market making, order book, market structure, adverse selectionHossain, Andrade, Zang, and Chen introduce evidence markets, a new mechanism that generalizes prediction markets to handle two limitations: prices reveal what the crowd believes but not why, and markets require external resolution events with known dates. The system uses a Logarithmic Market Scoring Rule with dynamic liquidity that responds to evidence quality. The authors prove bounded platform loss, show evidence rewards scale with uncertainty, and demonstrate epsilon-dominant strategy incentive compatibility for truthful reporting. An LLM-as-a-Judge verification layer with staking handles operational deployment.
Concepts: evidence markets, market scoring rules, incentive compatibilitySchema Research breaks down the five mechanisms powering every prediction market: LMSR, central limit order books, AMMs, bonding curves, and parimutuel pools. It explains the math behind each, their trade-offs, and how the strongest modern designs combine several mechanisms across different stages of a market's lifecycle.
Concepts: market scoring rules, parimutuel markets, market making, order book, price discoveryThe CFTC's recent event contract proposal marks a turning point for prediction markets as a new financial asset class, argues Rashan Colbert. Legal classification will determine whether event contracts develop as gambling products or as legitimate event-risk infrastructure for institutions and protocols. Part one of a planned summer series from the former dYdX and Senate policy lead.
Concepts: event contracts, regulatory classification, market structure, hedgingThe first rigorous estimate of U.S. user activity on offshore prediction market platforms finds that ~30% of Polymarket's $55.6B trailing twelve-month volume comes from U.S.-based users. Across all offshore platforms, U.S. activity totals $11-34B, representing 12.5-31.5% of all U.S. prediction market volume. Prepared for the Coalition for Prediction Markets, the report estimates offshore U.S. volume could reach $133B annually by 2030.
Concepts: regulatory arbitrage, market surveillance, federal preemptionA former CME board member who helped negotiate the CFMA 2000 argues for bifurcating prediction market regulation: sports and entertainment betting under state gaming laws, while economically valuable markets (politics, weather, economic releases) stay under CFTC oversight. Makes the case that the all-or-nothing regulatory debate misses a win-win compromise.
Concepts: regulatory classification, federal preemption, event contractsMakes the case that prediction markets are hitting the same infrastructure wall DeFi faced in 2020, with the oracle layer as the bottleneck to further growth. Explains the risks of optimistic dispute resolution and how Chainlink's deterministic data streams and AI-hybrid oracles are reducing settlement times from hours to minutes.
Concepts: oracle design, dispute resolution, resolution criteriaGalaxy Research breaks down Hyperliquid's HIP-4 protocol upgrade and positions it as the arrival of a third major prediction market model alongside Polymarket and Kalshi. Compares the three on infrastructure, fees, UX, oracle design, and regulatory posture across a 35-minute deep dive with live market data.
Concepts: platform competition, market structure, regulatory classification, oracle designSynthesizes four datasets — accuracy metrics, trader PnL, volume concentration, and skill classification — to reveal prediction markets' real structure: a 3% skilled minority drives the accuracy while 84% of traders lose money. A data-rich challenge to the 'wisdom of crowds' narrative.
Concepts: wisdom of crowds, forecasting accuracy, price discovery, information aggregationExamines Europe's 2018 binary options ban as a regulatory template for US prediction market legislation. Argues the key question is not whether prediction markets should exist but who should have access — retail traders lose money while institutions use them for hedging. Compares ESMA's framework with pending Congressional bills to map what works on both sides of the Atlantic.
Concepts: regulatory classification, retail flow, event contractsPolymarket matches orders off-chain for speed but settles on-chain for finality. This paper identifies a consistency gap they call Ghost Fills — orders matched off-chain that fail during on-chain settlement. Across 1.95M reverted transactions, the authors document four attack vectors that let traders selectively invalidate orders, realizing $1.49M in profit with 24.3% of filled orders reverting during peak hours.
Concepts: market manipulation, arbitrage, order bookMakes the case for prediction markets as a corporate treasury and forecasting tool, backed by data on 127x volume growth from 2022 to 2025. Profiles Enlivex's use of the Rain protocol on Arbitrum as its primary reserve asset, and traces how prediction markets are displacing expert opinion in pharma, climate science, and supply chain management.
Concepts: corporate prediction markets, info finance, decision marketsTerry Lee shows how prediction market parlays are systematically mispriced because traders treat events as independent when they are actually correlated. Using Fed rate decisions as a case study, he builds a conditional probability framework that reveals parlays up to 5x overpriced or 2x underpriced compared to naive multiplication. A practical guide to finding edges in event correlation.
Concepts: parlays, price discoveryLattica designs a shortfall-pricing risk engine that enables lending against collateralized prediction market positions, calibrated on Polymarket historical data. Uses Wang-distorted loss distributions within fixed-duration epochs to price lender tail risk where traditional liquidation-based lending fails due to bounded prices and discontinuous repricing. A dual-pool Monte Carlo simulation shows the risk engine pool earning 92.5% annualized return versus -27.2% for a flat-rate baseline.
Concepts: position collateralization, gap risk, adverse selection, liquidity provision, price discoveryYai Capital announces Yai Markets, a DeFi-native prediction market infrastructure built on Solana targeting advanced traders and institutional liquidity providers. The post outlines three key innovations including synchronous crankless execution, atomic smart circuit breakers, and cross-protocol atomic hedging, positioning the platform as a successor to Polymarket's hybrid Web2/Web3 architecture.
Concepts: platform competition, liquidity provision, hedging, adverse selectionDay One Law's weekly legal update for crypto builders covers the CFTC's June 10 proposed rules that would narrow the definition of prohibited event contracts to terrorism, gaming (narrowly defined), entirely random events, and specific sports micro-bets. Includes practical compliance guidance for prediction market builders during the 45-day comment period.
Concepts: event contractsMickey Hardy (Arcadia) argues prediction markets are not just better gambling but the next fundamental system for organizing human behavior — comparable to stock markets, insurance, and search engines — by converting probability itself into a measurable, allocatable asset. Covers applications across pharma, insurance, corporate forecasting, and geopolitics, with a long-term vision of society coordinating via financially-incentivized probability rather than narrative and hierarchy.
Concepts: info finance, futarchy, information aggregation, corporate prediction marketsWaPo analyzes 1,276 Kalshi markets and 829 Polymarket markets across 344 primary races and finds prediction markets are well-calibrated: candidates given 70-80% probability win about as often as predicted. High-profile misses (Spencer Pratt in LA, Massie in KY) are not failures of the market but the expected 25% outcome in a properly calibrated system where probabilities are often misunderstood as certainties.
Concepts: calibration, election markets, forecasting accuracy, information aggregationScott Alexander reviews the Metaculus US Democracy Threat Index, a prediction market index tracking expected threats to American democracy through 2027-2028. The index peaked at 47% in late 2025 following election uncertainty before settling to 39% after orderly special elections and failed politicized prosecutions. Discusses conditional prediction markets as a tool to measure how electoral outcomes affect democratic health, and proposes a wishlist including cross-platform duplicates (Polymarket/Kalshi), better question design, and wider forecaster participation.
Concepts: decision markets, election markets, forecasting accuracy, information aggregationDraws on Isaiah Berlin's two concepts of liberty to frame two competing visions of markets: the 'market as camera' (Hayek's price discovery aggregating scattered private knowledge) versus the 'market as engine' (actively shaping the outcomes it measures). Uses prediction markets as a live case study, covering their history from 1880s New York curb betting through the Iowa Electronic Markets to Polymarket and Kalshi's $24B monthly volume. Highlights recent research showing prediction market accuracy is driven by fewer than 3% of skilled traders, not wisdom of crowds, and explores the political stakes of which outcomes get priced at all.
Concepts: information aggregation, price discovery, election markets, forecasting accuracyArgues that the explosive growth of prediction markets like Kalshi and Polymarket reflects economic desperation and financial nihilism among young Americans, not innovation. Cites a Bloomberg analysis showing retail traders lost 31M while bots and institutional investors captured nearly all profits. Critiques Trump-era CFTC deregulation under Michael Selig and warns that prediction markets extend the gambling economy into every domain of life — from elections to wars to wildfires — while being sold as democratized finance.
Concepts: information asymmetry, insider trading, retail flowDetailed analysis of how Kalshi market makers (quant firms like SIG) shade lines against expected retail flow during the 2026 World Cup, creating persistent edges for informed sports traders. Covers the France vs Norway case study where public rotation news was ignored by models for three days, the fee structure that prevents clean arbitrage while allowing informed one-sided betting, and structural issues like the lack of taker speedbumps. Notes .7B in World Cup volume with highly skewed profit distribution toward sharp traders.
Concepts: market making, adverse selection, information asymmetry, retail flow, liquidity provisionInvestigates how easy it is to rig prediction markets on Rotten Tomatoes movie scores — a handful of compromised critics could swing scores enough to cash out on Kalshi. Chronicles multiple real insider trading cases: the Khai Rated critic who tanked Alien: Earth scores, the Maduro raid trader, MrBeast's video editor, the Google employee arrested for betting on search trends. Examines why CFTC enforcement has weakened under Trump while platforms like Kalshi struggle to self-police.
Concepts: insider trading, market manipulation, information asymmetry, retail flowArgues that prediction markets are following the same trajectory as social media — starting with idealistic narratives about information aggregation but devolving into gambling, manipulation, and regulatory arbitrage. Highlights Meta's new prediction market app Arena, insider trading cases like the US Special Forces Maduro raid bet, and regulatory crackdowns in Brazil, Minnesota, and Spain. Warns that putting prediction markets in Meta's hands repeats social media's biggest mistakes with the added danger of real-money incentives.
Concepts: platform competition, insider trading, information aggregationExamines how a $5.7 million Polymarket bet on whether a gamer said "donk" sparked a revolt over the platform's optimistic oracle resolution system. When one firm's token-weighted vote effectively decided the outcome, traders accused Polymarket of becoming a popularity contest with a wallet rather than a truth machine — exposing deep tensions in how decentralized prediction markets handle ambiguous outcomes.
Concepts: oracle design, dispute resolution, market manipulation, polymarket, platform competitionArgues that value in prediction markets is generated not at settlement but through the ongoing process of liquidity allocation and repricing. Explores how prediction markets differ from traditional markets at the microstructure level — the key distinction being the representation of the asset, not the mechanism — and how their bounded outcome space makes them composable, enabling liquidity to attach to things that previously had value but no market.
Concepts: liquidity provision, price discovery, market structure, information aggregationArgues that onchain prediction market betting profiles reveal deeper insights about individuals than traditional resumes. With structured data on specific questions, resolution criteria, and outcomes, a trader's history shows how they price information and manage risk — suggesting Polymarket and Kalshi profiles may eventually appear on job applications and investor decks.
Concepts: legibility, information aggregation, network effectsDeep-dive analysis of Polymarket International's likely blockchain migration as it pursues CFTC compliance. Covers three technical migration paths — mirror-and-cutoff, dual chain instances, and sunset switchover — along with changes to KYC requirements and dispute resolution mechanisms. Argues dual chain instances are the most probable outcome, balancing compliance with continuity.
Concepts: oracle design, regulatory classification, market structure, liquidity fragmentation, polymarketLays out an 8-point retention playbook for prediction market platforms, arguing the winners will be decided by habit-forming product mechanics rather than oracle quality or market breadth. Covers first-trade urgency, daily streaks, one-tap re-betting, leaderboard copy trading, shareable win moments, customizable flex cards, market-specific referral fees, and visible leverage controls. Borrows patterns from casinos, mobile games, and memecoins, and highlights Ares as a platform already shipping many of these ideas.
Concepts: network effects, platform competition, liquidity provisionAn academic paper from Stanford and Singapore Management University studying how prediction market participants can manipulate the settlement process itself — the mechanism by which outcome data feeds into market resolution. Extends the manipulation literature beyond the trading phase to examine oracle-level attacks, where traders profit by influencing or anticipating biased settlement rather than by trading on superior information. Formalizes the conditions under which settlement manipulation is profitable and discusses implications for oracle design, dispute resolution frameworks, and market integrity.
Concepts: market manipulation, oracle design, corruption value multiple, adverse selection, information aggregationA landmark experimental study comparing the accuracy of real-money (TradeSports) versus play-money (NewsFutures) prediction markets during the 2003-2004 NFL season. Both market types showed significant predictive power and remarkably similar accuracy — the play-money markets performed as well as the real-money markets. The authors hypothesize that this reflects two countervailing forces: real-money markets better motivate information discovery, while play-money markets may yield more efficient information aggregation by attracting a larger and more diverse pool of traders unconstrained by gambling regulations.
Concepts: information aggregation, calibration, continuous prediction markets, market scoring rules, adverse selection