Traces 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.

Analyzes 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.

Frames 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.

Traces 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.

Analysis 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.

Argues 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.

Compares 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.

Responds 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.

Examines 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.

Summarizes 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%).

Argues 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.

Manifesto 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.

Examines 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.

Compares 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.

Examines 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.

Argues 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.

Examines 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).

Large-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.

Argues 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.

Characterizes 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.

Introduction to decentralized prediction markets with a SWOT analysis of Polymarket. Covers how the platform works, its regulatory positioning, liquidity constraints, and growth opportunities.

Explains 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.