Seva 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.

A 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.

Robin 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.

Defense 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.

Prediction 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.

Frames 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.

Response 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.

Builder'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.

Bloomberg 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?

The 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.

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.

Proposes 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.

Screens 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.

Sets 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.

Congressional 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.

Traces 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.

Documents 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.

Written 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.

Argues 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.

Legal 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.