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.