In “Insider Trading in Prediction Markets: The Economics of Enforcement” authors Laurent Samuel, Greg Leonard, Nicole Moran, Jerrod Attias, and Jonas Dalmazzo of Cornerstone Research examine the central tradeoff in regulating prediction markets: suppressing trades based on misappropriated information without discouraging the legitimate research that keeps prices accurate. This article discusses the economics of informed trading, recent Commodity Futures Trading Commission (CFTC) enforcement, and the emerging “exchange-first” model.
Key Takeaways
- A different legal theory applies. With no shareholders, prediction markets rely on the misappropriation theory, not the “classical” insider-trading theory used in securities markets.
- Not all informed trading is equal. Legitimate research (e.g., “Theo,” who made $80M+ on 2024 election polls) differs from corporate insiders, government personnel, and outcome-influencers who can rig the result.
- Market size affects detection. Insider trades hide easily in high-volume markets but cause visible price swings in thin ones.
- Exchanges lead enforcement. Both cases in the CFTC’s February 2026 advisory were caught and sanctioned by Kalshi, not the CFTC.
- Direct enforcement is growing. In April 2026, the CFTC and the Department of Justice (DOJ) brought their first direct charges, and the CFTC signed a data-sharing deal with Major League Baseball.
- Over-enforcement can backfire. Curbing legitimate informed trading removes the incentive to research, eroding the price discovery that makes these markets useful.
Conclusion
Effective regulation must distinguish research-based trading from trading on misappropriated information. While any informed trading aids short-term accuracy, trading on stolen information ultimately harms price discovery—so the challenge is drawing that line reliably.
This article was originally published by Mealy’s Emerging Securities Litigation in July 2026.
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