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Who Counts as an Insider When Prediction Markets Bet on Human Behavior?

Our write-up of a story first reported by Gambling Insider

Who Counts as an Insider When Prediction Markets Bet on Human Behavior?

The CFTC's market oversight division issued a staff advisory on 22 September covering mention contracts. Exchanges were strongly recommended to identify potential controllers and known insiders on contracts whose outcome turns on what one person says or does.

The advisory follows the case of Gabriel Perez, a White House teleprompter operator. He traded contracts on Donald Trump's phrases between December 2025 and March 2026, knowing the text in advance. He made $107,539.02. The regulator ordered him to return the sum, pay a $65,000 penalty and barred him from trading for three years.

Election and sports contracts settle on an outcome no single person controls. Mention markets work differently. Everything depends on whether someone said a word, or turned up to a meeting. The CFTC therefore treats such conditions as easier to cause or prevent for profit. Thomas Rietz, a finance professor at the University of Iowa, separates knowledge from control. Knowledge makes prices sharper. Control gives one side a chance to beat the rest by shaping the outcome.

What exchanges are asked to do

There is no ban on listing mention contracts. Staff are ready to treat them as presumptively susceptible to manipulation, which raises the bar. An exchange would have to show a contract is not easily manipulated. The advisory names four markers: deterrent commitments from the person the outcome depends on, the risk of outside pressure, independent verification and broad public attention, and the quality of trade surveillance and controls. Noah Solowiejczyk, a partner at Fenwick, describes surveillance tuned to each contract. A universal template will not hold.

Insider screening starts at listing, before the first trade. Tiffany Magri, a regulatory adviser at Smarsh, advises mapping both the people who determine the outcome and those with access to scripts or guest lists. Open registries cover public figures. Private individuals, a podcast host for instance, are harder. Older surveillance models were built for issuers and standard lists. They fail here, because behaviour decides everything. Trading patterns still help: a new account placing a large, precise bet into a thin book is visible at once. That is how Kalshi caught Perez. Reason enough to check your own filters.

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