Prediction Markets Need Calibrated Insider-Trading Rules, Not Blanket Bans, Study Finds
A formal economic model published June 2 by Stevens Institute of Technology finance professor Balbinder Singh Gill concludes that banning insider trading outright in prediction markets would damage the very accuracy those rules are meant to protect. The finding lands as U.S. regulators and lawmakers are actively scrutinizing platforms like Kalshi and Polymarket over manipulation concerns.
Key takeaways
- A formal economic model published June 2 by Stevens Institute of Technology professor Balbinder Singh Gill finds that fully banning insider trading in prediction markets would damage the price accuracy such rules aim to protect.
- The model shows a 'hump-shaped' relationship between enforcement intensity and market accuracy, where both maximum and zero enforcement degrade price quality and the optimal point lies in the middle.
- Gill recommends tiered enforcement: minimal restrictions for traders with research-based edges, stronger enforcement for those using leaked confidential data, and the strictest scrutiny for anyone able to influence the outcome they bet on.
- The CFTC signaled in April that prediction-market insider traders could face enforcement, and by May U.S. lawmakers had opened investigations into Kalshi and Polymarket over manipulation concerns.
- Kalshi has already added measures requiring users in sensitive markets to disclose employers and a risk-scoring system to flag markets exposed to insider information, following an independent audit committee's recommendations.
A formal economic model published June 2 by Stevens Institute of Technology finance professor Balbinder Singh Gill concludes that banning insider trading outright in prediction markets would damage the very accuracy those rules are meant to protect. The finding lands as U.S. regulators and lawmakers are actively scrutinizing platforms like Kalshi and Polymarket over manipulation concerns.
The Mechanism: Why a Full Ban Backfires
Gill's model identifies what he calls a paradox at the core of prediction-market design. Insiders — traders who hold information the crowd does not — push prices closer to true probabilities faster than ordinary participants can. Remove them entirely, and the information flow dries up. Let them run unchecked, and regular participants exit, shrinking liquidity until prices become unreliable for a different reason.
The result is a "hump-shaped" relationship between enforcement intensity and market accuracy. Maximum enforcement and zero enforcement both degrade price quality; the optimal point sits in the middle. That framing runs directly against the instinct to simply prohibit insider activity and call it clean.
Who Gets the Strictest Scrutiny
Gill's model does not treat all insider edges as equivalent, and that distinction is where the policy prescription gets specific.
Traders who developed an advantage through their own research and analysis should face minimal restrictions — their edge reflects effort and pulls prices toward accuracy. Traders using leaked or misappropriated confidential data warrant stronger enforcement. The strictest category, Gill argues, should cover anyone capable of influencing the outcome they are trading on — the example being a political candidate betting on their own election result.
That tiered logic matters because it tells regulators where to concentrate resources rather than casting enforcement uniformly across a market.
Regulatory Pressure Already Reshaping the Platforms
The study emerges into a live policy fight. The Commodity Futures Trading Commission signaled in April that insider traders in prediction markets could face enforcement action. By May, U.S. lawmakers had opened investigations into both Kalshi and Polymarket over insider trading and manipulation concerns.
Kalshi has already moved. The platform announced new measures that include requiring users in sensitive markets to disclose their employers and introduced a risk-scoring system to flag markets with elevated exposure to insider information. Those changes followed recommendations from an independent audit committee.
Gill's conclusion is that enforcement should be calibrated rather than maximal — a finding that, given the current regulatory moment, amounts to a direct challenge to the reflex toward blanket prohibition. Whether the CFTC reads it that way is another question entirely.
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Filed by the digital assets desk of MarketPR on June 19, 2026. Source: coinpaper.com. Indicative figures are not investment advice.