An academic paper argues that a measured, calibrated approach to insider trading enforcement, rather than an outright ban, optimizes price accuracy in prediction markets. The research by Balbinder Singh Gill finds that optimal enforcement varies based on the information source, with trading on independently researched insights warranting the least punishment. The analysis comes as prediction market Kalshi introduces new user verification measures and U.S. regulators increase scrutiny.
Prediction market regulators should consider a measured approach to insider trading enforcement instead of a ban, according to new research. An economic model developed by Stevens Institute of Technology assistant professor Balbinder Singh Gill, released on June 2, examines how strictly such trading should be policed.
The model shows market price accuracy is “hump-shaped” relative to enforcement intensity. Too little enforcement lets insiders crowd out participants, while too much removes their genuine informational contribution.
“Tougher enforcement curbs the insider, raising participation, so accuracy is hump-shaped and optimal enforcement is interior, neither laissez-faire nor a ban,” Singh Gill stated. Regulators like the CFTC have recently warned of enforcement actions against prediction market insider trading.
The paper argues enforcement levels should depend on the information source. Trading on independently researched information should face the least enforcement to avoid discouraging valuable information production.
Misappropriated information, like leaks, should face higher enforcement. The stiffest enforcement should target those who can influence the outcome, such as a political candidate betting on their own campaign.
“Trading on a genuine, independently researched edge is the activity society should be most reluctant to punish […] And trading by those who can move the outcome warrants the stiffest enforcement, because their positions invite manipulation,” the paper concluded. Singh Gill argued enforcement should be “calibrated rather than maximal.”
This research coincides with Kalshi introducing new measures to combat insider trading. The platform will require users in sensitive markets to disclose employment information and has developed a specific risk score for markets with heightened manipulation risk.
Recent high-profile cases involving competitor Polymarket were referenced in the paper. In May, a Google employee was charged with using insider information to make $1.2 million, and a U.S. soldier was charged in April with trading on classified knowledge of a military operation.
