A researcher from the Stevens Institute of Technology argues that a complete ban on insider trading in prediction markets could do more harm than good. Assistant professor of finance Balbinder Singh Gill released a paper on June 2 that uses a formal economic model to explore how strictly regulators should police insider trading.
Gill highlights a key paradox: the same insider trade that improves price accuracy today can reduce participation tomorrow, making prices less informative. His model shows that prediction market accuracy follows a “hump-shaped” pattern based on enforcement intensity. Too little enforcement allows insiders to crowd out other participants, while too much enforcement removes the valuable information that insiders bring.
“Tougher enforcement curbs the insider, raising participation, so accuracy is hump-shaped and optimal enforcement is interior, neither laissez-faire nor a ban,” Gill wrote.
This comes as regulators push for crackdowns on insider trading in prediction markets. The CFTC’s chief enforcement director warned traders in April that violators face action. In May, U.S. House lawmakers launched a probe into Kalshi and Polymarket over insider trading concerns.
Different levels of enforcement needed
Gill argues that enforcement levels should depend on where insider information comes from. For researched information, where a trader has worked hard to learn something, there should be little or no enforcement. Crackdowns here discourage valuable information production.
Misappropriated information, such as leaked data or classified material, should face higher enforcement. Cases where the insider can influence the outcome, like a political candidate betting on their own campaign, require the most enforcement.
“Enforcement in a prediction market should be calibrated rather than maximal,” Gill concluded, adding that balanced enforcement provides optimal welfare.
Kalshi to check user employment details
Meanwhile, Kalshi is introducing new measures to combat insider trading. Users betting in sensitive markets, such as those involving company performance or national security, will need to disclose their employer via an online form. The platform has also developed a “specific risk score” for markets with heightened insider trading or manipulation risk.
These changes follow an audit committee report that recommended better data collection, as well as pressure from lawmakers and regulators. Gill’s paper references two recent high-profile insider trading cases on competitor Polymarket. In May, a Google employee was charged with using insider information about search trends to make $1.2 million on the platform. In April, a U.S. soldier was charged with trading on classified knowledge of a military operation.
