Stevens Institute Research Urges Calibrated Insider Trading Enforcement in Prediction Markets

A Stevens Institute of Technology researcher says regulators should take a measured approach to insider trading enforcement in prediction markets rather than imposing an outright ban. According to Cointelegraph, a June 2 paper by assistant professor of finance Balbinder Singh Gill presents an economic model examining how strictly insider trading should be policed and argues that enforcement intensity can affect both market participation and price accuracy.

Gill described a paradox in which insider trading can improve price accuracy in the short term but reduce future participation that helps keep prices informative. His model suggests prediction market accuracy is “hump-shaped” relative to enforcement: too little enforcement can allow insiders to crowd out other participants, while overly strict enforcement can eliminate insiders’ legitimate informational contributions. Gill concluded that optimal enforcement is “interior,” meaning neither laissez-faire nor a ban, and said enforcement should be “calibrated rather than maximal.” He also noted insider trading has remained a persistent issue, with regulators and lawmakers increasing scrutiny. The CFTC’s chief enforcement director warned in April that prediction market insider traders could face enforcement action, and in May, U.S. House lawmakers opened a probe into Kalshi and Polymarket over insider trading.

Gill further argued that enforcement should vary based on the source of the information. He said trades based on independently researched information—where a trader has worked to learn something—should face the least enforcement, warning that crackdowns at this level could discourage valuable information production. By contrast, he said misappropriated information, including leaked data or classified material, should face higher enforcement. He added that the strictest enforcement should apply when an insider can influence the outcome, such as a political candidate betting on their own campaign, because such positions can invite manipulation.

The paper coincided with Kalshi introducing new measures aimed at reducing insider trading. Users participating in certain sensitive markets, including those tied to company performance or national security, will be required to disclose their employer through an online form. Kalshi also developed a market “risk score” intended to flag heightened insider trading or manipulation risk. The changes follow an audit committee report recommending improved data collection and come amid pressure from lawmakers and regulators. Gill’s paper also referenced two recent Polymarket-related cases: a Google employee charged in May with using insider information about search trends to make $1.2 million on Polymarket, and a U.S. soldier charged in April with trading on classified knowledge of a military operation.