Stevens Researcher Says 'Maximal' Insider Trading Bans Could Misfire on Prediction Markets

Prediction market regulators should pursue a calibrated approach to insider trading enforcement rather than an outright prohibition, according to a working paper released on June 2 by Balbinder Singh Gill, an assistant professor of finance at the Stevens Institute of Technology. Gill developed a formal economic model examining how aggressively insider trading on prediction markets should be policed, concluding that price accuracy in those markets is "hump-shaped" with respect to enforcement intensity. Too little enforcement allows insiders to crowd out regular participants, while too much enforcement strips out the informational value insiders bring to prices. "Tougher enforcement curbs the insider, raising participation, so accuracy is hump-shaped and optimal enforcement is interior, neither laissez-faire nor a ban," Gill said. He framed the finding as a paradox: "the same insider trade that improves the accuracy of the price today can reduce the participation that makes the price informative tomorrow."

Gill further argued that the appropriate level of enforcement should depend on the source of the information being traded. Independently researched information, where a trader has invested effort to uncover an edge, should face the least or no enforcement, since punishing it would discourage valuable information production. Misappropriated information, such as leaked or classified data, should be subject to a higher level of enforcement. Cases where an insider can influence the outcome of the event being traded, such as a political candidate betting on their own campaign, should be policed most strictly. "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," Gill said, concluding that enforcement should be "calibrated rather than maximal" to deliver optimal welfare.

The paper lands as prediction market platforms face heightened scrutiny from U.S. regulators over insider trading. In April, the CFTC's chief enforcement director warned that violators on prediction markets would face enforcement action. In May, U.S. House lawmakers launched a probe into Kalshi and Polymarket over insider trading concerns. Separately, Kalshi has begun rolling out new measures aimed at deterring insider trading, including requiring users in sensitive markets such as those tied to company performance or national security to disclose their employer through an online form, and assigning what the company described as a "specific risk score" to markets with heightened insider trading exposure.