By Blockchain News | Created at 2026-06-10 17:59:41 | Updated at 2026-06-11 13:58:51
1 day ago
Zach Anderson Jun 10, 2026 05:59
New research suggests optimal enforcement of insider trading in prediction markets lies between extremes, as lawmakers and regulators target violations.
Insider trading in prediction markets is a tricky balancing act, according to new research by Balbinder Singh Gill, an assistant professor of finance at the Stevens Institute of Technology. In a recently published paper, Gill argues that neither a blanket ban nor a laissez-faire approach is optimal for these markets, which are increasingly under scrutiny from U.S. regulators.
Gill’s economic model highlights a paradox: insider trades can improve price accuracy in the short term but discourage broader market participation in the long term. "Tougher enforcement curbs the insider, raising participation,” Gill noted, but too much enforcement risks removing valuable information from the market entirely. His findings suggest a "hump-shaped" relationship between enforcement intensity and price accuracy, with the ideal level of enforcement falling somewhere in the middle.
The implications are significant as regulators ramp up efforts to police insider trading in prediction markets. In April 2026, the Commodity Futures Trading Commission (CFTC) identified insider trading as a top enforcement priority, warning violators of severe penalties. The agency has already acted, with Kalshi—a CFTC-registered platform—suspending and fining users for trading on non-public information multiple times this year.
Enforcement Should Vary by Source of Information
Gill emphasizes the need for tailored enforcement based on the type of insider information. Markets should be lenient on trades based on independently researched insights, which contribute valuable information, but impose strict penalties on trades involving leaked or classified data. The harshest enforcement should target scenarios where insiders can influence outcomes directly, such as a political candidate betting on their own election.
"Trading on a genuine, independently researched edge is the activity society should be most reluctant to punish," Gill stated, adding that manipulation-prone cases demand the stiffest oversight.
This "calibrated" approach contrasts sharply with the current regulatory climate. Both Kalshi and Polymarket have faced mounting pressure from lawmakers, with the U.S. House launching a probe into insider trading allegations at both platforms in May 2026. Recent cases include a Google employee allegedly using search trend data to generate $1.2 million on Polymarket and a U.S. soldier accused of betting on classified military operations.
Platforms Tighten Controls
Prediction markets are also taking proactive steps. On June 9, 2026, Kalshi announced new measures requiring traders in sensitive markets—such as those linked to corporate performance or national security—to disclose their employment details. These changes aim to mitigate insider trading risks following recommendations from an internal audit committee.
Kalshi has referred at least 20 suspected insider trading cases to law enforcement this year, reflecting the growing focus on compliance in the industry. Meanwhile, Polymarket, which operates under CFTC oversight after resolving legal issues in 2022, has also faced high-profile insider trading allegations.
Regulatory Uncertainty Persists
The broader regulatory framework for prediction markets remains in flux. The Prediction Market Act of 2026, proposed in April, seeks to formalize insider trading prohibitions for event contracts and would direct the CFTC to issue tailored rules for these platforms. Until then, enforcement relies on existing anti-fraud and anti-manipulation provisions under the Commodity Exchange Act.
As regulators, lawmakers, and platforms navigate this evolving space, Gill’s research underscores the need for precision in enforcement. Striking the right balance could ensure prediction markets remain both informative and inclusive—without falling prey to manipulation or overregulation.
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