Headline: New study warns blanket insider-trading bans could blunt Polymarket and Kalshi’s forecasting power
Prediction markets like Polymarket and Kalshi face a regulatory squeeze — but a new academic paper suggests heavy-handed enforcement could do as much harm as too little policing. Balbinder Singh Gill, an assistant professor of finance at Stevens Institute of Technology, argues in a June 2 research paper that prediction-market accuracy is maximized under a middle-ground enforcement regime, not a zero-tolerance ban on insiders.
Gill builds an economic model showing that insider-trading rules shape both who trades and what prices reveal. Stronger enforcement can widen participation by reducing unfair edges, improving price quality up to a point. But removing insiders entirely also strips markets of useful signals — researchers and well-informed insiders often contribute information that helps markets converge on accurate probabilities. The result, Gill finds, is a “hump-shaped” relationship: accuracy improves with enforcement until overly strict rules start to degrade information and reduce market informativeness.
The paper arrives amid stepping-up of regulatory scrutiny and high-profile enforcement actions. In April the CFTC’s enforcement division warned that bets placed with non-public information could trigger action, and a month later U.S. House lawmakers opened an investigation into Kalshi and Polymarket over insider-trading concerns. Authorities have also pursued criminal and civil cases tied to prediction-market bets:
- A probe was opened after Kalshi flagged unusual trading around former Rep. George Santos’ potential attendance at the State of the Union; prosecutors alleged Santos said he’d attend, wagered that he would not, then did not appear.
- Kalshi suspended trading and levied fines on local political candidates — including Virginia’s Mark Moran and Minnesota’s Matt Klein — who bet on races in which they were participating, raising concerns about participants who can influence outcomes.
- In April the CFTC and DOJ charged U.S. Army Master Sergeant Gannon Ken Van Dyke for trading on classified intelligence about an operation targeting Venezuelan President Nicolás Maduro — a case brought under Section 746 of Dodd-Frank (the so-called “Eddie Murphy Rule”), marking the first known use of that provision against prediction-market activity.
- Separately, a Google employee has been accused of using internal search-trend data to profit roughly $1.2 million on Polymarket.
Gill’s prescription: nuance. His framework recommends tailoring enforcement to the information source. Independent research and legitimate analysis — even if it gives traders an edge — should face light or no enforcement because it promotes information discovery. Leaks, stolen documents, classified material, and other misappropriations should draw stronger sanctions since the advantage comes from unauthorized access. The strictest rules, he says, are justified where participants can directly affect outcomes (e.g., candidates betting on their own races).
Platforms are already reacting. Kalshi has announced new safeguards for sensitive markets — requiring employment disclosures for certain contract categories and rolling out market-specific risk scores to flag elevated insider-trading or manipulation risks. Those moves follow recommendations from internal audits and mounting pressure from regulators and legislators.
Outside the platforms, corporate legal teams are updating compliance programs. Law firms are advising companies to revise insider-trading rules, employee handbooks and NDAs to explicitly cover prediction-market activity and the risks of material nonpublic information.
The stakes are growing as prediction markets expand: some financial firms project industry volumes could reach $1 trillion by 2030. Gill’s study underscores a central trade-off for regulators and platforms: clamp down too hard and you lose useful information; be too lax and you invite theft and manipulation. The paper suggests the best path lies in calibrated enforcement that differentiates between legitimate information gathering and illicit access or influence.
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