What did the new prediction market insider trading research find?

Research from Stevens Institute of Technology assistant professor Balbinder Singh Gill found that a total ban on insider trading may not produce the most accurate prediction markets. His model suggests that moderate enforcement can balance market participation with the value of genuine information held by informed traders.

Why could a complete insider trading ban hurt prediction markets?

According to the research, informed traders can improve market accuracy by incorporating valuable information into prices. If all insider trading is banned, markets may lose some of that information, potentially making predictions less accurate.

What does “hump-shaped” market accuracy mean in prediction markets?

The study found that prediction market accuracy improves as enforcement increases up to a point, then begins to decline if enforcement becomes too strict. In simple terms, both too little and too much enforcement can reduce the quality of market forecasts.

Why does insider trading remain a major issue for prediction markets?

Prediction markets rely on accurate information, but insider trading can create fairness concerns and discourage participation from ordinary users. Regulators are increasingly concerned that people with privileged information could gain unfair advantages or manipulate market outcomes.

What types of insider information did the research distinguish between?

The paper separates information into three categories: independently researched information, misappropriated information such as leaks or classified data, and information held by people who can directly influence an outcome. The study argues these categories should face different levels of enforcement.

Which types of prediction market trading should face the toughest enforcement?

The research argues that traders who can influence an outcome should face the strictest scrutiny. Examples include political candidates betting on their own elections or insiders whose actions could directly affect the event being traded.

Image for illustrative purposes only. Not a real photo.

Insider trading bans may hurt prediction markets

Written by Liezl GambePublished Jun 10, 2026, 5:28 AM

Prediction market regulators should adopt a balanced approach to insider trading enforcement rather than imposing a blanket ban, according to new research from Stevens Institute of Technology academic Balbinder Singh Gill.

In a paper released on June 2, Gill developed an economic model showing that market accuracy can decline when enforcement is either too weak or too strict, creating what he described as a “hump-shaped” relationship between enforcement and price accuracy.

“Tougher enforcement curbs the insider, raising participation, so accuracy is hump-shaped and optimal enforcement is interior, neither laissez-faire nor a ban,”

Said Stevens Institute of Technology assistant professor of finance, Balbinder Singh Gill.

The study argues that traders who uncover information through independent research should face little or no enforcement because penalising such activity could discourage the production of valuable information that improves market forecasts.

Gill said stricter enforcement should instead focus on traders using misappropriated information, such as leaked or classified data, while the toughest penalties should apply to participants who can directly influence an outcome, such as political candidates betting on their own campaigns.

The research comes as regulators increase scrutiny of prediction markets, with the CFTC warning insider traders of potential enforcement action and US lawmakers launching investigations into prediction platforms including Kalshi and Polymarket.

Kalshi is now requiring users in certain sensitive markets to disclose employment information and has introduced risk-scoring measures for markets deemed vulnerable to insider trading or manipulation, following recent cases involving a Google employee and a US soldier accused of using non-public information to place trades.