SKN | Should Insider Trading Be Fully Banned in Prediction Markets? New Research Suggests a Different Approach

Key Points

• New academic research argues that a complete ban on insider trading could reduce the accuracy and effectiveness of prediction markets.
• The study suggests enforcement should vary depending on how information is obtained and whether traders can influence outcomes.
• Regulators and platforms are increasingly focused on insider trading risks as prediction markets expand into politics, economics, and global events.

As prediction markets gain traction among investors, traders, and policymakers, a growing debate is emerging around one of their most controversial challenges: insider trading. While regulators have largely favored aggressive enforcement, new academic research suggests that an outright ban may actually undermine the very purpose of prediction markets—producing accurate forecasts.

A recent study by Stevens Institute of Technology finance professor Balbinder Singh Gill argues that prediction markets operate differently from traditional financial markets and may require a more nuanced regulatory framework. The findings arrive at a critical moment as platforms such as Kalshi and Polymarket face increasing scrutiny from lawmakers and regulators over allegations of insider trading and market manipulation.

Why Prediction Markets Depend on Information

Prediction markets function by allowing participants to buy and sell contracts tied to future events, ranging from elections and economic indicators to corporate performance and geopolitical developments. Their value lies in aggregating diverse information into a single market-based probability estimate.

According to Gill’s research, insider information can sometimes improve market efficiency by helping prices reflect reality more quickly. The study identifies a trade-off: information-rich traders contribute to more accurate forecasts, but excessive insider activity can discourage broader participation by making ordinary traders feel disadvantaged.

The research describes this dynamic as a balancing act. Too little enforcement can allow insiders to dominate markets and reduce confidence among participants. Too much enforcement, however, may eliminate valuable information that helps markets generate accurate predictions.

The result is what Gill describes as a “hump-shaped” relationship between enforcement and market accuracy, where the optimal outcome lies somewhere between no regulation and a total ban.

Not All Insider Information Is Equal

One of the study’s most significant contributions is its distinction between different forms of informational advantages.

The research argues that information obtained through independent research should face little or no regulatory intervention. Traders who spend significant time and resources gathering insights contribute positively to market efficiency and should not be discouraged from participating.

By contrast, information obtained through leaks, confidential documents, or classified sources warrants stronger enforcement because it creates unfair advantages and undermines trust in the marketplace.

The highest level of enforcement should apply when traders can directly influence the event outcome. For example, a political candidate wagering on their own election or an executive trading on company-related prediction contracts presents a clear conflict of interest that could encourage manipulation.

This framework moves beyond a one-size-fits-all regulatory approach and instead focuses on the source and nature of the information being used.

Regulators Increase Scrutiny

The debate is far from theoretical. Regulatory pressure on prediction markets has intensified throughout 2026.

The Commodity Futures Trading Commission has publicly warned that insider trading on prediction platforms could trigger enforcement actions. Meanwhile, congressional lawmakers have launched investigations into major operators, including Kalshi and Polymarket.

Several high-profile cases have amplified concerns. Authorities recently charged a Google employee accused of using internal search data to generate approximately $1.2 million through prediction market positions. In a separate case, a US military servicemember allegedly traded based on classified information related to military operations.

In response, Kalshi has begun implementing new compliance measures requiring employment disclosures in certain sensitive markets and assigning risk scores to contracts vulnerable to insider activity.

The Future of Prediction Market Regulation

As prediction markets continue expanding into mainstream finance and public discourse, regulators face a complex challenge. Excessive restrictions could weaken market participation and reduce forecasting quality, while insufficient oversight risks damaging credibility and public trust.

Gill’s research suggests that calibrated enforcement may provide the most effective path forward. Rather than attempting to eliminate all informational advantages, regulators may increasingly focus on distinguishing between legitimate research, unfair access, and outright manipulation.

With prediction markets becoming an increasingly important tool for forecasting economic, political, and societal outcomes, the decisions made by regulators over the coming years could determine whether these platforms evolve into trusted financial instruments or remain controversial speculative venues.