A new academic study is challenging the idea that insider trading in prediction markets should be completely outlawed, arguing instead that regulators may achieve better outcomes through carefully calibrated enforcement.
The research, published on June 2 by finance scholar Balbinder Singh Gill of Stevens Institute of Technology, explores how different levels of insider trading enforcement affect the accuracy and efficiency of prediction markets.
Using a formal economic model, Gill examined whether aggressive crackdowns or more moderate oversight would best serve market participants and regulators.
Accuracy Depends on Striking the Right Balance
According to the study, prediction markets face a difficult trade-off. Insider traders often possess information that can help market prices reflect reality more accurately.
However, their presence can also discourage ordinary participants from taking part if they believe insiders have an overwhelming advantage.
Gill’s model found that market accuracy does not improve indefinitely with stricter enforcement.
Instead, accuracy follows what economists describe as a “hump-shaped” pattern.
When enforcement is too weak, insiders dominate trading activity and discourage broader participation.
Conversely, when enforcement becomes too severe, markets lose valuable information that knowledgeable traders contribute.
The findings suggest that neither a completely hands-off approach nor a total prohibition produces the best results.
Instead, a moderate level of enforcement appears to generate the highest degree of price accuracy by balancing information flow with market participation.
Not All Insider Information Should Be Treated Equally
A central argument of the research is that enforcement policies should vary depending on the source of the information being used.
Gill contends that information obtained through legitimate research and analysis deserves the least regulatory scrutiny.
Traders who invest significant effort and resources into uncovering useful insights contribute valuable information to the market, and excessive punishment could discourage such activity.
By contrast, information acquired through leaks, unauthorized disclosures, or access to confidential and classified material should face stronger enforcement measures.
Such information provides unfair advantages that can undermine confidence in market integrity.
The study reserves its strongest criticism for situations in which traders can directly influence the outcome on which they are wagering.
Examples include political candidates betting on their own elections or individuals with the power to affect the events being predicted.
In these cases, the risk extends beyond information asymmetry to potential manipulation of outcomes.
Regulators Intensify Focus on Prediction Markets
The study arrives as regulatory attention toward prediction markets continues to increase.
In April, the enforcement chief of the Commodity Futures Trading Commission warned that insider trading in prediction markets could trigger enforcement actions.
The following month, lawmakers in the United States House of Representatives launched an investigation into prediction market platforms Kalshi and Polymarket over concerns related to insider trading practices.
These developments reflect growing concerns among regulators and policymakers about the risks associated with rapidly expanding event-based trading markets.
Kalshi Introduces New Compliance Measures
As scrutiny increases, Kalshi has begun implementing additional safeguards designed to identify and deter insider trading activity.
Under the new measures, users participating in certain high-risk markets—including those involving corporate performance or national security topics—will be required to disclose employment information through an online verification process.
The company has also developed a risk-scoring system that assigns heightened monitoring to markets considered especially vulnerable to insider trading or manipulation.
The changes reportedly stem from recommendations made by an audit committee and follow pressure from both regulators and lawmakers seeking stronger oversight mechanisms.
Recent Cases Highlight Ongoing Concerns
Gill’s paper also referenced recent high-profile cases involving Polymarket that drew attention to the challenges facing the industry.
One case involved a Google employee who was charged in May after allegedly using nonpublic information about company search trends to generate approximately $1.2 million in profits through trades on Polymarket.
Another case centered on a U.S. soldier charged in April for allegedly trading based on classified knowledge connected to a military operation.
These incidents have intensified debate over how prediction markets should be regulated and whether existing safeguards are sufficient.
Study Calls for Targeted Rather Than Maximum Enforcement
Ultimately, Gill concludes that the most effective regulatory framework is one that carefully distinguishes between different forms of insider information and applies enforcement proportionately.
Rather than pursuing blanket prohibitions, the research argues that prediction markets function best when enforcement is tailored to the specific risks involved.
Such an approach, the study suggests, can preserve valuable information within markets while reducing opportunities for unfair advantages and manipulation.
