Stevens Institute of Technology assistant professor of finance Balbinder Singh Gill released a research paper on June 2 examining insider trading enforcement in prediction markets. The study concluded that regulators should adopt a balanced enforcement approach rather than imposing outright bans, as complete prohibition could reduce market accuracy by eliminating valuable information. The research comes amid increased regulatory scrutiny, with the Commodity Futures Trading Commission warning in April that insider traders could face enforcement action and US lawmakers launching investigations in May into platforms including Kalshi and Polymarket over insider trading and market manipulation concerns.
Gill's Research Identifies Dual Effects of Insider Trading
Gill developed a formal economic model examining how insider trading affects prediction markets. The research found that insider trading can improve market accuracy by introducing valuable information, as insider traders often possess information that can help prices reflect real-world probabilities more quickly. However, the study also found that excessive insider activity can discourage participation and reduce liquidity, as ordinary participants may choose not to take part in the market.
Gill described this dynamic as a paradox, noting that the same insider trade that improves price accuracy in the short term can ultimately reduce the participation needed to maintain accurate markets in the future. His model found that market accuracy follows a "hump-shaped" relationship with enforcement intensity. Too little enforcement allows insiders to dominate markets and crowd out other participants, while excessive enforcement removes valuable information that insiders can contribute. Gill concluded that the optimal level of enforcement lies somewhere in the middle rather than at either extreme.
Study Recommends Tiered Enforcement Based on Information Source
The paper argues that regulators should distinguish between different types of insider information. Information obtained through legitimate research and analysis should face minimal restrictions because it reflects effort and contributes to market efficiency. Information acquired through leaks, misappropriation, or access to confidential data should be subject to stronger enforcement measures.
The study states that the strictest oversight should apply to individuals who have the ability to influence the outcome of an event while simultaneously trading on it, such as political candidates betting on their own elections. Gill concluded that enforcement in prediction markets should be calibrated rather than maximal.
CFTC and Lawmakers Increase Scrutiny of Prediction Markets
The Commodity Futures Trading Commission warned in April that insider traders could face enforcement action. US lawmakers launched investigations in May into platforms including Kalshi and Polymarket over concerns about insider trading and market manipulation.
Kalshi Introduces Disclosure Requirements and Risk-Scoring System
Kalshi announced new measures to reduce insider trading risks. The platform introduced a requirement for users participating in sensitive markets to disclose their employers. Kalshi also introduced a risk-scoring system for markets that may be vulnerable to insider information or manipulation. The changes follow recommendations from an independent audit committee and increasing pressure from regulators and policymakers seeking stronger safeguards for prediction market participants.
FAQ
What did Balbinder Singh Gill's research find about insider trading in prediction markets?
The research released on June 2 found that insider trading has dual effects on prediction markets. While insider trading can improve market accuracy by introducing valuable information, excessive insider activity can discourage participation and reduce liquidity. Gill's model found that market accuracy follows a "hump-shaped" relationship with enforcement intensity, with optimal enforcement lying somewhere in the middle rather than at either extreme.
What enforcement measures did Kalshi introduce in response to regulatory pressure?
Kalshi announced new measures requiring users participating in sensitive markets to disclose their employers. The platform also introduced a risk-scoring system for markets that may be vulnerable to insider information or manipulation. These changes follow recommendations from an independent audit committee and increasing pressure from regulators and policymakers.
