'Maximal' ban on insider trading would hurt prediction markets, says researcher

Crypto Insight Summary: Balbinder Singh Gill, an assistant professor of finance at the Stevens Institute of Technology, published a paper on June 2 arguing that prediction market regulators should adopt a calibrated rather than maximal approach to insider trading enforcement. His formal economic model demonstrates that price accuracy is "hump-shaped" in enforcement intensity, meaning optimal enforcement is interior between laissez-faire and a complete ban. Gill recommends varying enforcement levels based on the source of insider information: researched information should face little or no enforcement, misappropriated information moderate enforcement, and cases where insiders can influence outcomes the strictest penalties. The paper comes amid heightened regulatory scrutiny, including a CFTC enforcement warning in April and a US House probe into Kalshi and Polymarket launched in May. Kalshi has responded by introducing new measures requiring users in sensitive markets to disclose employment information and implementing risk scoring for high-risk markets. Two recent Polymarket cases referenced in the paper include a Google employee charged in May with making $1.2 million using insider search trend data, and a US soldier charged in April with trading on classified military operation knowledge. Crypto Insight Analysis: Direction: Neutral Impact: Minor Impact Horizon: Short-term (1-3 days) Reasoning: Regulatory debate on insider trading may cause short-term uncertainty but unlikely to significantly impact prediction markets.