Study: Calibrated Insider Trading Rules for Prediction Markets
A Stevens Institute of Technology study argues that prediction markets should not impose a maximal ban on insider trading. Finance professor Balbinder Singh Gill models how insider trading affects price accuracy, participation and liquidity.
The paper finds a “hump-shaped” link between enforcement intensity and market accuracy. Too little enforcement can let insiders dominate and crowd out outside traders, reducing longer-term price informativeness. Too much enforcement can also backfire by restricting insiders’ ability to provide legitimate information.
Gill’s key recommendation is calibrated enforcement based on the insider trading risk and the information source. Information gained from independent research should face lighter restrictions. Tighter penalties should target leaked or confidential data. The strictest oversight should apply when traders can influence an event’s outcome and trade on it, such as candidates betting on their own campaigns.
This comes as U.S. regulators step up scrutiny. The CFTC warned in April about potential insider trading enforcement actions. In May, lawmakers opened probes into platforms including Kalshi and Polymarket for insider trading and manipulation concerns. Kalshi also says it will add employer-disclosure requirements for sensitive markets and introduce a market risk-scoring system.
For crypto traders, the takeaway is that insider trading enforcement may shape market quality and liquidity more than outright bans—potentially affecting sentiment toward regulated prediction-market venues.
Neutral
The study’s policy stance is nuanced: it argues against a maximal ban on insider trading and instead calls for calibrated enforcement by information source. That could be broadly constructive for prediction-market price quality over the long run, because it aims to preserve legitimate insider informational value while reducing harm from leaked or outcome-influencing information. However, near-term effects depend on how regulators and platforms operationalize the approach—investigations and compliance changes (e.g., Kalshi’s disclosure and risk scoring) can temporarily increase uncertainty and affect liquidity or trading behavior. Overall, the direction is not a clear upside or downside for crypto prices; it is more likely to influence sentiment toward prediction-market venues and their market quality rather than drive a direct, sustained move in any single token’s price.