Study: Calibrated insider trading rules for prediction markets
One study from Stevens Institute of Technology tok say prediction markets no suppose to put full ban on insider trading. Finance professor Balbinder Singh Gill model how insider trading dey affect price accuracy, participation and liquidity.
The paper find one "hump-shaped" relationship between how strong enforcement be and market accuracy. If enforcement too weak, insiders fit dominate and push outside traders commot, wey dey reduce how useful prices dey for long term. If enforcement too tight e fit also backfire because e go stop insiders from giving legit information.
Gill recommend make enforcement dey calibrated based on insider trading risk and where the information come from. Information wey come from independent research suppose get lighter restrictions. Stronger penalties suppose target leaked or confidential data. The strictest oversight suppose apply when traders fit influence the outcome and trade on am, like candidates wey bet on their own campaigns.
This one come as US regulators dey increase scrutiny. CFTC warn for April about possible insider trading enforcement actions. For May, lawmakers open probes into platforms like Kalshi and Polymarket for concerns about insider trading and manipulation. Kalshi say dem go add employer-disclosure requirements for sensitive markets and bring in market risk-scoring system.
For crypto traders, the lesson be say enforcement on insider trading fit shape market quality and liquidity more than outright bans—this fit affect people feeling about regulated prediction-market venues.
Neutral
Di study get nuanced policy stance: e dey argue against total ban for insider trading and instead e dey call for calibrated enforcement based on wetin be the source of information. Dis fit generally help prediction-market price quality for long run, because e wan preserve legit insider information value while reduce harm from leaked or outcome-influencing information. But short-term effects go depend on how regulators and platforms take implement the approach—investigations and compliance changes (e.g., Kalshi’s disclosure and risk scoring) fit temporarily increase uncertainty and affect liquidity or trading behaviour. Overall, the direction no be clear upside or downside for crypto prices; e likely go influence sentiment toward prediction-market venues and their market quality rather than drive a direct, sustained move in any single token’s price.