The research found that while insider trading can improve market accuracy by introducing valuable information, excessive insider activity can discourage participation and reduce liquidity. Gill concluded that enforcement should vary depending on the source of the information, with stricter penalties for traders using leaked or confidential information and those capable of influencing outcomes. Banning Insider Trading Could Hurt Prediction Markets Prediction market regulators should adopt a balanced approach to insider trading enforcement rather than imposing outright bans. This is according to new research from Stevens Institute of Technology assistant professor of finance Balbinder Singh Gill. In a paper that was released on June 2, Gill developed a formal economic model examining how insider trading affects prediction markets and how regulators can best preserve market accuracy while limiting abuse. The research shed some light on a fundamental tradeoff in prediction markets. Balbinder Singh Gill’s paper abstract Insider traders often possess information that can improve the accuracy of market prices by helping prices reflect real-world probabilities more quickly. However, excessive insider trading can discourage ordinary participants from taking part in the market, which reduces overall liquidity and makes prices less informative over time. Gill described this dynamic as a paradox, as 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. As a result, Gill concluded that the optimal level of enforcement lies somewhere in the middle rather than at either extreme. According to the study, tougher enforcement can increase participation by limiting insider advantages, but completely eliminating insider trading may reduce the flow of useful information into markets. The paper also 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. On the other hand, information acquired through leaks, misappropriation, or access to confidential data should be subject to stronger enforcement measures. The strictest oversight should apply to individuals who have the ability to influence the outcome of an event while simultaneously trading on it, like political candidates betting on their own elections. Gill’s findings come as regulators and lawmakers are scrutinizing prediction markets. The Commodity Futures Trading Commission warned in April that insider traders could face enforcement action, while US lawmakers launched investigations into platforms including Kalshi and Polymarket in May over concerns about insider trading and market manipulation. The debate also prompted action from prediction market operators themselves. Kalshi recently announced new measures to reduce insider trading risks, including 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. The changes follow recommendations from an independent audit committee and increasing pressure from regulators and policymakers seeking stronger safeguards for prediction market participants. Gill ultimately concluded that enforcement in prediction markets should be calibrated rather than maximal, as carefully balanced oversight can improve both market accuracy and overall participant welfare.
