Regulators Push Insider Trading Controls for Crypto Prediction Markets

A scholarly framework from Stevens Institute of Technology argues for a measured approach to enforcing insider trading rules in prediction markets, rather than pursuing an outright ban. The work suggests that price accuracy in these markets responds to enforcement intensity in a non-linear way, and that policy should aim for a calibrated middle ground to maintain both market integrity and participation.

The paper, released on June 2 by Balbinder Singh Gill, assistant professor of finance, develops a formal economic model to explore how strictly insider trading in prediction markets should be policed. According to Cointelegraph, the model reveals that prediction-market price accuracy varies in a “hump-shaped” fashion with enforcement intensity: too little enforcement invites insiders to crowd out participants, while too much enforcement suppresses the insider’s informative contribution.

Gill explains that tougher enforcement can actually enhance participation by limiting insider-driven distortion, yielding an interior optimum where enforcement is neither minimal nor maximal. “Trade-offs matter,” he suggests, and the resulting policy recommendation favors calibrated enforcement aimed at preserving informative trading without stifling legitimate information discovery.

Key takeaways

1. Optimal enforcement for prediction-market insider trading is interior—neither a complete laissez-faire regime nor an outright ban.


2. The appropriate level of enforcement should depend on the provenance of the information driving trades.


3. Hard-won, independently researched edges warrant lower enforcement, while misappropriated information and manipulation risk justify stronger action.


4. Regulatory actions and platform responses are already evolving, with ongoing enforcement warnings and measures to increase disclosure and oversight in sensitive markets.


5. High-profile cases and congressional attention underscore the broader regulatory relevance for platforms, financial institutions, and market participants.

Calibrated enforcement in prediction markets

The central argument of Gill’s model is that price discovery in prediction markets benefits from a balanced enforcement regime. Inadequate enforcement allows insiders to crowd out diverse participation, undermining the informational content of prices. Conversely, excessive enforcement can suppress insider contributions that carry genuine, timely information, thereby degrading market efficiency. The resulting insight is that enforcement should be calibrated to achieve optimal welfare, rather than pursuing maximal crackdowns or laissez-faire tolerance.

Gill emphasizes that the impact of enforcement depends on the nature of the information and its source. Markets should be designed to tolerate the kind of information that participants obtain through legitimate, diligent efforts, while mitigating information flows that are misappropriated or susceptible to manipulation. The nuanced perspective aligns with a broader policy objective: preserve the integrity of price formation without disincentivizing information production and market participation.

Trading on a genuine, independently researched edge is the activity society should be most reluctant to punish And trading by those who can move the outcome warrants the stiffest enforcement, because their positions invite manipulation.

Kalshi’s response and enforcement landscape

The academic framing arrives as prediction-market operators increase their regulatory and operational safeguards. Kalshi, for its part, has begun introducing measures intended to curb insider trading by enhancing data collection and risk assessment in sensitive markets. Specifically, Kalshi is requiring users in certain markets—such as those tied to company performance or national security—to disclose their employer via an online form. It has also developed a “specific risk score” to flag markets with heightened insider-trading or manipulation risk.

The timing coincides with governance and regulatory developments following an audit-committee review and heightened scrutiny from lawmakers and regulators. The changes come amid broader enforcement attention on prediction markets: the Commodity Futures Trading Commission’s (CFTC) enforcement chief warned in April that insider-trading violators would face enforcement action, and in May U.S. House lawmakers opened a probe into Kalshi and Polymarket over insider trading concerns.

Two recent high-profile cases illustrate ongoing regulatory risk in this space. A Google employee was charged in May for allegedly using insider information about the company’s search trends to trade on Polymarket for substantial gains, and a U.S. soldier faced charges in April for trading on classified knowledge of a military operation. These incidents have fueled calls for stronger controls and more robust AML/KYC frameworks within prediction markets.

Regulatory context and policy implications

Gill’s framework sits amid a dynamic regulatory landscape that spans U.S. authorities and international approaches. In the United States, the CFTC continues to signal a zero-tolerance stance toward market manipulation and insider trading in derivatives-like markets, while lawmakers scrutinize platform conduct and enforcement effectiveness. The evolving oversight has implications for exchanges and liquidity providers, who must balance user privacy, data collection, and regulatory compliance requirements.

Beyond the U.S., the growing attention to stablecoins, cross-border activity, and regulatory harmonization—such as the European Union’s Markets in Crypto-Assets Regulation (MiCA) framework—highlights the need for consistent risk-management standards. Institutions engaged with prediction-market activity—banks, asset managers, and corporate treasury teams—face increasing compliance expectations around information governance, employee disclosures, and market manipulation controls. A calibrated enforcement approach that preserves legitimate information production while deterring misuses can help align market design with formal regulatory objectives and cross-border policy coherence.

From a risk-management and compliance perspective, the discussion underscores several practical implications for operators and participants. First, a tiered approach to information provenance—recognizing the difference between hard-earned research and misused confidential data—offers a path to more precise AML/KYC and surveillance requirements. Second, enhanced disclosure and risk-scoring mechanisms may be warranted in markets identified as susceptible to insider trading or manipulation. Finally, ongoing regulatory engagement—through supervisory guidance, enforcement actions, and legislative oversight—will continue to shape how prediction markets are structured and governed.

Closing perspective

As enforcement expectations evolve, the emphasis on calibrated, provenance-aware policies could refine how prediction-market platforms balance innovation with integrity. In the near term, continued regulatory scrutiny, platform-adjusted controls, and further empirical research will determine whether interior enforcement can reliably sustain price informativeness without stifling legitimate information discovery.

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