Prediction markets face liquidity gaps amid insider risk

Prediction markets are immature: insider trading, fragmentation, thin participation

according to PANews, investor nic carter argues prediction markets remain immature, citing insider trading risks, fragmentation across venues, and a lack of natural buyers and sellers. These structural issues can distort prices and limit the usefulness of event contracts.

Insider trading in prediction markets typically concerns material nonpublic information about policy decisions, corporate actions, or election processes. When such information is traded, price discovery may improve temporarily but fairness and confidence can degrade.

Fragmentation arises because platforms list overlapping questions with different wording, rules, and settlement criteria. These semantic differences make contracts non-fungible, complicate arbitrage, and can leave identical real-world events priced differently.

Thin participation compounds the problem. Without a balanced mix of informed traders, liquidity providers, and hedgers, spreads widen and adverse selection increases, deterring new entrants and compressing market depth.

Why insider trading in prediction markets, fragmentation, and compliance matter

Insider trading in prediction markets intersects with familiar market-abuse concepts like MNPI, surveillance, and conflicts of interest. The enforcement lens is tightening, and compliance expectations increasingly resemble those of regulated exchanges.

Regulatory posture is also turning more explicit. After stressing that traditional fraud standards still apply, Jay Clayton, U.S. Attorney for the Southern District of New York, said: “Prediction markets cannot hide behind the ‘prediction market’ label to escape fraud or insider information laws.”

Academic evidence suggests fragmentation has measurable pricing effects. Based on an arXiv study by Jonas Gebele and Florian Matthes, overlapping markets on different platforms exhibited persistent 2–4% price divergences, reflecting semantic non-fungibility and arbitrage frictions.

Practically, these dynamics hamper trust and usability. If users fear counterparties with informational advantages, or cannot rely on harmonized contract definitions, participation and liquidity may fall, raising execution costs.

Platform activity and integrity signals now matter more. news/detail/12560605212135″ target=”_blank” rel=”nofollow noopener”>As reported by Bitget News, citing defioasis.eth, Polymarket Builders reached approximately $125 million in weekly trading volume, highlighting demand even as structural frictions persist.

Institutionalization hinges on credible controls. According to Business Insider, Kalshi supports a targeted insider-trading ban for government officials and has implemented MNPI prohibitions modeled on NYSE and Nasdaq practices to bolster market integrity.

Liquidity quality depends on “natural” participants, those hedging real exposures, not just speculating. When such buyers and sellers stay on the sidelines, markets skew toward insiders and short-term traders, keeping depth thin and spreads wide.

Emerging fixes: market integrity and liquidity improvements

What concrete steps are platforms like Polymarket and Kalshi taking to improve market integrity and compliance?

Operators are being steered toward exchange-grade controls. According to JD Supra, core elements include MNPI policies, surveillance, KYC/AML, and jurisdiction-specific licensing frameworks. Sources reviewed highlight Kalshi’s MNPI prohibitions; platform-specific initiatives for Polymarket were not detailed.

What are regulators saying about prediction markets, and how does legal uncertainty impact users and operators?

State authorities are sharpening warnings. As reported by Yahoo News, regulators in several states, including Washington, have cautioned that event contracts may violate gambling laws, increasing operational risk for platforms and users. Legal ambiguity can elevate compliance costs and slow mainstream adoption.

FAQ about insider trading in prediction markets

Why are prediction markets fragmented across platforms, and how does that lead to price divergences?

Different question wording and settlement rules reduce contract fungibility, hindering arbitrage and allowing similar events to clear at different prices across venues.

Who are the natural buyers and sellers in prediction markets, and why does their absence hurt liquidity?

Natural participants hedge real-world risks. Without them, markets rely on speculators and insiders, worsening adverse selection, widening spreads, and weakening depth.