At the end of February 2026, the United States and Israel launched a joint military strike against Iran. Prior to this, Polymarket—the world’s largest decentralized prediction market—saw a series of contracts related to the timing of US military action against Iran reach a cumulative trading volume of over $529 million. This figure made it one of the largest markets in Polymarket’s history, rivaling the volume of bets placed on the 2024 presidential election.
What’s even more astonishing than the sheer size is the trading pattern itself. In the 24 hours leading up to the attack, more than 150 accounts placed hundreds of bets, each worth at least $1,000, wagering that the US military would strike the following day. The total amount reached approximately $855,000. At least 16 accounts profited over $100,000 by betting on an attack on the 28th, while another 109 accounts earned more than $10,000. Blockchain analytics firm Bubblemaps also found that six accounts created in February concentrated their bets just hours before the attack, collectively earning around $1.2 million.
This "uncannily precise" betting pattern sparked intense suspicions of insider trading. Prediction markets are supposed to price event probabilities effectively through "collective intelligence," but when informed traders leverage their information advantage ahead of time, the mechanism faces fundamental challenges.
How Do Prediction Markets Turn Future Events into Tradable Assets?
To understand the controversy, it’s essential to clarify how prediction markets operate. Platforms like Polymarket aren’t traditional gambling sites; they’re financial systems that turn "opinions" into tradable "assets."
In a typical binary market (for example, "Will the US strike Iran before the end of March?"), smart contracts generate "Yes" and "No" shares. These shares can be freely traded in secondary markets, with prices fluctuating between $0.00 and $1.00, directly representing the market’s implied probability of the event. For instance, if "Yes" shares trade at $0.60, the market believes there’s a 60% chance the event will occur.
Users can profit in two ways: first, by buying shares low and selling high before the outcome is revealed; second, by holding until settlement—if their prediction is correct, each winning share redeems for $1, while losing shares become worthless. The entire process is executed automatically by smart contracts, eliminating the need for trusted intermediaries. This ability to aggregate dispersed private information into a unified probability through financial incentives theoretically gives prediction markets an edge over traditional polling.
However, this mechanism relies on a core assumption: participants have symmetric information. Once this assumption breaks down, price signals can become distorted.
What Does It Mean When 2% of Users Generate 90% of Trading Volume?
Polymarket’s user structure reveals the true nature of prediction markets. On-chain data analysis shows that just 2% of users—high-frequency professionals (over 200 trades, more than $100,000 traded)—generate nearly 90% of the platform’s trading volume. Meanwhile, 69% of low-activity retail users average fewer than 10 trades each, with a median total investment of only $224.
From a broader perspective, the monthly trading volume in the prediction market industry surged from $1.2 billion at the start of 2025 to over $20 billion, with the number of independent wallets tripling in the six months leading up to February 2026, reaching 840,000. Geopolitical events, macroeconomic dynamics, and US politics now dominate trading activity, surpassing the previously "crypto-native" markets.
TRM Labs’ research found that the top 10 most profitable wallets on Polymarket in early 2026 employed three main strategies: betting on macro beliefs, algorithmic market making, and event-driven opportunities. The leading wallets earned $6.2 million across multiple markets, including Federal Reserve decisions, the World Cup, and elections. Six of these wallets traded daily for 80 consecutive days.
This structure means that Polymarket’s price signals largely reflect the judgments of a small group of professional traders, rather than true "collective intelligence." When these traders hold an information advantage, the reliability of price signals is significantly diminished.
The Cost of Precise Bets: Insider Trading Suspicions and Ethical Dilemmas
The core controversy exposed by Polymarket during the Iran incident isn’t a flaw in prediction market mechanisms, but rather the structural risks that arise when these markets intersect with geopolitical realities.
First are the allegations of insider trading. Dartmouth economics professor Eric Zitzewitz noted that the surge in last-minute bets before the attack "makes one suspect someone had advance knowledge of the exact timing." Bubblemaps’ CEO pointed out that the combination of conflict, war, and user anonymity creates "incentives for informed participants to act ahead of time." Notably, a similar pattern emerged in January 2026: four newly created wallets concentrated bets on US military action against Iran, with no other trading activity besides these predictions.
Second are ethical concerns. Democratic Senator Chris Murphy expressed outrage, suggesting the abnormal betting before the attack implied possible conflicts of interest among decision-makers. He stated, "I strongly suspect that some people involved in war decisions placed bets in these markets, creating economic incentives—that’s worse than simple insider trading." Former SEC official Amanda Fischer warned, "If people can profit from predicting others’ deaths, it creates dangerous incentives."
Additionally, extreme cases have emerged where participants attempted to manipulate markets by interfering with news reporting. During the Iran-Israel missile incident, Polymarket users harassed and threatened an Israeli journalist, demanding she alter her reporting on missile impacts to sway the outcome. This demonstrates that when the financial stakes are high enough, participants may not only exploit information but attempt to distort it.
Shifting Pricing Power: How Prediction Markets Are Reshaping Geopolitical Risk Valuation
Despite ongoing controversy, Polymarket’s performance during the Iran incident highlights a significant trend: pricing power is shifting from traditional institutions to market participants.
When the conflict escalated at the end of February, traditional financial markets were closed for the weekend, but on-chain markets completed the first round of risk expression. Polymarket data shows that contracts on "US strikes Iran before the end of March" had already accumulated over $500 million in trading volume before the conflict erupted. This data indicates that war scenarios, once analyzed by intelligence agencies and military think tanks, are now being voted on in real time by tens of thousands of market participants with their capital.
The probability curves formed by this "collective intelligence" are more liquid and responsive than any single institution’s forecasts. For the first time, geopolitical risk is being financialized in real time. Changes in prediction market probabilities spill over into traditional asset pricing through arbitrage and expectations. When Polymarket’s probability for "prolonged conflict" exceeds 50%, traders immediately buy oil call options to hedge.
For the crypto market, this linkage means asset characteristics are undergoing a thorough stress test. Amid macro turbulence triggered by the US-Iran conflict, Bitcoin behaved more like a high-beta risk asset than a safe haven. This shows that in the face of extreme uncertainty, crypto assets’ liquidity sensitivity outweighs their store-of-value function. Crypto assets are no longer isolated; they’ve become part of the global macro symphony.
Future Evolution: Regulatory Storms, Business Model Transformation, and Market Restructuring
The Iran incident on Polymarket won’t be an isolated case; its ripple effects will reshape the prediction market industry across multiple dimensions.
On the regulatory front, suspicious betting activity has prompted US lawmakers from both parties to propose stricter measures. Senators Chris Murphy and Mike Levin introduced a bill to restrict or ban betting on military actions, regime changes, or events that could incentivize conflict. Senators Richard Blumenthal and Andy Kim proposed the "Prediction Market Safety and Integrity Act," which more comprehensively bans betting on "material non-public information" and restricts operators from offering easily manipulated contracts. On March 23, 2026, Kalshi and Polymarket announced new measures to curb abnormal behavior, including tighter controls on access to non-public information.
On the business model side, Polymarket is undergoing a structural shift from "free user acquisition" to "fee-based monetization." On March 30, 2026, the platform completed a full rollout of its fee model, now charging taker fees across all core categories—crypto, sports, politics, finance, economics, culture, and weather. According to Dune Analytics estimates, with current daily trading volume around $160 million, the platform’s daily fee revenue is about $1.2 million, with protocol net income ranging from $570,000 to $950,000 per day, annualized at $209 million to $342 million. This revenue puts Polymarket among the highest-earning applications in the crypto industry.
In terms of market structure, prediction markets are moving from "single narrative" to "multi-topic diversification." TRM Labs analysis shows Polymarket’s trading isn’t concentrated on one story, but spread across leader succession probabilities, conflict scenarios, and policy events. This reduces dependence on any single event, but also creates regulatory fragmentation—different topics have vastly different legal statuses across jurisdictions.
Potential Risks: The Transparency Paradox, Information Asymmetry, and Moral Hazard
When considering the future of prediction markets, it’s crucial to confront their inherent structural risks.
The transparency paradox is one of the most ironic risks. Blockchain’s full transparency should be an industry moat, but in the insider trading controversy, on-chain data became "evidence for prosecution." The six accounts suspected of betting on the US-Iran conflict had clustered creation times, highly similar funding paths, and no other trading history besides these bets. This "pure" transaction history, in the blockchain context, actually serves as strong evidence of "clear intent and precise information." Transparency here becomes a double-edged sword.
The intensification of information asymmetry is another risk. While on-chain data is transparent, only institutional investors with high-speed algorithms and cross-market channels can interpret and act on it quickly. This further concentrates information advantage among professional traders, leaving ordinary investors more vulnerable to macro volatility. TRM Labs analysts have observed multiple signs of market manipulation, including wallets coordinating positions before major news and single players controlling prices in low-liquidity markets.
Moral hazard is equally concerning. Critics argue that when war and bloodshed are turned into financial products, this model not only encourages speculation and insider trading, but may even incentivize violent events. Prediction markets have long emphasized their value in helping investors hedge economic and geopolitical risk, but in the Iran-related trades, this case didn’t serve as a "validation sample" for prediction market value—instead, it’s a cautionary tale, showing these platforms still fall short in providing actionable signals.
Conclusion
Polymarket’s Iran war bets surpassing $500 million demonstrate the potential of prediction markets as new tools for information aggregation and pricing, but also expose structural flaws in information asymmetry, ethical boundaries, and regulatory adaptability. When 2% of users contribute 90% of trading volume, so-called "collective intelligence" is more like the collective judgment of a handful of professional traders. When informed traders can leverage their advantage ahead of time, price signals may shift from "collective insight" to "insider monetization." When the outcome of war can be precisely wagered on, the conflict between financialization and ethical boundaries becomes unavoidable.
The future of prediction markets will depend on how they find balance across three dimensions: between information transparency and preventing insider trading, between financial innovation and social ethics, and between global operations and localized regulation. Whatever direction the industry ultimately takes, Polymarket’s Iran incident has drawn a clear dividing line for the entire sector.
FAQ
Q: Are Polymarket’s prediction probabilities actually reliable?
Polymarket’s price signals offer some reference value in markets with ample liquidity and diverse participants. However, since about 90% of trading volume comes from just 2% of high-frequency professional users, these price signals mostly reflect the judgments of a small group of traders rather than true "collective intelligence." When information asymmetry exists, price signals can be distorted.
Q: Is betting on war events legal on Polymarket?
Polymarket currently operates in a legal gray area in the United States. The platform claims it should be regulated by the Commodity Futures Trading Commission rather than state gambling regulators. However, several US lawmakers have introduced bills explicitly banning bets on military actions, regime changes, or death events. No final legislation has been enacted yet.
Q: Is it easy to make money betting on Polymarket?
The data suggests otherwise. About 87.3% of users ultimately lose money on the platform. The core profit group consists of professional market makers with algorithmic strategies and high-frequency trading capabilities, not ordinary retail traders.
Q: Can insider trading be detected on Polymarket?
Thanks to blockchain transparency, on-chain analytics firms can track abnormal trading patterns, such as concentrated bets from newly created accounts and highly similar funding paths. However, on-chain data can only reveal "correlations" and "anomalies"—it cannot serve as "direct evidence" of insider trading.
