Article by: Long Yue
Source: Wall Street Journal
When a decentralized crowd can predict wars, policies, and market trends more accurately than U.S. federal agencies, U.S. regulators can no longer sit idle.
Prediction markets are undergoing rapid expansion. The Mises Institute recently published a lengthy article by Angelo Monaco that outlines the working mechanisms of prediction markets, their explosive growth, and why the U.S. government is eager to impose regulatory controls on them.
The article argues that U.S. regulators are cracking down on prediction markets under the guise of “protecting the public,” but in reality, they are “protecting themselves.” What regulators truly fear is not that these markets will fail, but that they will succeed too well—so well that they publicly expose the regulators’ own predictive shortcomings.
The logic of prediction markets is not complicated. Platforms like Polymarket and Kalshi are essentially financial exchanges: users buy and sell contracts based on the outcomes of real-world events, with contract prices fluctuating between one cent and 99 cents, directly reflecting the market’s collective judgment of the probability of an event occurring. If the event happens, the contract settles at one dollar; those who predicted correctly profit, while those who were wrong lose money. This mechanism forces every participant to back their judgment with real money.
Currently, the monthly trading volume of prediction markets has surpassed $24 billion. Analysts expect the overall market size to exceed $240 billion, with the potential to achieve annual trading volumes of over $1 trillion before 2030. This rate of growth is rare in the financial industry.
Iran war: Prediction markets beat the Pentagon press conference by several hours
The article uses the early 2026 Iran conflict as a core case study to demonstrate the practical value of prediction markets.
From late 2025 to January 2026, as initial unrest began in Iran, mainstream analytical institutions and media generally predicted that energy markets would remain stable, with Brent crude oil’s annual average price forecast ranging from $55 to $60 per barrel. However, during the same period, clear divergence signals emerged in the crude oil options market and decentralized geopolitical event contracts—while TV analysts told the public “there’s no need to panic,” traders risking real money were already significantly increasing the implied probability of the worst-case scenario.
The market had already priced in the structural fragility of the Strait of Hormuz weeks before the U.S.-led coalition launched its strike operations in February.
In March, Iran blockaded the Strait of Hormuz, disrupting approximately 20% of global oil supplies. At that time, prediction markets on Polymarket and IMF PortWatch had already provided a clear assessment—hours before the Pentagon held a press conference—by integrating satellite tracking data, surging insurance rate signals, and information from regional shipping companies.
The article points out that if you relied solely on traditional energy forecasts in January, you would have been told that a sharp rise in oil prices was a low-probability event.
The court has stated: The U.S. CFTC's concerns lack specific evidence.
Is the regulator's logic sound? The article argues that the answer is no.
The most representative legal case is Kalshi v. CFTC. The U.S. Commodity Futures Trading Commission (CFTC) attempted to obtain a federal court injunction to block contracts related to congressional elections, but the U.S. Court of Appeals for the District of Columbia Circuit explicitly denied the government’s request for a stay. The court’s language was direct: the CFTC’s concerns regarding market manipulation and threats to election integrity were deemed “speculative and unsupported by concrete evidence.”
The court further determined that the CFTC exceeded its statutory authority and failed to demonstrate that political outcome trading would cause immediate harm to the public interest. This ruling directly paves the way for the legalization of contracts based on U.S. commercial election events.
The CFTC cited as its largest example of a "national security threat" a case in April 2026, in which a U.S. Army soldier profited over $404,000 on prediction markets using classified information from Venezuelan operations. This case was heavily publicized by the federal government. However, the article points out that this remains the only significant case involving national security to date. Using a single isolated case to argue for systemic harm is logically unsound.
The real motive of U.S. states: not to protect the public, but to preserve tax revenue
If federal-level crackdowns are more about "narrative control," then the states' motivations are more direct—money.
The article cites tracking data from the American Gaming Association on commercial gaming revenue: since the beginning of 2025, prediction market platforms have cost state governments approximately $9.5 billion in potential gaming tax revenue.
The reason lies in a regulatory arbitrage loophole: traditional sports betting operators must pay high gross gaming revenue (GGR) taxes to state gaming commissions, while prediction market platforms classify themselves as "financial instruments," thus only paying standard corporate income tax and completely bypassing state gambling tax systems.
Using Minnesota as an example, when the state enacted its ban on prediction markets, the central argument in legislative debates was not “social harm,” but rather market share and tax revenue loss. The article concludes that the “harm” cited by states is often anticipated tax losses and threats to traditional gambling monopolies, rather than documented social issues.
Hayek long ago said this.
In arguing for the informational value of prediction markets, the article cites the classic assertion by economist Friedrich Hayek.
Hayek once pointed out that the decentralized price mechanism is the only tool capable of coordinating local knowledge across the globe. No single expert, federal agency, or algorithm can grasp the fragmented information scattered worldwide. Prediction markets essentially do one thing: crowdsource global wisdom.
In contrast, polls and regulatory reports are static snapshots—often outdated by the time they are published. Prediction markets, however, are dynamic and continuous. When a geopolitical event occurs or economic data is leaked, the immediate price movement in contracts tells you faster than any breaking news article just how significant that information truly is.
The article also mentions a everyday scenario: if a cable news host is shouting that a piece of legislation is "certain to pass," but the corresponding prediction market contract is trading at just 12 cents, you immediately understand the gap between rhetoric and reality. This is a real-time "reality check."