The monthly trading volume of prediction markets has surpassed $24 billion, and it is expected to reach $1 trillion by 2030. Authors from the Mises Institute believe that the U.S. government's regulation suppression of prediction markets is not motivated by public protection, but rather by "protecting itself." What they truly fear is that this mechanism can correct regulators' erroneous judgments in real time and openly.

Written 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, American regulators cannot sit idle.

Prediction markets are experiencing rapid expansion. The Mises Institute recently published a lengthy article written by Angelo Monaco, outlining the operational logic of prediction markets, their explosive growth trends, and why the U.S. government is eager to impose controls on them.

The article judges that the suppression of prediction markets by U.S. regulators appears to be "protecting the public," but in essence is "protecting themselves." What regulators truly fear is not that these markets will malfunction, but rather that they are functioning too well—so well that they can publicly slap regulators in the face regarding their predictive capabilities.

The logic of prediction markets is not complex. Platforms represented by Polymarket and Kalshi are essentially financial exchanges: users buy and sell contracts based on the outcomes of real events, with contract prices fluctuating between 1 cent and 99 cents, directly reflecting the market's collective judgment on the probability of an event occurring. When the event occurs, the contract settles at $1; those who predict correctly profit, while those who predict incorrectly incur losses. This mechanism forces every participant to put real money on the line for their judgment.

Currently, the monthly trading volume of prediction markets has surpassed $24 billion. Analysts expect the overall market size to exceed $240 billion, with a possibility of surpassing $1 trillion in annual trading volume by 2030. This growth rate is rare in the financial industry.

Iran War: Prediction Markets Were Hours Ahead of the Pentagon's Press Conference

The article uses the conflict in Iran at the beginning of 2026 as a core case to demonstrate the practical value of prediction markets.

From late 2025 to January 2026, when the local unrest in Iran was just beginning, mainstream analysis companies and media generally predicted that energy markets would remain stable, with the annual average price forecast for Brent crude oil set between $55 and $60 per barrel. However, during the same period, clear divergence signals appeared in the oil options market and decentralized geopolitical event contracts—while analysts on television were telling the public "not to panic," traders betting real money were significantly raising the probability of the "worst-case scenario."

Weeks before the U.S.-led coalition launched strike operations in February, the market had already begun pricing in the structural vulnerabilities of the Strait of Hormuz.

In March, Iran blocked the Strait of Hormuz, disrupting about 20% of the world's oil supply. At that point, the prediction markets on Polymarket and IMF PortWatch had already provided clear judgments hours before the Pentagon's press conference by integrating satellite tracking data, skyrocketing insurance rates, and regional shipping company data.

The article points out that if you relied only 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 Already Said: The CFTC's Concerns Lack "Concrete Evidence"

Do regulators' logic hold water? The article argues that the answer is no.

The most representative legal case is Kalshi v. CFTC. The Commodity Futures Trading Commission (CFTC) attempted to ban contracts related to congressional elections in federal court, but the D.C. Circuit Court of Appeals explicitly rejected the government's suspension request. The court's wording was very direct: the CFTC’s concerns about market manipulation and threats to election integrity were deemed "speculative and lacking concrete evidence."

The court further determined that the CFTC exceeded its statutory authority and failed to prove that trading on political outcomes would cause immediate harm to the public interest. This ruling directly cleared the path for the legalization of commercial election event contracts in the U.S.

The greatest "national security threat" case cited by the CFTC was a U.S. Army soldier profiting over $404,000 on prediction markets using confidential information from Venezuela's actions in April 2026. This case was heavily promoted by the federal government. However, the article notes that it remains the only major case involving national security. Using an isolated case to argue for systemic harm does not hold logically.

The Real Motive of U.S. States: Not Protecting the Public, but Maintaining Tax Revenue

If the federal-level suppression is largely about "narrative control," then the motivations at the state level are more direct—money.

The article cites data from the American Gaming Association on commercial gambling revenue tracking: since the beginning of 2025, prediction market platforms have cost state governments about $950 million in potential gambling tax revenue.

The reason lies in a regulatory arbitrage loophole: traditional sports betting entities must pay high gross gaming revenue (GGR) taxes to state gaming commissions, while prediction market platforms categorize themselves as "financial instruments," thus only needing to pay standard corporate income taxes, completely bypassing state-level gambling tax systems.

Taking Minnesota as an example, when the state enacted a ban on prediction markets, the core argument in legislative debates was not "social harm" but rather market share and tax revenue loss. The article's judgment is that the "harm" pointed out by the states often concerns anticipated tax losses and threats to traditional gambling monopolies, rather than substantiated social issues.

Hayek Said This Long Ago

In arguing the informational value of prediction markets, the article references the classic assertion by economist Friedrich Hayek.

Hayek once pointed out that the decentralized price mechanism is the only tool for coordinating global "local knowledge." No single expert, federal agency, or algorithm can comprehend the fragmented information scattered across the globe. Essentially, prediction markets are doing one thing: crowdsourcing global wisdom.

In contrast, polls and regulatory reports are static snapshots—by the time they are published, they are often outdated. Prediction markets are dynamic and continuous. When a geopolitical event occurs or economic data is leaked, the instant fluctuations in contract prices communicate the significance of that information faster than any editor can rush to report.

The article also mentions a common scenario: if a cable news host is shouting that a certain piece of legislation is "guaranteed to pass," but the corresponding prediction market contract price is only 12 cents, you immediately know where the gap between reality and rhetoric lies. This serves as a real-time "wake-up check."