Fed Economists Praise Prediction Markets as States Step Up Enforcement

Federal researchers are embracing prediction markets as policy-relevant tools just as state regulators move to curtail them.

In a working paper, Federal Reserve researchers say macro-focused prediction markets can provide policymakers with a real-time, market-based gauge of inflation and interest-rate expectations. 

The authors found that the models closely match, and even exceed, forecasts from traditional benchmarks while providing continuously updated probability distributions around key data releases and policy meetings.

Their findings suggest prediction markets can serve as a “valuable complement to existing forecast tools in both research and policy settings,” the authors wrote.

By offering “transparent, continuously updated, and economically interpretable measures of expectations” with competitive forecast performance, prediction markets could “open new avenues” for studying monetary policy transmission, market sentiment, and macroeconomic uncertainty, the authors said.

The Fed’s study arrives as state regulators intensify scrutiny of prediction markets.

Scrutiny and pushback

While Federal Reserve economists describe such contracts as real-time gauges of inflation and rate expectations, a Ninth Circuit ruling on Tuesday cleared the way for Nevada to pursue civil enforcement.

A request to pause Nevada’s enforcement action against a federally regulated prediction market was denied, allowing the state to move forward while the broader legal dispute continues. The decision complicates and adds weight to an ongoing debate over whether these markets fall under federal commodities law or state gambling regimes.

While the ruling allows Nevada to proceed with its enforcement case, it is only an interim decision that does not address the merits of the underlying appeal, a person familiar with the matter told Decrypt.

Supreme Court review, if pursued, would come only after the Ninth Circuit resolves the full appeal, not at this administrative stay stage, Decrypt was told.

Prediction markets allow participants to buy and sell contracts tied to the outcome of future events, with prices reflecting the implied probability of a given result. These platforms list contracts on elections, economic data releases, and central bank decisions, positioning themselves as tools for aggregating dispersed information into market-based forecasts.

But the sector has faced mounting regulatory pressure.

Federal regulators have previously challenged the scope of certain event contracts, while several states have argued that some prediction markets resemble unlicensed gambling operations, prompting cease-and-desist actions and enforcement disputes that now sit alongside the federal oversight framework.

Industry observers say the paper reveals tensions between prediction markets’ potential and ongoing regulatory uncertainty.

“Regulatory clarity is helpful, but certainty is never permanent; political and legal environments are always shifting,” Tom Chalmers, CEO of prediction market protocol functionSPACE, told Decrypt. “What matters for prediction markets as forecasting tools is whether participants can operate with enough stability to provide deep, continuous liquidity.”

The use for gambling is just one of many use cases, Chalmers noted.

“These markets only function as real-time gauges of inflation or rate expectations if a broad set of informed actors can participate without fearing abrupt shutdowns or jurisdictional conflict,” he said.

Political backlash has derailed prediction markets before, such as DARPA’s canceled 2003 project, though more structured regulatory frameworks have since enabled compliant, policy-focused versions to operate, Chalmers explained.

If prediction markets maintain predictive accuracy, Chalmers argues they could offer central banks a real-time gauge of inflation and rate expectations.

Such a use could “extend to decision markets as those outlined in the vision of Futarchy, where policy makers can go to the market to identify impacts of decisions,” he said, referring to Vitalik Buterin’s 2014 exploration of a governance model that uses prediction markets to forecast the outcomes of policy decisions before they are implemented.

Chalmers said “there is room” for prediction markets to become “even better forecasting engines,” including through the development of mathematical standards designed to represent full probability distributions, such as variance and skewness, which could provide “vital signals” for policymakers.