Researchers at the Federal Reserve System are examining whether data from the event-betting platform Kalshi can help improve how policymakers interpret economic expectations.
In a paper titled Kalshi and the Emergence of Macromarkets, published on February 12, Fed Chief Economist Anthony Diercks, Fed Research Assistant Jared Dean Katz, and Johns Hopkins University Research Fellow Jonathan Wright argue that prediction market data may offer advantages over traditional surveys and forecasts.
Their core argument is straightforward: betting markets update in real time and reflect financial stakes, potentially making them more responsive to economic news than periodic surveys.
Why Real-Time Market Odds May Matter
The researchers compared Kalshi’s market-implied probabilities with conventional forecasting tools. They found that prediction markets can rapidly adjust to macroeconomic events and policy signals.
For example, implied odds of a July rate cut reportedly climbed to 25% after public remarks from Fed Governors, only to fall again after a stronger-than-expected employment report. That kind of rapid repricing highlights what the authors describe as “rich intraday dynamics”, the ability to track shifts in expectations minute by minute.
Building A Probability Map Of Rate Decisions
The paper proposes using Kalshi data to construct risk-neutral probability density functions around Federal Open Market Committee (FOMC) decisions. In simple terms, that means mapping out the full range of possible rate outcomes, and the market’s estimated likelihood of each ahead of policy meetings.
The authors argue that current benchmark measures are often too distant from the actual policy decision, limiting their usefulness.
At the same time, they emphasize that the research is preliminary and intended to support discussion, not to dictate monetary policy.
The Rise Of Prediction Markets, Risks, And The Clarity Act
Prediction markets have expanded rapidly over the past year, with monthly trading volumes exceeding $10 billion across platforms. Alongside Kalshi, competitors such as Polymarket have drawn significant retail interest, even as some state regulators push for tighter oversight.
The appeal for policymakers is clear. Large, decentralized betting pools can sometimes produce more accurate aggregate forecasts than expert surveys – an idea often associated with the “wisdom of the crowds” theory, rooted in the work of economist Friedrich Hayek.
However, the lack of a federal regulatory framework, such as the proposed Clarity Act, adds uncertainty. Without the Clarity Act in place, there are no uniform rules governing prediction markets, leaving platforms like Kalshi in a legal gray area. Analysts warn that while the Fed may see value in using these markets to inform policy, the absence of clear legislation raises questions about the legality, transparency, and potential manipulation of market data.
Relying on market-based signals also introduces a classic economic dilemma known as Goodhart’s Law: when a metric becomes a target, it risks losing its reliability. If market participants know policymakers are watching a specific indicator, incentives to influence or manipulate that signal may increase.
The open question is whether central banks can extract useful information from prediction markets without becoming overly dependent on them, or inadvertently distorting them, especially in the absence of regulatory clarity.
