Polymarket and Kalshi Draw Fed Interest as Prediction Markets Match Traditional Forecasts
A set of recent studies of prediction markets has found that Kalshi’s contracts—and analyses that include Polymarket—produce real-time probability forecasts that rival traditional economist surveys and financial derivatives. The findings matter because policymakers and investors may gain continuous, tradable signals for inflation, unemployment and interest-rate moves at a frequency traditional tools do not provide.
Polymarket and Kalshi attract scrutiny from Federal Reserve economists
Researchers affiliated with the Federal Reserve examined Kalshi’s macro markets in a paper titled "Kalshi and the Rise of Macro Markets, " authored by Anthony Diercks, Jared Katz and Jonathan Wright. That analysis, which uses data from 2022 onward, concludes Kalshi’s probability distributions are well-behaved and broadly consistent with established instruments while sometimes outperforming them on point accuracy. Independent commentary on related work also highlights Polymarket alongside Kalshi as part of a new generation of prediction markets whose 24/7 trading offers continuously updated, money-backed expectations on economic releases and corporate outcomes.
Because these platforms embed financial incentives and react immediately to data releases and public statements by officials, their contract prices translate market views into tradable probabilities. The effect is a distributional readout of likely outcomes—showing not only medians but the spread of scenarios and tail risks—that updates in real time as new information arrives.
Kalshi study details: basis points, CPI errors and CFTC registration
The Fed-affiliated paper evaluated median contract outcomes across variables such as the federal funds rate and the consumer price index. On federal funds predictions, Kalshi’s median forecasts matched the accuracy of professional surveys, including the Federal Reserve Bank of New York’s Survey of Market Expectations. Notably, the mode from Kalshi’s distribution had a zero mean absolute error on the day of a Federal Open Market Committee meeting, a result driven by the September 2024 meeting when Kalshi assigned greater weight to a 50-basis-point cut rather than the 25-basis-point alternative and the larger move proved correct.
For CPI releases, Kalshi’s mean absolute error on release day was about 7 basis points, compared with roughly 8 basis points for the consensus. Unemployment and payroll contracts also produced medians close to consensus figures. The study highlights that Kalshi’s contracts can be tracked well in advance of standard survey consensus, providing an advance read on expectations.
Regulatory footing is part of the calculus: Kalshi operates as a designated contract market registered with the Commodity Futures Trading Commission, a status that aligns it with regulated exchanges. That registration, paired with the platforms’ market structure, is part of why researchers see these tools as potentially useful to both the Federal Reserve and Wall Street participants assessing real-time risks.
Implications for market participants and policymakers
Experts who have reviewed the work note the practical advantage of continuous markets that always react. That responsiveness means a single event—such as a data release or an official’s remarks—can immediately reweight probabilities, which in turn can sharpen short-term forecasts used by traders and analysts. What makes this notable is the combination of distributional richness and tradability: participants can both observe and act on evolving probabilities.
The broader implication is that prediction markets, including Kalshi and platforms like polymarket, may provide complementary inputs for policy and investment decisions—supplying high-frequency, financially incentivized signals that align closely with, and sometimes improve on, traditional forecasts. While researchers caution that the record is still developing, the measurable performance on metrics such as mean absolute error and the zero-error mode on FOMC day offer concrete evidence that these markets warrant continued attention.
As researchers expand datasets and compare outcomes across more release types and time spans, the immediate effect is already evident: market-based probability distributions are becoming a practical tool for gauging near-term macroeconomic risks and the likely path of monetary policy.
