Fed economists find Kalshi’s macro markets to be helpful complementary real-time forecasting tools, as CEPR research highlights pricing bias concerns that require careful interpretation.
A new Federal Reserve research paper is putting prediction markets, and Kalshi in particular, more squarely into the macroeconomic forecasting conversation.
Published through the Fed’s Finance and Economics Discussion Series (FEDS), the study analyzes data from Kalshi’s event contracts tied to inflation, Fed rate decisions, GDP and unemployment, examining whether those prices offer useful real-time signals alongside traditional forecasting tools.
The researchers found that prediction market data often tracks closely with established indicators like surveys, futures markets, and consensus economist forecasts. The paper argues these markets can provide what it calls “a high-frequency, continuously updated, distributionally rich benchmark that is valuable to both researchers and policymakers.”
Some other researchers are taking a more measured look. A separate analysis recently published by the Centre for Economic Policy Research (CEPR), a European economic research network, looks at prediction market accuracy from a different angle, highlighting evidence that market prices can provide useful information but may also contain pricing biases that complicate how they’re interpreted.
While the Fed paper highlights prediction markets’ potential as real-time economic signals, the CEPR analysis suggests those prices should be treated as useful indicators, not precise forecasts. Together, the two reports reflect a broader debate over how much weight prediction market data deserves in economic forecasting.
Inside the Fed study on Kalshi’s macro markets
The FEDS study, titled “Kalshi and the Rise of Macro Markets,” was authored by Federal Reserve economist Anthony Diercks along with Jared Dean Katz of Northwestern University and Jonathan Wright of Johns Hopkins University and the National Bureau of Economic Research. FEDS is a working paper program where staff economists circulate research for discussion before formal peer review. The papers are not official Federal Reserve policy statements but often spotlight topics gaining attention among Fed economists and outside academic researchers, particularly those evaluating new economic data sources.
Much of the analysis looks at how probabilities implied by Kalshi trades shift around major economic announcements and Federal Reserve policy statements. The researchers compare those market-based expectations with traditional forecasting tools over time, examining how quickly markets incorporate new information and how forecast accuracy stacks up across different approaches.
The study also explores how prediction-market pricing reacts to inflation data, employment reports and policy signals, with several examples showing expectations shifting rapidly following new information. The authors argue this real-time responsiveness could make prediction markets a useful complement to existing forecasting tools, especially in areas where traditional market-based probability measures have historically been limited.
One of the study’s key comparisons looks at how accurately different tools anticipate Federal Reserve rate decisions ahead of Federal Open Market Committee (FOMC) meetings. The chart below shows how forecast errors change over time, comparing Kalshi’s market-based expectations with Fed funds futures, a widely used market gauge of rate expectations, and the Survey of Market Expectations, a Fed survey of professional forecasters.
As the meeting date gets closer, forecast errors shrink across all three measures, with prediction market signals generally moving in line with traditional indicators. The authors say this reflects how quickly prediction markets incorporate new information, suggesting they can offer a competitive real-time read on policy expectations.
Some prediction market traders pointed to the comparison as validation of what they say has long been evident in rate markets.
In the paper’s conclusions, the authors argue that prediction markets can add useful context to economic forecasting even when their accuracy matches traditional tools. They highlight the ability to track expectations live and show how confident or uncertain traders are about different outcomes. The paper also notes that Kalshi attracts a larger share of retail traders than traditional rate or inflation derivatives markets, meaning its prices may reflect a different mix of views than institutionally dominated markets, potentially offering policymakers an additional perspective rather than a replacement for existing tools.
“These findings suggest that prediction markets can serve as a valuable complement to existing forecast tools in both research and policy settings,” the report concludes. “By providing transparent, continuously updated, and economically interpretable measures of expectations with competitive forecast performance, they open new avenues for studying monetary policy transmission, market sentiment, and macroeconomic uncertainty. As these markets mature and liquidity deepens, their potential to enhance real-time policy analysis and academic research will only grow over time.”
Kalshi highlights Fed study as validation
Kalshi executives have moved quickly to spotlight the Federal Reserve research, with CEO Tarek Mansour calling it “an incredible paper” and sharing what he described as its key takeaways. In a post on X, he said the markets “allocate probability mass that reflects the range of plausible macroeconomic outcomes better than traditional” forecasts, adding that the platform’s median and mode predictions had a “perfect forecast record” ahead of recent Federal Reserve rate decisions, meaning both the rate traders viewed as most likely and the middle of the market’s probability range matched what the Fed ultimately did.
Those claims broadly reflect the study’s findings, which show prediction market forecasts often track closely with traditional benchmarks and sometimes perform slightly better, while offering continuously updated probability data rather than periodic snapshots. But the paper consistently describes prediction markets as a complement to existing forecasting tools, not a replacement.
Highlighting the research strengthens Kalshi’s credibility with policymakers and investors, reinforcing its argument that prediction markets are becoming a more serious source of real-time economic signals even as debate continues over how heavily those signals should be weighted.
CEPR academic analysis puts accuracy claims in context
A separate academic analysis published through VoxEU, the policy commentary platform affiliated with CEPR, looks at Kalshi from a different angle, focusing less on whether prediction markets could inform policymakers and more on how accurately market prices reflect real-world probabilities.
Using large sets of historical contract data, the authors found that Kalshi prices often become more accurate as events approach. But they also identify a well-known pattern from betting markets known as the “favourite-longshot bias,” where low-probability outcomes tend to trade at prices that slightly overstate their chances, while heavily favored outcomes can be modestly underpriced. The implication isn’t that the markets lack useful information, but that prices don’t always translate cleanly into unbiased probability forecasts.
That nuance sits alongside the Federal Reserve research rather than directly contradicting it. The Fed paper highlights how prediction markets track economic expectations in real time and can work alongside traditional forecasting tools, while the CEPR analysis focuses more narrowly on pricing behavior and potential distortions. Together, the studies suggest prediction markets can provide useful signals but still require careful interpretation.
The CEPR research ultimately stresses that prediction markets can still be informative while cautioning against reading prices too literally as probabilities.
“These findings have implications for the use of prediction markets in policy and business contexts,” the study concludes. “While they are clearly a useful tool for aggregating information, our results suggest that Kalshi’s prices should not be interpreted as unbiased probability estimates.”
Fed interest boosts legitimacy while accuracy debate persists
The two analyses suggest prediction markets are moving deeper into mainstream economic conversations, but not without caveats. The Federal Reserve paper indicates that policymakers and central bank researchers are at least studying market-based probability data as a potential complement to surveys and futures markets. At the same time, the CEPR analysis points out that interpreting those prices requires care, particularly when treating them as precise probability estimates rather than signals shaped by trading behavior and market incentives.
For platforms like Kalshi, that combination is significant. Institutional attention from the Fed adds credibility to prediction markets as a serious data source, even if the research stops short of endorsing them as a primary forecasting tool. Meanwhile, academic scrutiny over pricing bias and market structure suggests the sector’s evolution will depend not just on accuracy claims, but on transparency, liquidity and how thoughtfully those signals are incorporated into broader economic analysis.
Mike Breen
Mike Breen has been a professional writer and editor covering a wide range of topics for more than 30 years. He’s been a freelance gaming industry writer since 2020, reporting on sports betting, online casinos, and more for various Catena Media sites, and he began reporting on prediction market industry news in 2025 for Prediction News. Prior to that, Mike was a founding editor at his hometown altweekly newspaper in Cincinnati, Ohio, where he extensively covered local arts, music and news.Mike’s published writing has received recognition and several awards from organizations like the Society of Professional Journalists and the Association of Alternative Newsmedia.When Mike is not working, he enjoys playing and listening to music, attending comedy shows, watching movies, and spending time with his family and three cats.
