Kalshi may be more useful than some traditional economic forecasting methods, according to a paper published by the US Federal Reserve Board.
The assertion, set out in the February 2026 working paper Kalshi and the Rise of Macro Markets, argues this is because the platform combines real-money incentives, high-frequency updating, and full probability distributions in ways that surveys and standard market instruments often cannot.
The paper refers to Kalshi providing “high-frequency, continuously updated, distributionally rich” forecasts that are valuable to both researchers and policymakers.
Framed against traditional tools, several advantages of these charateristics stand out.
First, Kalshi updates in real time, while surveys such as the Bloomberg Consensus or the Survey of Market Expectations are conducted at discrete intervals.
They provide snapshots at certain times, while Kalshi prices by contrast adjust intraday as news arrives. The paper documents sharp probability shifts around Consumer Price Index (CPI) releases, employment reports, and Federal Open Market Committee (FOMC) communications.
This allows analysts to observe not just what expectations are, but how they evolve in response to new information. Traditional surveys cannot capture that dynamic adjustment.
Second, Kalshi provides full probability distributions, not just point estimates. Many conventional tools deliver only a mean or modal forecast. Even federal funds futures require simplifying binomial assumptions that restrict outcomes to two possibilities.
Meanwhile Kalshi contracts, structured as Arrow-Debreu securities, allow researchers to construct the entire implied risk-neutral probability density.
That means policymakers can observe tail risks, skewness, and variance, rather than just the central forecast. In periods of elevated uncertainty, this distributional richness may be more informative than a single number.
Kalshi offers greater coverage than traditional instruments
Third, Kalshi fills gaps where traditional market-based instruments do not exist. Options markets for GDP growth, unemployment, or core CPI releases are generally unavailable.
Inflation swaps reference longer horizons and do not provide granular forecasts tied to specific data releases. Secured Overnight Financing Rate (SOFR) options are tied to repo rates and require assumptions to translate into federal funds expectations.
Kalshi contracts are directly linked to macroeconomic outcomes and specific FOMC meetings. In that sense, they provide cleaner signals about policy expectations.
Fourth, the forecasting performance is competitive. The paper shows that Kalshi’s mean absolute error for federal funds rate forecasts is similar to that of professional forecasters from the New York Fed’s Survey of Market Expectations.
In some cases, such as headline CPI, Kalshi’s median and mode outperform the Bloomberg consensus on statistically significant margins.
Importantly, Kalshi is not shown to be significantly worse than benchmark forecasts in any category tested, suggesting that data derived from the platform carries empirical credibility.
Fifth, Kalshi may reduce some distortions present in institutional derivatives markets.
The paper also recognises that SOFR markets are dominated by institutional players with hedging needs, which may result in risk premia (additional returns) and upward biases in probabilities.
Kalshi has a larger retail base and references the federal funds rate rather than repo-based rates.
Kalshi probabilities are still risk-neutral and may contain premia, but they offer a different perspective that may complement institutional market signals.
A deeper dive into the data
The sixth reason cited in the paper is that Kalshi facilitates event study analysis.
Because the platform provides high-frequency density data, researchers can measure how macro surprises affect not only the expected level of the federal funds rate, but also its variance and skewness.
The paper documents that inflation surprises shift the mean asymmetrically and reduce variance, while FOMC announcements affect higher moments as well. Traditional survey data rarely allows that level of granular distributional analysis.
Finally, Kalshi enhances transparency and accessibility. Surveys are often limited to professional forecasters, while some derivatives markets are thin or opaque.
Kalshi contracts are federally regulated by the CFTC and accessible through retail platforms, allowing for a broader participation base that may make expectations more reflective of diverse market beliefs.
This does not mean Kalshi should replace traditional forecasting tools, however. The authors acknowledge that prices reflect risk-neutral probabilities and may embed risk premia.
Liquidity in tail contracts can be thin, while surveys avoid financial incentives and may capture expert judgment differently. Each method has its strengths, according to the study.
However, taken together, the evidence suggests that Kalshi may be more useful than some traditional economic forecasting methods in three key respects: timeliness, distributional richness, and coverage of otherwise unavailable macro variables.
As a complement to surveys and established derivatives markets, it provides a new benchmark for measuring expectations, one that is market-based, continuously updating, and empirically competitive.