Kalshi Prediction Markets | FEDS 2026-010 Analysis

Why Macro Prediction Markets Matter for Economic Forecasting

The Federal Reserve’s Finance and Economics Discussion Series paper 2026-010, authored by Anthony M. Diercks of the Federal Reserve Board, Jared Dean Katz of Northwestern University’s Kellogg School of Management, and Jonathan H. Wright of Johns Hopkins University and the NBER, presents a groundbreaking analysis of how prediction markets are reshaping macroeconomic forecasting. Published in February 2026, the paper argues that Kalshi macroeconomic prediction markets represent a novel, high-frequency, and distributionally rich source of expectations data that can serve as a valuable complement to traditional forecasting tools used by researchers and policymakers.

Traditional approaches to gauging economic expectations have long suffered from fundamental limitations. Survey-based forecasts such as the Survey of Professional Forecasters and the Bloomberg consensus provide only periodic point estimates that become stale quickly between releases. These surveys lack real-time updating capability, fail to provide distributional information about uncertainty, and cannot capture the rapid shifts in market sentiment that characterize modern financial markets. The Bloomberg consensus, for instance, is typically available only shortly before economic data releases, offering no time series for ongoing analysis.

Market-based alternatives have also proven inadequate in important ways. Federal funds futures, while widely used, provide only mean forecasts and require restrictive binomial tree assumptions that limit outcomes to just two possibilities. Remarkably, the researchers note that fed funds futures options have not actually traded since the 2008 financial crisis. SOFR options, another common tool, cover quarterly periods rather than specific FOMC meetings and carry an approximately 6 basis point spread relative to the effective federal funds rate, introducing systematic bias. These structural limitations have created a significant gap in the forecasting toolkit available to central banks, institutional investors, and researchers. For those exploring how technology is transforming financial analysis across sectors, prediction markets represent one of the most significant innovations in decades.

Kalshi’s Institutional Design and CFTC Regulatory Framework

Kalshi occupies a unique position in the American financial landscape as the first federally regulated prediction market platform. Approved by the Commodity Futures Trading Commission (CFTC) as a Designated Contract Market—the same regulatory classification held by the Chicago Mercantile Exchange—Kalshi has operated since 2021 under a rigorous compliance framework that distinguishes it from unregulated alternatives like Polymarket, which operates in a legal gray area, or PredictIt, which offers fewer contracts with lower liquidity.

The platform’s contract design follows the Arrow-Debreu security model, where each contract pays exactly $1 if a specified economic outcome occurs and $0 otherwise. Prices are bounded between $0.01 and $0.99, and the exchange itself takes no directional position—every contract requires both a buyer and a seller. This binary structure creates natural probability estimates: a contract trading at $0.75 implies the market assigns a 75 percent probability to that outcome occurring. Maximum exposure per market is capped at $7 million, providing sufficient depth for meaningful price discovery while maintaining appropriate risk controls.

A critical feature of Kalshi’s institutional design is its accessibility. Unlike CPI Fixings, which are primarily institutional products with limited retail participation, Kalshi is available through popular retail brokerage platforms including Robinhood and Webull. Market making by sophisticated firms like Susquehanna ensures continuous liquidity, while the retail investor base means that the expectations captured by Kalshi prices reflect a broader cross-section of market participants than is typical for institutional derivatives markets. The FEDS 2026-010 paper documents that trading volume has grown substantially since launch, with peak volume approaching 100 million contracts for certain FOMC-related series and volume frequently exceeding 1 million in recent periods. Only Kalshi and Interactive Brokers currently operate prediction markets with CFTC regulatory approval in the United States, though Interactive Brokers maintains smaller position limits.

How Kalshi Prediction Markets Convert Prices to Probability Distributions

One of the most valuable contributions of the FEDS 2026-010 paper is its rigorous methodology for converting Kalshi market prices into complete probability distributions for macroeconomic variables. The researchers employ a model-free approach that treats the “Yes” contract price as the risk-neutral probability of the specified outcome. For exceedance contracts—where the contract pays if the realized value exceeds a given strike—the probability assigned to a specific bin equals the difference between adjacent strike prices on the cumulative distribution function.

The construction process involves several important technical steps. Monotonicity is enforced in the CDF by building outward from the mode toward both tails, ensuring that the resulting distributions are well-behaved and economically sensible. When no trades occur on a given day for specific contracts, the researchers apply a carry-over methodology using the last-traded price. This approach avoids the discontinuities that would arise from treating no-trade days as missing data while acknowledging that in thinly traded contracts, prices may be somewhat stale.

The paper carefully distinguishes between risk-neutral probabilities (the Q measure) and physical probabilities (the P measure), noting that Kalshi prices directly provide risk-neutral estimates. However, their probability integral transform analysis demonstrates that the resulting distributions are “fairly well calibrated,” meaning they approximate physical probabilities reasonably well. Some evidence suggests that risk premia lead Kalshi to slightly overstate the odds of higher inflation and higher unemployment outcomes, consistent with standard asset pricing theory where investors demand compensation for bearing downside macroeconomic risks. Researchers at the Federal Reserve’s Division of Research and Statistics have noted these risk premium properties as an important consideration when interpreting prediction market signals.

Federal Funds Rate Forecasting: Kalshi vs. Traditional Benchmarks

The paper’s analysis of federal funds rate forecasting delivers perhaps its most striking finding: Kalshi’s median and mode predictions achieve a perfect forecast record on the day before FOMC meetings, representing a statistically significant improvement over federal funds futures. The fed funds futures mean absolute error of 0.010 stands in sharp contrast to Kalshi’s 0.000 for both median and mode, with the improvement confirmed at conventional significance levels.

This remarkable accuracy stems from Kalshi’s structural advantages over competing instruments. Federal funds futures require a binomial tree assumption that restricts outcomes to just two possibilities, which becomes particularly limiting when the FOMC decision is uncertain between multiple rate paths. Kalshi, by contrast, provides the complete probability distribution across all possible rate outcomes, allowing median and mode estimates to capture the market’s true central tendency without restrictive parametric assumptions. Furthermore, Kalshi contracts directly reference the federal funds rate at specific FOMC meetings, eliminating the basis risk inherent in SOFR-based instruments.

The researchers also compare Kalshi’s federal funds rate mean forecasts to the Survey of Market Expectations and find them “very similar,” suggesting that Kalshi aggregates information comparably to carefully conducted institutional surveys while offering the crucial advantage of continuous, real-time updating. The paper documents rich intraday dynamics that illustrate this point: during the lead-up to the July 2025 FOMC meeting, the probability of a rate cut rose to 25 percent following remarks by Governor Waller and Vice Chair Bowman, then fell sharply after the release of a strong June employment report. These rapid intraday shifts would be completely invisible to survey-based approaches and are only partially captured by daily data. This type of real-time financial intelligence represents a paradigm shift in how monetary policy expectations are tracked.

CPI and Inflation Prediction Markets: Real-Time Price Discovery

Inflation forecasting represents another area where Kalshi prediction markets demonstrate substantial value. For headline CPI, the paper reports that Kalshi provides statistically significant improvements over the Bloomberg consensus. On the day of release, Bloomberg’s mean absolute error stands at 0.081, while Kalshi’s median and mode both achieve 0.063—a 22 percent improvement that is confirmed as statistically significant. Kalshi’s mean RMSE of 0.080 also represents a statistically significant improvement over the Bloomberg consensus, a particularly meaningful result given that RMSE penalizes large forecast errors more heavily.

For core CPI, Kalshi performs comparably to Bloomberg with no statistically significant differences (Bloomberg MAE of 0.070 versus Kalshi mean MAE of 0.070). This parity is itself notable because the Bloomberg consensus aggregates forecasts from dozens of professional economists at major financial institutions, while Kalshi prices emerge from a decentralized market of predominantly retail participants. That a retail-dominated prediction market achieves professional-grade accuracy for core inflation forecasting challenges conventional assumptions about the wisdom of crowds in macroeconomic contexts.

Beyond point forecast accuracy, Kalshi’s inflation contracts provide distributional information that is simply unavailable from alternative sources. The paper documents that Kalshi offers contracts on CPI month-over-month (since June 2021), CPI year-over-year (since November 2022), annual CPI (since 2022), core CPI month-over-month (since June 2022), core CPI year-over-year (since December 2022), and annual core CPI (since 2025). For core CPI in particular, Kalshi provides the only market-based distributional forecasts available anywhere. The Bureau of Labor Statistics CPI program publishes realized data, but no other market instrument provides forward-looking probability distributions for this critical inflation measure.

Distributional Dynamics and Higher-Moment Analysis

The FEDS 2026-010 paper moves beyond simple point forecast comparisons to analyze the rich distributional dynamics embedded in Kalshi prediction market prices. The event study regressions reveal how macroeconomic data releases and monetary policy communications systematically alter not just the mean of rate expectations, but the entire shape of the distribution including variance, skewness, and higher moments.

CPI releases emerge as the dominant driver of federal funds rate expectations during the 2022-2025 period analyzed. The regression coefficient of CPI surprise on the federal funds rate mean is 0.320 (significant at the 1 percent level), with an R-squared of 0.379—remarkably high explanatory power for a single-variable event study. The effect is even stronger on the mode (coefficient 0.413) and median (coefficient 0.388), suggesting that CPI surprises shift the most likely rate outcome even more than the average expected outcome.

A particularly striking finding concerns the asymmetric response to CPI data. The researchers demonstrate that positive CPI surprises produce a federal funds rate response four times larger than negative CPI surprises. This asymmetry has profound implications for understanding how markets perceive the Fed’s reaction function: investors believe the Federal Reserve will respond much more aggressively to upside inflation surprises than to downside surprises, consistent with a central bank that is more concerned about inflation remaining above target than about it falling below target.

Monetary policy communications also reshape the distribution in distinctive ways. FOMC statements increase variance (coefficient 0.249, significant at the 10 percent level), generating greater dispersion in market views about future rate paths. Press conference shocks, by contrast, significantly reduce skewness (coefficient -2.875, significant at the 5 percent level) without materially affecting the mean. The interpretation offered by the researchers is that the press conference effectively truncates the right tail of the distribution—more restrictive than expected communication removes the possibility of very high rate paths without changing the central forecast. Zero-surprise CPI releases produce the largest variance decline, as the confirmation of expectations resolves uncertainty on both sides.

Retail vs. Institutional Investors in Macroeconomic Prediction

The paper introduces an important distinction between the expectations revealed by Kalshi’s predominantly retail investor base and those embedded in institutionally dominated markets like SOFR options. This comparison provides novel insights into how different investor populations process macroeconomic information and form expectations about monetary policy.

Institutional investors in SOFR markets bring portfolio constraints and hedging demands that can systematically bias the probabilities implied by option prices. Banks, pension funds, and insurance companies purchase SOFR options not solely as expressions of rate expectations but as hedges against their existing interest rate exposures. These hedging demands may push implied probabilities upward, particularly for higher rate outcomes, creating a wedge between SOFR-implied probabilities and true market expectations. The SOFR-EFFR spread of approximately 6 basis points, which the paper notes is expected to grow amid Federal Reserve balance sheet runoff, compounds this bias.

Kalshi’s retail participants, by contrast, are largely free from portfolio constraints and hedging motives. Their contract purchases more directly reflect pure beliefs about economic outcomes, offering what the researchers describe as “a new lens on retail investor expectations, capturing their beliefs directly and in real time, independent of the portfolio constraints and hedging motives that can often shape the positions of institutional investors, potentially generating sizeable risk premiums.” This does not mean that Kalshi prices are necessarily more accurate—retail investors may have informational disadvantages compared to professional forecasters—but it does mean they provide a genuinely complementary perspective that enriches the overall information set available to policymakers. The growing body of research on prediction markets builds on earlier work analyzed in similar interactive financial research reports.

The paper also notes that PCE headline year-over-year surprises produce much smaller effects on federal funds rate expectations than CPI surprises (coefficient of 0.012 versus 0.320 for CPI), with lower R-squared values (0.060 versus 0.379). This suggests that despite the Federal Reserve’s official preference for PCE as its inflation target, market participants—particularly the retail investors on Kalshi—react primarily to CPI data, which receives more media coverage and public attention.

Stagflation Risk Monitoring Through Prediction Market Data

Among the most policy-relevant applications demonstrated in the paper is the use of prediction market data for real-time monitoring of stagflation risks. By tracking the joint behavior of Kalshi’s GDP growth and CPI inflation contracts, the researchers construct a real-time gauge of the probability that the economy experiences simultaneously low growth and high inflation—the dreaded stagflation scenario that poses particularly acute challenges for central banks.

The data reveal that as of early July 2025, Kalshi markets assigned a probability of approximately 0.4 to GDP growth falling below 1 percent, with investors placing increasing weight on outcomes combining sub-1 percent growth with CPI inflation above 3.5 percent following developments in trade policy. The July 2025 Blue Chip consensus projected GDP growth of just 1.4 percent and CPI of 2.9 percent, but Kalshi’s distributional data showed significantly more weight in the tails than these point estimates would suggest.

Critically, the paper finds that Kalshi places more weight on severe stagflation outcomes than the Survey of Professional Forecasters, suggesting that the retail investor base perceives higher tail risks than professional economists. Whether this represents superior information processing, behavioral biases, or rational risk pricing remains an open question, but the divergence itself is valuable information for policymakers who rely heavily on the SPF for distributional risk assessments. The ability to monitor these tail risks in real time, rather than waiting for quarterly survey releases, represents a meaningful advancement in the toolkit available to the Federal Reserve’s monetary policy apparatus.

Nonfarm payrolls surprises, interestingly, had no statistically significant effect on federal funds rate expectations during the 2022-2025 period (coefficient 0.053, not significant). The researchers interpret this as reflecting the inflation-focused posture of monetary policy during this period: with inflation well above target, employment data mattered less for the rate path than inflation data. This finding may shift as inflation continues to normalize and the dual mandate rebalances.

Policy Implications and the Future of Macro Prediction Markets

The FEDS 2026-010 paper concludes with a compelling case for integrating prediction market data into the regular toolkit of central banks, academic researchers, and financial market participants. The authors note that “prediction markets can serve as a valuable complement to existing forecast tools in both research and policy settings,” and announce plans to make distributional daily-level data publicly available through EconFutures.com, with trade-level data and code published on GitHub.

For central banks specifically, prediction markets offer a high-frequency check on the effectiveness of monetary policy communications. The finding that FOMC statements increase variance while press conferences reduce skewness provides granular insight into which elements of the communication strategy are working and which are generating confusion. The ability to observe these effects within minutes rather than waiting for the next survey release dramatically shortens the feedback loop available to central bank communications teams.

The paper also raises important questions about the future evolution of prediction market regulation. The CFTC’s decision to approve Kalshi as a Designated Contract Market established a precedent that additional prediction market platforms may seek to follow. Interactive Brokers has already obtained regulatory approval, and the competitive dynamics between regulated platforms, along with the continued operation of unregulated alternatives like Polymarket, will shape the liquidity, depth, and informational efficiency of prediction markets in the coming years.

The closest historical precedent cited by the researchers is Gürkaynak and Wolfers’ 2005 analysis of the now-defunct Economics Derivatives market developed by Goldman Sachs and Deutsche Bank. That earlier prediction market ultimately failed to achieve sufficient liquidity for sustained operation. Kalshi’s growing volume trajectory, regulatory approval, and retail accessibility via platforms like Robinhood suggest a fundamentally different growth path. Concurrent research by Swanson, Wang, and Wu (2025) on the Fed information effect using Kalshi data, and by Eichengreen and colleagues (2025) on Polymarket and Federal Reserve independence, further demonstrates the expanding academic interest in prediction market data as a research tool. The National Bureau of Economic Research continues to serve as a key venue for this emerging body of work, with co-author Jonathan Wright among the NBER-affiliated researchers advancing the field.