Kalshi’s Fed Hike Record Is Stronger Than Its Calibration Evidence

Kalshi’s Fed rate hike odds have encouraging near-decision forecasting evidence, but the draft’s sources do not establish a hike-only accuracy rate. Its reported 86.5% probability for September 2026’s quarter-point hike correctly favored the outcome that occurred. CME’s reported probability scored better on that single event. Neither result proves which venue is better calibrated across meetings.

Choose the actual outcome or a hypothetical miss; compare how confidence changes each forecast’s score.

An 87% Hike Probability Is Not an 87% Accuracy Rate

A YES contract costing 87 cents and paying $1 if its specified event occurs can be read as an approximately 87% market-implied probability. That is a forecast, not a guarantee or a measure of past success.

The economic research discussed below treats these prices as risk-neutral probabilities: compensation for risk can make them differ from actual event probabilities. It also identifies stale prices and low-volume tail contracts as limitations. Research methodology

The event definition comes first. “Any hike at the next meeting,” “exactly a 25-basis-point hike” and “the target rate above a threshold after December’s meeting” are different forecasts. A basis point is 0.01 percentage point; a 25-basis-point hike is a quarter-point increase.

For the underlying mechanics, see how prediction-market prices become probabilities.

September 2026 Favored CME on a Single-Event Score

On September 16, 2026, the FOMC raised its target range by a quarter percentage point to 3.75%–4.00%, approving the statement by a 12–0 vote. That establishes the policy outcome independently of the market forecasts. Federal Reserve statement

PredictionMarketsPicks reports the following September 15 pre-decision closing probabilities for a 25-basis-point hike, drawn from its 30-minute captures. These are the publisher’s reported snapshots, not an independently audited comparison of contract rules, synchronized timestamps or executable prices. Reported prices and capture method

A binary Brier score is (p − y)², where p is the forecast probability and y is 1 if the event happens, otherwise 0. Lower is better.

Venue	Reported Hike Probability	Brier Score
Kalshi	86.5%	0.0182
Polymarket, international book	88.5%	0.0132
CME futures-implied	94.3%	0.0032

Scores calculated from the reported probabilities, with the 25-basis-point hike outcome set to 1.

All three selected the correct favorite. CME had the lowest binary Brier score on these snapshots, because it assigned the greatest probability to the event that happened. Had the 25-basis-point hike not happened, that extra confidence would have produced the largest error. The tool above shows this hypothetical reversal; it is not a second historical observation.

Calling Kalshi “86.5% accurate” would be incorrect. Calibration asks whether events forecast around 86.5% occur roughly that often across a sufficiently large, comparable sample. One meeting cannot answer that.

Earlier Prices Answer a Different Forecasting Question

The same publisher reports Kalshi at 26% on August 15 and 58% intraday on September 2—but 54% at the September 2 daily close. Those are different observations, not interchangeable historical odds. Reported price history

A hike following a 26% forecast produces a large single-event error. It does not, by itself, prove that 26% forecasts are miscalibrated. To test that claim, the comparison needs other events assigned similar probabilities, not just later prices for the same meeting.

Research Supports Near-Decision Forecasting, Not a Hike-Only Rate

The February 12, 2026 working paper Kalshi and the Rise of Macro Markets, by Anthony M. Diercks, Jared Dean Katz and Jonathan H. Wright, evaluates FOMC rate forecasts in a historical sample beginning in 2022. It reports that Kalshi’s median and mode had a perfect day-before forecast record within that sample. The median is the distribution’s midpoint; the mode is its most likely rate outcome.

At roughly 150 days ahead, Kalshi’s mean absolute error was similar to professional forecasts from the New York Fed’s Survey of Market Expectations. Working paper

That is encouraging evidence, but it is not a hike-only accuracy rate, and the paper predates September 2026. Correctly selecting the most likely rate outcome also does not validate every quoted probability. A favorite can win while the probability assigned to it remains too high or too low across a broader sample.

The authors identify September 2024’s 50-basis-point cut as the observation driving the near-decision distinction from futures: Kalshi placed more weight on the cut size that occurred. Comparing a discrete modal forecast with a futures-implied mean is different from comparing both venues’ probabilities with Brier scores. Forecast-performance analysis

The paper’s distributional tests suggest fairly good calibration overall, with some evidence that risk premia overstate the odds of higher inflation and unemployment. Those tests cover several macroeconomic outcomes, not just hike contracts. It is preliminary research expressing the authors’ views—not a Federal Reserve endorsement or a guarantee about later markets. Distributional tests and research disclaimer

A Fair Comparison Needs Matched Contracts and Timestamps

A defensible hike-odds comparison must match exactly 25 basis points with exactly 25 basis points, rather than with any hike or a cumulative year-end rate change. Each contract’s resolution rules determine what counts as a correct forecast.

Forecast horizons also need to remain separate. A day-before forecast should be compared with another day-before forecast using the same cutoff—not with a rival’s month-before price. Repeated snapshots can show how a forecast evolved, but numerous snapshots of one meeting are not numerous independent outcomes.

The recorded price field matters. Last trade, bid, ask and midpoint can differ. Kalshi’s historical candles provide separate bid, ask and price fields, plus volume and open interest. If midpoint is the chosen measure, calculate it from the quotes rather than assuming the reported price field is a midpoint. Kalshi documentation

CME requires a separate methodological check. FedWatch derives probabilities from monthly average effective-rate expectations using assumptions including 25-basis-point increments and proportional effective-rate responses. It is not a directly traded binary probability. CME methodology

Across matched meetings, report average Brier scores and calibration by probability range, with sample counts. Keep trading returns separate: forecast scores do not include execution costs, while profitability requires executable prices and fees.

Kalshi’s winning September favorite and the working paper’s near-decision record support narrower claims than “Kalshi’s hike odds are accurate.” A broader verdict requires matched, timestamped probabilities across meetings. See Brier score versus calibration for the distinction. This is forecast analysis, not trading advice.