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Fed Rate Prediction Market — Historical Accuracy Record

How well did Kalshi prediction markets forecast Federal Reserve rate decisions? This is a citable primary source — all data is sourced from Kalshi contract prices and official FOMC announcements.

5

Meetings resolved

2 of 2

Correct calls (100%)

3

Remaining in 2026

5 meetings resolved · 2 gradable. A meeting is gradable only if we hold its final pre-decision Kalshi ladder; 3 predate the permanent archive and are no longer retrievable, so they are shown as rather than counted as a miss.

2026 Resolved Meetings

MeetingDecisionMarket ImpliedCorrect?
Jan 27–28, 2026HOLD
Mar 17–18, 2026HOLD
Apr 28–29, 2026HOLD
Jun 16–17, 2026HOLDHOLD
Jul 28–29, 2026HOLDHOLD

Upcoming 2026 Meetings

Methodology

Accuracy is defined as: the Kalshi market-implied favorite in the last polling cycle before 18:00 UTC on decision day (the 2:00 PM ET announcement) matches the Fed's actual decision. A cut-25 prediction is correct if the Fed cuts 25bp. It is incorrect if the Fed cuts 50bp (even though both are cuts) — this strict definition is more useful for trading, and it is the exact rule the table above applies.

Data is sourced from Kalshi contract prices and official FOMC press releases. At each decision-day boundary the final pre-decision ladder is copied into a permanent, immutable archive (fed_rate_final_snapshots) — one row per meeting per outcome, never rewritten. The live table behind it keeps only a rolling 7-day window, so before that archive existed a graded meeting expired roughly five weeks after it was earned.

The January, March and April 2026 meetings predate the archive and their market data is no longer retrievable from Kalshi, so they are reported as ungraded () rather than counted as missed calls. June and July 2026 were reconstructed from Kalshi's per-strike price history for the final trading hours before each announcement — never from settlement values, which would grade the market against the answer. Every meeting from September 2026 onward is archived live. This page serves as a citable primary source for researchers studying prediction market accuracy on monetary policy.

Academic Context

The academic literature on prediction market accuracy for monetary policy is limited. Key references: Gurkaynak, Sack & Swanson (2005) on macro surprises and asset prices; Nakamura & Steinsson (2018) on Fed information effects. This dataset contributes specifically to the Kalshi/binary-contract subset of that literature.

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