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Polymarket recession odds diverge across Democratic congressional sweep scenarios

Cointelegraph reports Polymarket conditional markets price a higher recession risk under a Democratic sweep, at 35 percent by the end of 2027 against 19 percent without a sweep, as of 4:50 p.m. UTC on Election Day.

According to Cointelegraph, Polymarket’s conditional markets put the probability of a US recession by the end of 2027 at 35 percent if Democrats sweep Congress, compared with 19 percent if they do not.

The figures reflect pricing as of 4:50 p.m. UTC on Election Day. The report was published on Nov 3, 2026, at 3:00 PM ET.

Two scenarios, different recession prices

The markets separate the recession outlook into two political scenarios: a Democratic congressional sweep and the absence of one. The higher quoted probability applies to the sweep scenario.

These are conditional prices, rather than a single forecast for whether a recession will occur. Neither figure, on its own, states the market’s overall probability of a US recession by the end of 2027.

Limits of the snapshot

The supplied summary does not include trading volumes, liquidity, resolution criteria or the probability assigned to a Democratic sweep. Those omissions limit assessment of both the prices’ robustness and how the two scenarios combine into an overall recession outlook.

The pricing difference also does not establish that a congressional result would cause a recession. It shows how the quoted market probabilities differ depending on that result.

What it means for prediction markets

  • Keep the conditions attached: The 35 percent price depends on a Democratic sweep; the 19 percent price applies without one.
  • Do not treat either as the overall forecast: Combining the scenarios requires the probability of a sweep, which the supplied summary does not provide.
  • Respect the timestamp: These figures describe pricing at 4:50 p.m. UTC on Election Day, not subsequent market moves.
PolymarketCongressElectionsInfiniti MarketsInfiniti Signals

Sources & attribution

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