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Kalshi leads midterm trading as election-day volume reaches $794 million

A Prediction Frontier analysis of 284,000 trades reports Kalshi accounted for 1.53 billion dollars of midterm volume and Polymarket for 474 million dollars, with Kalshi taking 88 percent of results-night volume. Kalshi priced Democratic control of each chamber at 99 percent about two hours before NBC News called them.

Kalshi and Polymarket recorded $2.0 billion in trading volume on 2026 midterm contracts, including $794 million during the election-day 24 hours, according to Prediction Frontier.

Trading concentrated on Kalshi

The analysis, published Nov 6, 2026, at 4:00 PM ET, examined 284,000 trades. Prediction Frontier attributed $1.53 billion in midterm volume to Kalshi and $474 million to Polymarket.

Kalshi’s share was particularly pronounced on results night, when it accounted for 88 percent of volume, according to the analysis. That results-night share is a separate measure from the platforms’ overall midterm totals.

Market pricing preceded network calls

Kalshi contracts put the probability of Democratic control of each chamber at 99 percent about two hours before NBC News called them, Prediction Frontier found.

The timing shows that Kalshi’s prices had reached near-certainty ahead of those network calls. It does not, by itself, establish how accurate the markets were throughout the election cycle.

What it means for prediction markets

  • Activity clustered around election day: The $794 million traded during that 24-hour period represented a substantial portion of the reported $2.0 billion total.
  • Venue concentration matters: Kalshi led overall volume and captured 88 percent of results-night trading, making platform-level differences important when interpreting market activity.
  • Prices and calls mark different milestones: The 99 percent readings preceded NBC News’s calls by about two hours; that comparison measures timing, not the markets’ full forecasting record.
KalshiPolymarketElectionsMidtermsInfiniti MarketsInfiniti Signals

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    Prediction Frontier
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