Kalshi, Polymarket face scrutiny from Democrats after midterms
KalshiECO
Prediction markets called the 2026 midterms correctly, and the party that won may now try to rein them in
Prediction markets spent election night looking very smart. Now they may have to explain themselves to the people they just predicted would win.
Democrats plan to crack down on platforms like Kalshi and Polymarket after strong gains in the 2026 US midterm elections. The companies called the outcome correctly. Their reward could be a lot more time in congressional hearing rooms.
Leading Democrats, including Sen. Jeff Merkley and Rep. Maxine Waters, have publicly criticized the platforms for letting people bet on sensitive subjects such as elections, war, and terrorism. As of November 2026, both lawmakers are preparing legislative action aimed at prediction markets.
The forecast that came true, and the backlash it fed
On Election Day, Kalshi priced the odds of a Democratic House majority at between 93% and 94.5%. It put a Democratic Senate majority at between 73.5% and 76%.
Both outcomes landed. Democrats took the House and the Senate, just as the markets suggested they would.
The race-by-race record was even sharper. According to Kalshi data, the market favorite won 479 of the first 487 races decided.
Money followed the attention. Election-related contracts across Kalshi and Polymarket drew more than $3 billion in trading volume. Kalshi alone accounted for more than $2.5 billion of that total.
What lawmakers are actually worried about
The Democratic critique boils down to three concerns: insider trading, market manipulation, and the gambling-like effect these markets could have on political events.
Insider trading is the most intuitive worry. If someone with nonpublic information can profit from a contract tied to an election, a policy decision, or a military event, the market starts to reward access rather than insight.
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Manipulation is the second issue. Critics fear that prices on high-profile contracts could be pushed around, and that those prices could then shape public perception of a race.
The third concern is more philosophical. Merkley and Waters have objected to the idea of wagering on outcomes like war and terrorism at all, regardless of how well the markets are policed.
The scrutiny is also not purely partisan. Before the election, Sen. Elizabeth Warren and Sen. John Curtis sent bipartisan letters questioning the integrity of election-related contracts. They asked the platforms what measures they had in place to prevent insider trading.
Warren is a Democrat and Curtis is a Republican.
Why the new Congress changes the math
With Democrats now holding majorities in both chambers, expectations are growing for more rigorous investigations and possible new rules targeting prediction markets.
Waters’ involvement is notable here. A senior House Democrat focused on financial services is exactly the sort of lawmaker who could turn criticism into formal oversight.
What this means for the platforms and their users
Election contracts are the obvious pressure point. They drove more than $3 billion in volume this cycle and are also the category lawmakers find most troubling.
Contracts tied to war and terrorism look even more exposed. Those markets are a small part of the public conversation, but they are central to the moral argument Merkley and Waters are making.
For Kalshi and Polymarket, the accuracy data gives them a strong talking point: markets that called 479 of the first 487 races correctly are clearly producing useful information.
The bipartisan Warren and Curtis letters may end up being the more important signal for the industry. Partisan criticism can stall when control shifts. Questions shared by members of both parties tend to stick around longer.
Watch for three things in the coming months. First, whether Merkley or Waters introduces specific legislation, and what it targets. Second, whether congressional committees schedule hearings on prediction markets. Third, how the platforms answer the insider trading questions already on the record.
Disclosure: This article was edited by John Chen. For more information on how we create and review content, see our Editorial Policy.

