Episode summary
Kalshi Head of Research on Midterms Accuracy
Bloomberg Businessweek Daily
· · Hosts: Carol Massar, Tim Stenovec · Guests: Nicole Kagan
Summary
Kalshi’s election markets were extremely accurate on midterm night, with Nicole Kagan saying the platform was directionally correct in 479 of 487 races processed by 06:00, or about 98% plus accuracy. She said accuracy improved as election day approached, rising from roughly 95% months out to about 98% by poll close as new polling, alternative data and early vote results came in.
Kagan highlighted how quickly traders reacted when information changed. In South Carolina, she said the market flipped within five minutes of polls closing, and traders correctly repriced the race before news networks called it. She argued that the key value of prediction markets is not just whether they “get it right,” but that they output probabilities that can be tested for calibration over large samples.
She also framed the platform as useful beyond politics. Kagan said institutional investors can use prediction-market probabilities for price discovery and hedging around policy risk, from CPI and payrolls to state-level issues like AI, data centers, gas prices and tariffs. Even with large races such as Talarico’s, she said the market does not need massive volume to be well calibrated because the incentive is to be right quickly.
Key takeaways
- Nicole Kagan said Kalshi was directionally accurate in 479 of 487 races, or about 98% plus, across Tuesday’s election markets.
- She said accuracy improved from about 95% months before the election to about 98% by poll close as traders absorbed new information.
- Kagan pointed to South Carolina as an example of fast repricing, saying the market flipped within five minutes of polls closing and before networks called it.
- She argued prediction markets should be judged on calibration and long-run probability accuracy, not just on whether a single race looks right or wrong.
- Kagan said institutional investors can use Kalshi for price discovery and hedging around policy and macro risks that are hard to trade directly.
Notable quotes
“Calci was directionally accurate in 479 of those 487. So we're talking about accuracy in the realm of about 98 plus percent across the elections we saw last night”
“The flip in South Carolina for Andrews happened within five minutes of polls closing in the state”
“what markets are providing to you is a probability of an event happening”
“there's a really useful price discovery component of the probabilities that we're producing from our markets”
“the incentive in our markets is to be right and to be right quickly”
How the episode opens
How accurate were the prediction markets? Yeah. Well, thank you so much for having me.
Well, the answer to that question is extremely accurate. So of the 487 races that had been processed by 06:00 this morning, if we look at what the markets were saying before the closure of polls, Calci was directionally accurate in 479 of those 487. So we're talking about accuracy in the realm of about 98 plus percent across the elections we saw last night.
How did the accuracy change as you got closer to the election? Yeah. So accuracy increases, as a function of time to the election.
You know, as more information comes out about the candidates, as more polling comes out, as more alternative data is revealed, traders may change their positions. And I think one thing that we saw last night that was really interesting and isn't included in this accuracy release, but is it an interesting thing to look at is just how the closure of polls in certain states in Kentucky closing first influenced the way that traders were trading other markets. So once we saw polls close, once we saw early voting results come in, other voting results come in and come in very blue, we saw traders reposition in other markets and begin to reposition in the house and the senate race as well.
Called making a trade with more information. Right? Exactly right.
Exactly right. Yeah. There were some markets that moved very...
In a very big way Mhmm. Very late. So I'm thinking of of South Carolina Mhmm.
For example, or the Texas governor's race, for example. What was unique about those races that caused such big movements to happen so late? And I don't recall if they were after the actual polls closed in in those ones, but it really flipped.
From the episode’s captions: roughly the first two minutes, lightly cleaned. Keep watching on YouTube →
Topics
- election prediction accuracy
- prediction markets
- midterm election probabilities
- price discovery
- institutional adoption
Companies and tickers mentioned
- Kalshi
Watch the episode
Watch “Kalshi Head of Research on Midterms Accuracy” on YouTube
Summary, takeaways and quote selection by Octagon. Quotes are short excerpts; the full conversation is in the episode.
More from Bloomberg Businessweek Daily
-
Bloomberg Businessweek Weekend - November 13th, 2026 | Bloomberg Businessweek -
Inside Jensen Huang’s Final Hours as Nvidia CEO | Bloomberg Businessweek -
Jensen Huang's Exit Kicks Off Nvidia Strategy Reset | Bloomberg Businessweek -
Special Episode: Bobby Jain on Why He Chose Millennium Again | Bloomberg Businessweek -
Bloomberg Businessweek Daily Podcast: Jensen’s Nvidia Departure -
Take-Two Releasing Long-Awaited Grand Theft Auto VI Next Week | Bloomberg Businessweek
Bloomberg Businessweek Daily, this episode and its artwork belong to Bloomberg. Octagon is not affiliated with the show; this page is an independent summary.
