Can We Trust Election Prediction Markets?

Summary

Podcast Summary: Can We Trust Election Prediction Markets?

Podcast: The Big Take (Bloomberg & iHeartPodcasts)
Date: July 29, 2026
Host: Sarah Holder
Guest: Liam Vaughn (Bloomberg investigations reporter and editor)

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Overview

This episode delves into the surging prominence—and accompanying vulnerabilities—of political prediction markets like Kalshi and Polymarket, particularly their influence on U.S. and global elections. Host Sarah Holder and investigations reporter Liam Vaughn explore whether these markets can be trusted as forecasting tools, the risks of manipulation, their integration into media narratives, and how their growing scale may shape democratic processes and public trust.

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Key Discussion Points & Insights

1. Case Study: Manipulation on Polymarket

• Matt Mahan's Odds Surge (01:44–04:20)
  • On the day Matt Mahan announced his run for California governor, a $44,000 bet on Polymarket suddenly spiked his odds from the low teens to 96%.
    • Liam Vaughn: “Suddenly, his odds go from like the low teens all the way up to 96%... what that market's saying is there's a 96% chance he’s going to be the governor of America's largest state.” (02:34)
  • Other traders quickly took the other side, betting against Mahan and pushing his odds back down—though not to original levels.
  • The major bet drew media attention, inflating Mahan’s profile and shaping press coverage.
  • The episode highlights the ease with which a single actor can sway market odds and public narratives.

2. Regulatory Shifts Enable Explosion of Political Betting

• Historical Restrictions and Regulatory Changes (05:21–08:24)
  • Until recently, political betting was banned in the U.S. by the Commodity Futures Trading Commission (CFTC).
  • Kalshi, a U.S.-based prediction market, challenged these restrictions in 2023. Legal ambiguity led to a loosening of regulations.
    • Liam Vaughn: “Kalshi and its competitors are allowed for the time being at least, to allow mass scale gambling on U.S. politics.” (06:15)
  • The Trump administration, post-2024, shifted regulators—removing Democratic commissioners—leading to a more permissive environment and a trading surge (over $6 billion monthly by late 2025).

3. How Kalshi & Polymarket Operate

• Contrasting Models (08:40–09:29)
  • Kalshi: Positions itself as the “official” market; requires customer identity verification (KYC); can track and address manipulation.
  • Polymarket: Crypto-based, anonymous; harder to regulate and more vulnerable to manipulation.
    • Liam Vaughn: “Kalshi should in theory know who’s behind that...Polymarket is crypto first...you could do it all anonymously.” (08:40)

4. Media Partnerships and Influence

• Mainstreaming Prediction Markets (09:29–11:50)
  • Both platforms ink deals with major outlets (CNN, CNBC, Fox, Wall Street Journal, etc.), incentivizing media to report their odds.
    • Some media (like CNBC) receive a fee for each Kalshi signup via their platforms.
    • Liam Vaughn: “A real strong financial incentive for some of these media companies to be citing these prediction market odds...which is a contrast with polls.” (09:44)
  • Concerns arise about referencing thinly traded or small markets, which are easier to manipulate yet cited as news.

5. Forecasting Reliability vs. Manipulation

• Market Corrections and Self-Regulation (11:50–14:02)
  • Kalshi argues their controls prevent manipulation; Polymarket points to self-correcting market dynamics.
  • Sarah Holder draws a parallel to the Mahan case: the market partially corrected, but not fully.
    • Liam Vaughn: “These markets have a sort of degree of ability to self correct...the Mahan example is testament to that.” (13:50)
  • Both platforms cite research indicating superior performance to polls under some circumstances.

6. Potential for Mis- and Disinformation

• Recent Examples in U.S. & Globally (17:19–20:36)
  • Influencer Manipulation:
    • Paid influencers used prediction market odds to sow doubt after Spencer Pratt lost the LA mayoral race.
    • Liam Vaughn: “...these markets kind of present themselves as truth machines, but they can also be utilized by people that are looking to spread mistruths.” (18:22)
  • International Cases:
    • In Romania, Russian disinformation campaigns targeted both social media and prediction markets, with suspicious betting patterns creating the false expectation of a pro-Russian candidate's win.
    • Liam Vaughn: “So it's almost like there's damage either way...it kind of degrades the sanctity and faith in the democratic process.” (20:12)

7. Are Risks Growing & What Can Be Done?

• Escalating Dangers and Possible Remedies (20:50–22:03)
  • The risks of manipulation and the impact on public discourse are likely increasing as the markets’ scale and reach expand.
    • Liam Vaughn: “I think so. You can have a situation where something is quite cheap to move, [and] have a huge amount of traction both in social media and in the media.” (20:50)
  • Possible Solutions:
    • Emulating Kalshi’s KYC standards in crypto markets.
    • Increasing market liquidity and participation to make manipulation more expensive.
    • Media using only robust markets—those with significant trading volume and diversity—or time-averaged prices.
      • Quote: “A professor at Stanford...suggests a charter for the media where they only cite markets of a certain size...or take some kind of average over the course of a day.” (22:17)

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Notable Quotes & Memorable Moments

• On sudden spikes:
  “Suddenly, his odds go from like the low teens all the way up to 96%. It's this huge spike.”
  — Liam Vaughn (02:34)

• On regulatory reversals:
  “Essentially the Trump administration comes in...they get rid of all the Democrats and...put in some conservative commissioners who are predisposed to and are supporters of prediction markets...as a result they explode.”
  — Liam Vaughn (07:54)

• On media incentives:
  “There was a real strong financial incentive for some of these media companies to be citing these prediction market odds.”
  — Liam Vaughn (09:44)

• On self-correction:
  “...these markets have a sort of degree of ability to self correct...the Mahan example is testament to that...”
  — Liam Vaughn (13:50)

• On misuse for disinformation:
  “These markets kind of present themselves as truth machines, but they can also be utilized by people that are looking to spread mistruths.”
  — Liam Vaughn (18:22)

• On risks to democracy:
  “So it's almost like there's damage either way...it kind of degrades the sanctity and faith in the democratic process.”
  — Liam Vaughn (20:12)

• On solutions for media:
  “...they only cite markets of a certain size. They just do some basic interrogation of like, is this price being determined by one or two wallets? Or is there a real diverse number of trades?”
  — Liam Vaughn (22:17)

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Timestamps for Important Segments

| Segment | Timestamp | |------------------------------------------------|--------------| | Matt Mahan/Polymarket case | 01:44–04:20 | | Regulation history and changes | 05:21–08:24 | | Kalshi vs. Polymarket models | 08:40–09:29 | | Media partnerships, incentives, and influence | 09:29–11:50 | | Market manipulation and platforms’ defenses | 11:50–14:02 | | Misinformation, disinformation, global examples | 17:19–20:36 | | Rising risks, solutions, media best practices | 20:50–23:08 |

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Summary & Takeaways

• Prediction markets can shape both perceptions and realities of political races, sometimes on the back of a single large trade.
• Regulatory decisions have accelerated their growth and integration into mainstream political reporting.
• Due to market structure and media partnerships, odds can be misleading—vulnerable to manipulation, and sometimes weaponized to seed doubt or misinformation.
• Solutions include stricter platform identity requirements, greater trading volume, and more responsible media use of market data.
• The legitimacy and influence of prediction markets are poised to rise in upcoming election cycles, placing renewed pressure on platforms, media, and regulators to ensure these tools inform rather than distort public debate.

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This summary preserves the original tone and language where relevant, distilling the central arguments and moments from the episode.

Transcript

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[Bloomberg Audio Studios Announcer]

Bloomberg Audio Studios Podcasts Radio

[Sarah Holder]

news When Matt Mahan officially entered the race for governor of California in late January, he wasn't exactly a household name. Mahan was the mayor of San Jose. A centrist with ties to Silicon Valley, he was polling around 4%. But on the morning Mahan announced his candidacy, he got a boost from an

[Liam Vaughn]

unexpected place on polymarket, which is the preeminent political prediction market in the world. There's suddenly a trade for $44,000 that comes into that market to back Matt Mahan's candidacy.

[Sarah Holder]

That's Liam Vaughn, a reporter and editor on Bloomberg's investigations team based in London. He's been covering prediction markets, which allow users to bet on the outcomes of real world events.

[Liam Vaughn]

Suddenly, his odds go from like the low teens all the way up to 96%. It's this huge spike.

[Sarah Holder]

If you bet 96 cents that Mahan would win, you'd get paid out a dollar if you did. But before that big bet, you only needed to bet about 12 cents for the same payout.

[Liam Vaughn]

And essentially the price fluctuates between 0 and 100. And that reflects the apparent odds, you know, according to that market, of an event happening. So when Matt Mahan's odds go from like 12% to 96%, at least for a few moments, what that market's saying is there's a 96% chance that he's going to be the governor of America's largest state.

[Sarah Holder]

Quickly, other traders on polymarket saw an opportunity to make money. They started taking the other side of the bet, wagering that Mahan would lose. That started pushing his odds back down.

[Liam Vaughn]

But the interesting thing about this market is it doesn't bring it back anywhere close to where it started. It goes to 36%, which is still very high for this guy who hasn't got much name recognition in a market full of, like, you know, very big, big names. And so immediately you start to see press coverage. For example, in the New York Post, there's a piece that runs that afternoon that says the smart money is backing Matt Mahan for California governor.

[Sarah Holder]

Mahan did not respond to requests for comment. Ultimately, his poly market bump wasn't enough to win him the primary.

[Liam Vaughn]

But eventually kind of gravity caught up. Other candidates with more money and more name recognition prevailed and ultimately Mahan kind of quietly exited stage left.

[Sarah Holder]

But the $44,000 question remained. Who had made the bet?

[Liam Vaughn]

We don't know very much, and I think that that's telling in and of itself because polymarket is crypto based, so the authorities don't know who it is. And unless this individual comes forward, nobody else knows who it is.

[Sarah Holder]

Whoever it was had shaped the conversation around a major primary race. With one big Polymarket bet. And with the US Midterms fast approaching, it's just one example of how prediction markets could be gamed in elections.

[Liam Vaughn]

What's fascinating to me about this incident is that it highlights the inherent vulnerability of some of these markets to manipulation. And so the questions that I was keen to explore were like, well, how is that able to happen? What safeguards are in place to stop it happening? And as we go into the midterms and, you know, elections globally, is this something that we're going to see much more of?

[Sarah Holder]

I'm Sarah Holder and this is the big take from Bloomberg News today on the show. The dangers of mixing prediction markets with politics. Until relatively recently, gambling on politics wasn't allowed in the U.S. previously, there was

[Liam Vaughn]

only a couple of forums that were allowing kind of political gambling, and they were associated with universities. And so it was, you know, studied. Like can these things reveal good information and are they useful forecasting tools?

[Sarah Holder]

For years that restriction on political betting was enforced by the Commodity Futures Trading Commission, the federal agency responsible for regulating derivatives markets in the US. But in 2023, Kalshi, the largest prediction market platform in the US and Polymarket's chief competitor, pushed for those restrictions to be loosened.

[Liam Vaughn]

The CFTC undertakes a review and ultimately it concludes as its predecessors had, that this is a bad idea, that it's against the public interest, that it's going to potentially encourage people to, to either try and make money from democracy or try and influence the outcome of elections and therefore it should be banned. That then goes before a judge. But ultimately the judge decided betting on politics is not gambling or it's not gaming as it's written in the statute. So it's a kind of nuanced argument, but ultimately it means that Kalshi and its competitors are allowed for the time being at least, to allow mass scale gambling on U.S. politics.

[Sarah Holder]

That opened the floodgates right before the U.S. presidential election.

[Liam Vaughn]

And so in the presidential election of 2024, I think it's like $4 billion is traded. And when both Kalshi and Polymarket predict that Trump is going to get in and the polls again are sort of prevaricating in some sense a tie, and the prediction markets perform much better in that question. That kind of adds to this sense of that this is a good thing and this is something that should be

[Sarah Holder]

embraced to prediction markets and their boosters. This outcome seemed to confirm something important, that by harnessing the wisdom of the crowds, crowds with real money on the line, these markets can predict the future more accurately than traditional polling can. What happens next when the Trump administration takes office? How does the regulatory approach to these markets change?

[Liam Vaughn]

I mean, It's a complete 180. Essentially the Trump administration comes in, they put their own people in charge of the various regulators and at the CFTC they get rid of all the Democrats and essentially put in some conservative commissioners who are predisposed to and are supporters of prediction markets. Essentially you see a real kind of shift in the administration's attitude towards these markets and as a result they explode.

[Sarah Holder]

In December 2024, the monthly trading volume on Kalshi was $226 million. A year later it was $6.4 billion. At this point, how do Kalshi and Polymarket's approaches to political wagering differ?

[Liam Vaughn]

Yeah, they're kind of the yin and yang of approaches. Kalshi has spent a huge amount of time and energy trying to position itself as the official market. It does KYC know your customer. So anyone that signs up has to provide details of who they are, where they live. And so anything that goes wrong or any sort of attempts at manipulation, Kalshi should in theory know who's behind that. Polymarket is crypto first and you don't need to provide your bank details. And essentially you could do it all anonymously. You could see via sort of a numerical or a kind of username what that account is up to on a kind of rolling basis, but you just don't know who it is.

[Sarah Holder]

So, Liam, there was another moment that really marked the entrance of prediction markets into sort of the polling mainstream. After the election, after the shifted regulatory approach, polymarket and Kalshi made a string of deals with major news outlets.

[Liam Vaughn]

Yeah, So I would describe it as a kind of feeding frenzy of deal making between Kalshi and polymarket and the mainstream media companies. So Kalshi have done deals with cnn, with cnbc, with Fox and several others. Polymarket have done deals with the Wall Street Journal, Dow Jones X substack. So there was a real strong financial incentive for some of these media companies to be citing these prediction market odds. And that's a kind of contrast with polls, because even though they carry polls, they're not getting paid by the polls to carry them.

[Sarah Holder]

We don't know the details of all of these deals, but in the agreement between CNBC and Kalshi, CNBC can earn a fee every time someone signs up for Kalshi from its website.

[Liam Vaughn]

The other criticism which we get into in the piece is that quite often, because these markets aren't very big outside of the major political events, they're easy to manipulate, and yet they're still cited regularly, just to give one example. But they're far from alone. But CNN has got this show called the Odds, and they will regularly use Kalshi odds as a sort of frame to talk about current political events. But often they cite markets that are very small. So, you know, there's a discussion around who's going to be the next speaker of the House. And we looked, and at the time it had like a total of 16,000 betting across its lifetime. So you sort of talk about a market where every day there's like, you know, a couple of hundred or a few hundred amounts being traded. So the question is like, is that a market that's robust enough to really be cited by mainstream media organizations? And there's Some people that argue, no, absolutely not.

[Sarah Holder]

Yeah, I mean, it's like the Matt Mahan example. The Matt Mahan market was vulnerable to being moved by this one big bet. Some major outlets ran that story about Mahan's Poly Market odds governor's race without additional context. Does that happen a lot?

[Liam Vaughn]

I mean, it happens more than you'd think, definitely. And it's not helped by the fact that because they've done these deals, they're sort of obliged to consistently refer to these polls.

[Sarah Holder]

CNN says that, quote, prediction markets offer just one source of data that journalists can use in telling a story, unquote, and that CNN staff are prohibited from trading. A Kalshi spokesperson says that its media partners have, quote, full control over what and how and we don't tell them which markets to cite, unquote. CNN and Kalshi both declined to comment on how much Kalshi pays CNN to cite its data. Bloomberg lp, the parent of Bloomberg News, provides polymarket and Kalshi data as part of its broader data offering for clients. What do prediction markets themselves say about their utility as forecasters of election results and these risks that they pose?

[Liam Vaughn]

So in terms of their ability to forecast events, Cauchy and polymarket point to research that suggests that they are consistently very good. They are at pains to say that they should be viewed as additive to existing forms of forecasting, whether that's polls or reporting or whatever else. In terms of the issues that we raise in the piece around the vulnerability of these markets to manipulation, Kalshi would say we are not the same as polymarket and we take our responsibilities to identify our customers and monitor what they're doing very carefully. And if there is anything untoward going on, then we will find it. Polymarket would say a couple of things. One is that in terms of market manipulation, these markets have a sort of degree of ability to self correct which is demonstrated. And so even if there are attempts at manipulation, they'll be quickly remedied. And so they would say, I think that the Mahan example is testament to that because ultimately, whilst these odds did move, they did also come back as the market kind of responded to that.

[Sarah Holder]

Coming up, how prediction markets can be used to help spread misinformation and so doubt in election results.

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[Sarah Holder]

Around the time Matt Mahan was surging on polymarket in the California governor's race, another candidate was riding his own wave of prediction market hype in the Los Angeles mayoral primary. Republican former reality TV star Spencer Pratt.

[Bloomberg Audio Studios Announcer]

Call sheet prediction markets are showing a 59% chance that Spencer Pratt finishes second place in the first round of the LA mayoral election.

[Liam Vaughn]

What if he clears 51% tonight? Kal, she's saying he's gonna go.

[Sarah Holder]

When the votes finally rolled in, Pratt came in third, failing to qualify for the November runoff. But not everyone was willing to accept that the prediction markets had been wrong.

[Liam Vaughn]

You saw lots of right wing supporters of Pratt coming forward and saying, this is ridiculous. The race was rigged. Like, there must be an issue here.

[Sarah Holder]

Some of the posts came from influencers who were being paid by Kalshee and Polymarket to cite their odds. According to Semaphore, after paid influencers for Kalshi questioned the integrity of the LA mayoral election on X, Kalshee asked them to take the post down.

[Liam Vaughn]

So Trump wrote on Truth Social, this is an outrage. Essentially Spencer was robbed and he's kind of citing how well he was doing in the race up until that point. And so the concern is that ultimately these markets kind of present themselves as truth machines, but they can also be utilized by people that are looking to spread mistruths.

[Sarah Holder]

In the past year, the platforms have missed the mark in high profile races like in Argentina, India and New York City. And one election in Romania showed they can also be vulnerable to potential influence from bad actors.

[Liam Vaughn]

There was an election in 2024 that was actually annulled because there was found to have been Russian disinformation campaign campaign that was enacted on TikTok and YouTube. It was so bad that they ended up canceling the election. They held another election. And that election was subject to a lot of speculation around whether the polymarket numbers for the kind of nationalist candidate who was supported by the Kremlin were being manipulated because there was such a big divergence between what the polls said and what the odds were on this political candidate. And there were lots of people that kind of suggested that this was like Russia cottoning on to the potential opportunity to mess with people's perceptions of reality by targeting prediction markets alongside TikTok or YouTube or, you know, other other forms of disinformation.

[Sarah Holder]

An active disinformation campaign Yeah. A firm Bloomberg asked to view the trades found that a few recently created accounts were driving a lot of the trading. Liam says maybe that was a coincidence, but for supporters of the right wing candidate George Simeon, the bets helped create the expectation that he'd win.

[Liam Vaughn]

When this guy Simeon lost again, you had all these people coming forward and saying, look, this is ridiculous. On Polymarket, he was, you know, a dead sir and now you expect us to believe that he lost. So it's almost like there's damage either way. Even if ultimately it doesn't succeed, or even if ultimately it's not manipulation, if everyone is suggesting it, then it kind of degrades the sanctity and faith in the democratic process.

[Sarah Holder]

I'm wondering, given all this potential for manipulation and just the rise of these prediction markets importance and reach, do you see the risks of this becoming more severe moving forward? Are we at an inflection point?

[Liam Vaughn]

I think so. I think that essentially you can have a situation where something is quite cheap to move, can have a huge amount of traction both in social media and in the media. And so I do think that the risks are increasing that people will try and manipulate these markets for kind of political ends. Having said that, I do think there's fairly easy solutions to some of these issues. A lot of the issues around Polymarket could be fairly easily remedied if it kind of emulated its rival Kalshi and, and started getting the identification of its, of its users.

[Sarah Holder]

Another thing that could minimize the risk of manipulation would actually be to increase the number of people placing bets, boosting the trading volume on the platforms. More gambling carries its own downsides, but more heavily traded markets are harder and more expensive to manipulate.

[Liam Vaughn]

I think that if you talk to Kalshi and Polymarket, they kind of hope that one day everyone's going to be sitting there watching television, betting on everything, and everyone's going to want to bet on the, you know, local mayoral race in, you know, in Sacramento. I'm not sure that that's going to happen.

[Sarah Holder]

Liam. The midterms are coming up in the US we're going to be seeing a lot more polls, a lot more prediction market election odds being reported. How should the public and the media be reading these results?

[Liam Vaughn]

What the media companies can do, and this is something that a professor at Stanford called Andy hall, he's done a lot of work on and essentially he sort of suggests a charter for the media where they only cite markets of a certain size. They just do some basic interrogation of like, is this price being determined by one or two wallets? Or is there a real diverse number of trades that have come to this price? He also suggests rather than taking like a price at a set moment in time, which can be pushed around, that you actually take some kind of average over the course of a day. So it's different things that the media companies could do fairly easily that wouldn't really undermine their use of these things, but would make the odds that they're citing more robust.

[Sarah Holder]

This is the Big Take from Bloomberg News. I'm Sarah Holder. To get more from the Big Take and unlimited access to all of bloomberg.com, subscribe [e-mail] podcast offer. If you like this episode, make sure to subscribe and review the Big Take. Wherever you listen to podcasts, it helps people find the show. Thanks for listening. We'll be back tomorrow.

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