Prediction markets have earned a reputation for striking accuracy, yet the engine behind that accuracy has remained surprisingly opaque. In their 2026 paper, Prediction Market Accuracy: Crowd Wisdom or Informed Minority? Roberto Gómez‑Cram, Yunhan Guo, Theis Ingerslev Jensen, and Howard Kung take the first comprehensive look inside Polymarket’s transaction universe and uncover a result that overturns the dominant narratives. Accuracy, they show, comes neither from the broad crowd nor from insiders, but from a tiny, persistently skilled minority roughly 3% of traders who react instantly to public news, correct pricing violations, and trade against the crowd’s behavioural mistakes. 

This Closer Look explores the motivations behind the research, the methods the authors used to separate skill from luck, and what their findings mean for the future of prediction markets as forecasting tools. Through our conversation with the authors, we dig into how the project began, why the “wisdom of crowds” story falls short, and how a small, informed minority quietly shapes the probabilities that policymakers 

Q: What motivated you to investigate who makes prediction markets accurate? Prediction markets are often praised for their forecasting ability, but the source of that accuracy is less understood. What sparked this research question?

Theis Jensen: Roberto and I were working on another project with the same co‑authors, studying analyst expectations and whether surprises around earnings announcements explained returns. Most of what we tested didn’t explain much. Then one of our co‑authors , came across these new earnings prediction markets. We added them to the regression almost casually, and suddenly they explained a lot. That caught our attention. Analysts are smart, so seeing prediction markets add information beyond their forecasts made them worth studying. Since then, we haven’t really looked back: we’ve gone fully into prediction‑market research. 
 

Q: Before you started the project, did you expect the results to support the “wisdom of crowds” view? How did your expectations compare with what you ultimately found? 

Roberto Gomez Cram: When we saw how accurate prediction‑market prices were and confirmed that in papers, we wanted to understand why. So we looked at what people were saying. The first explanation was the classic “wisdom of crowds”: many traders each contribute a bit of information, and it all gets incorporated into prices. That’s also the narrative pushed by the companies running these markets. So yes, we initially expected to find something like that, that information was spread across many traders. 

The other view, especially in the media, was that accuracy comes from insider trading. There are several high‑profile cases, like the Maduro case or the recent one involving a Google employee betting on Google markets. We had these two competing explanations: broad crowd wisdom, versus insider trading – and the goal of the paper was to figure out which one actually drives the accuracy. 

Q: For readers unfamiliar with prediction markets, what is the single most important takeaway from the paper? 

Theis Jensen: I think one key takeaway is that if you don’t have any particular informational advantage or you’re not especially good at things like programming ,then you probably shouldn’t bet in prediction markets with the goal of making money. What we find is that only about 3% of all accounts show significant skill. Most retail investors are not part of that 3%. So one takeaway is that prediction markets are not something you should rely on for long‑term investment success. That said, prediction markets can still be really interesting. I use them myself for things like the World Cup. I don’t expect to win money, but I get something else out of it the excitement of having a bit of money on the line. They can be fun, but I wouldn’t treat them as a long‑term investment strategy. 

Roberto Gomez Cram: And even if you win in one period, if you keep betting, it’s very likely you’ll lose over time. That goes back to what Theis said: they’re not designed for long‑term investment returns. 

Q: The paper argues that accuracy comes neither from the crowd nor from insiders. Why is that distinction important? What does it tell us about how information gets incorporated into markets?   

Theis Jensen: I think it’s important because it speaks directly to the public narrative. People who like prediction markets say the accuracy comes from the “wisdom of the crowd,” and people who dislike them say it’s all insiders. There’s value in academics looking at this in a rigorous way. What we find is that neither extreme is right, the truth sits somewhere in between, and that helps inform the broader debate. 

Prediction markets are controversial, and to make progress on questions like whether they should be allowed, we need a clear understanding of what actually drives them. Our job is to cut through the hype and the incentives people may have to push one story or the other, and simply say: here’s what the data shows. We don’t see insider trading as the dominant force, and we don’t see accuracy coming from all users. Instead, it comes from a very small group of highly skilled traders. And then people can decide what they make of that. 

Q:  How do you distinguish genuine skill from simple good luck? Why was that challenge central to the study? 

Theis Jensen: It’s actually very difficult to separate luck from skill. Our procedure is a bit technical, but the intuition is simple. We look at how well a trader has done historically, and then we simulate how they would have done if they were essentially just flipping a coin. 

From those simulations, we get a distribution of random outcomes: sometimes you’d win by luck, sometimes you’d lose, but on average you shouldn’t make or lose much. Then we compare that random distribution to the trader’s actual profit and loss. If their real performance is far above what you’d expect from the coin‑flip world, we say there’s statistically significant evidence that this trader is doing better than luck. That’s the basic idea behind how we identify genuine skill. 

Q: What characteristics define the traders you classify as “skilled”? Are they experts, fast information processors, disciplined traders, or something else entirely? 

Theis Jensen: We don’t actually know who they are, all accounts are anonymous ,but we can see how they trade. We focus on three behaviours that only the skilled group consistently shows, all related to trading efficiently on public news, exploiting psychological biases, and acting on arbitrage opportunities in prediction markets. 

One pattern is how quickly they react to news. For example, Polymarket might have a contract on whether Tesla will beat earnings. As soon as Tesla announces results, the skilled traders are the ones who immediately trade on that information before others react. They’re extremely fast at processing news. 

Another pattern is that they trade against well‑known psychological biases. A classic one is the favourite–long‑shot bias: people like betting on long shots for the big payout and avoid favourites because the return is small. That means favourites tend to be slightly underpriced and long shots slightly overpriced. Skilled traders do the opposite; they buy the underpriced favourites and sell the overpriced long shots. In that sense, they behave like cold, unemotional machines, while the rest of us are more hot‑headed humans. 

Q: What are the findings that generally surprised you during your research? Like any result that made you rethink your assumption? 

Roberto Gomez Cram: For me, the most striking result was how few accounts actually make money in prediction markets. Only about 3% consistently earn profits, while the vast majority don’t trade any better than flipping a coin. And those skilled traders essentially rely on the other 97% they’re trading against them. One way to interpret it is that the majority is effectively funding the profits of the minority, which is the opposite of what Polymarket’s CEO often claims about “everyone doing great.” The tiny number of skilled accounts kept surprising me. 

Theis Jensen: I agree. Another surprising result was how persistent skill is. In most financial markets, it’s extremely hard to beat the market year after year. Mutual fund managers are the classic example. They might outperform in one year, but very few do it repeatedly. Skill just isn’t persistent. But in prediction markets, the small group of skilled traders shows very persistent performance. So while only a tiny subset is skilled, those traders consistently beat the market. I hadn’t expected that at all. 

Q: Your study suggests that many participants contribute volume but relatively little information. What broader lessons does that offer about collective decision-making and expertise? 

Theis Jensen: I think one broad takeaway is that markets do a very good job of aggregating information, but that’s usually driven by a small minority of people. When you have retail traders like people from Reddit trading stocks, they’re not contributing much information. Their trades don’t really push prices toward the correct value. But what they do provide, and this goes back to what Roberto said, is an incentive for the really smart traders to trade. If you were only trading against other smart people, you wouldn’t make much money and might not trade at all. But if most people are trading for fun or acting behaviourally, then skilled traders have a strong incentive because there’s a big pot of money to be won. 

Roberto Gomez Cram: And this is especially true in prediction markets because they’re a zero‑sum game: my losses are your gains. That’s very different from financial markets, which aren’t zero‑sum. If you invest in an ETF and the economy grows, everyone can do well. There the benchmark is beating a passive strategy. Here, the benchmark is simply having a positive profit, which makes the dynamic Theis described even more important. 

Q: What does this research tell us about the future role of prediction markets in forecasting elections, economic events, sports, or technological developments? 

Roberto Gomez Cram: I think prediction markets are amazing, and they really do provide a new financial instrument, one where you can put money on very specific events. That specificity, with narrow questions and clear rules and timelines, is what makes prediction markets so interesting. Right now, most activity is speculation, like betting on whether GDP will be above a certain level. But there’s a lot of potential for hedging, because these markets let you hedge risks you can’t hedge with standard financial instruments. If volume and liquidity increase, more people could use them for hedging, and that would attract institutional investors, not just retail. At that point, prediction markets could become part of mainstream finance because they offer an instrument you simply can’t trade elsewhere. 

Theis Jensen: On future accuracy, our earnings paper shows prediction markets are extremely accurate, but our other paper shows that most of that accuracy comes from a small group of people. So going forward, our evidence suggests three things: prediction markets today are quite accurate; staying accurate requires that these skilled traders keep participating; and they also need people to trade against. For these markets to work well, they can’t be dominated only by not‑so‑smart money or only by very smart money they need a mix. That’s how I see their future. 

Q: If policymakers, businesses, or journalists rely on prediction markets, how should they interpret your findings? Should they have greater confidence in these markets or be more cautious? 

Theis Jensen: I would have confidence in these markets for accuracy. If you’re interested in some event and it’s traded on a prediction market, then more often than not, that’s probably the most accurate estimate you have. If I want to know the probability that Norway beats France in the World Cup, I check Polymarket. If I want to see where people think GDP is going, I still look at forecasts, but I’m increasingly drawn to prediction markets too. Most of our research suggests these markets are doing their job. They’re extremely accurate. 

Roberto Gomez Cram: And as liquidity improves and trading volume is increasing massively, these prices should become even more accurate. At least until the point where people start using them for hedging. Once they’re used for hedging, you’ll see a wedge between accuracy and the “true” price because risk premia start to matter. So they’ll stay very accurate up to that point, and then accuracy may decline a bit once hedging motives enter. 

Theis Jensen: I agree. Once they’re liquid enough to be used for hedging, they must already be extremely liquid. So there are two forces going in opposite directions, and it’s hard to know which one will dominate. 

Roberto Gomez Cram: But in the medium term, I think accuracy will keep improving. 

Q: What is the most important unanswered question that remains after this paper? If you were writing the sequel, what would you study next? 

Roberto Gomez Cram: We conclude the paper by saying that the next big question and what we’re currently working on is the hedging motives in prediction markets. 

Theis Jensen: Yes, essentially the hedging capacity. What can you actually hedge with prediction markets right now? How much can you hedge, and how valuable is that compared with standard financial markets? 

Roberto Gomez Cram: And we’ll have a working paper on this very soon , journalists, and investors increasingly rely on. 

ABOUT THE AUTHORS

Roberto Gomez Cram

Roberto Gómez‑Cram is an Assistant Professor of Finance at London Business School. His research spans asset pricing applied time‑series econometrics, and investments, with a focus on how information is incorporated into markets and how financial assets are priced. His work has been recognised with multiple awards and published in leading journals, including the Journal of Financial Economics. 
Roberto joined the LBS faculty in 2019, spent a year at NYU Stern, and returned to LBS in 2024. Before academia, he worked as an Assistant Economist at the Central Bank of Mexico. He holds a PhD in Finance from the Wharton School of the University of Pennsylvania. 

You can see more work by Roberto Gómez‑Cram on his SSRN Author page here.

Theis Ingerslev Jensen 

Theis Ingerslev Jensen is an Assistant Professor of Finance at Yale SOM. His research focuses on empirical asset pricing and the use of novel data especially measures of subjective expectations to understand how beliefs shape market outcomes. His projects are highly data‑intensive and often employ machine learning and other advanced statistical methods.
Originally from Denmark, Theis earned his PhD in Financial Economics from Copenhagen Business School in 2023. His expertise spans behavioural finance, big data, financial markets, and valuation.

You can see more work by Theis Ingerslev Jensen his SSRN Author page here.