{"id":21394,"date":"2026-06-14T10:07:52","date_gmt":"2026-06-14T08:07:52","guid":{"rendered":"https:\/\/thesmartcityjournal.cibeles.net\/sin-categoria\/wisdom-of-the-few-prediction-markets-are-driven-by-a-small-number-of-skilled-traders\/"},"modified":"2026-06-14T10:12:57","modified_gmt":"2026-06-14T08:12:57","slug":"wisdom-of-the-few-prediction-markets-are-driven-by-a-small-number-of-skilled-traders","status":"publish","type":"post","link":"https:\/\/www.thesmartcityjournal.com\/en\/business\/wisdom-of-the-few-prediction-markets-are-driven-by-a-small-number-of-skilled-traders","title":{"rendered":"Wisdom of the Few? Prediction Markets Are Driven by a Small Number of Skilled Traders"},"content":{"rendered":"

The prevailing view is that prediction markets like Polymarket and Kalshi get their accuracy by aggregating the views of vast numbers of participants. But a new study co-authored by Yale SOM\u2019s Theis Jensen finds that, in fact, a small group of informed traders drive prices\u2014and take home a large portion of the profits<\/span><\/i><\/p>\n

Characters in fairy tales rely on crystal balls to see into the future. In 2026, the world has prediction markets, in which traders bet on the likelihood of real-world events such as election outcomes or the winner of the Eurovision song contest\u2014often with surprising accuracy.<\/span><\/p>\n

The founders of these markets, which have grown by\u00a0<\/span>orders of magnitude<\/span><\/a>\u00a0in just the past two years, attribute their accuracy to the wisdom of crowds, the idea that, in aggregate, large numbers of bets by diverse people will ultimately trend in the right direction. \u201cEveryone has skin in the game, and a very strong incentive to state their true beliefs,\u201d explains Yale SOM\u2019s\u00a0<\/span>Theis Ingerslev Jensen<\/span><\/a>. \u201cIt feels reasonable when people say, \u2018Oh, they work because of the wisdom of crowds.\u2019\u201d<\/span><\/p>\n

But, he noted, prediction markets look a lot like traditional financial markets, which are shaped by the small minority of people trading with higher-than-average skill or information. \u201cWe wanted to test the conventional explanation for prediction markets against a more standard explanation that would apply to traditional financial markets,\u201d says Jensen.<\/span><\/p>\n

For a\u00a0<\/span>new working paper<\/span><\/a>, Jensen and his co-authors, Roberto G\u00f3mez-Cram, Yunhan Guo, and Howard Kung of the London Business School, tested the \u201cwisdom of crowds\u201d view of markets by looking at publicly available trading data from Polymarket, which bills itself as the world\u2019s biggest prediction market. They found that, in fact, prediction markets\u2019 accuracy derives from the bets of a handful of skilled traders\u2014who, unfortunately for the overwhelming majority of participants, also reap the lion\u2019s share of the financial winnings.<\/span><\/p>\n

In prediction markets, traders buy binary yes-or-no contracts on the outcome of events that often have nothing to do with the real economy. For example, in \u201cmention markets,\u201d traders bet on whether a public figure will mention a given word in a speech. (President Trump\u2019s Thanksgiving Day speech had\u00a0<\/span>multiple separate markets<\/span><\/a>, in which traders could bet on such questions as whether he would mention the word \u201cstuffing\u201d and which of two turkeys would receive a pardon.) A decentralized committee decides whether the event has occurred, leading to a payout of $1 per contract if the event occurs (a Yes), or nothing if it does not (a No). A higher likelihood of an event happening raises the price of the contract.<\/span><\/p>\n

Figuring out who drives trading on Polymarket and who profits from it was simplified by the fact that the market records transactions in a public blockchain. Jensen and his co-authors drew on two years of trading data, covering 1.72 million accounts whose owners collectively traded on 98,906 events and 210,322 markets, for a total trading volume of $13.76 billion.<\/span><\/p>\n

The records made it easy to see which anonymous accounts were profiting from each trade. The hard part was distinguishing luck from skill. To identify traders with a consistent edge, they took each trader\u2019s actual sequence of trades\u2014including the markets traded, timing, prices, and bet sizes\u2014but with one aspect randomized: whether they bought or sold the contract. By repeating this process 10,000 times, they simulated what each trader\u2019s profit and loss (PnL) would look like if they simply tossed a coin when deciding whether to buy or sell\u2014in other words, in the absence of any skill. They then compared the trader\u2019s actual PnL to this simulated coin-toss benchmark to test whether the trader out- or under-performed.<\/span><\/p>\n

Their analysis found that just 3% of accounts could be classified as \u201cskilled,\u201d with significantly positive PnL that could not be explained by random chance. About twice as many accounts performed even worse than the benchmark, a group they designated as \u201cunskilled.\u201d For the vast majority of traders, their outcomes were virtually indistinguishable from chance. The skilled group, alongside an even tinier group of market makers\u2014traders who primarily provide liquidity by posting buy and sell orders\u2014\u201crepresent fewer than 3.5% of all accounts, yet capture over 30% of total gains,\u201d the authors write.<\/span><\/p>\n

Where did the rest of the gains go? Another 29% of traders, who the authors call \u201clucky winners,\u201d managed to make money without any statistically discernible skill \u2014that is, they made a profit, but not one significantly larger than what the researchers\u2019 randomized simulations suggested might occur by chance alone. Those traders captured the remaining 69% of gains within the two-year period.<\/span><\/p>\n

Might the skilled traders simply have been unusually lucky? To avoid falling into the circular argument that \u201cskilled people made more money than the unskilled ones,\u201d Jensen says, they further scrutinized actual trades to see which accounts consistently performed better than chance.<\/span><\/p>\n

They divided the events that each trader bet on in half at random. A truly skilled trader would outperform the benchmark in both sets of trades\u2014and indeed, they found that 44% of traders classified as skilled based on the first set of trades are also classified that way in the second. That\u2019s a significantly higher share than in, say, the mutual fund market, where just 10% of fund managers consistently outperform the overall market.<\/span><\/p>\n

\u201cMost people think that if you beat the market in one period, then that was just luck, and so you’re not going to continue beating the market,\u201d says Jensen. \u201cWe found that for prediction markets, there’s an unusually high level of persistence. It appears that if you classify someone as skilled in one period, they are much more likely to be skilled in the next period than random chance would suggest.\u201d<\/span><\/p>\n

To test the \u201cwisdom of crowds\u201d hypothesis, the researchers also needed to determine which traders were responsible for the markets\u2019 accuracy. They set out to see how each of the different groups\u2014skilled and lucky winners, market makers, and unskilled and unlucky losers\u2014contributed to contract prices, which proxy the likelihood of an event. They looked at how each group traded around two recurring pre-scheduled events: the Federal Reserve\u2019s Open Market Committee (FOMC) meeting announcements, and quarterly corporate earnings reports.<\/span><\/p>\n

And indeed, they found that the traders they had deemed skilled consistently moved contract prices in the direction of the final outcome, by buying more contracts that ultimately resolved Yes and selling contracts that ultimately resolved No. Skilled traders also reacted more quickly to news. For example, following FOMC meetings or corporate earnings announcements, skilled traders quickly bought contracts in the direction of the announcement before the contract settled. \u201cThe remaining majority does not produce accuracy; rather, it funds it,\u201d the authors conclude.<\/span><\/p>\n

Despite some\u00a0<\/span>recent headlines<\/span><\/a>, their study finds that insider trading has only a small impact on prediction markets. \u201cFrom observation, we know there can be high frequency arbitrageurs, market makers, and real-time news trackers,\u201d says Jensen. \u201cWhile we do find evidence of insider trading, it\u2019s sporadic, it\u2019s small, and it’s not something that we think is systematically driving price discovery.\u201d<\/span><\/p>\n

Theis Ingerslev Jensen<\/span><\/p>\n

Assistant Professor of Finance<\/span><\/p>\n

Written by Anna Louie Sussman<\/span><\/p>\n

Image: Sean David Williams<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

The prevailing view is that prediction markets like Polymarket and Kalshi get their accuracy by aggregating the views of vast numbers of participants. But a new study co-authored by Yale SOM\u2019s Theis Jensen finds that, in fact, a small group of informed traders drive prices\u2014and take home a large portion of the profits Characters in\u2026<\/p>\n","protected":false},"author":2,"featured_media":21393,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_scj_primary_category_id":128,"_scj_featured":false,"_scj_featured_from":"","_scj_featured_until":"","_scj_featured_order":0,"_scj_visibility_class":"current","_scj_layout_family":"standard","_scj_article_style":"","_scj_display_overrides":[],"_scj_intro_image_id":21393,"_scj_intro_alt":"Wisdom of the Few? 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