Study Challenges 'Wisdom of Crowds' Theory in Prediction Markets

A new working paper from researchers at London Business School and Yale suggests that the perceived accuracy of prediction markets is not a result of collective intelligence, but rather the influence of a tiny group of highly informed traders. The study directly tests the industry claim that these markets function through the massed knowledge of their participants. Analyzing 1.72 million accounts and $13.76 billion in trading volume from 2023 to 2025, the authors concluded that just 3% of traders account for most price discovery. These skilled participants consistently move prices toward the correct outcome, while the remaining 97% of the crowd typically ends up on the losing side of trades, effectively funding the profits of the informed minority. To distinguish genuine skill from luck, the researchers simulated each trader's bets 10,000 times using a coin-flip benchmark. They discovered that among the top raw profit winners, only 12% actually beat the benchmark. Furthermore, roughly 60% of those identified as 'lucky winners' became losers when their performance was tested against a separate sample of events. The paper also highlights the risks associated with non-public information. In one instance involving the removal of Nicolás Maduro in Venezuela, three new accounts made over $630,000 by placing large bets before the event occurred. The study notes that such insider trades move prices 7 to 12 times more aggressively per dollar than typical skilled trades. Ultimately, the findings suggest that prediction markets function less as a democratic aggregation of knowledge and more as a mechanism where a small group of informed individuals extracts value from the uninformed majority.