Polymarket Study Finds 3.14% Drive Accuracy – Bitcoin News

Key Takeaways:

• Researchers from London Business School and Yale found only 3.14% of Polymarket accounts qualify as skilled, yet generate most price discovery.
• Skilled Polymarket traders retained their classification 44% of the time out-of-sample, compared to just 10% for skilled mutual funds.
• The CFTC filed an insider trading complaint on April 23, 2026 tied to a Polymarket contract on Nicolas Maduro’s removal from power.

Study Published to SSRN Covers 98,906 Events on Polymarket

The working paper, titled “ Prediction Market Accuracy: Crowd Wisdom or Informed Minority?” was published on April 20, 2026, on SSRN and revised on April 25, 2026. It was authored by Roberto Gomez-Cram, Yunhan Guo, and Howard Kung of London Business School, and Theis Ingerslev Jensen of Yale University.

The researchers analyzed the complete transaction history on Polymarket, the world’s largest prediction market by trading volume. The study covered 98,906 events, 210,322 markets, and $13.76 billion in total trading volume across 1.72 million accounts.

Using a statistical method called a sign-randomization test, the authors classified traders into distinct groups based on whether their profits reflected genuine skill or random chance.

The findings cut against a widely held assumption. Prediction market platforms, including Kalshi and Polymarket itself, regularly describe their accuracy as the product of collective intelligence from a diverse group of participants. The study directly challenges that framing.

Only 3.14% of Polymarket accounts qualified as skilled winners. These traders earned persistent profits that held up out of sample, traded across an average of 79 markets each, and consistently positioned in the direction of final outcomes. The remaining 96% of accounts either broke even by luck or lost money.

The authors found that skilled traders’ order flow predicted both next-period price changes and final market outcomes at statistically significant levels. A one-percentage-point increase in skilled net buying corresponded to an 8 basis point increase in the probability of the correct final outcome. Lucky winners, despite posting positive account balances, showed no meaningful predictive power in either test.

Polymarket‘s monthly trading volume climbed from $3.3 million in December 2023 to $1.98 billion in December 2025, a nearly 600-fold increase over two years. Over the same period, active accounts expanded from roughly 1,600 to more than 519,000. Despite that growth, the concentration of skill remained narrow.

The study also tested skill persistence. Researchers split events randomly into training and test sets. Among traders classified as skilled in training, 44% retained that classification in the test set. For unskilled losers, 51% remained in that category. By comparison, skilled mutual funds in a parallel test retained their classification only 10% of the time. The authors describe prediction markets as showing unusually high persistence of both skill and anti-skill.

Skilled traders also responded first when scheduled news arrived. In tests covering Federal Open Market Committee (FOMC) announcements and corporate earnings releases, only the skilled group shifted its order imbalance in the direction of the news surprise within a narrow window around each release. Other groups showed no consistent response. The paper separately examined insider trading.

Researchers identified 1,950 accounts that met timing and conviction criteria, suggesting they traded on non-public information. These accounts averaged roughly $15,000 in profits each and had large price effects when they did trade. One documented case involved three accounts that took positions in a contract tied to Venezuelan President Nicolas Maduro hours before a secret U.S. military operation on Jan. 3, 2026, collectively earning more than $630,000.

On April 23, 2026, the Commodity Futures Trading Commission (CFTC) filed a complaint alleging that an active-duty U.S. Army service member engaged in insider trading using one of those accounts. Despite those price effects, the researchers concluded that insider activity was too concentrated in isolated events to account for broad price discovery across the platform.

The majority of participants, the study found, funded accuracy rather than produced it. Unlucky and unskilled losers made up 67% of all accounts and absorbed the entirety of aggregate losses. Market makers and skilled takers together represented fewer than 3.5% of accounts but captured more than 30% of total gains.

The authors conclude that prediction market accuracy reflects the behavior of a small, identifiable group of informed traders whose participation is the mechanism behind price formation. Whether those traders continue participating as platforms grow and fees increase remains an open question the paper leaves for future research.