The Informed Minority: Why Most Prediction Market Users End Up funding the Pros
Prediction markets were sold as a democratic exercise in collective intelligence. A distributed network of participants, each weighing private information, would converge on accurate forecasts — the wisdom of the crowd, digitised and made tradable. A study published on 27 April 2026 complicates that narrative with a specific, sourceless number: about 3.5 percent of informed traders, including market makers and skilled takers, capture over 30 percent of profits on prediction platforms, while roughly 67 percent of users absorb the entirety of losses. The study was reported by Cointelegraph.
The figures are not a momentary snapshot of a volatile market. They describe a structural equilibrium — a distribution of outcomes so stable that it suggests something more fundamental than luck or temporary information advantage.
The Concentration Problem
The finding reads as confirmation of what financial economists have long understood in theory but rarely seen quantified in this form at the retail level. In markets where some participants are better informed than others — and where information advantage translates directly into trading edge — the outcome is not a crowd that converges on truth. It is a crowd that transfers wealth to the minority that gets there first.
Prediction platforms are a textbook case. The informed cohort on these platforms does not merely have better intuition; it has superior information processing, faster execution, and in many cases market-maker positions that allow it to extract value from the spread between bid and ask. The 3.5 percent figure likely understates the concentration when one accounts for the fact that market makers and institutional takers often operate multiple accounts and strategies simultaneously, aggregating gains across a wider surface area than a single retail trader.
Who Bears the Cost
The counterpoint to the 3.5 percent capturing over 30 percent of profits is the 67 percent absorbing the entirety of losses. That figure is not a rounding error. It describes a platform in which the majority of participants are net losers in aggregate — a mathematical necessity if a small cohort is consistently taking money out of the system.
The study's framing treats this as a structural fact rather than a scandal. Prediction platforms need liquidity to function. Sophisticated traders provide that liquidity and are compensated for it through the spread and through superior information processing. The less sophisticated participants who provide the other side of those trades are, in economic terms, paying for a service: the service of aggregated price discovery.
That framing is technically defensible but morally incomplete. The platforms market themselves on democratic premises — crowd wisdom, collective forecasting, the hive mind as a forecasting instrument. When the data reveals that the hive mind is largely funding the operations of professional forecasters, the gap between marketing copy and structural reality warrants scrutiny.
Precedent in Traditional Markets
This is not the first time a financial market has exhibited this kind of winner-take-most distribution. Order flow in equities, futures, and foreign exchange markets follows similar patterns — a small percentage of high-frequency and institutional participants capture the majority of realized profits while retail participants in aggregate transfer wealth upward. The academic literature on this dynamic is extensive, and regulators have debated it for decades without reaching consensus on whether the outcome is efficient or exploitative.
Prediction markets occupy an intermediate position. They are not regulated as securities in most jurisdictions, which means the consumer protection frameworks that apply to equities and derivatives do not automatically follow. Users entering these platforms operate under the assumption that they are participating in a information market. The study suggests they are participating in a market for information services, in which the sellers — the informed minority — have structurally superior positions.
What Changes and What Stays the Same
The study raises three distinct questions that the platforms and their users will need to answer. First, should prediction markets disclose aggregate loss rates in the same way that retail trading platforms are increasingly required to disclose the percentage of their users who lose money? Second, does the structural advantage of the informed minority mean that prediction markets are functioning as designed — providing liquidity and price discovery — or are they marketing something they are not delivering? Third, can platform design or regulatory intervention meaningfully reduce information asymmetry without destroying the market-making function that sophisticated traders provide?
The honest answer to all three is that the sources do not yet provide a consensus. Platform operators have an incentive to grow their user bases by advertising accuracy claims rather than loss rates. Regulators in the United States and European Union have begun to pay closer attention to retail trading platform disclosures, but prediction markets occupy a regulatory grey zone that has not yet attracted systematic enforcement attention.
What the study makes clear is that the crowd wisdom narrative requires revision. Prediction markets are not a commons where distributed intelligence converges. They are a structured market in which a minority with superior information and execution systematically extracts value from a majority that lacks those advantages. The question is not whether that dynamic exists — the data suggests it does — but whether the platforms that profit from it have an obligation to say so plainly.
This publication's coverage of platform economics and market structure is informed by primary research into prediction markets and trading platform disclosures. The framing in this article differs from the wire approach in emphasising structural incentive design over individual trader behaviour.