Just 3% of Traders Drive Prediction-Market Accuracy, Study Finds

Polymarket and Kalshi allow users to wager through contracts on the outcomes of political, economic and other events, with contract prices also viewed as a crowd-based measure of probability. Although such prediction markets can aggregate information, retail participants face information gaps and competition from professional quantitative firms, raising questions about whether profits are distributed fairly.

A recent Wall Street Journal analysis of Polymarket and Kalshi data found that just 0.1% of professional accounts captured 67% of total profits, while more than 70% of Polymarket users lost money. The report did not disclose the data cutoff date, sample size or actual profit amounts, but the figures show that gains were highly concentrated.

Polymarket and Kalshi are prediction markets where users trade contracts tied to the outcomes of political, economic and other events, with prices often viewed as crowd-sourced probability estimates. Such platforms have expanded rapidly in recent years, but whether ordinary participants have fair access to information and profit opportunities has become a major issue for market transparency and consumer protection.

A new analysis found that as many as 99.96% of ordinary users on Polymarket and Kalshi effectively do little more than provide market liquidity, while profits are concentrated among roughly 0.04% of traders alleged to possess inside information. The analysis also criticized the platforms for expanding their businesses by exploiting the economic anxiety of working-class people. Available information did not disclose the publication date or the sums involved.

Polymarket is a blockchain-based decentralized prediction market where contract prices are generally treated as the probability of an event occurring. Research found that the platform may not adjust prices simultaneously when markets have complex logical relationships, such as mutual exclusivity or inclusion. Quantitative traders can exploit those discrepancies by combining positions to lock in spreads, exposing a structural efficiency gap in Polymarket's pricing mechanism.

Research findings released as of July 2026 showed that traders used Bregman projections and the Frank-Wolfe algorithm to identify inconsistent probability pricing across Polymarket contracts and construct approximately risk-free arbitrage portfolios. The model estimated that such strategies generated close to $40 million in cumulative profit over the past year, indicating that the mispricing was not a short-lived anomaly confined to a single market.