Polymarket is a so-called prediction market: users can trade on the outcomes of real-world events. Bets can be placed on election outcomes, geopolitical decisions, corporate announcements, or even weather conditions. A contract associated with the correct outcome is worth $1 when the event ends, while the contract associated with the losing outcome is worth zero. If the “yes” contract is trading at 70 cents, the market is effectively pricing in an implied probability of roughly 70 percent.
This interpretation is not entirely unfounded. A major advantage of prediction markets is that they rapidly aggregate the scattered information held by participants. Since traders are risking real money, they have a strong incentive to price in their own probability/odds estimates rather than their preferences. Research suggests that the predictive performance of high-volume political prediction markets may, under certain circumstances, approach or exceed that of traditional public opinion polls.
However, the market price is not the same as the “true” probability. It is influenced by liquidity, risk appetite, transaction costs, and the participants themselves. What's more, at Polymarket, not everyone's opinion carries the same weight. The market reflects money-weighted expectations, so a single well-capitalized player can influence the price much more significantly than multiple smaller traders combined.
According to research, profits are also highly concentrated. More professional traders can profit from better information, faster reactions, limit orders, and arbitrage strategies, while retail investors are more likely to follow trends and execute trades at less favorable prices. In a low-liquidity market, a single “whale” can significantly shift the price of a trade for a short period of time. Although the price may later return to a more realistic level, a correct final forecast does not necessarily prove that the market was efficient throughout the entire period.
Insider information is the most critical issue. In several widely publicised cases, newly created or previously inactive accounts opened large, profitable positions shortly before major political and military events. Maduro's removal, the attacks against Iran, and the results of Google's Year in Search have all raised suspicions that certain traders not only made more accurate predictions about these events but also had access to non-public information.
This is where the fundamental paradox of prediction markets comes into play: a trade based on non-public information can make the price more accurate, while simultaneously giving the trader an unfair advantage over other traders. However, this advantage is gained at the expense of less-informed participants. The system is therefore more efficient from an informational standpoint, while being less fair from a market perspective.
The situation becomes even more problematic if the market participant not only knows the outcome of the bet but is also able to manipulate it. At a temperature market in Paris, the possibility arose that someone might have physically tampered with the weather station whose data was used to determine the outcome of the bet. In such cases, the prediction market encourages people not only to predict the future, but also to alter reality or the data that records it.
Addressing this problem requires a combination of market-based, technical, and regulatory measures. Stronger identity verification, checks for conflicts of professional interests, real-time trading monitoring, the use of multiple independent data sources, and the suspension of settlement for suspicious trades might be necessary. However, regulatory classification remains complicated: Polymarket resembles a financial market, a betting site, and a community forecasting system all at once.
Thus, Polymarket is not a flawless prediction machine, but rather a surprisingly informative market whose prices reflect not only knowledge but also the influence of capital power, speculation, information asymmetry, and, in some cases, manipulation. The real question is not whether it can predict the future, but at what cost it does so.
