Prediction market platforms allow people to bet on real-world events like politics, culture, economics, war, and sports. Their defining feature is the buying and selling of contracts. They operate on a peer-to-peer model in which participants buy and sell contracts tied to the outcome of future events rather than betting against a bookmaker. Most contracts are binary: an event either happens or it does not. Because contracts can be traded until they settle, prices change continuously as new information becomes available, with the market price reflecting the collective judgment about the likelihood of an outcome.

Supporters argue that this process aggregates publicly available information into a real-time forecast, allowing markets to reveal probabilities that sometimes outperform polls or expert opinion. Critics counter that these same incentives can reward speculation, manipulation, or insider knowledge, raising questions about whether they function primarily as forecasting tools, gambling platforms, or something in between.

A contract’s price fluctuates with the probability of an event occurring and reflects that prediction. The price is intended to serve as the implied probability of the event occurring. Every contract includes a resolution date and rules that indicate how and when it will be settled. When the resolution date arrives, the market closes automatically, and the bettor is informed about the outcome. One major advantage of prediction markets is their simplicity. Will a given event occur by a certain date? It’s either yes or no. On two of the most prominent markets, Kalshi and Polymarket, a user buys one or more contracts that pay out $1 each if they’re right and nothing if they’re wrong.

Unlike Kalshi, Polymarket relies on UMA (Universal Market Access), a decentralized oracle protocol, to resolve markets. Most outcomes are finalized automatically through UMA’s Optimistic Oracle, while disputed outcomes are referred to UMA token holders for decentralized arbitration. When a dispute arises, holders of UMA’s digital tokens debate the situation in forums on the social media platform Discord before voting on the outcome. UMA “governs this process to ensure fairness and transparency,” Polymarket says on its website. Because governance is decentralized and voting power depends on token ownership, critics argue that questions remain about transparency and influence over disputed outcomes.

Several prediction markets are relatively obscure. They include Good Judgment Open, a forecasting services firm; the Iowa Electronic Markets, a futures market operated for research and teaching; Manifold, a social prediction market; and PredictIt, a prediction market for political and financial events; among several others.

Prediction Markets Versus Sports Betting

Prediction markets are similar to, yet different from, sports betting. Both are based on the outcomes of future events. But while sports betting platforms set odds for a given outcome before a game and adjust them throughout the game based on real-time data feeds, prediction markets use a central limit order book that matches the highest available bid with the lowest available ask to facilitate peer-to-peer trading, much like traditional markets do. Unlike traditional sportsbooks, prediction markets generally allow participants to trade contracts with one another before an event is resolved, meaning prices can rise or fall continuously as new information becomes available. Supporters argue that this dynamic allows prediction markets to function not only as wagering platforms but also as forecasting systems that reflect changing expectations about future events.

Kalshi’s CEO and founder, Tarek Mansour, distinguishes between the “artificial risk” of a bet that requires a bookie and the “natural risk” of trading on real-world events. In his view, the bookie creates risk solely through the odds he sets. The risk in prediction markets follows from the actual possibilities they describe—the outcome of an election or the risk of a wildfire—and markets let users hedge against that natural risk.

The two leading prediction-market platforms, Kalshi and Polymarket, take different approaches to the same basic idea: letting people buy and sell contracts tied to future events. Founded in 2018, Kalshi operates as a federally regulated exchange under the oversight of the US Commodity Futures Trading Commission (CFTC). Polymarket, launched in 2020, built its global platform on blockchain technology and historically operated outside the US regulatory framework. It has since established a federally regulated US operation: QCX LLC, doing business as Polymarket US, now operates as a CFTC-designated contract market.

The distinction between Polymarket’s global platform and Polymarket US illustrates how rapidly the industry is evolving. Prediction markets increasingly combine elements of traditional financial exchanges, blockchain-based trading, and event wagering, complicating efforts to determine which regulatory frameworks should apply.

Why Prediction Markets?

Are prediction markets “truth machines,” as their advocates claim, or essentially casinos, as their detractors argue? Some also argue that these markets serve as vehicles for insider trading.

“Prediction markets are not just forecasting tools; they are decision-support tools,” says Richard Warr, a professor of finance at the Poole School of Management. In his view, they help people incorporate changing information into decisions about uncertain future events.

Prediction markets may legally fall outside the definition of gambling. But they may be no different from gambling. After all, platforms like Kalshi and Polymarket resemble poker, where a fee is collected on each hand even if it has a strategic component.

Supporters argue that prediction markets work because they create financial incentives for participants to reveal what they genuinely believe rather than what they merely hope or expect. This idea is often described as the “wisdom of the crowd”: market prices emerge from the combined judgments of many participants, each with money at stake. Critics, however, argue that market accuracy may depend less on the crowd as a whole than on a relatively small number of well-informed traders.

Where Prediction Markets Succeed

The outcomes of real-life events that prediction markets allow users to trade on range from election predictions to financial markets to pop culture. They have drawn particular attention for their election performance. During the 2024 US presidential election, leading platforms consistently assigned Donald Trump a higher probability of victory than many traditional polls suggested. Supporters point to this outcome as evidence that prediction markets can aggregate dispersed information faster than conventional polling, while critics caution that a successful prediction does not necessarily validate the broader forecasting model. Polls are scientific samples (with varying accuracy) that offer a snapshot of voter sentiment at any given time. No one faces consequences if those sampled—or talking heads—get it wrong. But for those betting on a particular candidate, the consequences are real: they either win or lose money. Financial incentives may encourage participants to focus less on personal preference and more on the outcome they believe is most likely.

Prediction markets may also provide useful signals for investors. Research has found that they can incorporate new information more quickly than traditional analyst forecasts. Because participants have money at stake, they have an incentive to update their expectations as new evidence emerges, allowing market prices to adjust rapidly and sometimes respond faster than conventional financial analysis.

Like stock markets, prediction markets summarize expectations about future events. But whereas stock prices reflect expectations about the future performance of individual companies, prediction markets estimate the probability that a specific event will occur. Supporters point to this ability to continuously aggregate information as one of the strongest arguments for their value as forecasting tools.

According to a 2026 Interactive Brokers analysis, prediction markets may already be outperforming traditional weather forecasts. By comparing weather forecasts from the firm’s prediction markets with those of the US National Weather Service, the analysis concluded that prediction markets were more accurate because financial incentives encouraged participants to incorporate new information more effectively.

Financial incentives, however, can create unintended consequences. Because prediction markets allow participants to profit from real-world events, they may also incentivize manipulation or exploitation of weaknesses in the data used to settle contracts. Concerns about weather-related prediction markets have ranged from attempts to influence official weather measurements to ethical questions about wagering on natural disasters. These examples illustrate the broader challenge of designing markets that reward accurate forecasting without encouraging harmful behavior.

Where Prediction Markets Break Down

Insider trading presents one of the greatest challenges for prediction markets. Because participants may have confidential government, corporate, or military information before it becomes public, regulators must distinguish legitimate expertise from the unlawful use of material nonpublic information. As prediction markets expand into politics, finance, and geopolitical events, that distinction has become increasingly important.

Supporters of prediction markets argue that participants with specialized knowledge can improve forecast accuracy. Critics counter that markets must prevent people from profiting from material nonpublic information, making the line between legitimate expertise and unlawful insider trading one of the industry’s most persistent regulatory challenges.

That concern is no longer merely hypothetical. In August 2026, the CFTC brought an insider-trading enforcement action against a former White House teleprompter operator who used advance access to presidential speeches to trade contracts based on words or phrases the president would mention. According to the CFTC, the trades generated more than $107,000 in profits from material nonpublic information.

Contract wording is essential because ambiguous language can lead to disputes. Contracts based on whether public officials mention particular words (so-called “mention markets”) can attract increased trading before events such as Federal Reserve press conferences. For example, a contract pays $1 if the Federal Reserve chair says a certain word and nothing if the chair doesn’t. Karlos Arregi, a linguistics professor at the University of Chicago, says in a Bloomberg opinion piece that the rules appear arbitrary and not based on any particular theory or philosophy of language. “This looks like the kind of rules you’d have in a game like Scrabble,” he says. “It’s obvious to me these rules were not done by a linguist.”

Rivka Levitan, a professor of computer science and linguistics at Brooklyn College, CUNY, sees prediction-market rules as “more legalistic than linguistic.” A good set of rules needs to prioritize logical consistency, she adds. That consistency applies not only to how individual contracts are written but also to how platforms determine which kinds of events may be traded. Kalshi’s rules, for example, distinguish between event types that might look similar at first glance.

Contract disputes are not merely theoretical. High-profile disagreements over how prediction-market contracts should be interpreted show that seemingly small wording differences can determine the outcome of wagers involving millions of dollars. As prediction markets expand into entertainment, sports, politics, and other domains, the precision and transparency of contract language become increasingly important to maintaining market confidence.

Regulatory Challenges 

Those different regulatory models create different legal challenges. Prediction markets can operate across national borders and under different financial, gambling, and technology regimes, meaning that regulation often depends not only on what is being traded but also on where and how a platform operates.

Regulation operates at two levels. Government agencies establish the legal framework under which prediction markets may operate, while the platforms themselves are responsible for monitoring trading activity, verifying users, investigating suspicious transactions, and cooperating with regulators and law enforcement. Supporters argue that these internal safeguards help preserve market integrity. Critics counter that voluntary self-policing cannot replace clear legal standards or effective enforcement.

Insider trading remains one of the most difficult regulatory questions. Existing securities and commodities laws do not always map neatly onto prediction markets, making it hard to determine when trading on nonpublic information becomes unlawful. As former CFTC enforcement director Aitan Goelman observed, many of these legal questions remain largely untested.

Regulatory priorities can also shift as administrations change, influencing how aggressively regulators supervise prediction markets and how quickly they approve new products.

States have also responded differently. Some have challenged whether prediction-market platforms operate as lawful financial exchanges or unlicensed gambling businesses. In contrast, federally regulated exchanges have argued that the Commodity Exchange Act gives the CFTC authority over their event contracts. That disagreement has produced conflicting federal court decisions over whether state gambling laws can apply to sports-related prediction markets. In September 2026, New Jersey asked the US Supreme Court to decide whether federal commodities law preempts state regulation of certain prediction-market contracts after federal appeals courts reached different conclusions. A separate federal appeals court ruling that month held that Kalshi’s sports-event contracts offered on tribal lands were likely subject to federal Indian gaming law. The disputes underscore how unsettled the legal boundary remains between federally regulated derivatives and gambling.

Regulatory debates extend beyond the United States. Different countries have adopted different approaches to prediction markets, with some allowing them under financial-market regulations, others treating them as gambling, and still others restricting or prohibiting them altogether. These differing approaches reflect the lack of an international consensus on how to regulate prediction markets.

Prediction Markets Enter the Mainstream

Prediction markets have grown rapidly in popularity. Combined monthly global trading volume on Kalshi and Polymarket rose from less than $5 billion in September 2025 to about $24 billion in April 2026, according to a Pew Research Center analysis. Sports, cryptocurrency, and politics were among the most popular categories.

Despite the controversies, Kalshi and Polymarket have attracted several prestigious affiliations. News organizations, financial firms, and professional sports leagues increasingly view them not simply as places to wager on uncertain events, but as sources of real-time information about public expectations and emerging trends.

Supporters argue that prediction markets can distinguish meaningful signals from short-term noise by aggregating the judgments of thousands of participants with financial incentives to be accurate. As a result, some news organizations, financial institutions, and other organizations have begun treating prediction markets not simply as wagering platforms but as another source of real-time information about uncertain events.

The growing interest from established institutions reflects a broader shift in how people perceive prediction markets. What began as a niche forecasting experiment is increasingly being treated as part of the modern information ecosystem, with proponents arguing that market prices can complement polling, expert analysis, and other traditional forecasting methods.

The Future of Trading on Uncertainty

Prediction markets offer both promise and risk. They can aggregate information, improve forecasting, and provide useful signals about uncertain events. At the same time, they raise important questions about regulation, insider trading, market manipulation, gambling addiction, and public trust.

Whether prediction markets become a widely accepted forecasting tool will depend not only on their predictive accuracy but also on the confidence participants, regulators, and the public place in the markets’ integrity. Like stock exchanges and other financial institutions, they ultimately succeed only if people trust that the rules are fair, transparent, and consistently enforced.