Can Prediction Markets Create The Future They Predict?

“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.

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.

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.

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.