Prediction market

Before the era of scientific polling, early forms of prediction markets often existed in the form of political betting. One such political bet dates back to 1503, in which people bet on who would be the papal successor. Even then, it was already considered "an old practice".^[2] According to Paul Rhode and Koleman Strumpf, who have researched the history of prediction markets, there are records of election betting in Wall Street dating back to 1884.^[3] Rhode and Strumpf estimate that average betting turnover per US presidential election is equivalent to over 50 percent of the campaign spend.^{[citation needed]}

As far back as 1907, Francis Galton found that the median estimate of a group can be more accurate than individual expert estimates.^[4] This is now known as the wisdom of the crowd.^[5]

Economic theory for the ideas behind prediction markets can be credited to Friedrich Hayek in his 1945 article "The Use of Knowledge in Society" and Ludwig von Mises in his "Economic Calculation in the Socialist Commonwealth". Modern economists agree that Mises' argument, combined with Hayek's elaboration of it, is correct.^[6] Prediction markets are championed in James Surowiecki's 2004 book The Wisdom of Crowds, Cass Sunstein's 2006 Infotopia, and Douglas Hubbard's How to Measure Anything: Finding the Value of Intangibles in Business.^[7]

Milestones

• One of the first modern electronic prediction markets is the University of Iowa's Iowa Electronic Markets, introduced during the 1988 US presidential election.^[8]
• HedgeStreet was the first prediction market to seek approval by the Commodity Futures Trading Commission as a designated contract market after the Commodity Futures Modernization Act of 2000, and it was granted in 2004.^[9] The exchange was acquired by the United Kingdom–based IG Group and rebranded to Nadex in 2007; Nadex was then acquired by Crypto.com in 2021.^[10]
• In July 2003, the U.S. Department of Defense publicized a Policy Analysis Market on their website, and speculated that additional topics for markets might include terrorist attacks. A critical backlash quickly denounced the program as a "terrorism futures market" and the Pentagon hastily canceled the program.^[11]
• In 2005, an article in Nature stated how major pharmaceutical company Eli Lilly and Company used prediction markets to help predict which development drugs might have the best chance of advancing through clinical trials by using internal markets to forecast outcomes of drug research and development efforts.^[12][13] Also in 2005, Google announced that it had been using prediction markets to forecast product launch dates, new office openings, and many other things of strategic importance. Other companies, such as HP and Microsoft, also conduct private markets for statistical forecasts.^[13]
• In October 2024, the Kalshi prediction market won a lawsuit against the Commodity Futures Trading Commission, allowing it to relist its election prediction markets. Kalshi's court victory led to a much broader range of prediction markets offered on their platform and by competitors.^[14][15][16]

Prediction markets are based on the theory that individuals with financial stakes in an outcome can collectively predict it more accurately than any single expert. Even if participants are not highly informed, the collective wisdom emerges from their shared incentive to avoid financial loss. Eric Zitzewitz, an economics professor at Dartmouth, explains "Financial markets are generally pretty efficient, and the evidence suggests that the same is true of prediction markets. There’s no virtue-signaling in an anonymous market when you’re betting", and that "what you’re seeing with the market is some average of all of those different opinions, weighted by their willingness to put their money where their mouth is."^[17]

The ability of the prediction market to aggregate information and make accurate predictions is based on the efficient-market hypothesis, which postulates that asset prices fully reflect all publicly available information. For instance, according to the efficient-market hypothesis, existing share prices always include all the relevant information for the stock market to make accurate predictions.^{[citation needed]}

While prediction markets tend to perform better than polling for the prediction of election outcomes, a study found that belief aggregation of participants who are asked to quantify the strength of their belief can beat prediction markets.^[18] When market participants have some intrinsic interest in trying to predict results, even markets with modest incentives or no incentives have been shown to be effective. When the group is more optimistic, they will 'bet' more in aggregate than the pessimists, raising the market price. The movement of the price will reflect more information than a simple average or vote count. Research has suggested that prediction markets' greater accuracy lies largely in superior aggregation methods rather than superior quality or informativeness of responses.^[18]

James Surowiecki raises three necessary conditions for collective wisdom: diversity of information, independence of decision, and decentralization of organization.^[19] In the case of a predictive market, each participant normally has diversified information from others and makes their decision independently. The market itself has a character of decentralization compared to expert decisions. For these reasons, a predictive market is generally a valuable source to capture collective wisdom and make accurate predictions.^{[citation needed]}

Prediction markets can aggregate information and beliefs of the involved investors and give a good estimate of the mean belief of those investors. The latter have a financial incentive to price in information. This allows prediction markets to incorporate new information quickly and makes them difficult to manipulate.^[20]

Numerous researchers have studied the accuracy of prediction markets:

• Steven Gjerstad (Purdue), in his paper "Risk Aversion, Beliefs, and Prediction Market Equilibrium",^[21] has shown that prediction market prices are very close to the mean belief of market participants if the agents are risk averse and the distribution of beliefs is spread out (as with a normal distribution, for example).
• Justin Wolfers (Wharton) and Eric Zitzewitz (Dartmouth) have obtained similar results to Gjerstad's conclusions in their paper "Interpreting Prediction Market Prices as Probabilities".^[22]
• Lionel Page and Robert Clemen have looked at the quality of predictions for events taking place sometime in the future and provide evidence for a favourite-longshot bias. They found that predictions are better when the event predicted is close in time. For events which take place further in time (e.g., elections in more than a year), prices are biased towards 50%. This bias comes from the traders' "time preferences" (their preferences not to lock their funds for a long time in assets).^[23]

Due to the accuracy of the prediction market, it has been applied to different industries to make crucial decisions. Some examples include:

• Prediction markets can be utilized to improve forecasting and have a potential application to test lab-based information theories based on their feature of information aggregation. Researchers have applied prediction markets to assess unobservable information in Google's IPO valuation ahead of time.^[24]
• In healthcare, predictive markets can help forecast the spread of infectious diseases. In a pilot study, a statewide influenza outbreak in Iowa was predicted by these markets 2–4 weeks in advance with clinical data volunteered from participating health care workers.^[25]
• Some corporations have harnessed internal predictive markets for decisions and forecasts. In these cases, employees can use virtual currency to bet on what they think will happen for this company in the future. The most accurate guesser will win a monetary prize as a payoff. For example, Best Buy once experimented with using the predictive market to predict whether a Shanghai store could open on time.^[26] The virtual dollar drop in the market successfully forecasted the lateness of the business and prevented the company from extra money loss.^{[citation needed]}

Although prediction markets are often fairly accurate and successful, there are many times the market fails in making the right prediction or making one at all. Based mostly on an idea in 1945 by Austrian economist Friedrich Hayek, prediction markets are "mechanisms for collecting vast amounts of information held by individuals and synthesizing it into a useful data point".^[27]

One way the prediction market gathers information is through James Surowiecki's phrase, "The Wisdom of Crowds", in which a group of people with a sufficiently broad range of opinions can collectively be cleverer than any individual. However, this information-gathering technique can also lead to the failure of the prediction market. Oftentimes, the people in these crowds are skewed in their independent judgments due to peer pressure, panic, bias, and other breakdowns developed out of a lack of diversity of opinion.^{[citation needed]}

One of the main constraints and limits of the wisdom of crowds is that some prediction questions require specialized knowledge that the majority of people do not have. Due to this lack of knowledge, the crowd's answers can sometimes be very wrong.^[28]

The second market mechanism is the idea of the marginal-trader hypothesis.^[27] According to this theory, "there will always be individuals seeking out places where the crowd is wrong".^[27] These individuals, in a way, put the prediction market back on track when the crowd fails, and values could be skewed.

In early 2017, researchers at MIT developed the "surprisingly popular" algorithm to help improve answer accuracy from large crowds. The method is built off the idea of taking confidence into account when evaluating the accuracy of an answer. The method asks people two things for each question: What they think the right answer is, and what they think popular opinion will be. The variation between the two aggregate responses indicates the correct answer.^[29]

The effects of manipulation and biases are also internal challenges that prediction markets need to deal with, i.e., liquidity or other factors not intended to be measured are taken into account as risk factors by the market participants, distorting the market probabilities. Prediction markets may also be subject to speculative bubbles. For example, in the year 2000, IEM presidential futures markets, seeming "inaccuracy" comes from buying that occurred on or after Election Day, 11/7/00, but, by then, the trend was clear.^{[citation needed]}

There can also be direct attempts to manipulate such markets. In the Tradesports 2004 presidential markets, there was an apparent manipulation effort. An anonymous trader sold short so many Bush 2004 presidential futures contracts that the price was driven to zero, implying a zero percent chance that Bush would win. The only rational purpose of such a trade would be an attempt to manipulate the market in a strategy called a "bear raid". If this were a deliberate manipulation effort, it failed, however, as the price of the contract rebounded rapidly to its previous level. As more press attention is paid to prediction markets, it is likely that more groups will be motivated to manipulate them. However, in practice, such attempts at manipulation have always proven to be very short-lived. In their paper entitled "Information Aggregation and Manipulation in an Experimental Market" (2005),^[30] Hanson, Oprea, and Porter (George Mason U), show how attempts at market manipulation can in fact end up increasing the accuracy of the market because they provide that much more profit incentive to bet against the manipulator.^{[citation needed]}

Using real-money prediction market contracts as a form of insurance can also affect the price of the contract. For example, if the election of a leader is perceived as negatively impacting the economy, traders may buy shares of that leader being elected, as a hedge.^[31]

These prediction market inaccuracies were especially prevalent during Brexit and the 2016 US presidential elections. On Thursday, 23 June 2016, the United Kingdom voted to leave the European Union. Even until the moment votes were counted, prediction markets leaned heavily in favor of staying in the EU and failed to predict the outcomes of the vote. According to Michael Traugott, a former president of the American Association for Public Opinion Research, the reason for the failure of the prediction markets is due to the influence of manipulation and bias, shadowed by mass opinion and public opinion.^[32] Clouded by the similar mindset of users in prediction markets, they created a paradoxical environment where they began self-reinforcing their initial beliefs (in this case, that the UK would vote to remain in the EU).^[32][33] Here, we can observe the ruinous effect that bias and lack of diversity of opinion may have on the success of a prediction market. Similarly, during the 2016 US Presidential Elections, prediction markets failed to predict the outcome, throwing the world into mass shock. Like the Brexit case, information traders were caught in an infinite loop of self-reinforcement once initial odds were measured, leading traders to "use the current prediction odds as an anchor" and seemingly discounting incoming prediction odds completely.^[34] Traders essentially treated the market odds as correct probabilities and did not update enough using outside information, causing the prediction markets to be too stable to represent current circumstances accurately.^[35] Koleman Strumpf, a University of Kansas professor of business economics, also suggests that a bias effect took place during the US elections; the crowd was unwilling to believe in an outcome with Donald Trump winning and caused the prediction markets to turn into "an echo chamber", where the same information circulated and ultimately lead to a stagnant market.^[36]

Prediction markets can yield better estimates of the mean opinion across a population than opinion polls. A study found that for the five U.S. presidential elections between 1988 and 2004, prediction markets gave a more accurate estimate of the voting result than 74% of the studied opinion polls.^[37] On the other hand, a randomized experiment from 2016 obtained that prediction markets were 12% less accurate than prediction polls, an alternative method for eliciting and statistically aggregating probability judgments from a crowd.^[38]

Whether prediction markets are regulated as financial products or betting platforms varies by jurisdiction. Some prediction market platforms have been banned from various countries, though often over registration issues rather than broad bans on prediction markets.

Asia

Singapore's Gambling Regulatory Authority blocked Polymarket as an illegal online gambling platform in December 2024. In January 2025, Thailand's Cyber ​​Crime Investigation Bureau announced plans to block Polymarket as an illegal gambling website due to its use of cryptocurrency.^[39]

Europe

Prediction markets are classified as betting platforms in the United Kingdom, and require operating licenses from the Gambling Commission.^[40]

The European Union has no unified regulatory structure for prediction markets, though some countries have taken action against various platforms. Belgium, France, Italy, Poland, and Romania have banned Polymarket as an unlicensed gambling platform. The EU's Markets in Crypto-Assets (MiCA) regulation, which comes into effect in July 2026, will apply to prediction markets using cryptocurrency assets.^[41]

North America

Early prediction markets were regulated by the Commodity Futures Trading Commission as futures contracts in the United States. Though Iowa Electronic Markets did not pursue registration with the CFTC, the University of Iowa granted a no-action letter in 1993 and allowed to continue operating the platform with restrictions on the number of traders and trading amounts. When Congress passed the Commodity Futures Modernization Act of 2000, prediction markets were required to "self-certify" their contracts. In 2010, the law was amended by the Dodd–Frank Wall Street Reform and Consumer Protection Act, which created a special rule in which the CFTC, could perform a public-interest review on contracts involving terrorism, assassination, war, gaming, activity that violates state or federal law, or activity deemed similar to any of the reviewable activities. In 2011, the CFTC deemed the North American Derivatives Exchange's political event contracts to be contrary to the public interest. In 2023, the CFTC made the same finding for Kalshi's event contracts on which political party would control chambers of Congress. Kalshi sued the CFTC, and in 2024, the District Court for the District of Columbia ruled in favor of Kalshi with an opinion that narrowly interpreted the CEA's mention of "gaming".^[42] The CFTC appealed the decision, but dropped the appeal under the Trump administration.^[43] After Kalshi's court victory, it and other prediction markets platforms dramatically expanded their offerings.^[44] CFTC Chairman Michael S. Selig has asserted that the agency has exclusive jurisdiction over prediction markets. However, state attorneys general and gaming regulators have taken enforcement action against prediction markets platforms that have offered sports-related event contracts, including Kalshi and Crypto.com.^[45] In February 2026, the CFTC submitted an amicus brief asserting its sole authority in a lawsuit by Crypto.com against the state of Nevada.^[46][47]

The Canadian Securities Administrators prohibited trading binary options in 2017. However, Polymarket has operated in all of Canada besides Ontario, from which it was banned in 2025. The CBC has described prediction market regulation as a "grey area", and a spokesperson for the CSA has said prediction markets not covered by the binary options ban could be considered "securities, derivatives or both".^[48]

Oceania

In August 2025, the Australian Communications and Media Authority determined that Polymarket was a "prohibited and unlicensed regulated interactive gambling service" and blocked access to the website.^[49]

In February 2026, the New Zealand Department of Internal Affairs ruled that prediction markets like Polymarket and Kalshi are prohibited under the Gambling Act 2003 and the Racing Industry Act 2020.^[50]

South America

Polymarket was deemed an unlicensed betting platform and banned nationwide in Argentina by a Buenos Aires judge in March 2026.^[51]

In Brazil, regulators have not established whether prediction markets fall under the purview of the country's Securities Commission or the Ministry of Finance's Secretariat of Prizes and Betting.^[52]

Some kinds of prediction markets may create controversial incentives. For example, a market predicting the death of a world leader might be quite useful for those whose activities are strongly related to this leader's policies, but it also might turn into an assassination market.^[53]

In 2026, United States Representative Chris Murphy announced plans to propose a bill regulating prediction markets, citing concerns that government officials might be profiting from inside knowledge, or that officials money riding on military-related events contracts could be influenced to make decisions that would be profitable to them.^[54]

• Augur is a now-defunct decentralized prediction market platform built on the Ethereum blockchain.^{[citation needed]}
• Good Judgment Open is a reputation-based prediction website.^{[citation needed]}
• The Iowa Electronic Markets is an academic market examining elections where positions are limited to $500.
• iPredict was a prediction market in New Zealand.^{[citation needed]}
• Kalshi, is a U.S. CFTC-regulated betting market^[55] and available only for U.S. residents.^[56][57] Robinhood has also partnered with Kalshi to offer prediction markets on its platform.^[58][59][60]
• Manifold is a reputation-based prediction market.^{[citation needed]}
• Metaculus is a reputation-based prediction website with the ability to make numeric-range or date-range predictions, inspired by SciCast.^[61]
• Polymarket is a decentralized prediction market built on the Polygon Chain that uses the USDC stablecoin.^{[citation needed]}
• PredictIt is a prediction market for political and financial events.^{[citation needed]}
• SciCast was a reputation-based combinatorial prediction market focusing on science and technology forecasting.^[62]

Reputation-based

Some prediction websites, sometimes classified as prediction markets, do not involve betting real money but rather add to or subtract from a predictor's reputation points based on the accuracy of a prediction. This incentive system may be better-suited than traditional prediction markets for niche or long-timeline questions.^[61][63] These include Manifold,^[64] Metaculus, and Good Judgment Open.

A 2006 study found that real-money prediction markets were significantly more accurate than play-money prediction markets for non-sports events.^[65]

Combinatorial prediction markets

A combinatorial prediction market is a type of prediction market where participants can make bets on combinations of outcomes.^[66] The advantage of making bets on combinations of outcomes is that, in theory, conditional information can be better incorporated into the market price.^{[citation needed]}

One difficulty of combinatorial prediction markets is that the number of possible combinatorial trades scales exponentially with the number of normal trades. For example, a market with merely 100 binary contracts would have ${\displaystyle 2^{100}}$ possible combinations of contracts. These exponentially large data structures can be too large for a computer to keep track of, so there have been efforts to develop algorithms and rules to make the data more tractable.^[67][68]

Election prediction markets

Election prediction markets are a type of prediction market in which the ultimate values of the contracts being traded are based on the outcome of elections. The main purpose of an election stock market is to predict the election outcome, such as the share of the popular vote or share of seats each political party receives in a legislature or parliament.^{[citation needed]}

Before World War II, election betting was widespread in the U.S., dating back to George Washington’s election and becoming organized by Lincoln's era. Though often illegal, it operated openly through “betting commissioners” who held stakes and charged a 5% commission. New York was the hub, with activity shifting from poolrooms to the Curb Exchange (precursor to AMEX) and Wall Street offices. By the 1930s, wagers involved large sums from anonymous business and entertainment figures. In some elections, the volumes traded rivaled those of stocks and bonds, with daily odds reported in major newspapers like The New York Times.^[69]

The CFTC has attempted to restrict election markets, arguing they resemble gaming rather than the financial derivatives it oversees. It previously allowed limited academic use, such as with PredictIt, but withdrew support in 2022 and became involved in litigation with the project. The CFTC also targeted Polymarket, a cryptocurrency-based prediction market, resulting in the company moving offshore and paying a $1.4 million fine.^[70]

In October 2024, prediction market Kalshi won a lawsuit against its regulator, the Commodity Futures Trading Commission, with a federal appeals court in Washington allowing it to revive the first fully regulated election prediction markets in the United States. Kalshi's court victory over the CFTC opened the market for election markets.^[14][15][16]

• Futarchy
• Futures exchange
• Prediction games
• Betting exchange
• Forecasting
• Simon–Ehrlich wager
• Collective intelligence