Prediction markets (also known as predictive markets, information markets, decision markets, idea futures, event derivatives, or virtual markets) are exchange-traded markets created for the purpose of trading the outcome of events.^[1] The market prices can indicate what the crowd thinks the probability of the event is. A prediction market contract trades between 0 and 100%. It is a binary option that will expire at the price of 0 or 100%. Prediction markets can be thought of as belonging to the more general concept of crowdsourcing which is specially designed to aggregate information on particular topics of interest. The main purposes of prediction markets are eliciting aggregating beliefs over an unknown future outcome. Traders with different beliefs trade on contracts whose payoffs are related to the unknown future outcome and the market prices of the contracts are considered as the aggregated belief.

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 has researched the history of prediction markets, there are records of election betting in Wall Street dating back to 1884.^[3] Rhode and Strumpf estimates that average betting turnover per US presidential election is equivalent to over 50 percent of the campaign spend.

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.^[4] 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.^[5] The research literature is collected together in the peer-reviewed The Journal of Prediction Markets ^[56], edited by Leighton Vaughan Williams and published by the University of Buckingham Press.

The ability of the prediction market to aggregate information and make accurate predictions is based on the efficient-market hypothesis, which states that assets prices are fully reflecting all available information. For instance, existing share prices always include all the relevant related information for the stock market to make accurate predictions.

James Surowiecki raises three necessary conditions for collective wisdom: diversity of information, independence of decision, and decentralization of organization.^[10]

Prediction markets have an advantage over other forms of forecasts due to the following characteristics.^[11] Firstly, they can efficiently aggregate a plethora of information, beliefs, and data.

Next, they obtain truthful and relevant information through financial and other forms of incentives.

Prediction markets can incorporate new information quickly and are difficult to manipulate.

The accuracy of the prediction market in different conditions has been studied and supported by numerous researchers.

Due to the accuracy of the prediction market, it has been applied to different industries to make important decisions.

Some examples include:

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".^[19]

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 judgements due to peer pressure, panic, bias, and other breakdowns developed out of a lack of diversity of opinion.

One of the main constraints and limits of the wisdom of crowds is that some prediction questions require specialized knowledge that majority of people do not have.

Due to this lack of knowledge, the crowd's answers can sometimes be very wrong.^[20]

The second market mechanism is the idea of the marginal-trader hypothesis.^[19] According to this theory, "there will always be individuals seeking out places where the crowd is wrong".^[19] 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.^[21]

The effects of manipulation and biases are also internal challenges 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.

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 was 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),^[22] 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.

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.^[23]

These prediction market inaccuracies were especially prevalent during Brexit and the 2016 US Presidential Elections.

On Thursday, June 23, 2016, the United Kingdom voted to leave the European Union. Even until the moment votes were counted, prediction markets leaned heavily on the side 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.^[24] 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).^[24][25] Here, we can observe how crippling bias and lack of diversity of opinion can be in 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.^[26] 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 accurately represent current circumstances.

^[27] 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.^[28]

However, when compared to results from polls, prediction markets are generally more accurate by 74%^[29].

Because online gambling is outlawed in the United States through federal laws and many state laws as well, most prediction markets that target US users operate with "play money" rather than "real money": they are free to play (no purchase necessary) and usually offer prizes to the best traders as incentives to participate.

Notable exceptions are the Iowa Electronic Markets, which is operated by the University of Iowa under the cover of a no-action letter from the Commodity Futures Trading Commission, and PredictIt, which is operated by Victoria University of Wellington under cover of a similar no-action letter.^[30]

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.^[31] The incentives involved in practical markets are so low, however, that this is extremely unlikely.

There are a number of commercial and academic prediction markets operating publicly.

A combinatorial prediction market is a type of prediction market where participants can make bets on combinations of outcomes.^[32] The advantage of making bets on combinations of outcomes is that, in theory, conditional information can be better incorporated into the market price.

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 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.^[33][34]