Prediction market


Prediction markets, also known as betting markets, information markets, decision markets, idea futures, or event derivatives, are open markets that enable the prediction of specific outcomes using financial incentives. They are exchange-traded markets established for trading bets in the outcome of various events. The market prices can indicate what the crowd thinks the probability of the event is. A typical prediction market contract is set up to trade between 0 and 100%. The most common form of a prediction market is a binary option market, which 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 purpose of prediction markets are eliciting aggregating beliefs about an unknown future event. 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.

History

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". 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. Rhode and Strumpf estimate that average betting turnover per US presidential election is equivalent to over 50 percent of the campaign spend.
As far back as 1907, Francis Galton found that the median estimate of a group can be more accurate than individual expert estimates. This is now known as the wisdom of the crowd.
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. 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. The research literature is collected together in the peer-reviewed , edited by Leighton Vaughan Williams and published by the University of Buckingham Press.

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.
  • Around 1990 at Project Xanadu, Robin Hanson used the first known corporate prediction market. Employees used it to bet on, for example, the cold fusion controversy.
  • HedgeStreet, designated in 1991 as a market and regulated by the Commodity Futures Trading Commission, enables Internet traders to speculate on economic events.
  • The Hollywood Stock Exchange, a virtual market game established in 1996 and now a division of Cantor Fitzgerald, LP, in which players buy and sell prediction shares of movies, actors, directors, and film-related options, correctly predicted 32 of 2006's 39 big-category Oscar nominees and 7 out of 8 top category winners.
  • In 2001, Intrade.com launched a prediction market trading platform from Ireland, allowing real money trading between members on contracts related to several different categories, including business issues, current events, financial topics, and more. Intrade ceased trading in 2013.
  • 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.
  • In 2005, the scientific monthly journal 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.
  • Also in 2005, Google Inc 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.
  • In October 2007, companies from the United States, Ireland, Austria, Germany, and Denmark formed the Prediction Market Industry Association, tasked with promoting awareness, education, and validation for prediction markets. The current status of the association appears to be defunct.
  • In July 2018, the first decentralized prediction market Augur was launched on the Ethereum blockchain.
  • In October 2024, Kalshi, a financial exchange and prediction market, 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.

    Accuracy

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."
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.
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. 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.
James Surowiecki raises three necessary conditions for collective wisdom: diversity of information, independence of decision, and decentralization of organization. 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.
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.
Numerous researchers have studied the accuracy of prediction markets:
  • Steven Gjerstad, in his paper "Risk Aversion, Beliefs, and Prediction Market Equilibrium", 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.
  • Justin Wolfers and Eric Zitzewitz have obtained similar results to Gjerstad's conclusions in their paper "Interpreting Prediction Market Prices as Probabilities".
  • 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, prices are biased towards 50%. This bias comes from the traders' "time preferences".
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.
  • 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.
  • 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. The virtual dollar drop in the market successfully forecasted the lateness of the business and prevented the company from extra money loss.
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".
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.
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.
The second market mechanism is the idea of the marginal-trader hypothesis. According to this theory, "there will always be individuals seeking out places where the crowd is wrong". 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.
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.
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", Hanson, Oprea, and Porter, 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.
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. Clouded by the similar mindset of users in prediction markets, they created a paradoxical environment where they began self-reinforcing their initial beliefs. 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. 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. 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.
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. 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.