Prediction markets (also known as betting markets, information markets, decision markets, idea futures or event derivatives) are open markets where specific outcomes can be predicted using financial incentives. Essentially, they are exchange-traded markets created for the purpose of trading the outcome of events. The
market prices can indicate what the crowd thinks the
probability
Probability is the branch of mathematics concerning numerical descriptions of how likely an Event (probability theory), event is to occur, or how likely it is that a proposition is true. The probability of an event is a number between 0 and ...
of the
event is. A prediction market contract trades 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 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.
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.
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''.
[Douglas Hubbard "How to Measure Anything: Finding the Value of Intangibles in Business" John Wiley & Sons, 2007] The research literature is collected together in the peer-reviewe
''The Journal of Prediction Markets'' 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 in order 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 a number of 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
The Pentagon is the headquarters building of the United States Department of Defense. It was constructed on an accelerated schedule during World War II. As a symbol of the U.S. military, the phrase ''The Pentagon'' is often used as a metonym ...
hastily canceled the program.
* In 2005, scientific monthly journal ''
Nature
Nature, in the broadest sense, is the physical world or universe. "Nature" can refer to the phenomena of the physical world, and also to life in general. The study of nature is a large, if not the only, part of science. Although humans ar ...
'' stated how major pharmaceutical company
Eli Lilly and Company
Eli Lilly and Company is an American pharmaceutical company headquartered in Indianapolis, Indiana, with offices in 18 countries. Its products are sold in approximately 125 countries. The company was founded in 1876 by, and named after, Colon ...
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 has 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.
Accuracy
The ability of the prediction market to aggregate information and make accurate predictions is based on the
efficient-market hypothesis
The efficient-market hypothesis (EMH) is a hypothesis in financial economics that states that asset prices reflect all available information. A direct implication is that it is impossible to "beat the market" consistently on a risk-adjusted b ...
, 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. In the case of 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 expertise decisions. Because of these reasons, predictive market is generally a valuable source to capture collective wisdom and make accurate predictions.
Prediction markets have an advantage over other forms of forecasts due to the following characteristics. 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.
* Steven Gjerstad (Purdue), 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 (as with a
normal distribution
In statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable. The general form of its probability density function is
:
f(x) = \frac e^
The parameter \mu i ...
, 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". In practice, the prices of binary prediction markets have proven to be closely related to actual frequencies of events in the real world.
* Douglas Hubbard has also conducted a sample of over 400 retired claims which showed that the probability of an event is close to its market price but, more importantly, significantly closer than the average single subjective estimate. However, he also shows that this benefit is partly offset if individuals first undergo
calibrated probability assessment
Calibrated probability assessments are subjective probabilities assigned by individuals who have been trained to assess probabilities in a way that historically represents their uncertainty. For example, when a person has calibrated a situation a ...
training so that they are good at assessing odds subjectively. The key benefit of the market, Hubbard claims, is that it mostly adjusts for uncalibrated estimates and, at the same time, incentivizes market participants to seek further information.
* Lionel Page and Robert Clemen have looked at the quality of predictions for events taking place some time in the future. They found that predictions are very good 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).
Due to the accuracy of the prediction market, it has been applied to different industries to make important decisions. Some examples include:
* Prediction market can be utilized to improve forecast and has a potential application to test lab-based information theories based on its 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 disease. In a pilot study, a statewide influenza 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 money prize as payoff. For example, Best Buy once experimented on using the predictive market to predict whether a Shanghai store can be 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".
[Mann, Adam. "Market Forecasts." Nature 538 (2017): 308–10. Web. 3 February 2017.]
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.
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 prediction markets need to deal with, i.e. liquidity or other factors not intended to be measured are taken into account as
risk factors
In epidemiology, a risk factor or determinant is a variable associated with an increased risk of disease or infection.
Due to a lack of harmonization across disciplines, determinant, in its more widely accepted scientific meaning, is often use ...
by the market participants, distorting the market probabilities. Prediction markets may also be subject to
speculative bubbles
An economic bubble (also called a speculative bubble or a financial bubble) is a period when current asset prices greatly exceed their intrinsic valuation, being the valuation that the underlying long-term fundamentals justify. Bubbles can be c ...
. 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), Hanson, Oprea and Porter (George Mason U), show how attempts at
market manipulation
In economics and finance, market manipulation is a type of market abuse where there is a deliberate attempt to interfere with the free and fair operation of the market; the most blatant of cases involve creating false or misleading appearan ...
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
A hedge or hedgerow is a line of closely spaced shrubs and sometimes trees, planted and trained to form a barrier or to mark the boundary of an area, such as between neighbouring properties. Hedges that are used to separate a road from adjoin ...
.
These prediction market inaccuracies were especially prevalent during
Brexit
Brexit (; a portmanteau of "British exit") was the withdrawal of the United Kingdom (UK) from the European Union (EU) at 23:00 GMT on 31 January 2020 (00:00 1 February 2020 CET).The UK also left the European Atomic Energy Community (EAE ...
and the
2016 US Presidential Elections. On Thursday, 23 June 2016, the
United Kingdom
The United Kingdom of Great Britain and Northern Ireland, commonly known as the United Kingdom (UK) or Britain, is a country in Europe, off the north-western coast of the European mainland, continental mainland. It comprises England, Scotlan ...
voted to leave the
European Union
The European Union (EU) is a supranational political and economic union of member states that are located primarily in Europe. The union has a total area of and an estimated total population of about 447million. The EU has often been ...
. 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
Michael Wolfe Traugott is an American political scientist, communication studies researcher, and political pundit.
As of 2022, he is a Professor Emeritus at the University of Michigan, Ann Arbor and a researcher at UM's Institute for Social Rese ...
, a former president of the
American Association for Public Opinion Research
The American Association for Public Opinion Research (AAPOR) is a professional organization of more than 2,000 public opinion and survey research professionals in the United States and from around the world, with members from academia, media, gover ...
, 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 (in this case, that the UK would vote to remain in the EU).
Here, we can observe the ruinous effect that bias and lack of diversity of opinion may have 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. 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. 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
Donald John Trump (born June 14, 1946) is an American politician, media personality, and businessman who served as the 45th president of the United States from 2017 to 2021.
Trump graduated from the Wharton School of the University of ...
winning and caused the prediction markets to turn into "an echo chamber", where the same information circulated and ultimately lead to a stagnant market.
However, when compared to results from opinion polls, prediction markets are generally more accurate by 74%. Prediction markets have also been used to assess successfully the reproducibility of scientific research in psychology. A recent randomized experiment showed that prediction markets were slightly (12%) less accurate than prediction polls, an alternative method for eliciting and statistically aggregating probability judgments from a crowd.
Other issues
Legality
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 A no-action letter is a letter written by the staff members of a government agency, requested by an entity subject to regulation by that agency, indicating that the staff will not recommend that the agency take legal action against the entity, sho ...
from the
Commodity Futures Trading Commission, and
PredictIt, which is operated by
Victoria University of Wellington
Victoria University of Wellington ( mi, Te Herenga Waka) is a university in Wellington, Wellington, New Zealand. It was established in 1897 by Act of New Zealand Parliament, Parliament, and was a constituent college of the University of New Z ...
under cover of a similar no-action letter.
Controversial incentives
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.
List of prediction markets
There are a number of commercial and academic prediction markets operating publicly.
Public prediction markets
* The
Iowa Electronic Markets is an academic market examining elections where positions are limited to $500.
*
PredictIt is a prediction market for political and financial events.
*
SciCast SciCast is a collaborative platform for science and technology forecasting created by George Mason University with the help of a grant from the Intelligence Advanced Research Projects Activity (IARPA) as part of its ForeST (Forecasting in Science an ...
was a reputation-based
combinatorial prediction market focusing on science and technology forecasting.
*
iPredict was a prediction market in
New Zealand
New Zealand ( mi, Aotearoa ) is an island country in the southwestern Pacific Ocean. It consists of two main landmasses—the North Island () and the South Island ()—and over 700 List of islands of New Zealand, smaller islands. It is the ...
.
*
Metaculus is a reputation-based prediction website with the ability to make numeric-range or date-range predictions, inspired by SciCast.
*
Good Judgment Open is a reputation-based prediction website.
*
Augur (software) is a decentralized prediction market platform built on the Ethereum blockchain.
Types
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.
These include Manifold Markets,
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.
Combinatorial prediction markets
A combinatorial prediction market is a type of prediction market where participants can make bets on combinations of outcomes. 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.
See also
*
Election prediction market
*
Futarchy
*
Futures exchange
A futures exchange or futures market is a central financial exchange where people can trade standardized futures contracts defined by the exchange. Futures contracts are derivatives contracts to buy or sell specific quantities of a commodity or fi ...
*
Prediction games A prediction game is a game which allow users to guess at the outcome of future events. Prediction games are generally operated online and are free for users to play. Points are awarded to players who most accurately predict the outcome of an event, ...
*
Betting exchange
A betting exchange is a marketplace for customers to bet on the outcome of discrete events. Betting exchanges offer the same opportunities to bet as a bookmaker with a few differences. Gamblers can buy (also known as "back") and sell (also know ...
References
Sources
;Academic papers
* Bell, Tom W
Prediction Markets For Promoting the Progress of Science and the Useful Arts– PDF file – ''George Mason Law Review'' (14 Geo. Mason L. Rev 37) (2006)
* Berg, Joyce E., & Thomas A. Rietz
The Iowa Electronic Market: Lessons Learned and Answers Yearned– PDF file – 2005-01-00
* Erikson, Robert S., & Christopher Wlezien. "Are Political Markets Really Superior to Polls as Election Predictors?" ''
Public Opinion Quarterly
''Public Opinion Quarterly'' is an academic journal published by Oxford University Press for the American Association for Public Opinion Research, covering communication studies and political science. It was established in 1937 and according to th ...
'' 72(2), Summer 2008, pp. 190–215.
* Gjerstad, Steven
"Risk Aversion, Beliefs, and Prediction Market Equilibrium,"University of Arizona Working Paper 04-17, 2005.
*
*
* Hanson, Robin
The Informed Press Favored the Policy Analysis Market- PDF file - 2005-05-05
* Manski, Charles F
Interpreting the Predictions of Prediction Markets– PDF file – Revised Aug 2005—Manski suggests that there needs to be a better theoretic basis for interpreting market prices as probability, and provides a simple model for this.
* Provides a detailed history of political prediction markets in the US, and shows early markets in the 19th and early 20th Centuries provided accurate forecasts and satisfied market efficiency.
* Discusses history of prediction markets internationally, as well as additional details on the historical US markets.
*
*
* Spann, Martin & Skiera, Bern
"Internet-Based Virtual Stock Markets for Business Forecasting"– PDF file – Discusses theory, design options and presents empirical comparisons on forecasting accuracy of prediction markets
* Wolfers, Justin, & Eric Zitzewitz
Prediction Markets– PDF file – 2004-05-00
* Wolfers, Justin, & Eric Zitzewit
Interpreting Prediction Market Prices as Probabilities– PDF file – Draft version 2007-01-08 – Expands on the work of Manski, providing a more general model wherein it is somewhat rational to interpret market prices as probabilities
* Watkins, Jennifer
Prediction Markets as an Aggregation Mechanism for Collective Intelligence– Proceedings of 2007 UCLA Lake Arrowhead Human Complex Systems Conference, Lake Arrowhead, CA, 25–29 April 2007.
* Storkey, A.J
Machine Learning Markets– Journal of Machine Learning Research C&WP 15:AISTATS. 2011.
* Storkey A.J., Millin, J., Geras, K
Isoelastic agents and wealth updates in machine learning markets– International Conference in Machine Learning. 2012.
External links
Video of Robin Hanson's ''Combinatorial Prediction Markets'' lecture at the 'Uncertainty in Artificial Intelligence' conference in Helsinki, 2008
{{DEFAULTSORT:Prediction Market
Social information processing
Market (economics)
Survey methodology
Forecasting