Prediction market

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. 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%.

Research has suggested that prediction markets are at least as accurate as other institutions predicting the same events with a similar pool of participants.[1]


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 ("Biography of Ludwig Edler von Mises (1881–1973)", The Concise Encyclopedia of Economics). One of the oldest and most famous is the University of Iowa's Iowa Electronic Markets, introduced during the 1988 U.S. presidential election.[2] 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. HedgeStreet, designated in 1991 as a market and regulated by the Commodity Futures Trading Commission, enables Internet traders to speculate on economic events.

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.

In 2001, 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 and 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.

Prediction markets are championed in James Surowiecki's 2004 book The Wisdom of Crowds, Cass Sunstein's 2006 Infotopia, and How to Measure Anything: Finding the Value of Intangibles in Business by Douglas Hubbard.[3]

The research literature is collected together in the peer reviewed The Journal of Prediction Markets, edited by Leighton Vaughan Williams and published by the University of Buckingham Press. The journal was first published in 2007, and is available online and in print.[4]

In John Brunner's 1975 science fiction story The Shockwave Rider there is a description of a prediction market that he called the Delphi Pool.

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.


Some academic research has focused on potential flaws with the prediction market concept. In particular, Dr. Charles F. Manski of Northwestern University published "Interpreting the Predictions of Prediction Markets",[5] which attempts to show mathematically that under a wide range of assumptions the "predictions" of such markets do not closely correspond to the actual probability beliefs of the market participants unless the market probability is near either 0 or 1. Manski suggests that directly asking a group of participants to estimate probabilities may lead to better results.

However, Steven Gjerstad (Purdue) in his paper "Risk Aversion, Beliefs, and Prediction Market Equilibrium",[6] 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, and also include some analysis of prediction market data, in their paper "Interpreting Prediction Market Prices as Probabilities".[7] In practice, the prices of binary prediction markets have proven to be closely related to actual frequencies of events in the real world.[8][9]

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.[3] However, he also shows that this benefit is partly offset if individuals first undergo calibrated probability assessment 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.

A series of laboratory experiments to compare the accuracy of prediction markets, traditional meetings, the Delphi method, and the nominal group technique on a quantitative judgment task, found only small differences between these four methods. Delphi was most accurate, followed by NGT and prediction markets. Meetings performed worst. The study also looked at participants' perceptions of the methods. Prediction markets were rated least favourable: prediction market participants were least satisfied with the group process and perceived their method as the most difficult.[1]

A common belief among economists and the financial community in general is that prediction markets based on play money cannot possibly generate credible predictions. However, the data collected so far disagrees.[8] Analyzed data from the Hollywood Stock Exchange and the Foresight Exchange concluded that market prices predicted actual outcomes and/or outcome frequencies in the real world. Comparing an entire season's worth of NFL predictions from NewsFutures' play-money exchange to those of Tradesports, an equivalent real-money exchange based in Ireland, both exchanges performed equally well. In this case, using real money did not lead to better predictions.[9]

Hollywood Stock Exchange creator Max Keiser suggests that not only are these markets no more predictive than their established counterparts such as the New York Stock Exchange and the London Stock Exchange, but that reducing the unpredictability of markets would mean reducing risk and, therefore, reducing the amount of speculative capital needed to keep markets open and liquid.

Sources of inaccuracy

Prediction markets suffer from the same types of inaccuracy as other kinds of market, 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),[10] 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.[11]

Other issues


Because online gambling is outlawed in the United States through federal laws and many state laws as well, most prediction markets that target U.S. 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.[12]

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.[13]

Public prediction markets

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

Use by corporations

Combinatorial prediction markets

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]

Decentralized prediction markets

Since 2012, decentralized platforms for prediction markets have been in development. These platforms utilize blockchain technology and cryptocurrencies to provide various advantages over centralized markets, but also more challenges for regulators.[35] One such example is open-source software Augur.

See also


  1. 1 2 Graefe, A.; Armstrong, J.S. (2011). "Comparing face-to-face meetings, nominal groups, Delphi and prediction markets on an estimation task" (PDF). International Journal of Forecasting. 27 (1): 183–195. doi:10.1016/j.ijforecast.2010.05.004.
  2. Stanley W. Angrist (1995-08-28). "Iowa Market Takes Stock of Presidential Candidates (Reprinted with Permission of THE WALL STREET JOURNAL)". The University of Iowa, Henry B. Tippie College of Business. Retrieved 2012-11-07.
  3. 1 2 Douglas Hubbard "How to Measure Anything: Finding the Value of Intangibles in Business" John Wiley & Sons, 2007
  5. Dr. Charles F. Manski (2005). ""Interpreting the Predictions of Prediction Markets"" (PDF). Northwestern University. Archived from the original (PDF) on 15 March 2016. Retrieved 2016-08-20.
  6. Steven Gjerstad. ""Risk Aversion, Beliefs, and Prediction Market Equilibrium"" (PDF). Archived from the original (PDF) on 12 April 2014. Retrieved 2016-08-20.
  7. Justin Wolfers; Eric Zitzewitz. ""Interpreting Prediction Market Prices as Probabilities"" (PDF). Archived from the original (PDF) on 12 November 2012. Retrieved 2016-08-20.
  8. 1 2 David M. Pennock, Steve Lawrence, C. Lee Giles & Finn Årup Nielsen (February 2001). "The real power of artificial markets" (PDF). Science. 291 (5506): 987988. doi:10.1126/science.291.5506.987. PMID 11232583.
  9. 1 2 "Prediction Markets: Does Money Matter?" (PDF). Retrieved 2016-08-20.
  10. "manipulation2.dvi" (PDF). Retrieved 2016-08-20.
  11. "Archived copy". Archived from the original on 20 April 2008. Retrieved 2008-10-05.
  12. Katy Bachman (2014-10-31). "Meet the 'stock market' for politics". Politico. Retrieved 2015-01-25.
  13. a scenario described by Jim Bell in 1997. Bell, Jim (1997-04-03). "Assassination Politics" (PDF). Infowar. Archived (PDF) from the original on 27 January 2011. Retrieved February 28, 2011.
  14. Vellacott, Chris (2010-10-07). "Betfair IPO values company at up to £1.5 billion | Reuters". Retrieved 2016-08-20.
  15. "the simExchange - The structure of simExchange game stocks".
  16. "Robin Hanson on the Sim Exchange". Midas Oracle.ORG - Predictions & Innovation.
  17. "simExchange a Keynesian Beauty Contest". Midas Oracle.ORG - Predictions & Innovation.
  18. "Keith Jacks Gamble: simExchange is somewhat OK, but will remained confined in play-money land.". Midas Oracle.ORG - Predictions & Innovation.
  19. "Prediction Markets - Real Time Intelligence - Concept Testing". Consensus Point.
  20. "Archived copy". Archived from the original on 7 September 2004. Retrieved 2004-09-21.
  21. "Archived copy". Archived from the original on 13 June 2008. Retrieved 2007-04-04.
  22. "Archived copy". Archived from the original on 8 October 2014. Retrieved 6 October 2014.
  23. Robert Charette (28 February 2007). "An Internal Futures Market". BI Review Magazine.
  24. "Official Google Blog: Putting crowd wisdom to work". Official Google Blog.
  25. "Using Prediction Markets to Track Information Flows: Evidence from Google" (PDF). Retrieved 2016-08-20.
  26. "Lauto G., Valentin F. (2016) How preference markets assist new product idea screening. Industrial Management & Data Systems 116(3):603-619".
  27. "Lauto G., Valentin F., Hatzack F., Carlsen M. (2013) Managing front-end innovation through idea markets at Novozymes. Research-Technology Management 56 (4), 17-26.".
  28. "Prediction Market - Market Research - Concept Testing - Crowdsourcing". Consensus Point.
  29. Totty, Michael (2006-06-19). "Business Solutions - WSJ". Retrieved 2016-08-20.
  30. "Archived copy". Archived from the original on 22 August 2006. Retrieved 2006-10-30.
  31. "Archived copy". Archived from the original on 8 May 2007. Retrieved 2007-02-21.
  32. Hanson, Robin (January 2003). "Combinatorial Information Market Design" (PDF). Information Systems Frontiers. 5 (1): 107–119. doi:10.1023/A:1022058209073.
  33. Sun, Wei; Hanson, Robin; Laskey, Kathryn; Twardy, Charles (16 Oct 2012). "Probability and Asset Updating using Bayesian Networks for Combinatorial Prediction Markets". Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012). arXiv:1210.4900Freely accessible.
  34. Sun, Wei; Laskey, Kathryn; Twardy, Charles; Hanson, Robin; Goldfedder, Brandon. "Trade-based Asset Model using Dynamic Junction Tree for Combinatorial Prediction Markets". arXiv:1406.7583Freely accessible.
  35. Lott, Maxim (2015-08-20). "New tech promises government-proof prediction markets | Fox News". Retrieved 2016-07-29.

Academic papers

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