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Prediction market

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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]

Contents

  • History 1
  • Accuracy 2
    • Sources of inaccuracy 2.1
  • Other issues 3
    • Legality 3.1
    • Controversial incentives 3.2
  • Public prediction markets 4
  • Use by corporations 5
  • Combinatorial prediction markets 6
  • Decentralized prediction markets 7
  • See also 8
  • References 9
  • Academic Papers 10
  • External Resources 11

History

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.

Prediction markets have a long and colorful lineage. Betting on elections was common in the U.S. until at least the 1940s, with formal markets existing on Wall Street in the months leading up to the race. Newspapers reported market conditions to give a sense of the closeness of the contest in this period prior to widespread polling. The markets involved thousands of participants, had millions of dollars in volume in current terms, and had remarkable predictive accuracy.[3] 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, 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 and on their website 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.[4]

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.[5]

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,[6] tasked with promoting awareness, education, and validation for prediction markets.

Accuracy

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",[7] 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,"[8] 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."[9] In practice, the prices of binary prediction markets have proven to be closely related to actual frequencies of events in the real world.[10][11]

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.[4] 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.[10] 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.[11]

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 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.[13]

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 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.[14]

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.[15]

Public prediction markets

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

  • By far the largest is Betfair which had a valuation in the region of £1.5 billion GBP in 2010.[16]
  • The Iowa Electronic Markets an academic market examining elections where positions are limited to $500.
  • Smarkets a prediction markets for sporting events.
  • iPredict is a prediction market in New Zealand.
  • Predictious is a bitcoin prediction market covering a variety of events.
  • Microsoft has launched Prediction Lab that initially focuses on 2014 U.S. Elections.
  • PredictIt is a New Zealand-based prediction market that offers prediction exchanges on political and financial events. PredictIt is owned and operated by Victoria University of Wellington with support from Aristotle, Inc. The market was launched on November 3, 2014. PredictIt's office is located in Washington, D.C.
  • SciCast is a combinatorial prediction market that focuses on science and technology forecasting. As of summer 2015, the market is closed as it seeks more funding.
  • Hypermind is a French prediction market covering a range of political, geopolitical, and financial events.
  • Augur is a decentralized prediction market built on Ethereum set to launch in 2015,

Use by corporations

  • The simExchange introduced a perpetual contract that it calls "stocks" to predict the global, lifetime sales of video game consoles and software titles. These stocks do not expire like most contracts on prediction markets because the founder, Brian Shiau, argued that video game sales can continue for years.[17] The premise for these stocks is that Shiau believes the video game industry suffers from a "lack of comprehensive sales data" and he compares the information problem of a game's sales to the information problem of evaluating a company's market value. Hanson warns that such a system may not work if a connection is not enforced.[18] Keith Gamble has described the simExchange as a Keynesian beauty contest[19] and that financial markets have certain remedies such as company buy-outs that cannot happen on the simExchange. Gamble concludes that such a prediction market can work but will be confined to play money.[20]
  • Best Buy, Motorola, Qualcomm, Edmunds.com, and Misys Banking Systems are listed as Consensus Point clients.[21]
  • Hewlett-Packard pioneered applications in sales forecasting and now uses prediction markets in several business units. Mentioned in academic publications from HP Labs. Also mentioned in Newsweek.[22] It is working towards a commercial launch of the implementation as a product, BRAIN (Behaviorally Robust Aggregation of Information Networks).[23]
  • Corning, Renault, Eli Lilly, Pfizer, Siemens, Masterfoods, Arcelor Mittal and other global companies are listed as NewsFutures customers.
  • Intel is mentioned in Harvard Business Review (April 2004) in relation to managing manufacturing capacity.
  • Microsoft is piloting prediction markets internally.[24]
  • France Telecom's Project Destiny has been in use since mid-2004 with demonstrated success.[25]
  • Google has confirmed in its official blog that it uses a predictive market internally.[26][27]
  • The Wall Street Journal reported that General Electric uses prediction market software from Consensus Point[28] to generate new business ideas.[29]
  • BusinessWeek lists MGM and Lionsgate Studios as two HSX clients.[30]
  • HSX built and operated a televised virtual stock market, the Interactive Music Exchange for Fuse Networks Fuse TV to be used as the basis of their daily live television broadcast, IMX, which ran from January, 2003 through July, 2004. The television audience traded virtual stocks of artists/videos/songs, and predicted which would make it to the top of the Billboard music charts. The first of its kind, Fuse Network and HSX won an AFI Enhanced TV (American Film Institute) Award for innoviation in television interactivity.[31]
  • Starwood embraced the use of prediction markets for developing and selecting marketing campaigns. Marketing department started out with some initial ideas and allowed employees to add new ideas or make changes to existing ones. Then subsequently incentives based prediction markets were leveraged to select the best of the lot.

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]

An example of a public combinatorial prediction market was SciCast, which operated until the summer of 2015.[35]

Decentralized prediction markets

In 2015, decentralized prediction markets have been in development.[36] These platforms utilize blockchain technology and cryptocurrencies to enable global betting. Augur has raised over $4 million USD in crowdfunding for further development on the platform, making it one of the top 25 crowdfunded projects of all time.[37]

See also

References

  1. ^ a b 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.  
  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. ^ BettingPaper Historical Prediction Markets: Wagering on Presidential Elections - by Paul W. Rhode and Koleman S. Strumpf - PDF file - 2003-11-10
  4. ^ a b Douglas Hubbard "How to Measure Anything: Finding the Value of Intangibles in Business" John Wiley & Sons, 2007
  5. ^ predictionmarketjournal.com
  6. ^ http://www.pmindustry.org
  7. ^ "Interpreting the Predictions of Prediction Markets" Northwestern University, Dr. Charles F. Manski (Revised: 2005)
  8. ^ "Risk Aversion, Beliefs, and Prediction Market Equilibrium" Steven Gjerstad
  9. ^ "Interpreting Prediction Market Prices as Probabilities" Justin Wolfers (Wharton) and Eric Zitzewitz (Stanford)
  10. ^ a b  
  11. ^ a b "Prediction Markets: Does Money Matter?" Servan-Schreiber (Electronic Markets, 2004)
  12. ^ Information Aggregation and Manipulation in an Experimental Market
  13. ^ David Schneider-Joseph - Ideas Futures Exchanges
  14. ^ Katy Bachman (2014-10-31). "Meet the 'stock market' for politics". Politico. Retrieved 2015-01-25. 
  15. ^ a scenario described by  
  16. ^ "Betfair IPO values company at up to £1.5 billion".  
  17. ^ "the simExchange - The structure of simExchange game stocks". thesimexchange.com. 
  18. ^ "Robin Hanson on the Sim Exchange". Midas Oracle.ORG - Predictions & Innovation. 
  19. ^ "simExchange a Keynesian Beauty Contest". Midas Oracle.ORG - Predictions & Innovation. 
  20. ^ "Keith Jacks Gamble: simExchange is somewhat OK, but will remained confined in play-money land.". Midas Oracle.ORG - Predictions & Innovation. 
  21. ^ "Prediction Markets - Real Time Intelligence - Concept Testing". Consensus Point. 
  22. ^ http://msnbc.msn.com/id/3087117/ (October 2004)
  23. ^ HP Labs : Solutions and Services Research : New Competitive Spaces : BRAIN
  24. ^ [1]
  25. ^ Robert Charette (28 February 2007). "An Internal Futures Market". BI Review Magazine. 
  26. ^ "Official Google Blog: Putting crowd wisdom to work". Official Google Blog. 
  27. ^ Microsoft Word - Information_Processing_Inside_the_Firm__draft_Jan_2
  28. ^ "Prediction Market - Market Research - Concept Testing - Crowdsourcing". Consensus Point. 
  29. ^ http://online.wsj.com/article/SB115073365085184192.html (June 2006)
  30. ^ http://www.businessweek.com/technology/content/aug2006/tc20060804_618481.htm (August, 2006)
  31. ^ "AFI.com Error". afi.com. 
  32. ^ Hanson, Robin (January 2003). "Combinatorial Information Market Design" (PDF). Information Systems Frontiers 5 (1): 107–119.  
  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). 
  34. ^ Sun, Wei; Laskey, Kathryn; Twardy, Charles; Hanson, Robin; Goldfedder, Brandon. "Trade-based Asset Model using Dynamic Junction Tree for Combinatorial Prediction Markets". 
  35. ^ SciCast https://scicast.org/. Retrieved 17 July 2015. 
  36. ^ "New tech promises government-proof prediction markets". Retrieved 2015-09-25. 
  37. ^  

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 72(2), Summer 2008, pp. 190–215.
  • Gjerstad, Steven. "Risk Aversion, Beliefs, and Prediction Market Equilibrium," University of Arizona Working Paper 04-17, 2005.
  • Graefe, A.; Armstrong, J.S. (2011). "Comparing face-to-face meetings, nominal groups, Delphi and prediction markets on an estimation task". International Journal of Forecasting 27 (1): 183–195.  
  • Gruca, Thomas S.; Berg, Joyce E.; Cipriano, Michael (2005). "Consensus and Differences of Opinion in Electronic Prediction Markets". Electronic Markets 15 (1): 13–22.  
  • 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.
  • Rhode, Paul; Strumpf, Koleman (2004). "Historical Presidential Betting Markets" (PDF). Journal of Economic Perspectives 18 (2): 127–142.   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.
  • Rosenbloom, E. S.; Notz, William (2006). "Statistical Tests of Real-Money versus Play-Money Prediction Markets". Electronic Markets 16 (1): 63–69.  
  • Servan-Schreiber, Emile; Wolfers, Justin; Pennock, David M.; Galebach, Brian (2004). "Prediction Markets: Does Money Matter?". Electronic Markets 14 (3): 243–251.  
  • Spann, Martin & Skiera, Bernd."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 Zitzewitz.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 H.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 Resources

  • The Journal of Prediction Markets
  • conference in Helsinki, 2008Uncertainty in Artificial Intelligence lecture at the Combinatorial Prediction MarketsVideo of Robin Hanson's
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