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Prediction markets are speculative markets created for the purpose of making predictions. Assets are created whose final cash value is tied to a particular event (e.g., will the next US president be a Republican) or parameter (e.g., total sales next quarter). The current market prices can then be interpreted as predictions of the probability of the event or the expected value of the parameter. Other names for prediction markets include information markets, decision markets, idea futures, event derivatives, and virtual markets.

Contents 1 Introduction 2 Legal issues 3 Theoretical challenges 4 Applicability to corporations 5 See also 6 References 7 External links

People who buy low and sell high are rewarded for improving the market prediction, while those who buy high and sell low are punished for degrading the market prediction. Evidence so far suggests that prediction markets are at least as accurate as other institutions predicting the same events with a similar pool of participants.

Examples of prediction markets open to the public include: The simExchange, Intrade, TradeSports, the Iowa Electronic Markets, NewsFutures, Hollywood Stock Exchange, Popular Science Predictions Exchange, FT Predict, IdeaWorth.com Futures Market HedgeStreet and eLab eXchange. One of the oldest and most famous is the University of Iowa's Iowa Electronic Market. 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 Oscarnominees and 7 out of 8 top category winners. HedgeStreet, designated in 2004 as a market and regulated by the Commodity Futures Trading Commission, enables internet traders to speculate on economic events. ZiiTrend, launched in 2007, is a community site that covers a wide range of topics submitted by its members.^[1]

These markets actually 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 scientific polling. The markets involved thousands of participants, had millions of dollars in volume in current terms, and had remarkable predictive accuracy.^[2]

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 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 cancelled 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]

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 Intrade/TradeSports, which escapes U.S. legal restrictions by operating from Dublin, Ireland, where gambling is legal and regulated, the Iowa Electronic Markets, which operates from the University of Iowa under the cover of a no-action letter from the CFTC, and finally US-based Bet2Give], a real-money market which doesn't qualify as "gambling" because it requires that trading profits be given to winner-chosen non-profit organizations.^[5]

Some academic research has focused on potential flaws with the prediction market concept. In particular, Dr. Charles F. Manski of the Northwestern University published “Interpreting the Predictions of Prediction Markets”^[6]., 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" ^[7]. has shown that prediction market prices are typically very close to the mean belief of market participants if the distribution of beliefs is smooth (as with a normal distribution, for example). Justin Wolfers (Wharton) and Eric Zitzewitz (Stanford) have obtained similar results, and also include some analysis of prediction market data, in their paper "Interpreting Prediction Market Prices as Probabilities" ^[8]. In practice, the prices of binary prediction markets have proven to be closely related to actual frequencies of event in the real world. ^[9][10]. 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^[11]. 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.

Prediction markets also 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. 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),^[12] Hanson, Oprea and Porter (George Mason U), show how attempts at market manipulation in fact end up increasing the accuracy of the market because they provide that much more profit incentive to bet against the manipulator.

Prediction markets may also be subject to speculative bubbles. For example in the year 2000 IEM presidential futures markets a flood of new traders in the final week of the election caused the market to gyrate wildly, making its "predictions" useless.

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.^[13] 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.^[14] Keith Gamble has described the simExchange as a Keynesian beauty contest^[15] 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.^[16]

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^[9]. 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^[10].

Some experimental systems are underway to provide data on alternatives to prediction markets that seek to avoid some of the theoretical pitfalls mentioned earlier. For example, polling firm TIPP Online has experimented with "national zeitgeist" questions which ask participants who they think will win rather than who they will vote for personally. This proved to be a more stable and accurate predictor in the 2004 US presidential race than traditional polls. Another experimental system is Owise which directly asks participants to estimate probabilities on a wide range of future events, and rewards accurate performance with status, titles, and small cash prizes. Owise functions as a hive mind or a kind of neural network in which each "neuron" is a human being whose predictions are assigned a weight based on past performance. In fact, this is not so different from what naturally happens in a prediction market where those who make good predictions do profit at the expense of those who make bad predictions, thus progressively increasing their relative influence on the market through how much money they can bring to bear to back up their predictions. There is currently not enough data and history to check how these alternatives will compare to prediction markets in terms of forecasting ability.

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.

Adding to the chorus of those who question the powers of markets to predict outcomes is 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.

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• 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.^[17] It is working towards a commercial launch of the implementation as a product, BRAIN (Behaviorally Robust Aggregation of Information Networks).^[18]

• Corning, Eli Lilly, Pfizer, Siemens, Masterfoods, Arcelor Mittal and other global companies are listed.^[19]
• Intel mentioned in Harvard Business Review (April 2003) in relation to managing manufacturing capacity.
• Microsoft is piloting prediction markets internally.
• France Telecom's Project Destiny has been in use since mid-2004, with very successful predictive behaviour.
• Google has confirmed that it uses a predictive market internally in its official blog.^[20]
• The WSJ reported that General Electric] uses prediction market software from Consensus Point to generate new business ideas.^[21]
• BusinessWeek lists MGM and Lionsgate Studios as two of HSX's clients.^[22]
• Abbott Labs, O'Reilly Media, and the Institute for the Future are listed as Inkling Customers.^[23]
• 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.^[24]
• HSX built and ran the Ultimate Hustler Xchange for Black Entertainment Television, as a prediction site for audiences to forecast who would be the winner of a 16 week reality Television Show contest.^[25]
• Starwoods 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. They used prediction marketplace from InnovateUs.

• Election Stock Market
• Policy Analysis Market
• Foresight Exchange
• Prediction games
• Futarchy - a form of government which would use prediction markets to evaluate public policy
• Futures market

• Prediction Markets For Promoting the Progress of Science and the Useful Arts - by Tom W. Bell - PDF file - George Mason Law Review (14 Geo. Mason L. Rev 37) (2006)
• The Informed Press Favored the Policy Analysis Market - by Robin Hanson - PDF file - 2005-05-05
• The Iowa Electronic Market: Lessons Learned and Answers Yearned - by Joyce E. Berg and Thomas A. Rietz - PDF file - 2005-01-00
• Prediction Markets - by Justin Wolfers and Eric Zitzewitz - PDF file - 2004-05-00
• Interpreting the Predictions of Prediction Markets - by Charles F. Manski - PDF file - 2004-02-00