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This article may contain inappropriate or misinterpreted citations that do not verify the text. Please help improve this article by checking for inaccuracies. (November 2007) (help, talk, get involved!) |
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. Prediction markets are thus structured as betting exchanges, without any risk for the bookmaker.
Other names for prediction markets include information markets, decision markets, idea futures, event derivatives, and virtual markets.
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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: Intrade, TradeSports, the Iowa Electronic Markets, NewsFutures, Bet2Give, Hollywood Stock Exchange, The simExchange, Popular Science Predictions 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.
Prediction 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 widespread polling. The markets involved thousands of participants, had millions of dollars in volume in current terms, and had remarkable predictive accuracy. BettingPaper Historical Prediction Markets: Wagering on Presidential Elections - by Paul W. Rhode and Koleman S. Strumpf - PDF file - 2003-11-10
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 HubbardDouglas W. Hubbard, *How to Measure Anything: Finding the Value of Intangibles in Business", John Wiley & Sons, July 2007.
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.predictionmarketjournal.com
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”“Interpreting the Predictions of Prediction Markets” Northwestern University, Dr. Charles F. Manski (Revised: 2005)., 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" "Risk Aversion, Beliefs, and Prediction Market Equilibrium" Steven Gjerstad. 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 (Dartmouth) have obtained similar results, and also include some analysis of prediction market data, in their paper "Interpreting Prediction Market Prices as Probabilities" "Interpreting Prediction Market Prices as Probabilities" Justin Wolfers (Wharton) and Eric Zitzewitz (Stanford). In practice, the prices of binary prediction markets have proven to be closely related to actual frequencies of event in the real world.David M. Pennock, Steve Lawrence, C. Lee Giles & Finn Årup Nielsen (February 2001). "The real power of artificial markets". Science 291 (5506): 987–988. PMID 11232583. "Prediction Markets: Does Money Matter?" Servan-Schreiber (Electronic Markets, 2004)
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 estimateDouglas Hubbard "How to Measure Anything: Finding the Value of Intangibles in Business" John Wiley & Sons, 2007. 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 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"The real power of artificial markets" Pennock et al Science, 2001. 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 "Prediction Markets: Does Money Matter?" Servan-Schreiber (Electronic Markets, 2004).
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.
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, a flood of new traders in the final week of the election caused the market to gyrate wildly, making its "predictions" useless.
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),http://hanson.gmu.edu/biastest.pdf 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.
Some experimental systems are underway to provide data on alternatives to prediction markets that seek to avoid some of the theoretical pitfalls mentioned above. 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.
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 Commodity Futures Trading Commission and allows bets up to $500, 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.
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.
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