Research Paper on Prediction Markets


The Business Nature of Prediction Markets
Simply put, the term “prediction markets” is a generic definition for a wide array of exchange platforms, on which traders can speculate on the probability of a specific event. That is, instead of trading financial instruments with an underlying asset (such as stocks, commodities or indexes), participants of prediction markets buy and sell contracts, whose value represent the probability of the underlying event between 0 and 1. Those events can be of almost any nature; for example, the prediction market service provider Intrade offered contacts on political issues (e.g. the probability that Ireland will approve the Lisbon Treaty), as well as economic (e.g. first weekend box-office returns of a new motion picture) sportive (e.g. will a North-American city will host the 2016 Summer Olympics) and legal (e.g. whether someone will be accused for Michael Jackson’s death).

Prediction markets share several characteristics with stock exchanges and gambling platform, although they cannot be associated with neither of the two latter platforms. On one hand, the market players exchange valuable financial assets, which resemble financial derivatives such as futures and exchange-traded notes (ETNs); however, prediction markets are not as closely regulated as mainstream financial markets, the values represent merely probabilities (and not trends), and the underlying events are much more diverse. Like gambling agencies, prediction markets are much more concerned with probabilities of specific events than with long-term fluctuations of the asset and offer many online service suppliers. Moreover, speculations on non-financial events (in particular sport-related) are the natural property of gambling companies. However, the gambling agencies and prediction markets is the latter’s structure as an exchange mechanism, whereas most “bookers” offer gamblers to play only “against the house.”

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Finally, the trading activities provide an accumulative overview on the traders’ opinions, attitudes and preferences, and can be used for interference purposes, even by non-traders. Therefore, decision-makers use insights from prediction markets in a wide array of areas, from marketing to politics and economic issues. Even the Department of Defence tried, at some point, to establish a Policy Analysis Market, which was supposed to indicate changes in various geopolitical risk factors (Wolfers & Zitzewitz, 2004).

Prediction Markets as a Source for Inference
Quoted probabilities of prediction market contracts can provide an efficient means for decision-making (Berg, Nelson & Rietz, 2003). This is because unlike respondents of poles and surveys, participants of prediction markets have an incentive to speculate what they think is going to happen, and not what the want to happen. In other words, these traders “put their money where their mouth is,” and thus they are less likely to lie or to bet on improbable events.

Surowiecki (2004) argues that prediction markets can provide faster and more accurate estimations than any other forecasting mechanism, either objective (e.g. statistical methods) or subjective (e.g. experts’ opinions). The author defines a set of several assumptions, among them independence and decentralization, under which prediction markets may operate more rationally than any other form of prediction. For example, players in the Iowa Electronic Markets make speculations on the trends of the Federal Reserve’s interest rate; if certain conditions are met, the current value of this contract can be used for further speculation, such as in FX markets.

Leading Vendors of Prediction Markets Tools
As mentioned earlier, the organizational structure of the prediction market sector is rather similar to gambling vendors than to financial exchanges. Moreover, the trading platforms and vendor vary considerably, although the basic trading methods are rather similar on all major markets. This section elaborates the points of difference and similarity among the major vendors in this industry, focusing on the major types and categories of contemporary prediction markets, as well as the chief members of the 2007-founded Prediction Market Industry Association (PMIA).

Hollywood Stock Exchange (HSX) (US)
This privately owned company allows traders to trade contracts, warrants and derivatives (such as options and bonds) on movie-related issues, mainly opening-weekend and total box office returns, as well as Oscar nominations and winnings. The service is based on virtual currency – the Hollywood Dollar, or H$ – which can be used as a predictor for box office revenues in a ratio of H$ 1=US$ 1 million. The company is registered in the US and owned by Cantor Fitzgerald LP, a New York-based brokerage and market research firm, whose financial expertise are noticeable, e.g. by posting the HSX Average – an index of the film industry revenues.

The business model of the HSX is rather typical for many enterprises. Traders can join, receive $H and trade for free, and even receive prizes for excellent performance. Real-money trading is impossible in the US, due to the prohibition on online gambling. However, the company applied for the US Commodities Futures Trading Commission (CFTC) to receive permission to operate real-money platform. Obviously, Cantor Fitzgerald operates HSX as an attractive website; as recently stated by a company official, “there is a market out there for real-money trading. […] Once approved by the CFTC, […] we will provide them the platform for trading futures contracts” (UseableMarkets, 2009).

Intrade (Ireland) and Betfair (UK)
As mentioned earlier, Intrade is an Irish for-profit company provides a large array of prediction market platforms on nearly every possible issues, excluding sporting events. The latter area is covered by Betfair, whose uniqueness in the “booking” market lays in the opportunity to bet on an open market and not “against the house.” Together, the two companies provide an immense variety of trading opportunities, from the question of whether Higgs Boson Particle will be observed this year, the results of worldwide political events and the market value of fine wines (Intrade), to numerous sporting events from around the world (Betfair). As a result, these companies (and several others) provide not only real-money trading opportunities, but also valuable insights for financial and business markets.

The business model is based on commissions from every transaction (just like brokerage agencies). In addition, the company produces and sells market data reports and other data services, such as real-time quota streaming for a fee. Whereas the companies’ operations are legal in their home countries, as well as the most of Europe and many other countries, US residents are currently not allowed to use the service.

The Iowa Electronic Markets (IEM) (US)
The IEM is a non-for-profit project of the University of Iowa, which aims to provide insight and conduct research on issues of prediction markets and collective wisdom. It offers real-money trading with a ceiling of US$500 per participant. The market is rather limited, and focuses on elections and monetary policy issues, i.e. alterations of the Federal Reserve’s interest rate (Wolfers & Zitzewitz, 2004).

Due to its academic nature and the limited trading sums, the IEM is approved by the CFTC. Nevertheless, each events has a turnover of hundreds of thousands of Dollars (ibid.). The IEM has several affiliate markets around the world, which are operated by universities in e.g. Canada, Austria and the UK.

Applications of the Concept and major Stakeholders
Although traders on prediction markets, especially real-money market, are not free of bias, their predictions were often proven as highly accurate. Traders on the HSX, for instance, were as accurate in predicting Oscar winners as a panel of experts (Pennock, Lawrence, Giles & Nielsen, 2001). Moreover, a longitudinal study conducted by Wolfers & Zitzewitz (2004) found that box office predictions of HSX traders “have been quite accurate” (p. 8) over a large number of observations. The predictions tend to scatter (i.e. to become less accurate) with smaller movies than with “blockbusters.” Hence, HSX predictions can be used by movie-related stakeholders such as distributors, advertisers and operators of movie theatres, thereby improving efficient allocation of promotion budgets, advertising funds, etc.

Berg & Rietz (2003) describe the Republican Party’s presidential nomination process as an example for the merits of prediction market can provide not only simple insights, but also for finding probabilities of conditional events, such as a candidate’s probability to win the presidential elections. As argued by the authors, the classical dilemma in party nomination is that only party members can vote for their candidate, but the nominated candidate can only be elected with the support of the general public; in this case, the challenge of “conditional prediction markets” (ibid., p. 79) is to predict which Republican candidate has the highest probability to receive the party’s nomination as well as to win the elections. Therefore, players in prediction markets can presumably provide more accurate prediction than any survey, as they bet on contracts such as “$1 times the Republican Candidate” (ibid.) – an opportunity to gain when the conditional predication will appear to be more probable near the elections themselves (or at any other point, inasmuch as the probability rises). This is not to say that the markets cannot be wrong, but their structure may improve the accuracy of their predictions, at least theoretically, and provide valuable and timely insights for the voters and candidates alike.

Finally, prediction markets can be used as a source of diversification for traders, vendors and market researchers. Traders, even gamblers on sporting events, may find arbitrage opportunities in prediction markets compared to traditional “bookers.” Vendors such as Cantor Fitzgerald can diversify their service portfolio and expand their operations both geographically and content wise. Also decision makers at all levels can benefit from this kind of collective wisdom, especially when inputs are scarce or nonexistent.

Prediction Markets and the Open/Lead User Innovation Theories
It has been previously discussed that prediction markets provide useful insights on people’s opinion on a wide range of questions. In addition to the uses described so far, these markets can be a source for generating ideas, assessing the likelihood of events and providing feedback for companies and organizations. Thus, even if the traders do not provide comprehensive answers, their estimations can replace costly internal resources and save money and time on R&D ventures.

As discussed by Von Hippel (1986), due to the lack of “stable basis for comparison such as that played by economic value for industrial goods” (P. 798) and the propensity of consumer preferences to change from time to time. For example, although it can be assumed that industrial buyers will give high priority to fuel economy in the automobile market (ibid.), the priorities of private consumers are much harder to predict. Hence, lead user inputs from prediction markets can be used to get insights on questions such as whether sales for SUVs will rise or fall in the short- and medium-run.


This section provides a project proposal for implementing HSX Research’s technologies for Apple in the Greek market. Apple, whose operations in Greece are mainly conducted through resellers, produces and sells several categories of consumer electronics, including computers, portable devices, cellular phones, software and application. This proposal aims to offset the company’s current weakness in the Greek market by raising awareness and interest in the company’s products, as well as state-of-the-art research and forecasting tools.

The rationale behind designing an Apple-oriented prediction market in Greece is twofold. First, the company seems to lack thorough knowledge of trends and prospects in Greece, as can be learn from its reluctance to engage in direct operations (except an online shop for iPhone in English), a lack that can be compensated by gathering first-hand data from predication markets. Second, a custom-made predication market can be modified to work well with the company’s products, and therefore to improve customer experience and even to generate revenues from gaming operations.

Apple’s Value from Prediction Markets
Apple is known for being a highly innovative company, which focuses on continuous improvement of its user experience as well as extending its market offerings. This process is extremely costly, especially in the context of a new non English-speaking market. Based on the above, it can be argued that the Apple Greek prediction should deliver value in three main ways:

First, by using the mechanisms described below, insights on trading volumes, value estimations, discussion-board activities and references to external links, can be gathered to determine and answer numerous business development questions. Classical issues are, for instance, on purchasing intentions of existing or prospective products; estimations of market acceptance of new applications and software; and the attractiveness of distribution channels. Second, the market provides an attractive marketing communication platform, which constitutes an alternative for the conventional platforms, which are saturated and thus provide rather limited returns (Kotler & Keller, 2006). Third, by offering benefits to users of Apple products, the prediction market can promote sales or at least provide superior customer value.

Market Structures
Apple’s real-money prediction market will be based on a trading server, which quote prices for “stocks,” “bonds” and derivatives according to buy and sell orders (Keiser & Burns, 2003). Each financial product is linked to either an underlying asset (e.g. sales forecast of a prospective iPhone application), or the probability of an event (e.g. “MacBook sales will rise by 20% compared to last year”). Whereas the trading will be made on the designated platforms, players will have to purchase their credit through the Apple Store website, thereby being exposed to its content.
The participation will not be limited to users of Apple products. However, existing customers will have several benefits, including slightly lower commissions, an iPhone trading applications, free data streams and benefits when purchasing credit. HSX Research will collect and analyze the trading data, manage the trading activities and provide Apple comprehensive analysis tools.

Apple, an expert on brand extension, often uses consumer groups and unconventional marketing methods to promote its sales (Kotler & Keller, 2006). The promotional strategy will be heavily based on the company’s excellent user-oriented image, as well as on the opportunity to become an active member of Apple’s group of Greek “consultants” or “analysts.” In addition, Payoffs can be made for purchasing Apple products with a special discount.

Moreover, excellent trading history may imply that a specific trader may have some valuable knowledge or skills. Hence, Apple and/or HSX will promise to invite experienced and successful traders to a job interview. Finally, advanced traders will be able to join “VIP” or “expert” clubs with both online and real exclusive events, seminars, etc.

The Service Provider’s Business Model
HSX Research’s representative in Greece will own the trading server and technology, and will charge Apple for several services:

  1. First, Apple will pay an initial fee for the adaption of the platform to its needs, including multilingual platforms. If appropriate, HSX and Apple can reach a countertrade (“barter”) agreement, e.g. by supplying hardware to HSX.
  2. Second, Apple will be charged a monthly fee, which is based on a fixed base plus a percentage of commission revenues, to cover overheads, customer support and so on.
  3. Third, as mentioned earlier, HSX has the knowledge and technical abilities to provide research services, which can generate additional revenues to the platform operator.

These services include, among others:

  • Trading results, volumes and browsing activities in regard to a specific product and/or product category
  • Geographical analysis of trading activities (e.g. which areas are more “optimistic” regarding a certain product)
  • Regression analyses and other statistical prediction methods
  • Insights from user-generated content (e.g. discussion boards)

Finally, as Apple has rather weak presence in the Greek market and no experience with prediction markets, the service provider can assist with promotion activities. The promotional strategy will include marketing communications with current traders as well as new users.

Similarly to its operations with movie-related products, HSX will conduct intensive PR and media activities. Considering the uniqueness of the platform in the Greek market, as well as the merits of the cooperation between the two companies, the project is expected to gain enormous public attention at the beginning; the challenge, however, is to keep this interest, and to continue the two companies’ proven innovative spirit.

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Custom Research Paper on Prediction Markets

Berg, J. E. & Rietz, T. A. (2003). Prediction Markets as Decision Support Systems. Information Systems Frontiers, 5(1), pp. 79-93. Retrieved September 9, 2009 from <>
Berg, J. E., Nelson, F. D., & Rietz, T. A. (2003). Accuracy and forecast standard error of prediction markets. University of Iowa Working Paper, July 2003. Retrieved September 9, 2009 from <>
Keiser, T. M. & Burns, M. R. (2003). U.S. Patent No. 6,505,174. Washington, D.C.: U.S. Patent and Trademark Office. Retrieved September 9, 2009 from <>
Kotler, P. & Keller, K. L. (2006). Marketing management (12th edition). Upper Saddle River, NJ: Prentice Hall.
Intrade. (2009). Intrade Predication Markets. Retrieved September 9, 2009 from <>
Pennock, D., Lawrence, S., Giles, C. L. & Nielsen, F. A. (2001). The power of play: Efficiency and forecast accuracy in web market games. NEC Research Institute Technical Report, 2000-168. Retrieved September 9, 2009 from <>
Prediction Market Industry Association. (2007). Prediction Market Industry Association is burn. Retrieved September 9, 2009 from <>
Surowiecki, J. (2004). The wisdom of crowds: why the many are smarter than the few and how collective wisdom shapes business, economies, societies, and nations. London: Abacus.
UseableMarkets. (2009, July 14). Interview with Alex Costakis of HSX. Retrieved September 9, 2009 from <>
Von Hippel, E. (1986). Lead users: A source of novel product concepts. Management Science, 32(7), pp. 791-805. Retrieved September 9, 2009 from <>
Wolfers, J. & Zitzewitz, E. (2004). Prediction Markets. Stanford Research Paper Series. Retrieved September 9, 2009 from <>

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