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Prediction markets

By Andrew Leigh and Justin Wolfers - posted Monday, 16 July 2007


In 2006, Google decided that it wanted to obtain better forecasts of what the company and its competitors were planning to do. As with most firms, better predictions of product launch dates, office openings and so on are of significant strategic importance to Google. But instead of using the standard approach - forecasting the future by asking designated experts - Google opted to set up a market, in which any Google staff member could bet on the chances of an event coming true.

If a Google staffer had strong information that an event would come true, then regardless of her rank in the company, she could bet on that outcome. The results - based on the aggregated bets of thousands of Google staff members - were strong predictors of the actual outcomes. Where the trading volume was high, the forecasts were even more precise. And as closing time drew closer, the markets became steadily more accurate.

This example describes an increasingly important information-aggregation tool, known as “prediction markets”. Analytically, these are markets where participants trade in contracts whose payoff depends on unknown future events - just as in any financial or betting market. The defining feature of a prediction market is that the price of these contracts can be directly interpreted as a market-generated forecast of some unknown quantity.

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Much of the enthusiasm for prediction markets derives from the efficient markets hypothesis: in a truly efficient market, the price of a financial security or prediction market contract reflects all available information. Thus, efficient prediction market prices hold the promise of yielding efficient and unbiased forecasts.

A number of successes in these markets, both with regard to predicting public events and corporate outcomes, have generated substantial interest among social scientists, policymakers and the business community.

We begin by outlining the types of prediction markets currently on offer, and then discuss their accuracy. We review what is known about how to design an effective market, and the constraints of the present legal regime. Finally, we conclude with our own prognostications on what the future might hold.

Prediction markets - an overview

Perhaps the best-known type of prediction market are election-betting markets. The Iowa Electronic Market, established by political scientists at the University of Iowa in 1988, is perhaps the world’s best known election market. These academics operate an electronic market in which traders can purchase “futures contracts” that consist of a promise to pay $1 if the candidate wins the popular vote. Thus, the price of this contract reflects the probability of a candidate winning the election.

Assuming market efficiency, these prices should yield assessments that reflect all available information - including polls, the state of the economy, and recent policy pronouncements.

For United States presidential elections, the Iowa Electronic Market has tended to be more accurate than opinion polls. Research has suggested that this is because the betting market focuses on the underlying dynamics of the race, and is therefore better able to parse out events that occur several months before the election, but will not change the outcome.

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Conversely, the betting market responds rapidly to occurrences that affect the underlying dynamics of the race (such as the appointment of a new campaign manager), even if these events elicit relatively little response in the media or polls.

In Australia, looser regulation of sports betting in the Australian Capital Territory and the Northern Territory has led several bookmakers - including Betfair, Centrebet, International All Sports, SportingBet and SportsAcumen - to offer punters the ability to bet on election outcomes.

True to national traditions, Australian gamblers wagered more than $1.5 million on the 2001 federal election, and more than $2.6 million on the 2004 federal election. While betting markets do not “look” like financial markets, the betting odds yield the same directly interpretable forecasts offered by the Iowa Electronic Markets.

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This is an edited version of an article that first appeared in the Melbourne Review.



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About the Authors

Andrew Leigh is the member for Fraser (ACT). Prior to his election in 2010, he was a professor in the Research School of Economics at the Australian National University, and has previously worked as associate to Justice Michael Kirby of the High Court of Australia, a lawyer for Clifford Chance (London), and a researcher for the Progressive Policy Institute (Washington DC). He holds a PhD from Harvard University and has published three books and over 50 journal articles. His books include Disconnected (2010), Battlers and Billionaires (2013) and The Economics of Just About Everything (2014).

Dr Justin Wolfers is an Assistant Professor of Economics at Business and Public Policy Department of the Wharton School, University of Pennsylvania.

Other articles by these Authors

All articles by Andrew Leigh
All articles by Justin Wolfers

Creative Commons LicenseThis work is licensed under a Creative Commons License.

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