Understanding Prediction Markets: Types, Uses, and Real-World Examples

What Is a Prediction Market?

A prediction market is a platform where participants trade event-based contracts whose prices reflect collective forecasts of future outcomes.

These markets, such as Continuous Double Auction, Automated Market Maker, and blockchain-based systems, are used to predict elections, economic trends, and business results. Real-world examples like the Iowa Electronic Markets have shown that such markets can outperform traditional forecasting methods in accuracy.

How Prediction Markets Function

Prediction markets are similar to futures markets for commodities or other financial asset prices. In futures markets, traders bid up or down the price of a future contract based on their expectation of what the future price of the underlying asset will be. Prediction markets are just futures markets where the future event being traded upon is something other than the price of an asset at some point in the future. Prediction markets involve a collection of people speculating on a variety of events—exchange averages, election results, quarterly sales results, or even gross movie receipts.

Robin Hanson, a professor at George Mason University, is an advocate of prediction markets. He makes the case for prediction markets by emphasizing the removal of reliance on self-interested punditry by so-called experts. "Instead, let us create betting markets on most controversial questions, and treat the current market odds as our best expert consensus. The real experts (maybe you), would then be rewarded for their contributions, while clueless pundits would learn to stay away," Hanson says on his web page.

The price in a prediction market is a bet that a particular event will occur. It also represents an estimated value that the person placing the bet assigns to the parameters being considered in the bet. Unlike public markets, where bets are placed indirectly on intangibles, such as government policy or the possible outcomes of an election (via the effects these things are expected to have on asset prices), prediction markets enable users to bet directly on a piece of information that they believe is valuable.

For example, it is impossible for a speculator to bet directly on an election in the U.S. Instead, the trader will have to find stocks that might increase in value if a certain candidate is elected. However, prediction markets allow traders to bet directly on the possibility of actual candidates being elected to office.

Practical Applications of Prediction Markets

Because they represent a wide variety of thoughts and opinions—much like the markets as a whole—prediction markets have proven to be quite effective as a prognostic tool. As a result of their visionary value, prediction markets (sometimes referred to as virtual markets) have been utilized by a number of large companies.

The blending of economics, politics, and more recently, cultural factors, has only made the demand for prediction even greater. Add the benefits of data analytics and artificial intelligence; we're living in the golden age of data and statistical utility.

Over the past 50 years, prediction markets have moved from the private domain to the public. Prediction markets can be thought of as belonging to the more general concept of crowdsourcing. Crowdsourcing is specifically designed to aggregate information on particular topics of interest. The main purpose of prediction markets is eliciting aggregating beliefs over an unknown future outcome. Traders with different beliefs trade on contracts whose payoffs are related to the unknown future outcome; the market prices of the contracts are considered as the aggregated belief.

In theory, by pulling information from every available source, estimation methods should improve and become more accurate and consistent. In reality, as we're currently learning, data manipulation brings a host of new ethical and human biases. As leaders of all varieties help everyday individuals trust and appreciate prediction markets, their use and effectiveness will only improve further.

Various Models of Prediction Markets

There are several models for prediction markets, depending on the mechanism and frequency of forecasting.

Continuous Double Auction

A continuous double auction is a type of trading mechanism to match buyers to sellers, much like the stock market. In the case of prediction markets, traders can buy or sell their bets on a certain outcome, with the price rising or falling if that outcome appears more or less likely. This requires the operator of the prediction market to maintain a ledger of each trade, delivering the payoff to the final owner of each bet.

Automated Market Makers

An automated market maker is used to provide liquidity for markets where there may not be enough buyers or sellers. In this system, the operator of the prediction market acts as a counterparty to all trades, similar to the "house" in a casino. With each trade, the operator can adjust the payoffs, based on the number of bets placed on each outcome. This system is commonly used in sports betting.

Play Money Markets

While most prediction markets rely on using real money to incentivize accurate forecasts, this can run into trouble in jurisdictions where online gambling is illegal. Some prediction markets allow trades in virtual tokens instead of money, with prizes or other incentives to players that collect the most tokens. This allows markets to operate legally, while providing a low-risk platform for traders.

Blockchain-Based Prediction Markets

Developments in blockchain technology have enabled the creation of decentralized prediction markets that can operate without being controlled by a single party or operator. Typically, these markets use smart contracts to mediate bets between different traders, and a complex voting system to determine the final outcome.

Decentralized prediction markets have attracted controversy, both for ethical reasons and the possibility of manipulation. Augur, one of the first decentralized prediction markets, became infamous after traders began betting on the deaths of political figures, raising the possibility that it might become an "assassination market."

Other Crowdsourcing Methods

There are also less formal ways to crowdsource forecasting, such as opinion polls or betting without rewards. These options offer a convenient way to collect crowd forecasts, without a financial incentive for correct forecasting.

Benefits of Prediction Markets

Although they are sometimes controversial, the advantage of prediction markets is that they can benefit from the wisdom of crowds. By collecting and weighing the predictions of a large number of traders, they can provide a market-wide forecast that is generally more reliable and balanced than any single expert opinion. The potential payoffs incentivize traders to reach accurate predictions.

Many real-world securities are traded with the same mechanism as bets in a prediction market. Binary options trades represent a bet on the likelihood of a real-world event, with the price rising or falling as the likelihood of each outcome changes.

Real-World Example of Prediction Markets

One of the pioneers of online predictions markets is the Iowa Electronic Market (IEM), an experiment in market-based forecasting run by faculty of the University of Iowa's Tippie School of Business. Using real money, speculators on the IEM have been able to forecast the outcome of presidential elections with greater long-run accuracy than traditional opinion polls.

The IEM is not regulated by either the CFTC or the SEC, and regulators have issued two no-action letters stating that they would not attempt to regulate it. This was due to the academic nature of the IEM, and the fact that it is not operated for profit.

The Bottom Line

Prediction markets let participants trade contracts tied to future events, with prices reflecting the crowd's collective forecast. Platforms like the Iowa Electronic Markets have shown strong accuracy in predicting outcomes such as elections.

While these markets harness collective intelligence for better decision-making, they also raise ethical and regulatory concerns, especially in decentralized systems. When used responsibly, they can be valuable tools for individuals and companies seeking data-driven insights into future trends.