Prediction markets are contractbased markets that track the outcome of specific events.
Traders buy shares in a market (priced 0 < x < 100), and depending on the event’s outcome, those shares are either worth 0 or 100.

A market is created to determine if the price of Ethereum is >= 3500 at the end of October.

YES, shares are selling for 60c, implying a 60% probability that ETH >= 3500 on the settlement date.

Trader X buys 100 YES shares for $60, whereas Trader Y buys 100 NO shares for $40.

At the end of October, ETH is 3700. Trader X redeems his 100 shares for $100 (~1.66x), and Trader Y is zeroed out.
The only constraints on a prediction market’s existence are a willing external party to create the market and traders willing to purchase contracts for both sides.
There are three different types of prediction markets:

Binary: These markets are YES/NO, without a possibility for a third answer. The market above is binary.

Categorical: These markets include multiple outcomes. A simple example is a prediction market on the first crypto protocol to airdrop. The market will include a predetermined set of outcomes, and each outcome will have everchanging, varying probabilities assigned.

Continuous: These markets handle events with many different possible settlements. Predicting the close of BTC on a given date would be a continuous market, as there are infinitely possible prices at which BTC could close. Due to this, continuous markets typically integrate predetermined constraints, such as >= 70,000, 60,000 < X < 70,000, and <= 60,000.
There are several different realworld practical applications for prediction markets:

Political: Political markets are arguably the reason prediction markets start seeing accelerated growth and volume. The majority of volume stems from presidential elections and senate/house races. The U.S. presidential election alone has 128.5M outstanding contracts, with more than five months left until the election.

Economic: Economic markets are normally continuous and consist of different financial indicators, such as the CPI rate, unemployment/housing figures, and GDP growth.

Corporate: Corporate markets are typically used to predict the sales of a certain product or merger. However, they can also be used in less sophisticated ways, such as “What is the probability Delta Airlines has a commercial during the Super Bowl?”

Entertainment: Entertainment markets are prevalent because sportsbooks under the hood are effectively prediction markets with a house edge. These markets can commonly be arbitraged, as discussed here. In a nuanced fashion, prediction markets are inefficient, so there is typically a disparity between sportsbook offerings and probability assigned to prediction markets.

Arbitrary: Arbitrage prediction markets are effectively any market not categorized under the above four.
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