Benjamin_Sturisky

Prediction markets are contract-based 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. 1. A market is created to determine if the price of Ethereum is >= 3500 at the end of October. 2. YES, shares are selling for 60c, implying a 60% probability that ETH >= 3500 on the settlement date. 3. Trader X buys 100 YES shares for $60, whereas Trader Y buys 100 NO shares for $40. 4. 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 ever-changing, 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 real-world 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. *