Exploring the Design Opportunities of Prediction Markets

A prediction market is a special financial instrument that allows participants to predict the outcomes of specific events through trading. The operation of these markets is similar to a free market economy, where prices are adjusted based on the collective wisdom of the participants. Users can trade the probabilities of certain events occurring, and the final market price reflects the expected likelihood of these events.

The openness of prediction markets is one of its most critical features. Unlike traditional betting, prediction markets start with the same odds, and as participants trade based on their knowledge and insights, the market naturally adjusts prices to reflect the most likely outcomes.

Taking the final of the FIFA World Cup in December 2022 as an example, suppose Argentina is facing England. In the prediction market, there will be two outcome tokens available for trading: ARGWIN( for Argentina winning) and ENGWIN( for England winning). These tokens initially trade at the same price, and as participants buy based on expectations, the price will fluctuate according to supply and demand. Ultimately, the market will self-adjust, and the token prices will reflect the most likely outcomes.

Prediction markets can also be viewed as derivative markets. They allow participants to trade contracts based on the outcomes of future unknown events, and the resulting market prices can be seen as a collective prediction. If these contracts are linked to the prices of certain assets, prediction markets effectively become derivative markets.

As a derivative market, prediction markets have some advantages: they can operate without underlying assets; automatic market making is relatively simple to achieve; by designing suitable prediction events, they can provide universal products; they have an isomorphic relationship with European options; they are capital efficient; and there is no risk of a short squeeze.

However, prediction markets also face some challenges: liquidity providers are at risk, especially during black swan events; as an emerging concept, participants need time to understand its mechanisms; there may be undiscovered unknown risks.

Continuous two-way auction ( CDA ) and logarithmic market scoring rule ( LMSR ) are the two main mechanisms that support the operation of prediction markets. CDA allows traders to interact by placing buy and sell orders directly into the order book. When buy and sell orders match, the trade is executed at the matched price. CDA performs excellently in high liquidity markets, but may face liquidity issues in markets with fewer participants.

LMSR is a specially designed automated market maker mechanism aimed at addressing common liquidity issues in prediction markets. It involves a central automated market maker that acts as the counterparty for all trades. LMSR continuously provides buy and sell quotes, using a logarithmic scoring rule to calculate these quotes, adjusting prices based on the total amount of unmatched contracts. The advantage of LMSR lies in its ability to provide constant liquidity, ensuring that traders can execute trades at any time without waiting for matching orders from other participants.

Prediction markets can take various forms, including binary markets, categorical markets, scalar ( range ) markets, and composite markets. Each form is suitable for different scenarios, ranging from simple yes/no predictions to complex multivariable forecasts.

Compared to traditional polls, prediction markets encourage accurate predictions through financial incentives, and the natural dynamics of the market ensure that overpriced shares will be corrected, thus providing more reliable data.

A prediction market is a powerful tool that can be used to predict various outcomes. The goal of any prediction market platform should be to create a user-friendly environment that attracts liquidity and provides quick responses. Decentralization and permissionless participation further enhance the platform's potential, allowing users to discover valuable data about the world around us.