Understanding Prediction Markets

A prediction market is essentially an open market where participants trade to predict the outcome of specific events. These markets operate similarly to a free market economy, with market prices adjusting based on the collective wisdom of the participants. Prediction markets allow users to trade the probability of certain events occurring, and the final market price reflects the expected likelihood of these events.

By definition, a prediction market is “a trading market created for the purpose of trading the outcome of an event. Market prices can reflect the public’s views on the probability of an event occurring.” While this definition summarizes the basic concept, the depth and complexity of prediction markets go far beyond this and deserve further exploration.

The key role of openness

The openness of prediction markets is one of their most important features. Unlike traditional gambling, where the odds are set by the bookmaker according to a specific formula, prediction markets start with the same odds. As participants trade based on their own knowledge and insights, the market will naturally adjust prices to reflect the most likely outcome.

To illustrate how prediction markets work, consider a hypothetical example of the FIFA World Cup final, perhaps Argentina versus England, in December 2022. Based on available data, a centralized bookmaker might set the odds at 67% for Argentina to win and 33% for England.

In contrast, prediction markets do not require a centralized bookmaker. Participants can create a market by asking a question like “Who will win the FIFA World Cup final?” and listing possible outcomes, such as “Argentina” or “England.” This setup is called a binary prediction market.

In our example, there will be two result tokens available for trading:

ARGWIN (Argentina wins)

ENGWIN (England wins)

The tokens start trading at the same price, say 50/50. As participants buy tokens based on expectations, the price fluctuates based on supply and demand. If more people buy ARGWIN, its price will rise, while ENGWIN will fall. Over time, the market will adjust itself, and the token price will reflect the most likely outcome, perhaps in line with the 67/33 odds set by the bookmaker.

Therefore, prediction markets are able to achieve accurate predictions without the need for dedicated forecasters or data analysts. Most participants will only participate in predictions when they have some insight or information about the possible outcomes.

Prediction Markets as Derivative Markets

Prediction markets can also be viewed as derivative markets. Since markets are essentially information processors, they can be designed within an information-theoretic framework, making prediction markets particularly amenable to this model.

Prediction markets, also known as betting markets, information markets, decision markets, idea futures or event derivatives, allow participants to trade contracts based on the results of unknown future events. The market prices formed by these contracts can be regarded as the collective predictions of market participants. If these contracts are linked to the prices of certain assets, the prediction market actually becomes a derivative market.

Advantages of prediction markets as derivative markets:

No underlying asset required: These markets do not require an underlying asset to operate. All that is required is an oracle that brings in information about the underlying asset and a currency to be used as collateral to establish such a market.

Automated Market Maker (AMM): It is relatively simple to implement an automated market maker in prediction markets. Research on prediction markets has played a key role in the development of AMM algorithms.

Versatility: By designing appropriate prediction events, prediction markets can provide general-purpose products.

Isomorphism with European options: The prediction market is isomorphic to European options, so the option pricing model can be migrated to the prediction market.

Capital Efficiency: Prediction markets are extremely capital efficient, often more so than traditional betting markets.

No short squeeze risk: In the prediction market, the liability of participants is limited by their collateral assets, eliminating the risk of short squeeze.

Disadvantages of prediction markets as derivative markets:

Liquidity provider risk: Liquidity providers hold positions, especially during black swan events, and face high risk. However, for risk-neutral investors, this may be acceptable.

Novelty and learning curve: Prediction markets are a relatively new concept and participants may need time to fully understand their mechanics. However, novelty is a common feature in the blockchain space.

Unknown risks: As with any new design, there may be shortcomings that have not yet been discovered.

Mechanism: CDA and LMSR

Prediction markets are specialized financial markets where participants trade contracts based on the outcomes of future events, such as political elections, sports results, or economic indicators. The prices of these contracts reflect the collective beliefs of market participants about the likelihood of these events. The two main mechanisms that underpin the operation of prediction markets are the Continuous Double Auction (CDA) and the Logarithmic Market Scoring Rule (LMSR). Each mechanism has its own unique advantages, while also facing specific challenges in terms of liquidity and price accuracy. This article explores the complexity of these mechanisms, their application in prediction markets, and their relationship to automated market makers (AMMs).

Continuous Double Auction (CDA)

Continuous Double Auction (CDA) is one of the most commonly used mechanisms in financial markets, including prediction markets. In CDA, traders interact by placing buy (bid) and sell (ask) orders directly into the order book. The order book is the core part of the CDA mechanism and lists all unfilled orders, with bids on one side and asks on the other. When a bid matches an ask, a trade occurs and is executed at the matched price. The dynamics of the CDA mechanism can be described by using a sigmoid function for bids and asks. The sigmoid function is defined as:

Here, PPP represents the price level. The bid function gradually decreases as the price increases, while the ask function increases, forming a natural equilibrium point where the two curves intersect. This intersection point represents the price at which the transaction occurs. The S-shaped function is used to simulate the gradual change in the number of orders as the price deviates from the central value.

A key feature of CDA (Continuous Double Auction) is that it relies on direct interactions between traders to facilitate price discovery. Traders can place orders at any time, and these orders remain in the order book until matched by an order in the opposite direction. The flexibility of CDA allows traders to set their desired prices, which enables efficient price discovery in highly liquid markets. However, in markets with fewer participants, this mechanism of reliance on direct interactions may become a limitation. In illiquid markets, CDA may suffer from low liquidity and widen bid-ask spreads because there are not enough traders to quickly match orders. This reduces market efficiency and makes accurate price prediction more difficult.

In the context of prediction markets, the CDA mechanism has been widely used due to its simplicity and ability to facilitate direct transactions. However, the low liquidity problem caused by the limited number of participants in prediction markets has prompted people to explore alternative mechanisms such as LMSR.

Logarithmic Market Scoring Rule (LMSR)

The Logarithmic Market Scoring Rule (LMSR) is a specially designed automated market maker (AMM) mechanism that aims to solve common liquidity problems in prediction markets. Unlike CDA (Continuous Double Auction), where trades are conducted directly between participants, LMSR involves a central automated market maker that acts as the counterparty to all trades. This market maker continuously provides buy and sell quotes and uses a logarithmic scoring rule to calculate these quotes, adjusting the price based on the total amount of open contracts.

The LMSR mechanism can model price adjustments through logarithmic functions and liquidity through logistic functions. The logarithmic function of price adjustment is expressed as:

Where TTT represents the number of transactions. This function reflects that as the number of transactions increases, the price rises at a decreasing rate, thus preventing the price from becoming too extreme. Liquidity can be modeled by a logistic function:

This function shows how liquidity changes with the number of transactions, with liquidity reaching a peak at a certain transaction volume and then gradually decreasing.

A significant advantage of LMSR is its ability to provide constant liquidity, ensuring that traders can execute trades at any time without having to wait for matching orders from other participants. LMSR achieves this by automatically adjusting prices as more contracts are bought or sold. Price adjustments are logarithmic, meaning that as the number of contracts favoring a certain outcome increases, the price of that outcome rises at a slower rate. This mechanism prevents prices from becoming too extreme and stabilizes the market even in the presence of a large number of one-sided trades.

LMSR is particularly well suited for use in prediction markets as it mitigates the risks that come with low liquidity. In markets with a small number of participants, LMSR ensures that trades can proceed smoothly and prices reflect the collective sentiment of the market, even when there are fewer active traders. However, this also means that market makers may face potential losses as it may need to subsidize trades to maintain liquidity. Nonetheless, the design of LMSR ensures that these losses are capped, making it a sustainable mechanism for market owners.

Ken Kittlitz, CTO of Consensus Point, highlighted the practical benefits of using LMSR in prediction markets. He noted that the presence of an automated market maker "has a huge impact on the success of the market" because it provides stable liquidity and simplifies the trading process for participants. By ensuring that there are always buy and sell orders across a wide range of prices, LMSR makes the market more accessible and intuitive, which may lead to higher participation and, therefore, more accurate predictions. Comparing the use of CDA and LMSR in prediction markets

Although both CDA (Continuous Double Auction) and LMSR (Logarithmic Market Scoring Rule) mechanisms are used for prediction markets, they serve different purposes and are best suited for different market conditions. CDA performs well in highly liquid markets where there are enough participants to ensure that buy and sell orders are matched regularly. In such an environment, CDA can promote efficient price discovery, allowing the market to reflect the true collective beliefs of participants. However, in less liquid markets, CDA's reliance on direct trader interactions can lead to inefficiencies such as wide bid-ask spreads and inaccurate price predictions.

On the other hand, LMSR excels in environments where liquidity is an issue. Its automated market-making capabilities ensure that trades can be made at any time, regardless of the number of participants. This continuous provision of liquidity makes LMSR particularly valuable in prediction markets, especially when participation may be intermittent or limited. LMSR's ability to dynamically adjust prices based on trading volume also helps stabilize the market and prevent extreme price volatility, which is critical to ensuring the reliability of market predictions.

Automated Market Maker (AMM)

Automated market makers (AMMs), such as LMSR, play a critical role in maintaining liquidity, especially in markets that may suffer from liquidity issues due to low trading volume. In prediction markets, where the number of participants can fluctuate significantly, the presence of AMMs ensures that the market remains functional and that prices continue to reflect the collective sentiment of traders.

AMMs work by using algorithms to set prices and automatically provide trades. In the case of LMSR, this algorithm is based on a logarithmic function that adjusts prices based on changes in trading volume. This constant adjustment helps prevent the market from being overly biased towards a particular outcome, ensuring that prices remain within reasonable ranges. By providing this stabilizing effect, AMMs like LMSR enable prediction markets to operate effectively even with a small number of participants.

Classification of prediction markets

Prediction markets can take many forms, each suitable for different scenarios:

1. Binary Market: Involving two possible outcomes, such as “yes” or “no”. For example, the FIFA World Cup example is a typical binary market.

2. Categorical markets: Similar to binary markets, but with more than two options. For example, predicting the winner of a tournament where there are multiple teams competing.

3. Scalar (range) markets: predicting outcomes within a certain range, such as predicting the future price of an asset. Participants are rewarded based on how close their predictions are to the actual outcome.

4. Combination Markets: The most complex form, where users combine multiple prediction markets to create predictions for multiple levels of outcomes.

Classified and Scalar Markets

In a classified market, let’s say we want to predict the winner of the FIFA World Cup after the quarter-finals, with eight teams remaining, each outcome token might start at a price of 0.125 ZTG. If you accurately predict the winner early on before the market closes, you can make a significant profit.

In the scalar market, suppose you are predicting the price of the Polkadot token (DOT) at the end of Q3 2022. Participants can predict any price within a set range (e.g., $0 to $20), and their reward will depend on how close their prediction is to the actual price.

Combination Market

Combination prediction markets make more complex predictions by combining multiple prediction markets. For example, predicting the success of a new iPhone launch may involve multiple variables, such as color options, included accessories, and pricing. By combining these factors, participants can generate more accurate predictions about the success of the product.

Combination markets are particularly useful in scenarios such as weather insurance, where multiple variables influence the outcome. A dedicated article on the complexity of combination prediction markets explores this topic further.

Prediction Markets vs. Traditional Polls

Prediction markets have distinct advantages over traditional polling methods. Prediction markets encourage accurate predictions through financial incentives rather than relying on labor-intensive surveys. The natural dynamics of the market ensure that overpriced shares are corrected by participants through the purchase of underpriced shares, providing more reliable data.

in conclusion

Prediction markets are a powerful tool that can be used to predict a variety of outcomes, from sporting events and asset prices to political decisions and weather events. Participants with valuable insights are incentivized to participate and correct market imbalances, while participants with less information are naturally dissuaded from taking significant risks.

The goal of any prediction market platform should be to create a user-friendly environment that attracts liquidity and provides fast responses, ensuring that prediction markets are easy to create and participate in. Decentralization and permissionless participation further enhance the potential of the platform, enabling users to discover valuable data about the world around us. Continuous Double Auction (CDA) and Logarithmic Market Scoring Rule (LMSR) are two different mechanisms that each address different needs in prediction markets. CDA promotes direct interactions between traders and performs well in highly liquid markets, while LMSR, as an automated market maker, ensures continuous liquidity and price stability, making it a good fit for markets with less participation. Understanding the strengths and limitations of each mechanism is critical to designing effective prediction markets that accurately aggregate information and generate reliable predictions. As the prediction market space continues to grow, automated market makers like LMSR will likely become increasingly important in ensuring the robustness and accuracy of market predictions.