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Beyond Polymarket: Exploring Opportunities in Prediction Market Design

Beyond Polymarket: Exploring Opportunities in Prediction Market Design

BlockBeatsBlockBeats2024/09/03 06:02
By:BlockBeats

Prediction markets can be derivative markets. They reflect the collective predictions of market participants on the outcomes of unknown events through contract transactions. They have advantages such as no need for underlying assets and automated market makers (AMMs).

Original title: Beyond Polymarket: Exploring Opportunities in Prediction Market Design
Original source: Aquarius


Understanding Prediction Markets


A prediction market is essentially an open market where participants trade to predict the outcome of a particular event. These markets operate similarly to a free market economy, with market prices adjusted 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 trading the outcome of an event. Market prices can reflect the public's view 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 knowledge and insights, the market naturally adjusts prices to reflect the most likely outcome.


To illustrate how prediction markets work, consider a hypothetical example of the FIFA World Cup final in December 2022, perhaps with Argentina versus England. Based on existing data, a centralized bookmaker might set the odds at a 67% chance of Argentina winning and a 33% chance of 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 known as a binary prediction market.


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


ARGWIN (Argentina wins)


ENGWIN (England wins)


These tokens start trading at the same price, for example 50/50. As participants buy tokens based on their expectations, the price will fluctuate 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.


Thus, prediction markets are able to achieve accurate predictions without the presence of dedicated forecasters or data analysts. Most participants will only participate in predictions when they have some insight or information about the likely outcome.


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 well suited 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 outcomes of unknown future events. The market prices formed by these contracts can be viewed as collective predictions of market participants. If these contracts are pegged to the price of some asset, prediction markets effectively become derivative markets.


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 introduces information about the underlying asset and a currency for collateralization to establish such a market.


Automated Market Makers (AMMs): It is relatively simple to implement automated market makers 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 products.


Isomorphism with European options: Prediction markets are isomorphic to European options, allowing option pricing models to be migrated to prediction markets.


Capital efficiency: Prediction markets are extremely capital efficient and are often more efficient than traditional gambling markets.


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


Disadvantages of prediction markets as derivative markets:


Risks of liquidity providers: Liquidity providers hold positions, especially during black swan events, and face high risks. However, this may be acceptable to risk-neutral investors.


Novelty and learning curve: Prediction markets are a relatively new concept, and participants may need time to fully understand their mechanisms. However, newness 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.


Mechanisms: CDA vs. 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 occurring. 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 to 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 the 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:


Beyond Polymarket: Exploring Opportunities in Prediction Market Design image 0


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 represents the price at which the trade occurs. The sigmoid 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 its reliance 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 that relies on direct interactions may become a limitation. In illiquid markets, CDA may suffer from low liquidity due to not enough traders matching orders quickly, resulting in wide bid-ask spreads. 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 to address common liquidity issues 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 via a logarithmic function and liquidity via a logistic function. The logarithmic function of the price adjustment is expressed as:


Beyond Polymarket: Exploring Opportunities in Prediction Market Design image 1


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


Beyond Polymarket: Exploring Opportunities in Prediction Market Design image 2


This function shows how liquidity changes with the number of trades, peaking at a certain 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 waiting for matching orders from other participants. LMSR achieves this by automatically adjusting prices as more contracts are bought or sold. The price adjustment is logarithmic, meaning that as the number of contracts that favor a certain outcome increases, the price of that outcome increases 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 because it mitigates the risks that come with low liquidity. In markets with a small number of participants, LMSR ensures that trades can be conducted smoothly and prices reflect the collective sentiment of the market, even if 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.


Comparison of CDA and LMSR in Prediction Markets


Although both CDA (Continuous Double Auction) and LMSR (Logarithmic Market Scoring Rule) mechanisms are used in 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 the 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 functionality ensures 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 where 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 Makers (AMMs)


Automated Market Makers (AMMs), such as LMSR, play a key 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 continuously reflect the collective sentiment of traders.


AMMs set prices and automatically provide trades by using algorithms. 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 becoming overly biased towards a particular outcome, ensuring that prices remain within a reasonable range. 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 Markets: Involving two possible outcomes, such as "yes" or "no". For example, the example of the FIFA World Cup is a classic binary market.


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


3. Scalar (range) markets: Predicting outcomes within a specific 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 create predictions for multiple levels of outcomes by combining multiple prediction markets.


Categorical and Scalar Markets


In a categorical 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 a scalar market, let's say predict 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 rewards will depend on how close their predictions are to the actual price.


Combinatorial Markets


Combinatorial prediction markets combine multiple prediction markets to make more complex predictions. 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 of the product's success.


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


Comparison of Prediction Markets to Traditional Polls


Prediction markets offer unique 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 by purchasing underpriced shares, providing more reliable data.


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 both the creation and participation of prediction markets are easy. Decentralization and permissionless participation further enhance the platform's potential, 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 meet different needs in prediction markets. CDA facilitates direct interactions between traders and excels 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 field of prediction markets continues to grow, automated market makers like LMSR may become increasingly important in ensuring the robustness and accuracy of market predictions.


This article is from a contribution and does not represent the views of BlockBeats.

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Disclaimer: The content of this article solely reflects the author's opinion and does not represent the platform in any capacity. This article is not intended to serve as a reference for making investment decisions.

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