jakiro
Author: Eli Qian, Investment Partner of Multicoin Capital; Translation: Bitcoin Vision xiaozou
For simplicity, we can roughly divide assets into two categories:
1.cash flow assets– Mainly stocks and bonds.Such assets generate the cash flows that investors value;
2. Supply and demand assets——Mainly applicable to commodities and foreign exchange.Prices fluctuate with supply and demand.
In recent years, the crypto space has given rise to a new type of asset—one whose value is measured by attention.Currently, “attention assets” are mainly user-generated assets, such as NFT, creator tokens and Memecoin.Such assets serve as Schelling points on the tide of cultural attention and reflect the ebb and flow of attention through price fluctuations.
While Memecoin is culturally interesting, its financial properties are flawed.An efficient attention asset should allow market participants to build exposure to direct attention to something specific.Through this mechanism, participants will be willing to trade assets they believe are mispriced,The market can thus collectively form prices that reflect attention expectations.
We believe that through reasonable structural design, attention assets are expected to be upgraded to a formal asset class.To advance this idea, this article proposes the concept of “attention oracle” – this new oracle architecture can support the creation of “attention perpetual contracts”, allowing traders to perform long and short operations on the attention of cultural symbols.
In short, the attention oracle collects topic-specific binary prediction market data and combines price, liquidity and time dimensions to construct a weighted composite index to capture attention changes.To ensure effective operation, the underlying market needs to be carefully selected to represent real world attention input.Using prediction markets as a data source naturally has built-in manipulation costs – malicious traders need to invest money to affect the index, which can theoretically inhibit tampering.
1, Why you need to pay attention to the perpetual contract
User-generated assets (UGAs) have achieved product-market fit in the purely speculative realm and are adept at tracking attention for things started from scratch, such as emerging internet trends and memes.
The core value of UGAs is to create assets for targets that cannot exist through traditional financial channels.The traditional asset issuance process is slow, costly and has high regulatory thresholds, which greatly limits the scope of targets.Attention assets must maintain Internet speed to match the evolution of global trends of thought.The combination of permissionless token issuance, smart pricing mechanisms such as bonding curves, and decentralized exchanges enables anyone to create assets for free, channel liquidity, and open trading to the world.
It has been observed that UGA prices usually start from zero.This isn’t a flaw but a feature—when you create a new meme, it gets zero initial attention.Entering at a low level is intuitive and allows those who are good at detecting trends early to realize cash through low-cost underlying assets.But this also makes it difficult for UGAs to effectively track existing things that already have high attention.
For example, suppose you are optimistic about the attention of LeBron James and want to go long.Although meme coins can be created, there are dozens of LeBron tokens available. How do you choose?Moreover, new coins need to start from scratch. As the world’s top celebrity, its attention should be at a high level and it is impossible for it to surge hundreds of times in a short period of time.What if you want to short its attention?Meme coins are harder to support this.
So, what characteristics should assets with high-profile targets have?The following requirements must be met:
-
A two-way trading mechanism is required to support long and short operations;
-
Need to anchor real-world attention measurement benchmarks;
-
Initial valuation should not start from scratch.
If you take a step back and look at these requirements, you will find that perpetual contracts fit the requirements perfectly: they have two-way operation, have an oracle pricing mechanism, and do not need to start from scratch as a derivative.The real challenge lies in building an oracle system for attention perpetual contracts.
Some teams are already working on solving this problem, such asNoise.On this platform, traders can conduct long and short operations on the community mindshare of crypto projects such as MegaETH and Monad.Noise uses Kaito as an oracle to generate numerical values representing topic popularity by aggregating social media and news data.
However, there is still room for optimization in the existing design.The core goal of the attention oracle is to collect attention-related data and process it through algorithms to output value indicators that can be used for long and short transactions.
The drawback of using social media as a data source is that it can be easily manipulated – a testament to Goodhart’s Law: In adversarial markets, traders will attempt to manipulate pricing inputs.Kaito has toRedesigned leaderboards and anti-spam filtersto deal with this problem.
Furthermore, social media is not a perfect measure of attention.Take Shohei Ohtani as an example: he has a global fan base using different social applications, and these data may not be fully included by Kaito.If he wins the World Series again, his popularity will further increase, but the number of fans and mentions will not necessarily increase linearly.
2, Attention Oracle: Market-Based Solution
Going back to the case of LeBron James, let’s say you want to trade his attention.The first step in building a LeBron attention oracle is to collect (or create if it does not exist) multiple binary prediction markets about him, such as “Can LeBron James have more thanA complete oracle needs to include more underlying markets, but this example will take these three as examples.Index prices will be calculated by weighted aggregation of each market’s price, liquidity, settlement time and event importance.
For each prediction market, we need to consider the following four dimensions: price, liquidity, remaining settlement time and event importance coefficient.To simplify the explanation, we use the basic weight calculation formula: the importance coefficient of each market is 1-10 points, and the weight is calculated based on the liquidity and time factors.
Assuming that the importance scores of the three markets are 8 points, 2 points and 10 points respectively, the weight of each market is calculated as follows:
The final attention index is as follows:
If it is assumed that the settlement periods of the three prediction markets are 180 days, 20 days and 180 days respectively, and their event importance coefficients are 8, 2 and 10 in order, the comprehensive calculation is as follows:
Obviously there are more complex calculation methods for attention indicators, such as using open interest to replace trading volume, considering related events, adjusting market depth, non-linear relationships among variables, etc.we have createdinteractive website, for readers to build custom indices via the real-time Kalshi market.
The main advantage of this way of building an oracle based on prediction markets is that manipulation will have real costs.If a trader is long LeBron Focus and is trying to push the index higher, they will need to buy the underlying binary prediction market position.Assuming sufficient liquidity in the underlying market, this means that a position needs to be opened at a price deemed high by the market.
Another advantage that becomes increasingly important as the market expands is that binary prediction markets provide spot hedging channels for market makers.If a market maker is short the Attention Index, the risk can be hedged by taking a long position in the underlying forecast markets that make up the index.
AdjacentThe real-time liquid market on Kalshi has been used to create political trend indices (such as Democrats vs Republicans, New York mayoral election, etc.).We believe that this method can be generalized to attention tracking of any topic.As prediction markets develop, the range of feasible topics will continue to expand.
3, Design trade-offs of attention oracles
Our oracle architecture requires weighing multiple factors.When looking at attention oracles from a more macro perspective, the following are the core dimensions to consider:
-
The strength of the correlation of the input data;
-
Practical feasibility of data acquisition;
-
Manipulation level of input variables;
-
Algorithm function design for calculating attention index.
The most significant trade-off of our proposed oracle scheme is the difficulty of data acquisition.To build a LeBron James attention oracle, you first need to create multiple highly liquid prediction markets for its related topics, and these markets need to remain liquid and be replaced in time when old topics expire.Therefore, this design is only suitable for niche high-profile topics where there are mature prediction markets (such as Trump or Taylor Swift).
Another contradiction is that regardless of the outcome of the incident, attention is likely to increase.For example, even if LeBron fails to win another championship, discussion about his decline may actually increase attention.In the real world, attention often flows to unexpected events, while prediction markets only measure the probability of an event occurring – if the market expects LeBron to be elected MVP but loses, the public discussion may be more heated when the index falls, and fans will argue that the selection is unfair.
The optimal solution may be a hybrid solution that combines prediction markets, social media and other data sources.Google TrendsRecently, the search trend API has been opened to developers. Search volume and attention are obviously related, andDeduplication mechanismMaking it more resistant to manipulation than social media metrics.LLM can also be used to analyze easily manipulated data sources (such as mainstream media headlines or X platform hot posts) and filter spam information to build a more robust evaluation system.
We believe that mature exchanges like Kalshi and Polymarket are in the best position to launch attention perpetual contracts because they already have a large liquid underlying market and trading user base.However, opportunities for attention assets are not limited to industry giants.
One possible solution is to set up a vault dedicated to trading prediction markets and target long/short positions on specific themes.For example, “Long Taylor Swift Vault” allows you to buy “yes” contracts for events such as her songs entering the top ten, Super Bowl performances, etc. The treasury manager determines which markets are related to increased attention.
Another model is to utilize HyperliquidBuilders deploy perpetual contract functionality.The HIP-3 proposal gives market deployers the flexibility to define oracles—indexes can be constructed by combining data sources such as Kalshi/Polymarket prices, social media indicators, Google search trends, news headlines, etc.
4, the potential of attention assets
Ironically, the first mature application scenario of the attention economy may appear in the stock market.Stock price contains two major elements: discounted cash flow value (i.e. intrinsic value) and memetic value.
Historically, most stocks have not had significant memetic value.But in recent years, with the rise of 24×5 retail trading platforms such as Wall Street Gambling Forum and Robinhood, more and more stocks have begun to continue to carry meme value.
The core task of an equity research analyst is to determine stock prices.Although there are mature methods for calculating DCF components, how to quantify the memetic value?As more assets are traded on memetic value, it is imperative to develop methods for modeling memetic value.Professional investors have begun to use indicators such as number of fans, likes, and exposure to evaluate market sentiment, and prediction markets combined with other oracles can become an effective tool for measuring stock attention and optimizing trading models.
But the potential of attention assets extends far beyond stock pricing.We believe that predicting attention is an economically valuable activity—attention is a leading indicator of consumer preferences and spending.As companies allocate R&D, recruiting and marketing budgets based on where attention flows, the key is to build new heuristic models to track these flows.
