Multicoin: Attention Assets and Prediction Markets

Author: Eli Qian, Venture Partner at Multicoin Capital; Translator: Jinse Caijing xiaozou

For simplicity, we can broadly categorize assets into two types:

1. Cash Flow Assets—primarily stocks and bonds. These assets generate cash flows that investors value;

2. Supply and Demand Assets—primarily commodities and foreign exchange. Their prices fluctuate with supply and demand.

In recent years, the crypto space has spawned a new type of asset—assets whose value is measured in attention. Currently, "attention assets" primarily manifest as user-generated assets, such as NFTs, creator tokens, and memecoins. These assets serve as Schelling points for the tides of cultural attention, reflecting the ebb and flow of attention through price fluctuations. While Memecoin is culturally interesting, its financial attributes remain limited. An effective attention asset should allow market participants to gain exposure to the direct attention paid to a specific event. Through this mechanism, participants will be willing to trade assets they believe are mispriced, allowing the market to collectively form a price that reflects expectations of attention. We believe that, through appropriate structural design, attention assets have the potential to become a formal asset class. To advance this vision, this paper proposes the concept of an "attention oracle"—a novel oracle architecture that supports the creation of "attention perpetual contracts," allowing traders to trade long and short on the attention paid to cultural symbols. In short, an attention oracle collects binary prediction market data on a specific topic and, combining price, liquidity, and time dimensions, constructs a weighted composite index to capture fluctuations in attention. To ensure effective operation, the underlying market must be carefully selected to represent real-world attention input. Using prediction markets as a data source inherently imposes a cost for manipulation—malicious traders must invest capital to influence the index, which theoretically discourages tampering. 1. Why is there a need for perpetual attention contracts? User-generated assets (UGAs) have achieved product-market fit in the purely speculative sector and excel at tracking the attention of things that have been created from scratch, such as emerging online trends and memes. The core value of UGAs lies in creating assets for entities that cannot be accessed through traditional financial channels. Traditional asset issuance processes are slow, costly, and involve high regulatory barriers, significantly limiting the scope of assets. Attention assets, on the other hand, must maintain the speed of the internet to keep pace with the evolution of global thought. The combination of permissionless token issuance, smart pricing mechanisms like bonding curves, and decentralized exchanges enables anyone to create assets for free, guide liquidity, and open up trading globally. Observations show that UGAs typically start at zero. This isn't a flaw, but a feature—when you create a new meme, its initial attention starts at zero. Entering the market at a low point makes intuitive sense and allows early trend spotters to cash in on low-cost underlying assets. However, this also makes it difficult for UGAs to effectively track existing items with high attention. For example, suppose you're bullish on LeBron James's popularity and want to go long. While it's possible to create a memecoin, there are already dozens of LeBron tokens. How do you choose? Furthermore, a new coin must be created from scratch. As a top global celebrity, his attention should already be high; it's unlikely to skyrocket a hundredfold in a short period of time. What if you want to short his attention? Memecoins make this even more difficult. So, what characteristics should an asset with already high attention possess? The following requirements must be met:

• It must have a two-way trading mechanism supporting long and short trades;

• It must be anchored to a real-world attention benchmark;

• The initial valuation should not be zero.

Taking a step back and examining these requirements, perpetual swaps perfectly meet the requirements: they offer two-way trading, an oracle pricing mechanism, and as derivatives, they don't need to be built from scratch. The real challenge lies in building an oracle system for perpetual attention swaps.

Some teams are already working on this problem, such as Noise. On this platform, traders can trade long and short on the community mindshare of crypto projects like MegaETH and Monad. Noise uses Kaito as its oracle, aggregating social media and news data to generate a numerical value representing topic popularity. However, the existing design still has room for improvement. The core goal of an attention oracle is to collect attention-related data and, through algorithmic processing, output a value indicator that can be used for long and short trading. The drawback of using social media as a data source is that it is easily manipulated—this confirms Goodhart’s Law: in adversarial markets, traders will attempt to manipulate pricing inputs. Kaito has already had to redesign its leaderboards and anti-spam filters to address this issue. Furthermore, social media is not a perfect measure of attention. Take Shohei Ohtani, for example: he has a global fan base across various social apps, data that Kaito may not fully capture. If he wins the World Series again, his popularity will increase further, but the number of followers and mentions may not increase linearly. 2. Attention Oracle: A Market-Based Solution Returning to the LeBron James example, suppose you want to trade his attention. The first step in building a LeBron attention oracle is to collect (or create if none already exist) multiple binary prediction markets about him, such as "Will LeBron James reach X million followers by the end of this month?", "Will LeBron win the championship in 2026?", and "Will LeBron be named MVP in 2026?" A complete oracle would require more underlying markets, but this example uses these three. The index price is calculated by weightedly aggregating the prices, liquidity, settlement time, and event importance of each market. For each prediction market, we consider the following four dimensions: price, liquidity, remaining settlement time, and event importance. To simplify the explanation, we use a basic weighting formula: each market's importance coefficient is scored on a scale of 1-10, and the weight is calculated based on liquidity and time factors. Assuming that the importance scores of the three markets are 8, 2 and 10 respectively, the weight of each market is calculated as follows: The final attention index is as follows: If we assume 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, respectively, the comprehensive calculation is as follows: Obviously, more complex calculations for the attention index exist, such as using open interest as a proxy for trading volume, accounting for correlated events, adjusting for market depth, and nonlinear relationships between variables. We've created an interactive website that allows readers to construct custom indices using the live Kalshi market. The primary advantage of this prediction market-based oracle approach is that manipulation incurs real costs. If a trader goes long on LeBron's attention and attempts to drive up the index, they must buy a position in the underlying binary prediction market. Assuming the underlying market is liquid, this means entering the position at a price that the market deems high. Another advantage, which will become increasingly important as the market expands, is that binary prediction markets provide market makers with a spot hedging channel. If a market maker goes short on the attention index, they can hedge their risk by taking a long position in the underlying prediction market that comprises the index. Adjacent has leveraged real-time, liquid markets on Kalshi to create political trend indices (e.g., Democrat vs. Republican, New York City mayoral election, etc.). We believe this approach can be generalized to track attention for 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 balancing multiple factors. When examining attention oracles from a broader perspective, the following are key considerations: the strength of the correlation between the input data; the practical feasibility of data acquisition; the level of controllability of the input variables; and the design of the algorithmic function used to calculate the attention index. The most significant trade-off with our proposed oracle solution is data accessibility. To build a LeBron James attention oracle, we first need to create multiple highly liquid prediction markets for related topics. These markets must maintain liquidity and be promptly replaced when old topics become ineffective. Therefore, this design is only suitable for niche, high-profile topics (such as Trump or Taylor Swift) for which established prediction markets already exist. Another paradox is that attention can increase regardless of the outcome of an event. For example, even if LeBron doesn't win another championship, discussions about his decline could actually drive up 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 he doesn't, public discussion may intensify as the index drops, leading fans to argue that the selection was unfair. The optimal solution may be a hybrid approach that combines prediction markets, social media, and other data sources. Google Trends recently opened its search trend API to developers. Search volume and attention are clearly correlated, and its deduplication mechanism makes it more resistant to manipulation than social media metrics. LLMs can also be used to analyze easily manipulated data sources (such as mainstream media headlines or trending posts on Platform X) and filter out spam, thereby building a more robust evaluation system. We believe that established exchanges like Kalshi and Polymarket are best positioned to launch perpetual attention contracts, as they already have a large, liquid underlying market and a user base. However, the opportunities in attention assets are not limited to industry giants. One possible solution is to establish a dedicated vault for trading prediction markets, specifically long or shorting specific themes. For example, a "Long Taylor Swift Vault" could buy "yes" contracts for events like her songs reaching the top ten or her Super Bowl performance. Vault managers would then determine which markets correlate with increased attention. Another model utilizes Hyperliquid's builders to deploy perpetual contract functionality. The HIP-3 proposal gives market implementers the flexibility to define their own oracles—indices can be constructed by combining data sources such as Kalshi/Polymarket prices, social media metrics, Google search trends, and news headlines. 4. The Potential of Attention Assets Ironically, the first mature application scenario of the attention economy may appear in the stock market. Stock prices are composed of two major components: the discounted cash flow value (i.e., intrinsic value) and meme value. Historically, most stocks have not possessed significant meme value. However, in recent years, with the rise of Wall Street gambling forums and 24/5 retail trading platforms like Robinhood, more and more stocks have begun to carry meme value. The core task of stock research analysts is to determine stock prices. While there are established methods for calculating DCF components, how can one quantify the value of memes? As more assets are traded based on meme value, developing meme value modeling methods is imperative. Professional investors have begun using metrics like follower count, likes, and exposure to assess market sentiment, and prediction markets and other oracle constructions can become effective tools for measuring stock attention and optimizing trading models.

However, the potential of attention assets goes far beyond stock pricing. We believe that predicting attention is an economically valuable activity—attention is a leading indicator of consumer preferences and spending. Companies allocate R&D, recruitment, and marketing budgets based on the flow of attention. The key lies in developing new heuristic models to track this flow.