Intent-centric Asset Class
Have you ever made the right judgment on the earning report of a company based on your knowledge, but witnessed the stock price go against your expectation?
For example, we expected Tesla’s revenue and EPS would beat analysts’ expectation since 4 out of 5 your friends bought brand new Teslas, however, on the earning release day, even though both revenue and EPS were in fact beat analysts’ expectations, the stock of Tesla went down by 5%, and it turns out to be the future expectation of productivity were not as high as expected.
Why it happens? Despite operating in the same capital market, various participants have distinct views on the future movement of asset prices, leading them to incorporate information in varied ways. The interpretation of the same news can vary significantly among these parties, and the actions they take may pursue entirely different future price from yours. The cumulative effect of these diverse actions creates a high level of uncertainty in the market. This explains why it often seems like the market moves contrary to your expectations.
Can we make something different? Imagine a scenario where you trade not the usual financial assets, but specifically the likelihood of Tesla surpassing its earnings report expectations. This would be the sole focus of the trade. If another person takes the opposite position in this trade, it creates a small-scale market. Your reward would depend on the outcome aligning with your prediction. This approach simplifies the entire capital market to hinge solely on your prediction, separating the asset from its traditional market price.
Intent-centric asset class
Simplifying an asset class into a series of events for betting (without the complexities of options and futures) is quite feasible. For instance, users could wager on events like Tesla’s earnings calls, car production figures, or the number of tweets from Elon Musk. The challenge is ensuring sufficient liquidity for the asset by offering traders options for early exit. How can we achieve this? Is this the right timing to start a prediction market?
We believe it is.
Attention war in the AI era
Once we’ve expanded the concept of asset classes significantly, the main challenge becomes ensuring liquidity in the event market. Given that individual users have a limited attention span and can’t monitor all market events, it’s crucial to have an AI matching system. This matching system should not only match counterparties effectively but also matching a group people’s attention (to maintain asset liquidity) and the profitability fairness of creating the asset.
Personalized Emotional Topics
Events featured by the platform need to offer significant emotional appeal to capture and retain people’s interest. They should be topics of lively debate. For instance, speculating on whether “Sam Altman will resume the role of CEO at OpenAI in three weeks” is likely to garner more attention than predicting Tesla’s stock price movement over the same period.
The AI system will connect various individuals based on their interests and attentiveness using an ‘intent book’. This approach is how we first establish an intent-based asset, which then leads to the formation of an order book.
Topic Profitability
The trading topics on the platform should instill a sense of expertise and assurance in traders, rather than feelings of uncertainty. Under this asset-providing platform, users ought to have a clear understanding of their trading options, as opposed to a confusing mixture of traders with varying investment horizons. The platform should justly reward informed users within a set framework, possibly through reduced transaction fees or more attractive bid/ask prices. It’s about fostering a personalized connection between user groups and the platform, enabling them to collaboratively develop asset classes that are enjoyable, profitable, and highly liquid.
Information Value
Professional traders, hedge funds, which contribute huge volume in the market, often rely on extensive data to place large bets, with their confidence stemming from their depth of knowledge. In traditional markets, obtaining sufficient data is usually feasible. However, acquiring enough data for our event-based trading might seem challenging. But is it really the case? The data needs aggregated and restructuring at an event level, tailored to demand. The feasible solution seems to be for an exchange to implement a hybrid revenue-sharing model with internal and external data vendors to contribute event-specfic data.
Conclusion
For thousands of years, people have trade on assets, but an asset in the modern capital market has been a treated as a collection of concepts. This complexity is explains why there are numerous valuation models co-exist in finance, like DCF-based, cost-based, relative valuation, PS-based, and Net-net. Now, it’s time to shift the paradigm, allowing people to bid on specific opinions and sub-levels, thereby creating a new group-to-group trading market driven by intent, not by traditional asset classes defined in exchanges.
In the AI era, we have the opportunity to redefine trading spaces by focusing on distilling people’s intents. This approach moves away from competing over the limited availability of traditionally defined, ambiguous aggregates known as ‘asset prices.’ Instead, it emphasizes a clearer and more direct engagement with market dynamics, based on individual and collective intents.
