Polymarket returns to the US, where is the next opportunity in the prediction market?

Author: Noveleader, Castle Labs; Compilation: Ismay, BlockBeats

Editor’s note: As Polymarket is set to reopen to U.S. users, prediction markets are rapidly emerging in the crypto space, evolving from a niche activity into an important tool for hedging risks and capturing event outcomes. This article delves into the operational logic of prediction markets, liquidity challenges, and potential paths for scalability through specific case studies, while also exploring their application value in high-impact events such as elections and drug approvals.

The article points out that prediction markets not only provide professional investors with a new type of hedging tool but also open up a new entry for ordinary users to participate in the crypto world, covering diverse trading scenarios from popular culture to new technology products. For both new and old readers interested in innovations in crypto finance, the evolution of market structures, and risk management, this article offers highly valuable insights.

The following is the original text:

Prediction markets are rapidly evolving and have become one of the hottest narratives today. The more I read, the more I realize that prediction markets could be an excellent tool for hedging against certain global or local events, depending on the direction of my exposure in trading. Clearly, this application is not yet widely utilized, but I anticipate that as liquidity improves and prediction markets reach a broader audience, this use case will experience a boom.

Vitalik recently mentioned prediction markets in his article about low-risk DeFi. In this regard, "hedging event risks" is the most important use case for prediction markets. It not only helps maintain the circulation of liquidity but also creates more opportunities for ordinary participants in prediction markets.

We already know that an increasing number of new companies are leveraging the trend to enter the prediction market sector, resulting in a rise in the number of projects under construction in this field to 97. Although the service directions of these projects vary and will grow with the development of their respective industries and user groups, data on trading volumes still indicates that a few products stand out significantly.

In addition, it should be noted that the prediction market, as a niche track, is still in the exploratory stage. In the future, new winners will continue to emerge alongside mature projects such as @Polymarket, @Kalshi, and @trylimitless, which have already captured significant shares in the prediction market trading volume.

The Evolution of Prediction Markets

Here I will briefly review the history of prediction markets (not too long ago). Prediction markets have gradually entered the mainstream in recent years, especially during the 2024 U.S. presidential election, when Polymarket's trading volume significantly increased, bringing the necessary attention to prediction markets.

Since that peak, although trading volume has not been able to maintain the levels of 2024, it has consistently remained within a relatively high range. Recently, as shown in the monthly trading volume chart above, the trading volume has begun to grow again. With the development of this trend, companies like Kalshi and Limitless have also gradually achieved higher trading volumes, growing into strong competitors that can now rival Polymarket.

In addition, different types of prediction markets are continuously emerging, targeting various user groups and application scenarios to develop their respective niche tracks. A good example is @noise_xyz (which is still in the testing phase), which allows users to engage in leveraged trading based on the "attention" for a specific project.

Prediction Markets as Hedging Tools

Next, I will present my core viewpoints.

Future prediction markets will become more efficient and liquid, thus becoming valuable hedging tools. I'm not saying that they are not currently used for hedging at all, but rather that their current scale is not sufficient to play a role on a larger level.

If we observe the existing application cases, an article by @0xwondr explains this well. He illustrates how he used prediction markets for hedging when Trump Token launched earlier this year. On one hand, he bought $TRUMP tokens, while also purchasing shares for the event "Will Trump be hacked?" in the prediction market, specifically the "Yes" shares. This way, if a hack did occur, he could offset his losses with the "Yes" shares he held; conversely, if no hack event occurred, the token itself would have significant upside potential (and indeed we saw it eventually rise very high).

Let me try to illustrate the "hedging" opportunity with another example. Suppose a certain investor has a significant portion of their investment portfolio in a specific pharmaceutical company. The company is waiting for FDA approval for its new product. If the approval is granted, the company's stock price is likely to soar; if it is denied, the stock price may plummet. If there is a prediction market for the same outcome, this pharmaceutical company investor could hedge their stock position by purchasing a "no" share.

Of course, there are different views on this approach: some believe that there are better and more liquid channels to achieve this hedging. Investors can completely short the stock directly and then wait for the approval results. But the question is, can investors strictly maintain the hedge based solely on an uncertain FDA approval result? The answer is clearly no.

By using prediction markets, hedging decisions that are not yet known to anyone becomes smooth and feasible. Perhaps in the long run, prediction markets will become a hedging tool that can complement existing hedging channels. If used efficiently, prediction markets can indeed become very effective hedging tools.

Similar examples include election results, macroeconomic events, interest rate adjustments, etc. For these event-based risks, there are almost no other viable hedging methods.

What does the prediction market need to achieve scalability?

The evolution of prediction markets and the new liquidity brought by users help them become a liquid venue for hedging against specific events or markets. But the question is, is the liquidity of these markets sufficient for large-scale hedging?

A simple answer is: not enough, at least for most markets.

You may have seen some impressive trading volume data at the beginning of the article. Polymarket's trading volume last month was close to $1 billion, which is quite remarkable for a binary market that does not offer leverage and is based on relatively emerging narratives. However, this trading volume is spread across different markets and topics, not targeted at a single event, but rather the platform's total trading volume. In fact, only a few events truly contributed to the majority of the trading volume.

Putting aside trading volume, the core issue we need to discuss is liquidity, as the growth of trading volume relies on deeper liquidity.

Deeper liquidity can ensure that prices are less susceptible to manipulation, that a single transaction does not have a significant impact on the entire market, and can also minimize slippage during trading.

Currently, prediction markets primarily obtain liquidity through two means:

1. Automated Market Maker (AMM): Under the classic AMM structure, users trade with the liquidity pool. It is suitable for the early stages of the market, but not suitable for large-scale expansion. At this time, the order book has more advantages.

2. Order Book: The order book relies on active traders or market makers to maintain liquidity, making it very suitable for market scaling.

I recommend reading @Baheet_ 's article to gain a deeper understanding of how prediction markets operate:

Since our focus here is on scalability, I will concentrate on the Orderbook. In the Orderbook, liquidity can be achieved through traders or market makers (MM) actively placing orders, or a combination of both. Structures involving market makers are typically more efficient.

However, due to the significant differences between traditional markets and event contracts (prediction markets), market making in binary markets like Polymarket or Kalshi is not easy.

Here are some reasons why market makers may be reluctant to participate:

1. High Inventory Risk: The market is expected to experience significant volatility due to specific news events. A market may perform well in one direction, but it can quickly reverse. If the market maker's pricing is contrary to the market direction at this time, they may face significant losses. While hedging can alleviate this, these tools often do not have convenient hedging options.

2. Traders and Insufficient Liquidity: The market lacks sufficient liquidity. This sounds like the "which came first, the chicken or the egg" question, but the market requires frequent traders or buyers to bring profits to market makers through bid-ask spreads. However, if the trading volume and number of trades in a specific market are too low, it fails to incentivize market makers to actively participate.

To address this issue, some projects are actively exploring solutions, such as Kalshi, which utilizes third-party market makers while also having an internal trading department to maintain liquidity. In contrast, Polymarket mainly relies on the naturally occurring supply and demand dynamics within the order book.

Ultimately, to gain trading volume and users, a market that everyone wants to participate in must be built, and such a market should have three characteristics:

1. High Leverage: It is not easy to achieve in binary markets with Yes/No questions, as users cannot pursue higher returns through leverage. Some platforms like @fliprbot offer leveraged trading in prediction markets, but the trading volume is usually low. In addition, Limitless provides daily and weekly exercise markets, allowing users to participate in faster-settling markets, which can potentially increase returns.

2. High-frequency markets: The more markets users can choose from, the more likely they are to trade on the same platform. More markets mean more trading volume.

3. High market outcome value: If the outcomes of the market have a significant impact, it will lead to substantial trading volume. This is particularly evident in markets related to elections or drug approvals, as the results of these events can significantly affect broader market reactions.

Summary and Reflection

The prediction market has undoubtedly made a profound impact on the industry. Just this week, its trading volume has surpassed that of meme coins, demonstrating a clear growth trend and signs of widespread adoption.

I would also like to point out that prediction markets have indeed boosted the state of "Hyperfinancialization." Frankly speaking, as long as people are not losing large sums of money, there is no problem with this state itself. I even wrote an article exploring how we are moving towards a state where "everything can be marketized" and why this trend has both advantages and disadvantages.

If you want to read, you can find the article here:

Finally, I sincerely believe that prediction markets are an excellent way to bring new users into the crypto space, as these markets are often aimed at a general audience outside of the CT (crypto traders) circle. Almost everything and any topic has a corresponding market, including pop culture, celebrity gossip, new Apple product launches, and almost anything you can think of. Allowing anyone to trade on things they are interested in is a powerful potential in itself, and it is a field that I am very eager to observe and participate in.

So, it can be said that prediction markets are alpha.