When the prediction market enters the "High Trading Volume Era": Structural Divergence of Kalshi, Polymarket, and Opinion
Author: 137Labs
Prediction markets are experiencing a critical inflection point.
By mid-January, the daily trading activity density, turnover speed, and user engagement frequency on mainstream prediction market platforms all rose simultaneously, with multiple platforms breaking their historical records in a very short period. This is not merely a coincidental “event-driven peak,” but more like a collective leap in the product form and demand structure of prediction markets.
If in the past few years prediction markets were still regarded as a “niche information game experiment,” now they are gradually showing a more mature form: a trading market centered on event contracts, characterized by high-frequency participation, and capable of continuously attracting liquidity.
This article will analyze the structural changes behind the growth in trading volume of three representative platforms—Kalshi, Polymarket, and Opinion—and explore how they are heading towards three distinctly different paths.
1. The essence of the trading volume leap: prediction markets are “de-low-frequencying”
A core limitation in the history of prediction markets has been trading frequency.
Traditional prediction markets are closer to “betting participation”:
- Users enter
- Place bets
- Wait for results
- Settle and exit
This model naturally limits the ceiling of trading volume because the same funds can only participate in one pricing per unit time.
Recently, a surge in trading activity indicates that prediction markets are systematically undergoing a transformation:
From “result-oriented betting” to “process-oriented trading.”
Specifically reflected in three points:
- Events are broken down into sustainable trading price paths
No longer just “will it happen,” but “how does the probability change over time.” 2. Multiple entries and exits within the contract lifecycle become normal
Users start to repeatedly adjust positions like trading assets. 3. Prediction markets begin to exhibit “intra-day liquidity” features
Price fluctuations themselves become a reason for participation.
In this context, the rapid increase in trading volume does not mean “more people betting once,” but rather the same group of users engaging in multiple bets on the same event.
2. Kalshi: When prediction markets are thoroughly rewritten by sports
Among all platforms, Kalshi’s trading structure change is the most radical.
It did not attempt to shape prediction markets into “more serious information tools,” but chose a more realistic path:
Enabling prediction markets to have the same level of participation frequency as sports betting.
1. The significance of sports is not “the theme,” but “the rhythm controller”
Sports events have three decisive advantages:
- Very high frequency (daily, multiple matches)
- Strong emotional drive (users willing to participate repeatedly)
- Fast settlement (funds quickly flow back)
This gives prediction markets for the first time an attribute similar to “intraday trading products.”
2. The true meaning of trading volume: improvement in capital turnover rate
Kalshi’s growth in transaction volume is not entirely due to new users, but from the same funds being repeatedly used within shorter cycles.
This is a typical consumption-type trading volume structure:
- Closer to entertainment
- More reliant on frequency
- Easier to scale up
Its advantage is high scalability, but the risk lies in:
When sports hype declines, whether users can be retained on other event contracts.
3. Polymarket: When prediction markets become “public opinion trading layers”
If Kalshi’s trading activity comes from rhythm, then Polymarket’s trading density comes from topics.
1. Polymarket’s core asset is not the product itself, but “issue selection rights”
Polymarket’s strengths include:
- Extremely fast new listings
- Covering highly emotional topics like politics, macroeconomics, technology, and crypto
- Naturally fluctuating in sync with social media opinion waves
Here, trading is not always based on informational advantage, but on opinion expression.
2. Another explanation for high trading volume: repeated hedging of viewpoints
A large amount of trading on Polymarket is not “betting from 0 to 1,” but involves:
- Changing stances
- Emotional reversals
- Repricing after public opinion shocks
This makes it more like a decentralized public opinion futures market.
Its long-term challenge is not whether trading is active, but:
When everyone is trading opinions, can the prices still reliably carry signals of “true probabilities”?
4. Opinion: For growth-oriented platforms, the key issue is not “volume,” but “stickiness”
Compared to the first two, Opinion is more like a platform still validating its own positioning.
1. Trading volume shows more “strategic growth” characteristics
Opinion’s activity depends more on:
- Incentive mechanisms
- Product design
- External distribution
This type of trading volume can grow rapidly in the short term, but the real test is after incentives fade.
2. The truly important thing is not the peak, but the retention curve
For platforms like Opinion, what matters more is not the trading performance on a certain day, but whether:
- Users continue trading on multiple events
- Form a fixed participation habit
- Naturally generate buy-sell depth
Otherwise, trading volume can easily become a one-time growth display.
5. The next stage of prediction markets: shifting from “scale competition” to “structural competition”
Overall, the current high activity in prediction markets is not a single phenomenon, but the result of three different directions advancing simultaneously:
- Kalshi is turning prediction markets into commodified, entertainment-oriented products
- Polymarket is making prediction markets more opinionated and emotional
- Opinion is exploring the replicability of growth models
This indicates an important turning point is emerging:
Prediction markets are no longer solely about “growing trading volume,” but are beginning to differentiate into various types of market infrastructure.
The true determinants of success in the future are not just daily trading performance, but three longer-term questions:
- Can trading volume be converted into stable liquidity?
- Do prices still have interpretability and reference value?
- Does user participation come from genuine demand, rather than short-term incentives?
Conclusion: Prediction markets are no longer a question of “whether they will be popular”
As prediction markets begin to feature continuous, high-density trading behavior, one fact has become quite clear:
They are moving from experimental edges toward a market mechanism that can be repeatedly used.
What truly matters is no longer whether a specific number has been refreshed, but:
Which form of prediction market can ultimately balance high-frequency participation and effective pricing.
This is the real signal indicating prediction markets have entered a new stage.
