Author: 137Labs
The prediction market is experiencing a critical turning point.
As we enter mid-January, the daily trading activity density, turnover rate, and user participation frequency of mainstream prediction market platforms have all risen simultaneously, with several platforms refreshing their historical performance in a very short time. This is not a random "event-driven peak," but rather a collective leap in the product form and demand structure of prediction markets.
If in the past few years prediction markets were still seen as "niche information gaming experiments," now they are gradually presenting 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 in prediction markets, focusing on three representative platforms—Kalshi, Polymarket, and Opinion—and how they are heading down three distinctly different paths.
I. The Essence of Trading Volume Leap: Prediction Markets are "De-Low-Frequency"
A core limitation in the history of prediction markets has been trading frequency.
Traditional prediction markets are closer to "betting-type participation":
Users enter
Place bets
Wait for results
Settle and exit
This model inherently limits the ceiling of trading volume because the same amount of capital can only participate in pricing once within a unit of time.
The recent surge in trading activity is underpinned by a systematic transformation in prediction markets:
From "result-oriented betting" to "process-oriented trading."
This is specifically reflected in three points:
Events are broken down into sustainable trading price paths
It is no longer just "will it happen," but "how does the probability change over time."
Multiple entries and exits during the contract lifecycle become the norm
Users begin to adjust positions repeatedly like trading assets.
Prediction markets begin to exhibit "intraday liquidity" characteristics
Price fluctuations themselves become a reason for participation.
In this context, the rapid increase in trading volume does not mean "more people are betting once," but rather that the same group of users is starting to engage in multiple rounds of betting on the same event.
II. Kalshi: When Prediction Markets are Completely Rewritten by Sports
Among all platforms, Kalshi's trading structure changes are the most radical.
It has not tried to shape prediction markets into "more serious information tools," but has chosen a more realistic path:
To make prediction markets have participation frequencies on par with sports betting.
1. The Significance of Sports is Not "Subject Matter," but "Rhythm Controller"
Sports events have three decisive advantages:
Extremely high frequency (daily, multiple events)
Strong emotional drive (users are willing to participate repeatedly)
Quick settlement (funds quickly flow back)
This gives prediction markets properties similar to "intraday trading products" for the first time.
2. The Real Meaning of Trading Volume: Increased Capital Turnover Rate
Kalshi's growth in transactions does not primarily come from new users, but from the same amount of capital being reused in 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 strong scalability, while the risk lies in:
When the excitement of sports declines, can it retain users on other event contracts?
III. Polymarket: When Prediction Markets Become the "Trading Layer of Public Opinion"
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, but the "Topic Selection Right"
Polymarket's strengths lie in:
Extremely fast new product launches
Coverage of highly emotional topics in politics, macroeconomics, technology, and crypto
Naturally fluctuating in sync with social media opinions
Here, trading is not always based on information advantage, but rather on expression of opinions.
2. Another Explanation for High Trading Volume: Repeated Hedging of Opinions
The large volume of transactions on Polymarket is not "betting from 0 to 1," but rather:
Changes in stance
Emotional reversals
Repricing after public opinion shocks
This makes it more like a decentralized futures market for public opinion.
Its long-term challenge is not whether trading is active, but rather:
When everyone is trading opinions, can prices still reliably convey signals of "real probabilities"?
IV. Opinion: The Key Issue for Growth-Oriented Platforms is Not "Volume," but "Stickiness"
Compared to the first two, Opinion resembles a platform that is still validating its positioning.
1. Trading Volume Exhibits "Strategic Growth" Characteristics
Opinion's activity relies more on:
Incentive mechanisms
Product design
External distribution
This type of trading volume can grow rapidly in the short term, but the real test comes after the incentives recede.
2. What Truly Matters is Not the Peak, but the Retention Curve
For platforms like Opinion, what is more critical is not the trading performance on a particular day, but rather:
Whether users continue to trade across multiple events
Whether fixed participation habits are formed
Whether natural buy-sell depth can be generated
Otherwise, trading volume can easily become a one-time growth showcase.
V. The Next Stage of Prediction Markets: From "Scale Competition" to "Structural Competition"
In summary, the current high activity level in prediction markets is not a singular phenomenon, but rather the result of three different directions advancing simultaneously:
Kalshi is commercializing and entertaining prediction markets
Polymarket is turning prediction markets into a platform for public opinion and emotions
Opinion is exploring the replicability of growth models
This indicates that an important turning point is emerging:
Prediction markets no longer have only the path of "increasing trading volume," but are beginning to differentiate into different types of market infrastructure.
What will truly determine the outcome in the future is not the daily transaction performance, but rather three longer-term questions:
Can trading volume be converted into stable liquidity?
Do prices still possess explainability and reference value?
Is user participation driven by real demand rather than short-term incentives?
Conclusion: Prediction Markets are No Longer About "Whether They Will Take Off"
As prediction markets begin to exhibit continuous, high-density trading behavior, one fact has become quite clear:
They are transitioning from a marginal experiment to a reusable market mechanism.
What truly deserves attention is no longer whether a specific number is refreshed, but rather:
Which form of prediction market can ultimately find a balance between high-frequency participation and effective pricing.
This is the true signal of prediction markets entering a new stage.
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