When prediction markets enter the "high-volume era": the structural divergence between Kalshi, Polymarket, and Opinion.

Author: 137Labs

The market is predicted to be at a critical turning point.

In mid-January, the daily trading activity density, turnover rate, and user participation frequency of mainstream prediction market platforms all increased simultaneously, with many platforms breaking historical records in a very short period of time. This was not a random "event-driven peak," but rather more like a collective leap forward in the prediction market in terms of product form and demand structure.

If prediction markets were still considered "niche information game experiments" in the past few years, they are now 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 in the forecasting market by focusing on three representative platforms— Kalshi, Polymarket, and Opinion —and how they are heading down three distinct paths.

I. The essence of the leap in trading volume: Predicting that the market is "de-frequenting" low-frequency trading.

A key limitation in the history of market prediction is the frequency of trading .

Traditional prediction markets are closer to "betting participation":

• User login

• Betting

• Waiting for the result

• Settlement and departure

This model inherently limits the ceiling of trading volume because the same amount of money can only participate in pricing once per unit of time .

The recent surge in trading activity is based on the prediction that the market is undergoing a systematic transformation:

Specifically, this is reflected in three points:

1. The event was broken down into price paths for sustainable transactions.

   It's no longer just about "whether it will happen," but rather "how the probability changes over time."

2. Multiple entries and exits within the contract's lifecycle have become the norm.

   Users began to adjust their positions repeatedly, just like trading assets.

3. The market is beginning to exhibit characteristics of "intraday liquidity".

   Price fluctuations themselves become a reason to participate.

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 are starting to gamble on the same event multiple times .

II. Kalshi: When the prediction market was completely rewritten by sports

Of all the platforms, Kalshi has undergone the most radical changes in its trading structure .

Instead of attempting to mold prediction markets into "more serious information tools," it chose a more realistic path:

This would allow prediction markets to have the same level of participation frequency as sports betting.

1. The significance of sports lies not in its "subject matter," but in its role as a "pacing controller."

Sporting events have three decisive advantages:

• Extremely high frequency (daily, multiple sessions)

• Strong emotional drive (users are willing to participate repeatedly)

• Fast settlement (rapid return of funds)

This gave prediction markets, for the first time, attributes similar to "intraday trading instruments".

2. The true meaning of trading volume: an increase in capital turnover.

Kalshi's transaction growth does not entirely come from new users, but rather from the repeated use of the same funds within a shorter period .

This is a typical consumer-driven transaction volume structure :

• Closer to entertainment

• More dependent on frequency

• Easier to scale

Its advantage is its high scalability, but the risk lies in:

When the popularity of a sport declines, can users be kept on other event contracts?

III. Polymarket: When Prediction Markets Become a "Trading Layer for Public Opinion"

If Kalshi's trading activity stems from its rhythm, then Polymarket's trading density comes from its topics .

1. Polymarket's core asset is not its products, but its "issue selection power."

Polymarket's strengths lie in:

• The speed of new product launches is extremely fast.

• Covering highly emotional topics such as politics, macroeconomics, technology, and cryptography.

• Natural fluctuations synchronized with social media public opinion

Here, transactions are not always based on informational advantage, but rather on the expression of opinions .

2. Another explanation for high trading volume: repeated hedging of viewpoints.

A large portion of the trading on Polymarket is not “betting from 0 to 1,” but rather:

• Change of stance

• Emotional reversal

• Repricing after public opinion shock

This makes it more like a decentralized public opinion futures market .

Its long-term challenge lies not in the activity of trading, but in:

IV. Opinion: The key issue for growth platforms is not "volume," but "stickiness."

Compared to the former two, Opinion is more like a platform that is still validating its positioning.

1. Trading volume exhibits more "strategic growth" characteristics.

Opinion activity depends more on:

• Incentive mechanism

• Product Design

• External distribution

These types of transactions can grow rapidly in the short term, but the real test comes after the incentives fade .

2. What really matters is not the peak value, but the retention curve.

For platforms like Opinion, what's more crucial is not the trading performance on a single day, but rather:

• Does the user continue trading across multiple events?

• Whether a fixed habit of participation has been formed

• Can trading depth be generated naturally?

Otherwise, trading volume can easily become a one-off display of growth.

V. Predicting the Next Stage of the Market: From "Scale Competition" to "Structural Competition"

In summary, the current high activity in the prediction market is not a single phenomenon, but rather the result of three different directions advancing simultaneously:

• Kalshi is commodifying and entertaining the prediction market.

• Polymarket is turning market predictions into a speculative and emotional market.

• Opinion is exploring the replicability of its growth model.

This signifies that a major turning point is emerging:

What will truly determine the outcome in the future is not single-day trading performance, but three longer-term issues:

1. Can trading volume be converted into stable liquidity?

2. Does the price still have interpretability and reference value?

3. Is user engagement driven by genuine needs, rather than short-term incentives?

In conclusion, predicting the market is no longer a question of "whether it will be popular".

When the prediction market begins to exhibit continuous, high-density trading behavior, one fact becomes quite clear:

What truly deserves attention is no longer whether a particular number has been surpassed, but rather:

Which prediction market model can ultimately find a balance between high-frequency participation and effective pricing?

This is the real signal that the market is entering a new phase.