When the prediction market enters the "High Trading Volume Era": Structural Divergence of Kalshi, Polymarket, and Opinion

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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 have simultaneously increased, with multiple platforms setting new historical records in a very short period. This is not merely an accidental “event-driven peak,” but more like a collective leap in the product form and demand structure of prediction markets.

If prediction markets in the past few years were still regarded as “niche information game experiments,” now they are gradually showing a more mature form: a trading market centered around 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 the three very different paths they are heading toward.

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”:

User enters

Places a bet

Waits for results

Settles and exits

This model naturally limits the ceiling of trading volume because the same funds can only participate in one pricing per unit time.

Recently, the surge in trading activity is underpinned by a systemic transformation of prediction markets:

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.”

Multiple entries and exits within the contract lifecycle become normal

Users start to repeatedly adjust positions like trading assets.

Prediction markets begin to exhibit “intraday 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 Completely Rewritten by Sports

Among all platforms, Kalshi’s structural change is the most radical.

It does not attempt to shape prediction markets into “more serious information tools,” but chooses a more realistic path:

Enable prediction markets to have the same level of participation frequency as sports betting.

1. The significance of sports is not “theme,” but “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 attributes similar to “intraday trading products.”

2. The true meaning of trading volume: increased capital turnover rate

Kalshi’s growth in transaction volume is not entirely from new users but from the same funds being repeatedly used over shorter cycles.

This is a typical consumption-based trading volume structure:

More entertainment-like

More reliant on frequency

Easier to scale up

Its advantage is extremely strong scalability, but the risk is:

When sports enthusiasm wanes, can it retain users on other event contracts?

3. Polymarket: When Prediction Markets Become the “Opinion Trading Layer”

If Kalshi’s trading activity comes from rhythm, then Polymarket’s trading density comes from topics.

1. The core asset of Polymarket is not the product itself, but “topic selection rights”

Polymarket’s strengths are:

Rapid new listings

Covering highly emotional topics like politics, macroeconomics, technology, and crypto

Naturally fluctuating in sync with social media opinions

Here, trading is not always based on informational advantage, but on expressing viewpoints.

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

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

Changing positions

Reversing emotions

Repricing after public opinion shocks

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

Its long-term challenge is not whether trading remains 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 features “strategic growth”

Opinion’s activity depends more on:

Incentive mechanisms

Product design

External distribution

Such trading volume can grow rapidly in the short term, but the real test is after the 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 showcase.

5. The Next Stage of Prediction Markets: 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 commercializing and entertainment-izing prediction market products

Polymarket is politicizing and emotionalizing prediction markets

Opinion is exploring the replicability of growth models

This indicates an important turning point:

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?

Do user participation stem 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 marginal experiments toward a market mechanism that can be repeatedly used.

What truly matters now is not whether a specific number is refreshed, but:

Which form of prediction market can ultimately balance high-frequency participation and effective pricing.

This is the real signal that prediction markets are entering a new stage.