As Prediction Markets Enter the 'High Trading Volume Era': The Structural Divergence of Kalshi, Polymarket, and Opinion

Author: 137Labs

Prediction markets are experiencing a critical inflection point.

By mid-January, the density of daily trading activity, turnover speed, and user participation frequency on mainstream prediction market platforms simultaneously increased, with multiple platforms刷新ing historical performance in an extremely short time. This is not an偶然的 "event-driven peak," but更像 a collective leap in the product form and demand structure of prediction markets.

If prediction markets were still regarded as a " "niche information博弈 experiment" in the past few years, they are now 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 focus on three representative platforms—Kalshi, Polymarket, and Opinion—to analyze the structural changes behind the growth in prediction market trading volume and how they are heading down three distinct paths.

I. The Essence of the Trading Volume Leap: Prediction Markets Are Becoming " "De-Low-Frequency"

A core historical limitation of prediction markets has been trading frequency.

Traditional prediction markets were closer to " "betting-type participation":

• User enters

• Places a bet

• Waits for the outcome

• Settles and exits

This model naturally limited the ceiling for trading volume because the same capital could only participate in pricing once per unit of time.

The recent surge in trading activity is背后 a systematic shift:

From " "outcome-oriented betting" to " "process-oriented trading".

This is具体体现在 three points:

1. Events are broken down into可持续交易的价格路径

   No longer just " "will it happen," but " "how does the probability change over time?".

2. Multiple entries and exits during a contract's lifecycle become常态

   Users begin adjusting positions repeatedly, like trading assets.

3. Prediction markets begin to exhibit " "intraday liquidity" characteristics

   Price fluctuations themselves become a reason to participate.

In this context, the rapid rise in trading volume does not mean " "more people placing a single bet," but rather意味着 the same group of users beginning to博弈 multiple times on the same event.

II. Kalshi: When Prediction Markets Are Radically Rewritten by Sports

Among all platforms, Kalshi's trading structure change is the most激进.

It did not try to shape prediction markets into a " "more serious information tool," but chose a more realistic path:

Giving prediction markets a participation frequency on par with sports betting.

1. The Significance of Sports is Not " "Subject Matter," but a " "Rhythm Controller"

Sports events have three decisive advantages:

• Extremely high frequency (daily, multiple events)

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

• Fast settlement (funds quickly回流)

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

2. The True Meaning of Trading Volume: An Increase in Capital Turnover Rate

The growth in Kalshi's volume本质上 does not come entirely from new users, but from the same capital being used repeatedly within a shorter cycle.

This is a typical consumption-type trading volume structure:

• Closer to entertainment

• More reliant on frequency

• Easier to scale up

Its advantage is极强的 scalability; the risk lies in:

Whether users can be retained on other event contracts when sports热度 declines.

III. Polymarket: When Prediction Markets Become the " "Trading Layer for 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 " "Issue Selection Right"

Polymarket's strengths lie in:

• Extremely fast上新 speed

• Covering highly emotional topics like politics, macroeconomics, tech, crypto

• Natural synchronization with social media舆论波动

Here, trading is not always based on information advantage, but on expression of opinion.

2. Another Explanation for High Trading Volume: The Repeated Hedging of Opinions

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

• Changes in stance

• Emotional reversals

• Repricing after舆论冲击

This makes it more like a decentralized民意 futures market.

Its long-term challenge is not whether trading is active, but:

When everyone is trading opinions, can prices still stably carry the signal of " "true probability"?

IV. Opinion: For Growth Platforms, the Key Question is Not " "Volume," but " "Stickiness"

Compared to the first two, Opinion更像 a platform still validating its own positioning.

1. Trading Volume Has More " "Strategic Growth" Characteristics

Opinion's activity relies more on:

• Incentive mechanisms

• Product design

• External distribution

This type of trading volume can be scaled up quickly 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 given day, but:

• Whether users持续 trade on multiple events

• Whether fixed participation habits are formed

• Whether买卖 depth is naturally generated

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

V. The Next Stage of Prediction Markets: From " "Scale Competition" to " "Structure 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 commoditizing and娱乐化 prediction markets

• Polymarket is舆论化 and emotionalizing prediction markets

• Opinion is exploring the replicability of its growth model

This意味着 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 success in the future is not single-day trading performance, but three more long-term questions:

1. Can trading volume be transformed into stable liquidity?

2. Do prices still possess interpretability and reference value?

3. Does user participation come from genuine demand, not short-term incentives?

Conclusion: Prediction Markets Are No Longer a Question of " "If They Will Take Off"

When prediction markets begin to exhibit continuous, high-density trading behavior, one fact is already quite clear:

They are moving from a fringe experiment to a market mechanism that can be used repeatedly.

What is truly worth paying attention to is no longer whether a certain number is刷新ed, but:

Which form of prediction market will ultimately find a balance between high-frequency participation and effective pricing.

This is the true signal that prediction markets have entered a new phase.