2025 Market Landscape Reassessment: From Speculative Tool to New Financial Paradigm

Author: @MrRyanChi, Nox Ventures

1. Current State of Prediction Markets: A Game Changer in Finance

Prediction markets, an emerging event contract market driven by blockchain technology, are subtly reshaping the rules of the financial domain. As a new financial instrument, it is providing unprecedented wealth-building opportunities for investors, institutions, and individual users.

Looking at the prediction market landscape in 2025, three core trends have already been established:

User-driven breakout growth: Market boundaries are significantly expanding from Web3 native users to a broader audience;

Mature compliance frameworks: Regulatory certainty has become the industry's moat;

Inherent evolution of technology and ecology: Transitioning from single platforms to ecosystems.

1.1 Trend One: Exponential Growth and Market Traction

Entering the fourth quarter of 2025, the prediction market sector has shown astonishing explosive power.

Core Data Review:

Surge in Trading Volume: As of the week ending December 1, 2025, the total nominal weekly trading volume in prediction markets has surpassed $3.8 billion. This figure is more than double the historical high of $1.9 billion recorded during the 2024 U.S. presidential election.

User Activity: The number of transactions has jumped from 3 million in Q4 2024 to 11 million currently, fully validating the solidification of the user base and the popularization of this financial tool.

Open Interest: The total value of open contracts across the network is nearing $300 million (Dune Analytics), indicating that the funds on the platform are not merely transient but reflect substantial, ongoing gaming and hedging demands.

This round of growth is attributed not only to Kalshi's strong rise since August 2025 but also to the rapid expansion of emerging platforms like Opinion Labs, Limitless, and Myriad (Dune Analytics).

From a blockchain-native perspective, prediction markets are becoming a third asset allocation choice beyond cryptocurrency and token investments.

Meme market decline: Data shows that meme coin trading volume has plummeted by 80% (vijays);

On-chain derivatives cooling: On-chain perpetual contract (Perp Dex) trading volume has declined by 30% (uwusanauwu);

New highs in prediction markets: Against the backdrop of the decline of the above two, prediction market trading volume has reached historical highs.

This indicates that prediction markets no longer solely rely on token price fluctuations but provide an event-driven financial tool similar to binary options. It has successfully created a new liquidity pattern independent of the traditional crypto market, and this "asset isolation" is crucial for the current crypto space—it introduces new blood and utility, becoming a key entry point for Web3's mass adoption.

1.2 Trend Two: Regulatory Hammer and Compliance Dividends

The narrative of "mass adoption" is not empty talk; it is backed by milestone progress in compliance and regulation within prediction markets. As highlighted in A16Z's "2025 State of Crypto Report" (Trifan), regulation has become a core issue for blockchain developers and founders over the past year. As a pioneer of compliance, prediction markets are ushering in a bright growth outlook.

Key Regulatory Dynamics in 2025:

Traditional Giants Entering the Arena: Intercontinental Exchange (ICE) has officially invested in Polymarket (Intercontinental Exchange);

CFTC Green Light: Following last year's approval of Kalshi, the CFTC has formally approved Polymarket US to operate in the United States;

FinTech Integration: Existing fintech giants like Robinhood and Coinbase are beginning to delve deeply into this field (Nikhilesh);

Derivatives Giants Collaborating: The CME Group, a pioneer of Bitcoin futures in 2017, announced a partnership with FanDuel to launch a prediction market platform (CME Group).

Global Regulatory "Ice and Fire":

While the U.S. market is seeing good news, globally, the space for regulatory arbitrage is shrinking, and compliance thresholds remain.

European Union: Prediction markets are still often viewed as unlicensed gambling platforms rather than financial event contracts, facing strict scrutiny from gambling commissions.

Asia: In countries with strict regulations like Singapore, prediction markets remain explicitly banned.

The regulatory path in the U.S. is redefining prediction markets from "gambling" to "event contracts." This clarification of legal status is a prerequisite for institutional capital to enter. While other countries are still observing, the demonstration effect of the U.S. is likely to trigger a follow-up in global regulatory frameworks in the coming years.

1.3 Trend Three: Technological Iteration and Ecosystem Building

In addition to the expansion of market size and clarity in regulation, prediction markets in 2025 have also achieved a qualitative leap in technological transparency and practicality. The industry is shifting from an early "internally driven R&D" model to a "ecosystem collaboration and cross-platform integration" model.

1. Tokenization and On-Chain Integration of Regulated Platforms: Kalshi, initially based on an off-chain order book system, has begun collaborating with blockchains like Solana to tokenize its event contracts (Bambysheva).

Strategic Intent: This move aims to directly compete with Polymarket by introducing more efficient on-chain order matching and global liquidity.

Compliance Support: KYC/AML (Anti-Money Laundering) regulatory measures for on-chain markets have also been implemented simultaneously, achieving a hybrid model of "on-chain technology + off-chain compliance."

2. Developer Co-Building Initiatives: Platforms like Polymarket, Kalshi, and Opinion Labs have opened APIs and launched developer programs. This has spurred the emergence of third-party trading terminals, arbitrage bots, and other tools.

Data Support: According to Dune Analytics, the ecosystem built by third-party platforms on Polymarket alone has contributed over $200 million in trading volume ("Polymarket Builders"). This marks a transition of prediction markets from a single DApp to a protocol layer.

3. Deep Integration with Mainstream Media: To compete for the narrative of "truth discovery," prediction market platforms are actively integrating with mainstream media, branding themselves as information markets for accurate news and public opinion.

Implementation Cases: Media giants like Google Finance and CNN have now integrated prediction market data to provide audiences with more forward-looking insights (Wile; CoinCentral).

Theoretical Foundation: This trend perfectly aligns with the "Wisdom of Crowds" theory, which posits that market prices often reflect true probabilities better than expert opinions.

2. The Essence and Operating Mechanism of Prediction Markets

2.1 Crowd Wisdom and Prediction Markets

The core value of prediction markets is anchored in the concept of "Crowd Wisdom." As early as 1907, the idea of crowd wisdom began to take shape, with studies showing that the aggregation of independent judgments often provides more accurate estimates than recognized experts (Galton). In recent years, empirical data has further confirmed the existence of crowd wisdom. For example, in the 2004 book "The Wisdom of Crowds," the author elaborates on the effectiveness of this mechanism in business, economics, and other fields (Surowiecki).

Essentially, prediction markets are a combination of market efficiency in price discovery and crowd wisdom.

Although the sector exhibited exponential growth during 2024-2025, relevant academic research laid the groundwork as early as 2004. Research published in the Journal of Economic Perspective indicated that prediction markets are more accurate than traditional polls in reflecting future outcome expectations because they incentivize users to actively seek information, provide real intelligence, and offer a superior way to aggregate diverse viewpoints (Wolfers and Zitzewitz 107). In 2008, a study of data from the Iowa Electronic Markets (IEM) since 1988 also showed that prediction markets historically outperformed traditional surveys in predicting election outcomes (Snowberg, Wolfers, and Zitzewitz).

Turning back to 2024, the scale of prediction markets for the U.S. presidential election has grown to $2.4 billion, while the market size in 2004 was only $355,000. Online prediction markets like Kalshi and Polymarket have demonstrated significant accuracy and efficiency. Research shows that driven by the efficient market hypothesis, although irrational behavior may lead to specific distortions, overall, the accuracy of prediction markets exceeds that of other data sources (Clinton and Huang).

As a consensus-driven digital value native carrier, blockchain provides a programmable, automated, decentralized, and immutable infrastructure for the operation of prediction markets. This part of the report will delve into the specific structure of on-chain prediction markets. Here, we categorize prediction markets into two types: one is native on-chain markets, such as Polymarket, Opinion Labs, and Limitless; the other is tokenized off-chain markets, such as Kalshi.

2.2 Pricing and Clearing Mechanisms

Before delving into technical details, it is crucial to clarify the actual operational logic of prediction markets.

In most cases, prediction markets involve user-tradable binary (yes/no) contracts. Initially, the prices for "yes" and "no" are both set at $0.50. At the final clearing moment, for the side with the correct outcome, the price per share will rise to $1.00; for the side with the incorrect outcome, the price per share will drop to zero.

Before the final clearing, the prices per share for "yes" and "no" are dynamically fluctuating. In other words, the odds change over time and are influenced by public or insider news and information. However, regardless of how the odds fluctuate, the sum of the share prices for "yes" and "no" always equals 1. This means that prediction markets are essentially a zero-sum game, where the gains of the winners come from the losses of those who chose incorrectly.

This logic also applies to more advanced prediction markets, where there are multiple options beyond just "yes" and "no." For example, in an election, there may be A, B, and C competing for a single winner. In such cases, prediction markets can be ternary, quaternary, or even more diverse. Nevertheless, the sum of the prices per share for all options must still equal 1.

Overall, whether on-chain or off-chain markets, the pricing and clearing strictly adhere to the following two rules:

Regarding Clearing: The correct outcome always settles at $1.00 per share, while the incorrect outcome settles at $0.00. Payments are executed automatically. After the designated clearing time, the odds no longer change, and users can no longer trade. Due to the possibility of variable outcomes requiring cross-validation, a time lag may sometimes be observed between the designated clearing time and the actual payment time.

Regarding Pricing: Like other contract forms such as futures and options, the price per share in prediction markets depends on supply and demand. Therefore, although Polymarket initially used a liquidity pool model based on automated market makers (AMM) for logarithmic pricing, most prediction markets have now widely adopted the order book model.

Based on this, two types of orders influence pricing: liquidity-providing limit orders and liquidity-consuming market orders. Prices are determined by the matching and trading of contract orders. In this process, users can also choose to "sell yes" or "sell no," similar to binary options, where the result is equivalent to shorting the opposite side (Wang et al. 46).

2.3 On-Chain Prediction Market Architecture

Based on the aforementioned pricing and clearing systems, the construction of on-chain prediction markets requires three core components:

Gnosis Conditional Token Framework (Gnosis CTF): Used to tokenize events and achieve on-chain settlement of outcomes.

Trading Matching System: Initially, many used automated market makers (AMM), but the mainstream has shifted to a centralized limit order book system (CLOB) for real-time pricing before order matching and clearing.

Oracle: Used to track the actual outcomes of events in the real world, ensuring the fairness of final settlements.

This section will explore these three components one by one.

2.3.1 Gnosis Conditional Token Framework (Gnosis CTF)

The Gnosis Conditional Token Framework is a condition framework based on If-Else logic, primarily consisting of three conditions: the question, oracle results, and optional outcomes. Since the index set is a 256-bit array, each specific market can support up to 256 different outcomes ("Overview - Polymarket Documentation").

In execution, CTF covers three main stages in the lifecycle of prediction market tokens: Minting, Transactions, and Redemption.

Minting: Initially, tokens are minted as "yes," "no," or other tokens based on specific outcomes. To obtain CTF tokens, users need to stake other tokens, typically stablecoins. At any point in time, minted tokens can be sold and used to redeem the initially staked tokens.

Trading and Redemption: Subsequently, CTF-based tokens will be traded on the market via CLOB or AMM, with prices reflecting users' judgments on the probabilities of different events. At settlement, the oracle will return the final outcome, allowing users to redeem based on the initially staked stablecoins at the time of minting.

2.3.2 Automated Market Makers and Centralized Limit Order Book Systems

AMM Mechanism: In the early stages of Polymarket, the automated market maker (AMM) system it adopted was similar to traditional decentralized exchanges, with Uniswap being a typical representative ("Glossary"). In traditional AMMs, the number of tokens in a trading pair follows a constant product formula. For example, if the initial quantity of SOL is x and USDT is y, the product x·y in the liquidity pool remains a constant k. If a user exchanges SOL for USDT, the USDT balance in the pool increases, and the SOL balance decreases accordingly, leading to an increase in the price of SOL in terms of USDT.

The AMM mechanism of prediction markets is slightly more complex because it involves not only "yes/no" tokens under the CTF framework but also requires pricing based on a third token. As mentioned earlier, the CTF framework requires users to mint "yes/no" tokens by staking specified tokens (such as USDC and USDT). In short, the AMM specifically designed for prediction markets contains two pools: one is the staking pool, used to mint "yes/no" tokens and provide rewards after clearing; the other is the AMM-type liquidity pool, which uses the ratio of "yes" and "no" tokens for conversion and valuation, with its value anchored to USDC in the staking pool.

CLOB Mechanism: Today, Polymarket has adopted a model that combines CLOB (centralized limit order book) with AMM. This shift aims to improve trading speed and reduce friction costs caused by factors like gas fees.

The current CLOB system consists of a two-layer architecture: an off-chain order book layer and an on-chain settlement layer ("CLOB Introduction - Polymarket Documentation").

Off-Chain Order Book Layer: Similar to traditional order books for perpetual contracts, prices are determined by order depth. For example, if there are 100 buy orders at a price of 0.57 and 100 sell orders at a price of 0.59, with the latest transaction price being 0.58, the token price will be 0.58. This model ensures that the fundamental principles of dynamic pricing in prediction markets are maintained. Additionally, when users choose to sell "yes/no," the system matches them with the opposite side for better settlement rather than merely executing buy orders. In other words, "buying yes" can be viewed as "selling no," and vice versa.

On-Chain Settlement Layer: Similar to the staking pool mentioned in the AMM section, users stake assets to mint "yes/no" tokens in ERC1155 format ("CLOB Introduction - Polymarket Documentation"), and "yes" and "no" tokens are programmatically complementary. Therefore, specific dynamic pricing trades occur off-chain, with only the settlement, minting, and redemption stages happening on-chain.

Furthermore, to ensure liquidity in CLOB when order book depth is insufficient, Polymarket offers market-making rewards to liquidity providers ("Liquidity Rewards - Polymarket Documentation") and partially relies on the AMM mechanism as a supplement when liquidity is lacking.

2.3.3 UMA Optimistic Oracle Integration

For on-chain prediction markets like Polymarket, the UMA Optimistic Oracle is used for outcome resolution. In short, under the UMA system, whitelisted proposers directly submit outcomes to the system. After a 2-hour public notice period (Liveness Period), if there are no objections, the resolution will be automatically settled; if there are objections, the contract will reset ("Resolution - Polymarket Documentation").

Similar to various proof-of-work mechanisms that effectively prevent the Byzantine Generals Problem in Bitcoin, successful proposals will be rewarded, while the cost of cheating will exceed potential profits. Therefore, UMA can achieve fair resolution in a decentralized and transparent system ("FAQs | UMA Documentation").

Currently, the UMA Optimistic Oracle has about 37 unique addresses as proposers, including employees from Risk Labs and Polymarket, as well as community users with a good track record. As of now, the accuracy rate of proposals has reached 99.7% ("UMA Restricts Resolution Proposers to 37 Whitelisted Addresses on Polymarket"). While this has a degree of centralization, community members outside the whitelist can still propose by tagging whitelisted addresses on Discord, and initiating disputes is open to everyone.

It is worth noting that although UMA occupies a significant share of prediction markets requiring real-world resolutions, specific markets (especially those related to cryptocurrency prices) may use alternative oracles like Chainlink. Other on-chain markets, such as Opinion Labs and Limitless, also have different oracle solutions.

2.4 Off-Chain Prediction Markets and Tokenization of Prediction Markets

In off-chain prediction markets like Kalshi, there is no oracle mechanism. Therefore, final resolutions are typically centralized. Nevertheless, according to CFTC requirements, Kalshi must adhere to a strict set of resolution handling rules, which helps minimize potential errors (U.S. Commodity Futures Trading Commission; Kalshi).

In terms of pricing, Kalshi uses a centralized limit order book (CLOB) through a matching engine and standardized cloud infrastructure. Therefore, its final pricing model is not significantly different from Polymarket's ("Orderbook Responses").

However, Kalshi is not a native on-chain platform; it is attempting to leverage blockchain to unlock global liquidity and achieve composability. Similar to tokenized stocks, RWA (real-world assets), or stablecoins, Kalshi has chosen to tokenize prediction markets on the Solana blockchain (Wang, John).

Here, a hybrid model is employed: Kalshi retains its core order book settlement and regulatory system while allowing users to place orders on-chain. This is achieved by routing orders as request-for-quote (RFQ) to liquidity providers in Kalshi's off-chain order book system. Upon successful matching, SPL tokens will be automatically minted on-chain (for buying) or burned (for selling/settling). Thus, on-chain users can settle and purchase through wallet terminals (Nath).

The RFQ model in Kalshi's prediction market and the concurrent liquidity programs supporting on-chain asynchronous and intent-based trading are realized through DFlow and integrated into Jupiter. Through the DFlow API, trading tokenized Kalshi prediction market contracts on existing Web3 platforms becomes possible ("Welcome to DFlow").

2.5 Comparison of On-Chain and Off-Chain Prediction Markets

Overall, the differences between Polymarket and Kalshi can be summarized as follows:

In summary, the key differences lie in how each platform resolves disputes and the barriers to user participation. For Kalshi, the centralized CLOB and its status as a pioneer in obtaining CFTC approval provide deeper liquidity depth, leading to a better order matching experience. For Polymarket, its decentralized nature lowers the barriers for global user participation. Additionally, in terms of dispute resolution, both have their pros and cons depending on users' value judgments.

In the next section, we will discuss regulatory and compliance aspects in detail.

3. Regulation of Prediction Markets

3.1 Historical Regulation of Prediction Markets

The first prediction market to be formally regulated was the Iowa Electronic Markets (IEM). This was a futures market for political outcomes such as presidential elections, established in 1988, and received a "no-action letter" from regulators in 1992 (U.S. Commodity Futures Trading Commission).

As indicated by documents from the U.S. Commodity Futures Trading Commission (CFTC), the reason IEM was able to be regulated was primarily due to its non-profit nature and academic research purposes. To ensure that educational value took precedence over commercial interests, participant qualifications were also restricted. At this stage, the positive externality of information provision was seen as a key component of regulated prediction markets.

However, despite IEM's successful compliance, the development of prediction markets after the millennium was not smooth.

In 2011, HedgeStreet, a designated contract market (DCM) and derivatives clearing organization (DCO) regulated by the CFTC, attempted to launch political event contracts. However, this application was rejected by the CFTC on the grounds that its nature involved "gambling," which violated Section 5c of the Commodity Exchange Act (CEA) (Commodity Futures Trading Commission).

Fast forward to 2018, when the CFTC sued Intrade. Intrade, as an "over-the-counter commodity options platform" providing event contract services, was penalized for not being registered. Intrade was fined $3 million and was directly ordered to cease operations (Commodity Futures Trading Commission).

During this period, some positive signals emerged, as PredictIt, another prediction market platform focused on the political domain established by Victoria University of Wellington, received a no-action letter from the CFTC. However, similar to IEM, although PredictIt prioritized educational purposes, the amount of investment that participants could stake was also capped (Clarke et al. v. Commodity Futures Trading Commission).

Overall, this period exhibited three main trends:

First, according to the CEA, event contracts based on CLOB (central limit order book) were classified as "gambling," leading to approval rejections.

Second, legal operation in the U.S. requires registration with the CFTC.

Third, while the informational value of prediction markets is fully recognized, commercialization is strictly prohibited, and investment scales are capped to serve the "public interest."

As a result, both Polymarket and Kalshi, established in 2020 and 2018 respectively, initially faced similar restrictions. For Kalshi, the CFTC issued an order prohibiting its contracts due to "gambling" (KalshiEX LLC v. Commodity Futures Trading Commission, Complaint); on the other hand, Polymarket was fined $1.4 million in 2022 for operating without regulation in the U.S. and blocked access from U.S. IPs (Commodity Futures Trading Commission).

However, since 2024, both Kalshi and Polymarket have benefited significantly from changes in leadership and shifts in public opinion. As the innovative and hedging benefits of prediction markets have been uncovered, lawsuits against these two companies have been withdrawn, and the CFTC has also approved the operation of their commercial event contract systems (Commodity Futures Trading Commission; Polymarket).

In summary, prediction markets have faced high restrictions throughout their long history, primarily because they are defined as "gambling" that violates public interest under the CEA. However, recent developments in public opinion and the discovery of the value of prediction markets as hedging tools have opened new doors for platforms like Kalshi and Polymarket, making them among the first prediction market platforms regulated by the CFTC.

3.2 Current Global Regulatory Landscape

Although prediction markets have seen growth in the U.S., strict regulatory frameworks have yet to be established in countries outside the U.S. Moreover, most countries still focus on the gambling attributes of prediction markets rather than their event contract attributes. To this day, the overall regulatory landscape can be summarized as follows:

3.3 Current Status of Other Prediction Markets and Prediction Market Derivatives

As mentioned, most countries outside the U.S. lack a clear regulatory framework for prediction markets. Therefore, for most countries, on-chain prediction markets exist in a gray area, functioning without the need for geographical blocking based on users' IP addresses.

For other prediction markets currently operating outside of Kalshi and Polymarket (such as Opinion Labs and Limitless), their legal status remains unclear. Although these platforms can operate in most regions globally, they have implemented geographical blocking for U.S. IPs to avoid potential fines for illegal operations in the U.S.

Additionally, while Kalshi and Polymarket are regulated by the CFTC, products built within their ecosystems are not regulated. For example, specific projects built on prediction markets, such as Gondor, which allows users to collateralize Polymarket positions for lending, may fall under the jurisdiction of the U.S. Securities and Exchange Commission (SEC) due to their nature resembling collateral ("What Is Gondor?").

4. Legal Risks and Concerns

Although prediction markets are gradually being accepted as a new type of financial tool, many legal and ethical risks remain unresolved.

This section will explore and reflect on potential legal or ethical issues surrounding prediction markets.

4.1 Insider Trading and Market Manipulation in Prediction Markets

Like other financial markets, prediction markets frequently face issues of insider trading. However, the situation is worse because, compared to traditional finance, it is much more challenging to delineate the boundary between malicious and benign insider trading in prediction markets. In traditional financial instruments like stocks, bonds, and futures contracts, the behavior of insiders profiting from undisclosed information at the expense of retail investors is clearly and reasonably seen as detrimental to the market, as they merely take liquidity from those without information.

However, the essence of prediction markets is to encourage users to reveal their insights on specific events, thereby creating public utility. Therefore, in certain cases (for example, predicting potential political changes that could impact the global economy), insider trading that influences prices can be beneficial, as it reveals insights that businesses and individuals can use to hedge risks. As some viewpoints suggest, insider trading reveals private knowledge, thus providing positive externalities (Hanson).

Nevertheless, even in prediction markets, there are still evident negative insider trades, which serve no purpose other than to extract liquidity from retail investors. The Solomon MetaDAO financing case is an example. This case involved a prediction market regarding the total commitment amount publicly sold by Solomon on MetaDAO. Given the performance of the crypto market, many users believed the commitment amount would not exceed $40 million; however, at the last moment, a massive buy order surged in, pushing the total commitment amount above $100 million. As a result, all traders betting "no" (i.e., believing it would not reach that upper limit) were liquidated. Thus, insiders at Solomon profited massively by betting "yes" internally and manipulating the market. In such cases, insider manipulation does not possess any foreseeable positive value.

4.2 Anti-Money Laundering (AML) Risks in Prediction Markets

Like any cryptocurrency market or financial market, prediction markets (especially those that are not strictly regulated on-chain) can easily be exploited as tools for money laundering. The specific methods may vary, but similar to traditional stock markets or decentralized exchanges (DEX), "wash trading" is certainly feasible (Miro).

To address potential anti-money laundering risks, traditional trading platforms or brokers provide a suitable solution: utilizing KYC (Know Your Customer) to verify the source of funds and track profits. For Polymarket US and Kalshi, U.S. residents must comply with strict KYC procedures. However, for on-chain Polymarket and the upcoming tokenized Kalshi Global, specific guidelines are still to be assessed. Given that KYC technology has been widely used and is quite mature in major centralized exchanges, the technical implementation is expected to be straightforward. Nevertheless, prediction market platforms still face the trade-off between lowering user entry barriers and implementing comprehensive KYC to eliminate money laundering risks.

4.3 Outcome Resolution Disputes

Despite the maturity of oracle technology used in on-chain prediction markets today, or the regulated information sources used in centralized settlement systems like Kalshi, outcome resolution disputes may still arise frequently, often requiring strict rules and clear filtering mechanisms to resolve.

A notable case is the Zelensky "suit" market case that occurred in July 2025, a market with a scale of $200 million, betting on whether Ukrainian President Zelensky would appear in a suit. The resolution of this market sparked debates over the definition of "suit," i.e., what constitutes a suit, which largely fell into the realm of subjectivity (Nguyen; Adejumo).

Due to the complexity of real-world situations, especially when outcomes deviate from expectations or are difficult to define, resolving disputes remains complex and tricky. Discussions about fairness may also arise, such as how to ensure the fairness of settlements for events where outcomes may change (like sports games).

5. Comparison of Prediction Market Products

As 2026 begins, the prediction market sector has shifted from the election-driven frenzy of 2025 to more sustainable and diversified growth. The total trading volume across the industry in 2025 was approximately $44 billion, with Polymarket contributing about $21.5 billion and Kalshi about $17.1 billion, forming a de facto duopoly.

Emerging platforms like Opinion Labs and Limitless, while having smaller shares, have shown strong momentum in the macro permissionless and L2 high-frequency sectors, cumulatively contributing over $5 billion in volume. This reflects the core evolution of the sector: from single hot speculation to structural expansion into institutional hedging, opinion quantification, and daily volatility trading.

5.1 Polymarket: The On-Chain Liquidity Dominator of Global Hotspots

Polymarket solidified its absolute leadership in the on-chain prediction market in 2025, with an annual trading volume of $21.5 billion (Forbes data), accounting for nearly half of the industry. The platform was founded by Shayne Coplan in 2020, a 27-year-old heir (valued at $9 billion in 2025) who started from a Manhattan apartment, transforming Polymarket into a "decentralized truth engine." The core team members mostly come from quantitative and blockchain backgrounds, emphasizing an open ecosystem rather than closed operations.

In terms of technical design, Polymarket employs a hybrid model combining centralized limit order books (CLOB) and AMM mechanisms, supporting the ERC1155 conditional token framework (Gnosis CTF), with dynamic pricing reflecting supply and demand; resolutions primarily rely on the UMA Optimistic Oracle, supplemented by Chainlink/Pyth, enhancing dispute handling efficiency (accuracy rate over 99%). New expansions in 2025 include launching a real estate price index market in collaboration with Parcl and deep integration with mainstream media (Google Finance displaying odds in real-time).

Polymarket's customer acquisition channels heavily rely on viral hotspot dissemination: political elections, crypto price fluctuations, and even geopolitical events (such as a single user making over $400,000 on a Maduro-related market) amplified through platforms like X and KOLs; in addition, the Polymarket Builder developer program contributes over 30% of trading volume, further strengthening user retention through third-party terminals and bot ecosystems.

Its unique positioning lies in global low-barrier access and event breadth—covering everything from internet culture gossip to macro policies, with virtually no blind spots; DeFi-native composability allows for collateralized lending (like the Gondor protocol), making Polymarket not just a prediction tool but also a hub for the on-chain circulation of information assets.

5.2 Kalshi: An Institutional-Level Hedging Platform on a Compliance Path

Unlike the aggressive approach of on-chain platforms, Kalshi has chosen to engage in direct confrontation with regulators since its inception in 2018, founded by MIT graduates Tarek Mansour and Luana Lopes Lara. Mansour's early experiences in Lebanon shaped his risk sensitivity, while Lopes Lara transitioned from professional ballet to quantitative finance, with their cross-cultural backgrounds driving the platform's emphasis on professional risk control. Following financing in 2025, the valuation reached $11 billion, with both co-founders becoming billionaires, and Lopes Lara becoming the youngest self-made female billionaire globally.

Kalshi's core technology is a centralized off-chain order book, combined with Solana tokenization to achieve DeFi bridging (DFlow RFQ model, supporting intent-based trading and Jupiter integration). Resolutions are fully centralized but strictly regulated by the CFTC, ensuring zero dispute risk; market design is highly specialized, such as sports contracts covering real-time NFL scoring lines, and macro contracts supporting multi-level probability expressions for Federal Reserve interest rates (e.g., "25bp vs 50bp vs no change").

Kalshi's growth primarily stems from traditional channels: sports event advertising and institutional collaborations drive it, with single market volumes often exceeding $200 million during NFL weekends in 2025; mainstream media like CNN/CNBC/Yahoo Finance provide real-time access to odds, attracting U.S. retail investors and hedge funds. Open interest remains stable in the $100-200 million range, with order book depth supporting single trades of millions of dollars without slippage.

In recent years, Kalshi has accelerated its crypto transformation: in August 2025, it hired 23-year-old crypto influencer and entrepreneur John Wang (former founder of Armor Labs and Penn dropout) as Head of Crypto, driving on-chain expansion and the onboarding of crypto-native users. In December, Wang's team led the launch of tokenized event contracts on Solana (realized through DFlow API and deeply integrated with Jupiter), bridging off-chain liquidity into the Solana ecosystem, unlocking global crypto capital pools and supporting intent-based trading. By the end of 2025, the proportion of Kalshi's crypto users and on-chain trading volume rapidly increased, with the platform aiming to integrate all major crypto apps (like Phantom wallet with 15 million users) within 12 months, further challenging Polymarket's on-chain dominance while retaining its compliance core advantage.

Kalshi's growth primarily comes from traditional channels and emerging crypto bridges: sports event advertising and institutional collaborations drive it, with single market volumes often exceeding $200 million during NFL weekends in 2025; mainstream media like CNN/CNBC/Yahoo Finance provide real-time access to odds, combined with Solana tokenization to attract crypto traders. Additionally, Kalshi's differentiated advantages are evident: as the only fully CFTC-regulated platform in the U.S., it redefines prediction markets as legitimate event contracts, providing institutional-level deep liquidity (e.g., average daily trading in sports markets exceeds $500 million) and specialized multi-outcome contracts (supporting fine hedging, such as layered interest rates or real-time sports lines), particularly suitable for risk management under macro uncertainty, while expanding to high-frequency on-chain users through crypto transformation rather than pure speculation.

5.3 Opinion Labs: The Dark Horse Challenger of Permissionless Opinion Quantification

Opinion Labs emerged as a strong contender in the second half of 2025, with trading volume surpassing $10 billion within just 55 days of launch (MEXC, AInvest data), and peak open interest reaching $110 million, primarily focused on macroeconomic data contracts. Incubated by YZi Labs (seed round of $5 million), the team is low-key but focused on the intersection of DeFi and AI, positioning the platform as a "dynamic opinion and continuous prediction market."

The technology stack is fully on-chain and permissionless: users can create markets using any ERC-20 token, and AI-enhanced oracles significantly reduce resolution disputes; product innovations include continuous probability spectra (not just simple binary "yes/no") and social opinion quantification, allowing trades like "Will the Federal Reserve cut rates in 2026?" or "Specific policy sentiment index."

Customer acquisition relies on DeFi incentive mechanisms: points farming and yield activities quickly gather communities, with users earning points by participating in macro contracts (like interest rate decisions), and potential airdrop expectations further amplifying stickiness. By the end of 2025, it rapidly entered the top three in the industry, reflecting the strong resonance of the narrative "opinion as an asset" in uncertain environments.

The core value of Opinion Labs lies in its thorough permissionless innovation—anyone can instantly create and trade custom opinion markets, combined with AI oracles to achieve transparent continuous pricing, filling the gap left by traditional prediction markets in macro depth and social quantification, becoming a representative experiment in the deepening of crowd wisdom into assetization.

5.4 Limitless: A Short-Term Volatility Capturer in L2 High-Frequency

Built on the Base chain, Limitless focused on niche segments in 2025, with trading volume soaring from $8 million in July to $760 million in December (Yahoo Finance, CoinMarketCap), raising $10 million in seed funding (led by 1confirmation). The team background focuses on high-frequency trading and L2 infrastructure, with the platform clearly targeting hourly/same-day crypto and stock price predictions.

Product design is extremely simple and efficient: natural language market creation (e.g., directly inputting "Will BTC break $70,000 before 8 PM tonight?"), with Base L2 ensuring near-zero gas fees and rapid order matching; settlements are instantaneous with no clearing risk, blurring the boundaries between prediction markets and perpetual contracts. The LMTS token further incentivizes liquidity after its TGE.

Growth stems from the low-cost advantages of L2: attracting high-frequency players like Deribit, achieving viral spread through social sharing and direct wallet connections (e.g., potential Trust Wallet integration); short-term markets like "post-market stock fluctuations" or "crypto hourly lines" have become hallmarks, leading in daily high-frequency trade counts among peers.

Limitless's positioning is clear: by providing near-real-time volatility experiences through rapid execution, extremely low friction costs, and natural language accessibility, it offers a fast-paced gaming experience particularly suitable for short-term traders in crypto and stocks, establishing a unique high-frequency barrier within the L2 ecosystem.

5.5 Multi-Dimensional Comparative Summary

In summary, the four platforms have formed a clear ecological stratification: Polymarket dominates global hotspots and on-chain liquidity, Kalshi provides compliant institutional hedging, Opinion Labs drives permissionless opinion deepening, and Limitless locks in L2 high-frequency niches. In 2026, as cross-platform aggregators emerge, token economies mature, and global regulatory follow-ups occur, the systemic importance of prediction markets as core infrastructure for "information as an asset" will further manifest.

6. Core Trading Profit Models

The trading ecology of prediction markets has evolved from early simple binary bets to a highly specialized battlefield of "information finance." In 2025, on-chain transaction counts surpassed 95 million (Phemex Research; KuCoin Research), but the profit landscape is extremely asymmetric: the vast majority of users ultimately incur losses, while a small number of professional players capture most of the positive returns through systematic strategies.

As mentioned above, the essence of this zero-sum game is that while crowd wisdom continuously enhances market pricing efficiency, it concentrates returns among participants who master tools, data edges, and execution discipline—namely, attracting genuine information seekers to participate through arbitrage based on information asymmetry. Retail investors are often driven by emotions and luck, while top traders build multi-strategy portfolios to achieve a risk balance from passive cash flow to high-frequency speculation.

Based on the sharing of on-chain traders on social media in 2025, as well as specific on-chain trading record reviews, the following section will analyze the six dominant profit models of 2025 one by one.

6.1 Information and News Arbitrage: Capturing Mispricing through Information Lags

The logic of information and news arbitrage lies in the existence of lags in information dissemination. After a news breakout, market prices do not immediately reflect the consensus of all participants, leading to short-term deviations. Professional players monitor intelligence in advance, establishing positions before retail investors react, capturing the price convergence towards true probabilities.

The execution process includes four steps. First, establish real-time information sources, such as API news subscriptions or Discord alert channels. Second, assess the impact of events, such as calculating the gap between the implied probabilities in the market after the news and one's own judgment (if over 5%). Third, quickly buy shares of the undervalued side. Fourth, close the position after the market adjusts (within minutes to hours).

In the hot events of 2025, this strategy averaged capturing 20-50% of fluctuations. Typical cases include early layouts by suspected insider addresses in geopolitical events (Rootdata). The risk is that false news or pre-pricing could lead to losses, making it suitable for players with strong information advantages.

6.2 Cross-Platform Arbitrage: Locking in Low-Risk Returns through Price Differences between Platforms

The logic of cross-platform arbitrage stems from platform isolation. The differences in global liquidity on Polymarket and institutional depth on Kalshi lead to short-term price discrepancies for the same event. Players hedge to lock in deviations without clearing risks.

The execution process includes three steps. First, use aggregators (like DeFi Rate) to monitor price differences (threshold of over 0.5%). Second, buy equal shares on the lower-priced platform and sell on the higher-priced platform. Third, wait for natural settlement at clearing (correct side $1, incorrect side $0).

After Kalshi's tokenization in 2025, opportunities increased, with average returns of 0.5-3% (QuantVPS). For example, in the interest rate market, bots captured 2-5% deviations (AInvest). The risk is low, primarily execution delays. This strategy repairs market efficiency and may reduce due to aggregators in 2026.

6.3 Copy Trading and Whale Tracking: Sharing the Overflow of Smart Money on the Blockchain

The logic of copy trading is based on blockchain transparency: whale addresses have advantages, and retail investors automatically copy to share profits. Whales are large-scale, and the cost of misleading reversely is high, making following sustainable.

The execution process includes four steps. First, select tools like insiders.bot, polycule, etc. Second, filter high-win-rate addresses (win rate > 60%). Third, set proportional copying and add stop-loss. Fourth, monitor adjustments.

6.4 Providing Liquidity and Market Making: Contributing Depth for Subsidies

The logic of providing liquidity is that platforms need buy/sell depth to maintain low slippage. LP limit orders provide stability, and platforms pay fee-sharing and rewards as incentives.

The execution process includes four steps. First, choose high-liquidity markets. Second, place limit orders to form depth. Third, earn shares and rewards (like Polymarket programs). Fourth, monitor adjustments.

In 2025, liquidity providers earned an annualized return of 10-50%, contributing 15-20% of volume (Polymarket Documentation, "Liquidity Rewards"). The risk is low, primarily opportunity cost. Suitable for conservative capital.

6.5 High-Probability Bond Strategy: Rolling Tail Discounts for Compound Interest

The logic of high-probability bonds lies in tail irrationality: events with a 99% probability approach settlement, causing psychological and liquidity frictions that lead to discounts. Buying and rolling converts to high annualized returns.

The execution process includes three steps. First, filter high-probability events. Second, buy discounted shares (0.95-0.99). Third, roll to the next market after settlement.

6.6 Domain Expertise and Public Dimming: Reverse System Bias

The logic of domain expertise lies in domain bias: emotions amplify hotspots, and experts model recognition and reverse bet.

The execution process includes four steps. First, accumulate data modeling. Second, identify biases. Third, build positions counterintuitively. Fourth, hold for settlement.

The Fade strategy succeeded in 2025, with a win rate >70% (Moontower Meta). The risk is potential black swans, but it has long-term sustainability.

6.7 Multi-Model Comparative Summary

7. Opportunities and Future Outlook

7.1 Opportunities

As prediction markets evolve into an emerging financial tool, they have also spawned a variety of opportunities. Today, various products have been built within the prediction market ecosystem to expand its functionality.

7.2 Derivative Uses

1. Automation and Decentralization of Parametric Insurance

The most direct derivative value of prediction markets lies in their evolution into a key infrastructure for parametric insurance, fundamentally changing the model of risk coverage. Traditional insurance claims often rely on manual loss assessment, which is lengthy and opaque, while insurance products based on prediction markets achieve an automated closed loop of "trigger and pay."

Imagine users can purchase "yes" shares for specific catastrophic events (e.g., "Will Florida experience a hurricane of category 3 or above in 2025?"). Once the oracle determines the event has occurred, the $1 return per share essentially serves as an instant payout policy. This model eliminates moral hazard and claims friction costs in traditional insurance, particularly in agricultural insurance and flight delay insurance, where decentralized platforms are gradually replacing traditional actuarial models.

Research shows that this decentralized insurance model can reduce operational costs by up to 70%, significantly lowering premium thresholds (Gatteschi et al. 305). For farmers or small businesses in developing countries, this provides a permissionless and low-threshold risk hedging tool, allowing insurance services to reach endpoints that traditional financial institutions cannot cover.

2. Micro-Hedging Tools for Long-Tail Risks

On a more macro level, prediction markets fill the gap in traditional financial markets for managing long-tail risks, providing institutions with the ability for "micro-hedging" against geopolitical and macro events.

While traditional tools like CME futures can hedge interest rate or exchange rate risks, they often fall short in addressing specific, non-standardized geopolitical turmoil. Today, multinational supply chain companies are no longer passively accepting risks; instead, they can precisely hedge by purchasing specific prediction market contracts (e.g., "Will the Suez Canal close due to conflict in Q3?"). If the canal closure leads to increased logistics costs, the high returns from prediction markets can effectively offset losses in physical business.

Data shows that some medium-sized hedge funds have begun allocating about 2-5% of their funds to specific event contracts on Kalshi, a precision hedging capability that traditional derivatives markets cannot provide, allowing companies to proactively manage external shocks previously considered "acts of God."

3. Deep Financialization and Assetization of Sports Economics

Meanwhile, the sports sector is also undergoing a financialization transformation driven by prediction markets. The market is no longer solely focused on the outcomes of individual games but is beginning to assetize athletes' careers, seasonal performances, and even transfer decisions. Similar to high-frequency trading on Limitless, the new generation of sports prediction markets allows users to trade contracts like "Will LeBron James exceed X total points this season?" or "Will a certain star player join Real Madrid during the transfer window?" This effectively establishes a synthetic asset for athletes' personal market values, transforming what was once a consumer behavior of "gambling" into an investment behavior based on data analysis.

This trend has become particularly evident in recent years, with relevant data indicating that the integration of sports betting and prediction markets is expected to drive the global market size in this field to $155 billion by 2030 (Grand View Research). For quantitative teams well-versed in sports data, this has become a new source of Alpha; for ordinary fans, it blurs the lines between fan economy and financial investment, providing a way to monetize knowledge, turning every positive outlook on a player into a long-term investment of real money.

4. The "Truth Serum" of Organizational Governance and Decision Optimization

Finally, in terms of organizational governance and decision-making, prediction markets are reviving the Futarchy governance model and serving as a "truth serum" within large enterprises. In DAOs and some avant-garde tech companies, decision-making no longer solely relies on voting but establishes prediction markets to assess the potential impacts of different policies on token prices or company revenues, using the principle of "skin in the game" to combat voter apathy.

Similarly, in traditional hierarchical enterprises, to penetrate the blind optimism of management and address the "spiral of silence" in information transmission, companies are beginning to establish internal prediction markets (e.g., "Will Product X launch on time in October?"). This mechanism utilizes the dispersed knowledge of frontline employees to calibrate project progress. Early corporate experiments have already confirmed its effectiveness; for example, Google's early internal prediction markets had an accuracy rate nearly 20% higher than official forecasts in predicting product launch dates and user numbers (Cowgill and Zitzewitz 4).

With the maturation of enterprise-level SaaS prediction tools, this use case welcomed a new growth point in 2025, becoming a remedy against the ailments of large corporations.

7.3 Future Outlook

By the end of 2025, we will see an increase in the adoption rate of prediction markets in the U.S. and globally. However, compared to more established financial markets, the trading volume of prediction markets remains small.

In the coming year, with the launch of Polymarket US and Kalshi Global, we expect prediction markets to be used more widely as a financial tool, enabling everyone to gain higher information transparency and more targeted financial hedging opportunities.

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