The rise of prediction markets is not just a technological innovation in crypto finance, but an institutional experiment on “how the future is priced.”

Introduction: In the evolutionary path of crypto finance, prediction markets have long occupied a delicate position: they are both regarded as information pricing tools and often misunderstood as “decentralized gambling.” However, as the real-world financial system begins to formally accept this mechanism, prediction markets are completing an identity leap—from a marginalized crypto experiment to a new type of asset pricing layer in the mainstream financial structure.

In late 2025, Polymarket, the world’s largest prediction market platform, obtained regulatory clearance from the U.S. Commodity Futures Trading Commission (CFTC) and officially returned to the U.S. market with compliant status. This event not only marked the end of the years-long regulatory gray period for prediction markets, but also meant that a new asset form is being recognized by the system: the right to predict itself is becoming a tradable, priceable, and hedgeable financial asset.

When future events are broken down into standardized contracts, when social consensus is transformed into liquidity curves, and when probability itself becomes the object of financial pricing, a new financial era is unfolding. Prediction markets are no longer just “betting tools” on the future, but are evolving into financial infrastructure connecting real-world events, capital games, and information efficiency.

01 Regulatory Breakthrough: Prediction Markets Officially Enter the U.S. Compliance System. The legalization process of prediction markets in the United States was not achieved overnight, but a regulatory game that lasted for many years.

As early as 2022, the CFTC fined Polymarket $1.40 million for “operating an unregistered derivatives trading platform” and required it to completely stop providing services to U.S. users. This penalty forced Polymarket to withdraw from the U.S. market and labeled the entire prediction market track as “high regulatory risk.”

The real turning point came in 2025. Polymarket completed the restructuring of its compliance architecture by acquiring a clearing and trading institution holding a CFTC license, and built an operating framework that complies with the U.S. derivatives regulatory system. Subsequently, the CFTC issued a No-Action Letter, allowing it to re-enter the U.S. market in a compliant manner.

This means that prediction markets have been formally included in the U.S. financial regulatory order for the first time, and their transaction structure, clearing path, fund custody, and information disclosure are fully aligned with traditional futures market standards. U.S. users no longer directly participate in on-chain interactions, but participate in prediction contract transactions through regulated intermediaries such as futures brokers. Prediction markets have thus completed the identity transformation from “crypto-native applications” to “financial infrastructure components.”

From an institutional perspective, this release is of landmark significance: prediction markets are no longer simply classified as gambling or gray financial tools, but are recognized as a new type of event derivatives market, whose core function is to price uncertainty.

02 New Asset Paradigm: How Events Become Tradable Assets. The real innovation of prediction markets lies not in “prediction” itself, but in its construction of an unprecedented asset paradigm—event assetization.

In the traditional financial system, asset pricing revolves around continuous variables such as corporate profits, interest rates, exchange rates, and commodity supply and demand; while the object of prediction market pricing is the discrete future event itself.

For example: whether a policy is passed, whether a candidate is elected, whether a company completes a merger, and whether a technology is implemented on schedule. These social events that were originally impossible to financialize are packaged into standardized contracts, forming financial assets that can be bought, sold, hedged, and cleared.

In the Polymarket system, each event is broken down into two results, “yes/no,” corresponding to two types of Tokens, with prices fluctuating between 0 and 1, essentially reflecting the market’s pricing of the probability of the event occurring. When the market believes that the probability of an event occurring is 70%, its contract price will approach $0.70.

This structure gives prediction markets three financial attributes: priceable (the probability of an event is quantified as a continuous price curve), tradable (users can buy, sell, go long, or short probabilities at any time), and clearable (settlement is based on the actual results after the event occurs).

As a result, prediction markets construct a new asset class: event-driven assets. It is different from stocks and bonds, and also different from commodity futures, but a kind of “probability finance” that unfolds around real-world uncertainty. In this system, price no longer simply reflects supply and demand, but becomes a comprehensive result of collective wisdom, information density, and capital game. Prediction markets are therefore regarded as an efficient information aggregator, and its essence is a social consensus formation mechanism of “voting with real money.”

03 AI Enters the Scene: Prediction Markets Enter the Era of Intelligent Pricing. If prediction markets have completed the institutional construction of “future assetization,” then the addition of artificial intelligence is reshaping the operating efficiency and pricing logic of this market.

As AI matures in natural language processing, sentiment analysis, and event recognition, prediction markets are evolving from “human game markets” to “human-machine collaborative pricing systems.” AI models can analyze news, social media, policy documents, regulatory dynamics, and public opinion changes in real time, and transform this unstructured information into quantifiable signals to participate in odds adjustment and liquidity allocation.

In practical applications, AI has been used to: monitor emerging events in real time and automatically generate new prediction contracts, analyze market sentiment changes and warn of abnormal trading behavior, optimize bid-ask spreads, improve liquidity efficiency, and identify pricing deviations and assist in arbitrage decisions.

More importantly, predicted probabilities are entering the public information system. Search engines, financial terminals, and information platforms are beginning to access prediction market data, embedding “how much the market believes an event will occur” as a new information indicator into users’ daily decision-making processes. In this process, prediction markets are gradually transforming from a crypto finance sub-track into a social-level information pricing system. It is no longer just a trading tool, but is beginning to assume functions similar to “public opinion index,” “risk indicator,” and “expected curve,” becoming the probability infrastructure of the digital economy era.

04 Risk Game: When Prediction Begins to Shape Reality in Reverse. The pricing mechanism of prediction markets is essentially a system that monetizes social expectations. However, when probability curves become an important reference indicator for public opinion, this mechanism inevitably introduces one of the most classic problems in financial markets—reflexivity.

In traditional financial theory, price is regarded as a reflection of fundamentals; but in the real market, price often affects behavior in reverse, thereby changing the fundamentals themselves. Prediction markets also face this structural risk: when the market gives a “high probability result,” it itself may become a force driving the event in that direction.

In the fields of political elections, policy games, and corporate mergers, changes in the odds of prediction markets are often cited by the media, spread on social platforms, and interpreted by the public as “trend signals.” When a certain result is priced as a high-probability event, it may trigger herd behavior, which in turn has a substantial impact on real-world decision-making. This makes prediction markets no longer just “observers,” but may become participants in the real process.

In addition, prediction markets also face three typical risk structures: First, whale manipulation and liquidity shock risks. In markets with relatively concentrated liquidity, large amounts of funds can quickly push up or lower the odds by sweeping orders in a concentrated manner, thereby creating trend signals and guiding follow-up transactions. This behavior is no stranger to the crypto market, but in prediction markets that are highly tied to real-world events, its spillover effects are more significant.

Second, insider information and information asymmetry issues. Prediction markets naturally encourage “information pioneers” to profit, which itself helps information to be priced quickly, but also introduces gray space. When some participants have access to key decision-making information that has not yet been made public, ordinary traders will be at a structural disadvantage, and the credibility of the market will be challenged.

Third, oracle adjudication and result dispute mechanisms. Prediction markets rely on oracle systems to adjudicate real-world results on-chain, and many events in the real world do not have absolutely objective judgment standards. Issues such as whether a policy is “substantially implemented,” whether a project “constitutes a breach of contract,” and whether a certain behavior “meets the conditions” often involve legal interpretation and subjective judgment. Once a dispute arises, the adjudication process itself may become a new game field.

Therefore, although prediction markets have advantages in information efficiency, their institutional design must strike a balance between openness and security. The core competitiveness in the future is not only reflected in the scale of trading volume, but also in the ability to govern manipulation risks, adjudication disputes, and systemic shocks.

05 Global Differentiation: Different Development Paths of Prediction Markets. As prediction markets gradually move towards the mainstream financial vision, this track is showing obvious path differentiation, and the development directions in different regions and different institutional environments are different.

In the U.S. market, prediction markets are evolving along the direction of “compliant derivatives finance.” Platforms represented by Polymarket and Kalshi are being incorporated into the regulatory framework for futures and event contracts, and the transaction structure, clearing mechanism, and fund custody are all aligned with traditional financial infrastructure. The positioning of prediction markets in this system is closer to a new type of macro hedging tool and risk pricing layer.

In the European market, prediction markets are more regarded as a supplementary form of financial instruments, subject to the dual constraints of the Markets in Financial Instruments Directive and the regulatory system for crypto asset services, emphasizing investor protection and anti-money laundering compliance, and the pace of market development is relatively cautious.

In the Web3 native ecosystem, prediction markets are evolving along a completely decentralized path, emphasizing community governance, on-chain autonomy, and open market creation. Users are both traders, rule makers, and adjudicators. Prediction markets are regarded as a social collaboration experiment, not just a financial product.

The development path in the Chinese-speaking region is more challenging. On the one hand, public event-type predictions are strictly restricted in most jurisdictions, and political, policy, and macro topics are highly sensitive; on the other hand, localized liquidity and market-making systems are not yet mature, and event question banks, participation habits, and risk control models are still in the early stages.

But at the same time, the Chinese-speaking market also has unique advantages: in highly localized scenarios such as consumption, technology, real estate, film and television, and sports, there are a large number of event targets with clear result calibers, high social attention, and high discussion heat. This provides a natural application soil for prediction markets. If a localized event asset system can be built within a compliant framework, prediction markets are expected to form a differentiated development path in Asia.

From a global perspective, prediction markets will not move towards a single model, but are more likely to evolve into a multi-layered structure: the upper layer is the compliant financial pricing layer, the middle layer is the decentralized experimental layer, and the bottom layer is the vertical field application layer. Different paths complement each other and jointly constitute the infrastructure network for “future assetization.”

Conclusion. The rise of prediction markets is not just a technological innovation in crypto finance, but an institutional experiment on “how the future is priced.” When events are assetized, when probabilities are securitized, and when expectations are monetized, the boundaries of financial markets are further pushed outward, extending from enterprises and commodities to the operation of society itself.

Prediction markets are building a new financial language, expressing consensus with prices, expressing judgments with liquidity, and expressing beliefs with capital.

Polymarket’s regulatory clearance in the United States marks the first time that this system has been formally accepted by the mainstream financial order. Prediction markets have thus completed a key leap from the edge of crypto to the core of finance. “Assetization of prediction rights” is no longer just an internal narrative of the crypto world, but is beginning to become a new variable in the global financial system.

In an era when uncertainty is increasingly becoming the norm, the ability to price the future is becoming a new infrastructure capability. Whoever can more efficiently aggregate information, manage expectations, and price risks will master the key interface of the next-generation financial system. A future that can be traded, hedged, and quantified is coming.

[RWATech]