Forecast Market: Information Aggregation Mechanism Supported by Modern Finance

Summary

Although prediction markets originated from informal betting, they have combined modern financial market mechanisms and information analysis to form a unique information aggregation mechanism, allowing predictions to extract different values under the support of modern financial systems. With the emergence of blockchain-integrated modern prediction markets like Polymarket, the prediction market has entered a period of rapid development.

The so-called Prediction Markets are open trading markets that aggregate dispersed information through financial incentives to forecast the outcomes of specific future events—contracts that target the result of an event. The core logic is "voting with money," where contract price fluctuations reflect the market’s collective consensus on the probability of an event occurring. On the surface, prediction markets seem very similar to informal betting activities, but we believe that prediction markets can aggregate market information and, through modern financial market formats, showcase collective judgment that surpasses individual intuition, elevating them to regulated prediction markets.

As early as 1988, the University of Iowa developed IEM (Iowa Electronic Markets), aiming to create an internet-based teaching and research tool, making it one of the earliest electronic prediction markets. It allows students to invest real money (from $5 to $500) and trade various contracts. A typical example is predicting U.S. political elections, where students can trade "shares" of political candidates or parties (returns depend on election outcomes). Students can also trade contracts whose final payoffs depend on future events, such as economic indicators, quarterly corporate earnings, stock returns, or box office revenues. IEM was initially developed as a university teaching tool, and it’s not surprising that only a few projects are open for trading.

In the Web3.0 era, more open, innovative prediction markets covering a broader range of topics are rapidly emerging, with Polymarket and Kalshi being typical representatives. Polymarket is a blockchain-based prediction market platform founded by Shayne Coplan in 2020; users can predict the outcomes of real-world events in areas like pop culture, weather, sports, economics, politics, and other fields of interest. As a product of the combination of prediction markets and Web3.0, Polymarket has inherently different innovative qualities compared to IEM, making it a representative of prediction markets in the Web3.0 era.

What makes Polymarket particularly special is that it operates on blockchain technology, attracting many users from the cryptocurrency market. Users can bet with cryptocurrencies, demonstrating greater flexibility, which makes Polymarket naturally aligned with Web3.0, especially through deep integration with decentralized finance (DeFi) systems. This fusion allows Polymarket to appear as an alternative prediction market—more like a DeFi product—while the events it covers are closely related to the traditional economy and financial markets.

With the rapid growth of the market, the CFTC has adopted a more proactive stance toward prediction markets. To meet CFTC requirements, Polymarket has implemented upgraded monitoring systems, clearing procedures, and regulatory reporting functions. It’s clear that U.S. regulators are actively engaging with the rapid rise of prediction markets, which is a key reason for their swift development.

Major crypto exchanges like Coinbase have also announced entering the prediction market space. As regulatory recognition of the value of prediction market information aggregation mechanisms increases, this market is expected to enter a period of rapid growth, facing more intense competition.

Risk Warning: Blockchain technology development may fall short of expectations; regulatory policies are uncertain; the commercial implementation of prediction market models may not meet expectations.

1. Core Viewpoints

Prediction markets, though originating from informal betting, have integrated modern financial market mechanisms and information analysis, forming a special information aggregation system that allows predictions to derive unique value under the support of modern finance. With the emergence of blockchain-based prediction markets like Polymarket, the prediction market is entering a period of rapid development.

This article analyzes the origins of prediction markets and the application of emerging modern prediction markets.

2. The essence of prediction markets supported by modern finance: Information aggregation

2.1 Prediction markets: From informal betting to a financial incentive-driven information aggregation mechanism

Prediction markets (Prediction Markets) are open trading platforms that aggregate dispersed information through financial incentives to forecast the outcomes of specific future events—contracts that target the event’s result. The core logic is "voting with money," where contract prices reflect the market’s collective consensus on the probability of an event. Although prediction markets appear very similar to informal betting, we believe they can better aggregate market information and, through modern financial formats, demonstrate collective judgment superior to individual intuition, thus elevating them to regulated prediction markets.

As early as 1988, the University of Iowa developed IEM (Iowa Electronic Markets), aiming to create an internet-based teaching and research tool, making it one of the earliest electronic prediction markets. It allows students to invest real money (from $5 to $500) and trade various contracts. A typical example is predicting U.S. political elections, where students can trade "shares" of candidates or parties (returns depend on election results). Students can also trade contracts whose payoffs depend on future events, such as economic indicators, quarterly corporate earnings, stock returns, or box office revenues.

The most notable example of IEM is the 2008 U.S. presidential election, where the prediction market’s forecasts clearly outperformed other polling agencies. This demonstrates that market value fluctuations of contracts can reflect the collective judgment of the probability of an event, and under the support of modern financial markets, prediction markets are no longer just informal betting activities but resemble financial incentive markets that showcase the value of information aggregation—collective judgment surpassing individual intuition.

Currently, IEM’s operation seems not very active. As of May 14, 2026, only four open prediction markets remain active, including three political election forecasts and one financial return market (used as a classroom case). There are no open projects for economic indicators. We believe that IEM was initially developed as a university teaching tool, so only a few projects being open is not surprising; perhaps a more significant reason is that market operation may not be its strong suit—plus, the limit of $500 per bet reduces its appeal to a broader market.

The currently open financial return market involves a cross-industry profit prediction project—winner-takes-all contracts—where the payout is entirely determined by the dividend-adjusted yields of six different industry companies: ExxonMobil (XOM), General Motors (GM), Microsoft (MSFT), Simon Property Group (SPG), WBA (Walgreens Boots Alliance), and Disney (DIS). Among these, the highest-yield contract will pay $1.00 per share, while others pay nothing. Each month, new winner-takes-all contracts are launched.

Clearly, such financial market contracts are more aligned with educational purposes.

In summary, prediction markets like IEM are not merely informal betting; supported by modern financial markets, they can aggregate fragmented market information and objectively reflect collective judgment through trading. This mechanism of information aggregation driven by financial incentives is a hallmark of modern prediction markets.

2.2 Web3.0 and prediction markets sparking brighter innovations

Compared to prediction markets like IEM with a specialized background, more open, innovative, and topic-diverse prediction markets are rapidly emerging. Examples include Polymarket and Kalshi.

Polymarket is a blockchain-based prediction market platform founded by Shayne Coplan in 2020; users can predict outcomes of real-world events in areas like pop culture, weather, sports, economics, politics, and other fields. As a product of prediction markets and Web3.0 integration, Polymarket inherently possesses different innovative qualities compared to IEM, making it a representative of prediction markets in the Web3.0 era.

We believe that Polymarket has expanded to cover more prediction scenarios and events. Its blockchain operation attracts many cryptocurrency users, who can bet with crypto assets, demonstrating greater flexibility. This makes Polymarket naturally compatible with Web3.0, especially through deep integration with DeFi systems—the intersection of DeFi and prediction markets can generate many imaginative products. This positions Polymarket as an alternative prediction market—more like a DeFi product—while the events it covers are closely related to traditional economic and financial markets. Overall, Polymarket exhibits more flexibility and openness in market operation, embodying Web3.0’s core traits.

Another feature of blockchain support is that all prediction betting data on Polymarket is fully recorded on the blockchain, ensuring the validity and fairness of bets through an open network. Anyone who believes a prediction bet is erroneous can propose an objection. Once proposed, it enters a 2-hour challenge period. If no objections are raised, the decision is considered valid, and the proposer recovers their deposit with a reward. During the challenge period, anyone can submit a challenge deposit equal to the proposer’s deposit via the UMA dapp to contest the proposal. This initiates a debate lasting 24-48 hours (with voting the next day, at least 24 hours for discussion). Participants can present evidence on the UMA platform. After debate, UMA token holders vote (about 48 hours), resulting in one of four outcomes:

1. Proposal wins; proposer recovers deposit plus half of the challenger’s deposit as a bounty. Challenger loses their deposit.

2. Challenger wins; challenger recovers deposit plus half of the proposer's deposit as a bounty. Proposer loses their deposit.

3. Too early; applicable to proposals about events not yet occurred, e.g., ongoing sports results. Challenger gets a refund plus half of the proposal’s deposit as a bounty. Proposer loses their deposit.

4. Unknown/50-50; used when other options are not applicable. The market price is ultimately set at 50 votes for and 50 against. Challenger recovers their deposit plus half of the proposal’s deposit as a bounty. Proposer loses their deposit.

This dispute resolution approach exemplifies the DAO (Decentralized Autonomous Organization) model typical of Web3.0, reflecting blockchain’s characteristics. Therefore, we see Polymarket as a DeFi application fundamentally based on blockchain.

Polymarket’s prediction topics cover many issues, with dedicated “channels” for high-profile figures and events, such as a separate channel for U.S. President Trump, and others for current global hotspots, economic policies, etc., making the scene very rich. According to market forecasts, the trading volume of the 2024 U.S. presidential election prediction market on Polymarket exceeds $3 billion, with some optimistic estimates approaching $3.6 billion.

Recently, the Hantavirus outbreak has attracted global attention, prompting Polymarket to launch a prediction market on whether the Hantavirus will become widespread by 2026. The rules are simple: if, by December 31, 2026, the WHO’s official statement explicitly classifies the Hantavirus outbreak as a “pandemic,” the market outcome is “Yes.” Otherwise, it’s “No.” The “Yes” and “No” positions each have independent order books, and bettors can place orders based on the order book prices. Currently, the “Yes” contract price is about 9.4 cents, corresponding to roughly a 9% probability of outbreak (rounded). Bettors can wager at this price; if the outbreak is confirmed, the probability jumps to 100%, and the contract pays $1 at settlement, yielding substantial profit. Conversely, if the outbreak is not confirmed, the “No” contract settles at $1.

Given the current low probability of Hantavirus becoming widespread, betting “Yes” offers a larger potential profit margin. Supported by modern financial markets, prediction trading objectively reflects collective judgment—remember, behind this are bettors risking real money, aggregating fragmented market information into a collective assessment, displayed through order book prices. This mechanism of information aggregation via financial incentives is a key feature of modern prediction markets.

3. As regulatory positions clarify, prediction markets will face fiercer competition

3.1 Major crypto giants like Coinbase entering prediction markets

Initially, the U.S. Commodity Futures Trading Commission (CFTC) maintained a cautious stance toward prediction markets, closely monitoring them, often viewing them as academic research objects like IEM. However, with the rapid market expansion, the CFTC has adopted a more proactive approach. To meet CFTC requirements, Polymarket has upgraded its monitoring, clearing, and reporting systems. The startup states: “Polymarket remains subject to the Commodity Exchange Act and all provisions of CFTC regulations applicable to designated contract markets, including self-regulatory obligations.” This indicates that U.S. regulators are actively engaging with the rise of prediction markets, which is a crucial reason for their swift growth.

In July 2025, Polymarket acquired a derivatives trading and clearing platform, QCEX, regulated by the CFTC. In September 2025, the CFTC issued a no-action letter—essentially a written assurance that if Polymarket meets certain conditions, the CFTC will not pursue enforcement actions—effectively greenlighting its operations in the U.S.

Following positive regulatory interactions, in October 2025, Polymarket secured up to $2 billion in funding commitments from Intercontinental Exchange (ICE), the parent company of the New York Stock Exchange. ICE aims to bring prediction markets into mainstream finance, connecting its traditional products with Polymarket’s financial offerings, asset tokenization plans, and event-driven data. This exemplifies the rapid integration of traditional finance with Web3.0 financial markets.

According to The Block, in October 2025, the company sought $15 billion in funding to cope with intensifying market competition. In recent months, the prediction market arms race has intensified, with Polymarket and competitor Kalshi competing fiercely, boosting market attention. Founded in 2018 and regulated by the CFTC, Kalshi holds a designated contract market license (DCM), with each transaction publicly available via API. The chart shows both companies’ total sales are growing well. In March and April 2026, their monthly trading volumes exceeded $20 billion, indicating the prediction market has rapidly become an emerging sector.

Kalshi’s homepage features a broad range of prediction scenarios and categories, similar to Polymarket, contrasting sharply with the conservative approach of IEM.

On December 22, 2025, U.S. crypto exchange giant Coinbase announced acquiring prediction market startup The Clearing Company, whose team will join Coinbase to expand its products. Just a week prior, Coinbase had launched a partnership with Kalshi, allowing users to access Kalshi’s markets via Coinbase’s interface. This shows Coinbase regards prediction markets as an important financial infrastructure.

Coinbase’s move into prediction markets is a natural choice. As regulatory recognition of their value grows, this market is expected to develop rapidly, with increasing competition.

3.2 Prediction markets will influence the real economy in multiple dimensions

Earlier this year, CFTC Chairman Michael Selig announced plans to establish new rules for the prediction market industry, which now exceeds tens of billions of dollars in scale. He stated, “It’s time to set clear rules and make the public aware that the CFTC supports legitimate innovation in these markets,” indicating ongoing regulatory progress. These actions are based on the fact that predictions are increasingly impacting real-world economic activities.

In April, FIFA announced a partnership with ADI Predictstreet, making it the official prediction market partner for the 2026 World Cup. This is FIFA’s first use of prediction markets to enhance fan engagement. Fans can predict match results, event data, player performances, and key incidents, with some free betting options. This marks prediction markets beginning to influence football economics—an example of their participation in real economic activities.

On April 7, Fox News announced a new sponsorship agreement with Kalshi, integrating prediction market data into its media system, providing real-time odds of major events in news reports. Kalshi’s data on political, economic, and cultural event probabilities will be incorporated into live broadcasts and digital content, influencing market expectations. This is a typical case of prediction markets not just observing but actively shaping economic expectations.

Prediction markets are thus not merely “spectators” but are deeply affecting the real economy from multiple dimensions.

4. Risk Warnings

Blockchain technology development may fall short of expectations: The underlying blockchain technologies and projects like Bitcoin are still in early stages, with risks of underperformance.

Regulatory policy uncertainty: The operation of blockchain and prediction markets involves multiple financial, online, and other regulatory policies. Currently, regulatory frameworks are still under research and exploration, lacking mature models, which poses risks of regulatory uncertainty.

Commercialization of prediction market models may not meet expectations: Infrastructure and regulatory rules are still developing, and there is a risk that the commercial implementation of prediction market models may not achieve anticipated success.