Meta enters the prediction market, where will Asia go
Source: Tiger Research
Author: Ryan Yoon
Compiled and organized by: bitPushNews
Core summary
The forecasting market has become a mainstream industry, with monthly trading volume of $14 billion, and Meta's “Arena” project shows that big tech companies have recognized its value.
The mechanism is simple: if an event occurs, the contract is settled at $1, and if not, it is $0, so the transaction price can be used as a real-time probability, and the oracles confirm the results after expiration.
This is based on “skin in the game” (skin in the game): participants lose money if they make mistakes in judgment, which gives credibility to the information they provide.
Western markets have incorporated the forecasting market into a formal system, while Asia's participation is limited, which is leading to capital outflows, loss of information sovereignty, and lack of user protection.
Asia's task now is not to block these markets, but to determine how to use this data responsibly within the formal system, because avoiding discussions is tantamount to handing over dominance overseas.
1. The prediction market has found PMF
The forecasting market was in the conceptual phase for many years. This changed around 2020, when a small number of small projects began to accumulate meaningful transaction volume and remove regulatory barriers one by one, marking the beginning of the forecasting market as an industry.
Since then, growth has accelerated. The current monthly trading volume has surpassed $140 billion, and the leading platform's valuation has reached around $40 billion.
Meta's entry made the industry's trend toward maturity even more evident. The New York Times recently reported that Mark Zuckerberg is personally leading a team to develop a prediction market app called “Arena.” The investment of this level of resources by a major technology company shows that the industry has gone beyond the experimental stage and established a business model with proven product and market fit points.
2. Where did the prediction market originate?
Predicting markets is not a new invention. Blockchain technology existed in academia and finance for years before it brought it into the wider public eye and helped shape the industry.
2.1. informal use
The term “forecast market” appeared later than in its actual history. In the 1980s, the concept had many names, including information markets and decision markets, and it wasn't until 2004 that an economics paper identified “forecast market” as the standard term.
However, its history of practice dates back several centuries. Its earliest form was a political gamble on election results. In 18th-century London, cafes are full of bets on parliamentary scandals and prime minister changes, and the results and odds are sometimes reported in newspapers. In 19th century New York, an informal futures market that predicted presidential election results operated actively in the roadside market near Wall Street.
2.2. Academic applications
In academia, the starting point was an attempt at the University of Iowa in 1988. Confused by the poll's failure to predict Jesse Jackson's victory in the Michigan preselection, three economists designed a marketplace where people could trade election results. This is the Iowa Electronics Marketplace (IEM).
In 1992 and 1993, IEM was approved by the US Commodity Futures Trading Commission (CFTC) for research purposes. The market is open to anyone willing to invest $5, and between 1988 and 2004, its forecast performance outperformed traditional polls by about three-quarters. It operates as an effective laboratory that combines collective judgment into price. Even so, there was no regulatory framework at the time that allowed it to operate as an open market.
2.3. binary options
Early prediction markets are very similar to binary options in financial markets: contracts are paid based on yes/no bets to determine whether the price crosses a certain threshold within a set period of time. This structure, which settles to 1 if an event occurs and 0 otherwise, is completely consistent with the logic of today's prediction market.
Binary options have also entered regulated exchanges. The 2007 US Stock Exchange's fixed return options (Fixed Return Options) and the 2008 Chicago Board Options Exchange (CBOE) binary options based on the S&P 500 index are notable examples. Frequent fraud on offshore platforms led to several major jurisdictions banning the retail sale of these products between 2017 and 2021. Despite setbacks, the basic contract structure (i.e. binary bet) is still the logic for predicting the operation of the market today.
3. How is today's prediction market traded?
Today, the prediction market covers almost any event imaginable.
Sporting events account for the largest trading volume in any category, and thanks to the ongoing league calendar and global events, the ongoing World Cup is currently generating additional interest. Politics, geopolitics, and macroeconomics have surpassed indicators such as inflation data to forecasting private company valuations, turning the information itself into a tradable asset. Cryptocurrency and stock prices, along with some smaller gossip-driven events, form a spectrum of popular interest and specialized information needs.
Every contract that predicts the market is settled on a binary yes/no basis. Take the market for whether J.D. Vance (J.D. Vance) will be the 2028 Republican presidential candidate. If Vance is confirmed as the nominee, the contract pays the YES bettor $1. If not, the person who bet “NO” (NO) is paid $1.
The easiest way to understand this structure is to think of $1 as 100%. The contract pays $1 (or 100%) when the event occurs and $0 when it doesn't happen, so its transaction price naturally reflects probability. A contract traded at 40 cents represents 40% of $1, which means the market is 40% likely to price the event. If the bid-ask spread and transaction costs are not factored in, the cent value can be read directly as a percentage.
Prices are formed through an order book and not through any central party. Buy orders (such as a purchase offer of 39 cents) and sell orders (such as an offer of 40 cents) accumulate at each price level, and the transaction is executed where the parties agree. Prices (and, by extension, implied probabilities) are generated in real time through an integrated capital game involving many participants. Traders can effectively trade profitably using their views on events by selling their positions before maturity to lock in gains or limit losses.
The results were recorded by the Oracle (Oracle). No matter how accurate the contract pricing is, after the event is over, someone still needs to determine whether the outcome is “yes” or “no”; oracles are the mechanism responsible for that determination. In the example above, this is the final step in determining whether Vance has actually been confirmed as the Republican nominee.
Oracles work in two ways:
Decentralized oracles: The proponent deposits a deposit and submits a proposed result, and if no objections are raised within a set window thereafter, the result becomes the final result. If an objection is raised, the result will enter the re-proposal process, and the matter will only enter the voting process if there is another objection.
Centralized oracles: The criteria for judging results are set in advance. After the incident is over, the exchange directly applies the official results and immediately settles the market. This structure places all power of judgment in the hands of a single exchange.
For example, the Limitless platform finalizes results based on pre-determined rules after a deadline has passed. Oracles (a service that reports real-world results to the blockchain) are responsible for this report: for most markets, such as those that track cryptocurrency prices or stocks, results are automatically reported via Pyth Network; for customized markets covering sports or politics, the operations team manually judges them within 24 to 72 hours.
A prediction market like Limitless is best understood as an information system that compresses the opinions of a large number of participants into a number reflected in the price, and determines whether the forecast is correct based on pre-determined rules after the event is over.
4. “Stakes of Interest” and the Evolution to Information Finance
The prediction market is no longer just a simple betting platform; it has become the core infrastructure of “Information Finance” (Information Finance), which transforms future uncertainty into real-time price information. The fundamental difference between these markets and traditional polls or expert predictions is the “skin in the game” (skin in the game) mechanism, where participants invest their own capital to make predictions.
Under traditional methods, experts who make mistakes in predictions have little to no reputational cost, and public opinion polls can't filter out respondents' indifference or strategic misreporting. If a mistake is made in predicting market prices, participants are responsible for financial costs, which encourages participants to verify their judgments based on the most objective and up-to-date information. This willingness to bear costs translates directly into the reliability of the market.
The performance of this “stakeholder” mechanism in actual data is reflected in many fields:
Accuracy of financial and monetary policy predictions: A study by a Federal Reserve economist published in February 2026 explains why predicting good market performance is predicted. Since 2022, forecasted market rate expectations prior to the FOMC meeting have been statistically consistent with actual results and outperformed federal funds futures and the Bloomberg consensus. The reason is that if participants misjudge, they face an immediate loss of capital, which leads them to more rigorously analyze the available information and price accordingly.
Transparent probability estimates in politics and elections: In South Korea's local elections in June 2026, Polymarket accurately predicted the winners of 14 campaigns in 16 metropolitan and provincial elections. When export polls can only describe the election as “difficult to divide”, the prediction market provides real-time prices for participants to invest in real money. This is the result of many participants weighing various variables to make collective judgments, not simple predictions.
Response to market events and corporate valuations: When the issue of limiting interest income from stablecoins occurred in March 2026, predicting that the market immediately priced the possibility that Coinbase's share price would fall at 97.6%. It played a role as a real-time risk indicator rather than a retrospective analysis, proving how sensitive participants are when their own capital is at risk. Academic research came to a similar conclusion: A 2015 study looking at the internal forecasting market of companies, including Google and Ford, found that they reduced forecast errors by up to 25% compared to official forecasting models, which showed that when internal knowledge is combined with venture capital, forecast accuracy increases.
Information asymmetry remains a limitation. A January 2026 case involving Venezuela revealed that someone used insider information to trade, revealing a real weakness in these markets. However, this use of privileged information to distort prices has been criminalized and prosecuted, proving that the market is designed to operate on a transparent and responsible basis.
In a field where information is widely distributed, predicting the market is an accurate analytical tool. In areas where information is concentrated on a small number of participants, they act as a monitoring mechanism that can identify this concentration itself. Since participants' capital is indeed at risk, the prices generated by these markets constitute objective information relevant to evaluating the value of financial assets.
5. Predictive markets absent from Asian policy discussions
The nature and trajectory of forecasting markets varies greatly depending on each country's regulatory framework. The US has incorporated prediction markets into the regulated financial system through judicial rulings, and major Asian jurisdictions largely continue to view them as a form of traditional gambling.
In the US, lawsuits have resolved much of the regulatory uncertainty. The US Commodity Futures Trading Commission (CFTC) tried to classify Kalshi's election prediction contract as gambling and sanction the platform, but the court ruled that election predictions were not a game of chance, and regulators had no authority to ban them. The ruling changed the regulatory stance and became a decisive catalyst for traditional financial institutions such as the Intercontinental Exchange (ICE), Robinhood, and the Chicago Mercantile Exchange (CME) to enter the market.
In contrast, in major Asian jurisdictions, the mainstream view equates the predictive market's “binary settlement” structure with traditional gambling. The regulatory perspective mainly focuses on gambling control and public order rather than financial policy. Although different countries have adopted different approaches, forecasting markets are largely outside of formal policy discussions in the Asian region, with the exception of India and Indonesia.
How to treat the field of predicting the market ultimately depends on whether regulators view the market as a financial innovation or a matter of social control.
6. A prediction market at the crossroads of regulatory difficulties and institutionalization
The forecasting market has become a core part of the global financial and information infrastructure. There is already a huge gap between global trends and the inflexible attitude of Asian regulators. At a time when the boundaries between technology and finance have largely disappeared, trying to confine a new market to an old regulatory framework faces inherent limitations. The regulatory approach currently adopted by major Asian jurisdictions raises three major issues.
The first is the paradox of regulatory arbitrage.
The prediction market operates on a digital network without borders, so blocking a platform or restricting users in a country doesn't eliminate potential demand. Instead, users will migrate to offshore platforms that have no regulatory reach, and take greater risk in the process. Capital flows out of this jurisdiction, and regulators have lost both control over the market and the tax revenue associated with it. This dynamic has long weakened the region's financial competitiveness.
The second is the loss of sovereignty over a country's information infrastructure.
The prediction market is more than just a gambling place; it is also a complex information infrastructure that transforms complex social issues into accurate numerical estimates. Recent elections in Asia have shown that predicting the market is a faster and more accurate interpretation of public sentiment than traditional polls. When these markets are excluded in the name of regulation, the data that most directly reflects social sentiment is instead accumulated on overseas servers. The result is an imbalance where foreign media and institutions understand domestic society more clearly than domestic analysts.
Third, user protection has been abandoned.
Users are placed in a blind spot where there are no institutional guarantees. A policy that simply denies the market without sufficient prior discussion only exposes users to risk and pushes them out of the system.
The center of this discussion needs a complete shift.
The question is no longer how to block this market, but how to use this data healthily within that formal system. This shift in perspective requires dedicated research, but discussions in this area are still limited.
In this space, Limitless Research is filling this gap, turning forecast data from Asian markets such as Korea and Japan into information assets. In the future, more participants will need to take on this role in building a health data ecosystem.
Regulation should not be a dam blocking the flow of water, but rather a channel to guide its proper flow.
What Asia needs now is not more stringent enforcement, but rather a forward-looking discussion in response to this shift. Pushing deals that have already taken place into the shadows is the worst policy. Integrating these activities into a formal system through constructive discussions, establishing a transparent monitoring system, and returning the data generated in this process as a national and social asset — this will require continuous effort.
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