Key Takeaways
Prediction markets have become a mainstream industry, with $14 billion in monthly volume and Meta’s own “Arena” project signaling recognition from major tech companies.
The mechanism is simple: a contract settles at $1 if an event occurs and $0 if it does not, so its trading price serves as a real-time probability, and an oracle confirms the outcome after expiry.
This rests on skin in the game: participants lose money if they are wrong, which lends credibility to the information they provide.
Western markets have brought prediction markets into the formal system, while Asia’s limited engagement is costing it capital outflow, loss of informational sovereignty, and unprotected users.
Asia’s task now is not to block these markets but to determine how to use this data responsibly within the formal system, since avoiding the discussion is already ceding leadership abroad.
1. Prediction Markets Have Found Product-Market Fit
Prediction markets stayed largely conceptual for years. That changed around 2020, when a handful of smaller projects began accumulating meaningful trading volume and clearing regulatory barriers one at a time, marking the point at which prediction markets started to take shape as an industry.
Growth has accelerated since then. Monthly trading volume now exceeds $140 billion, and the valuations of the leading platforms have reached roughly $40 billion.
Meta’s entry has made the shift beyond the early stage more apparent. The New York Times recently reported that Mark Zuckerberg is personally leading a team developing a prediction market app called Arena. A large technology company committing resources at this level indicates that the industry has moved past experimentation and established a business model with validated product-market fit.
2. Where Did Prediction Markets Begin?
Prediction markets are not a recent invention. They were used informally in academic and financial circles for decades before blockchain technology brought them to a broader public and helped establish them as an industry.
2.1. Informal Use
The term “prediction market” itself is more recent than its history suggests. Through the 1980s, the concept went by various names, including information market and decision market, before a 2004 economics paper fixed “prediction market” as the standard term.
The underlying practice, however, predates the terminology by centuries. Its earliest form was political betting on election outcomes. In 18th-century London, coffeehouses hosted wagers on parliamentary scandals and changes in the prime minister, with the resulting odds sometimes reported in newspapers. In 19th-century New York, informal futures markets predicting presidential election results operated actively in the curb markets near Wall Street.
2.2. Academic Use
In academia, the starting point was a 1988 effort at the University of Iowa. Three economists, puzzled that opinion polls had failed to predict Jesse Jackson’s win in the Michigan caucus, designed a market where people could trade on election outcomes themselves. This became the Iowa Electronic Markets (IEM).
In 1992 and 1993, the IEM received approval from the CFTC to operate for research purposes. Open to anyone willing to put in $5, the IEM outperformed traditional polling roughly three-quarters of the time between 1988 and 2004, functioning as a working laboratory for aggregating collective judgment into a price. Even so, no regulatory framework existed that would have allowed it to operate as a public market.
2.3. Binary Options
These early prediction markets closely resemble binary options in financial markets: contracts that pay out based on a yes-or-no bet over whether a price will cross a given threshold by a set time. That structure, settling at 1 if the event occurs and 0 if it does not, is identical to the logic underlying prediction markets.
Binary options also found their way into regulated exchanges. The American Stock Exchange’s Fixed Return Options in 2007 and the Chicago Board Options Exchange’s S&P 500-based binary options in 2008 are notable examples. Frequent fraud on offshore platforms led several major jurisdictions to ban retail sales of these products between 2017 and 2021. Despite that setback, the basic contract structure, a binary bet on yes or no, remains the same logic that prediction markets operate on today.
3. How Are Prediction Markets Traded Today?
The range of topics covered by prediction markets today extends to nearly any event imaginable.
Sports draws the largest volume of any category, supported by a continuous calendar of leagues and global events, with the ongoing World Cup currently driving additional interest. Politics, geopolitics, and macroeconomics have expanded beyond indicators such as inflation data to include forecasts of private company valuations, turning information itself into a tradable asset. Cryptocurrency and stock prices, along with smaller gossip-driven events, round out a spectrum that spans both popular interest and demand for specialized information.
Every contract in a prediction market settles on a binary yes-or-no basis. Consider a market on whether J.D. Vance will be the Republican presidential nominee in 2028. If Vance is confirmed as the nominee, the contract pays $1 to those who bet YES. If he is not, it pays $1 to those who bet NO.
The simplest way to understand this structure is to treat one dollar as 100 percent. A contract pays $1, or 100 percent, if the event happens and $0 if it does not, so the price at which it trades in between naturally reflects a probability. A contract trading at 40 cents represents 40 percent of that dollar, meaning the market prices the event’s likelihood at 40 percent, and the cent value can be read directly as a percentage. This reading, however, assumes that spreads and transaction costs are ignored.
Prices form through an order book rather than through any central party. Buy orders, such as an offer to buy at 39 cents, and sell orders, such as an offer to sell at 40 cents, accumulate at each price level, and trades execute where the two sides meet. The price, and by extension the implied probability, emerges in real time from the combined capital of many participants pushing against each other, and traders can sell their position before expiration to lock in a gain or limit a loss, effectively trading their view on an event for money.
Outcomes are recorded through an oracle. However precisely a contract has been priced, someone still has to determine whether the outcome was YES or NO once the event concludes, and the oracle is the mechanism responsible for that determination. In the example above, this is the final step that establishes whether Vance was in fact confirmed as the Republican nominee.
Oracles operate in one of two ways.
Decentralized oracle: a proposer posts a bond and submits a proposed outcome, which becomes final if no one disputes it within a set window. If a dispute is raised, the outcome goes through a re-proposal process, and only if a further dispute follows does the matter go to a vote.
Centralized: the criteria for judging the outcome are set in advance, and once the event concludes, the exchange applies the official result directly and settles the market immediately. This structure places full authority over the determination with a single exchange.
Limitless, for example, finalizes results according to predetermined rules once the deadline passes. An oracle, a service that reports real-world outcomes onto the blockchain, handles this reporting: for most markets, such as those tracking crypto prices or stocks, the outcome is reported automatically through Pyth Network, while custom markets covering sports or politics are judged manually by the operating team within 24 to 72 hours.
Prediction markets such as Limitless are best understood as an information system that compresses the views of a large number of participants into a single number reflected in price, and that, once an event concludes, determines according to predetermined rules whether that prediction was correct.
4. Skin in the Game and the Evolution Toward Information Finance
Prediction markets have evolved beyond simple betting platforms into core infrastructure for information finance, the conversion of future uncertainty into real-time price information. What fundamentally distinguishes these markets from traditional polling or expert forecasting is the skin in the game mechanism, meaning that participants commit their own capital to the positions they take.
Under conventional methods, an expert who is wrong faces little reputational cost, and opinion polls cannot filter out respondent indifference or strategic misreporting. Prediction market prices carry a real cost for being wrong, since a mistaken position loses money, which pushes participants to verify their convictions against the most objective and current information available. That willingness to bear a cost translates directly into market reliability.
How this skin-in-the-game mechanism plays out in actual data can be seen across several areas.
Accuracy in financial and monetary policy forecasting: a Federal Reserve economist’s research published in February 2026 illustrates why prediction markets perform well. Since 2022, prediction market rate expectations ahead of FOMC meetings have aligned statistically with actual outcomes, outperforming both fed funds futures and the Bloomberg consensus. The reason is that participants face an immediate loss of capital if they are wrong, which leads them to analyze available information more rigorously and price it accordingly.
Transparent probability estimates in politics and elections: during South Korea’s local elections in June 2026, Polymarket correctly called the winner in 14 of 16 metropolitan and provincial races. Where exit polls could only describe a race as too close to call, prediction markets offered real-time prices reflecting probabilities that participants had staked their own money on, a result of collective judgment across many participants pricing in a wide range of variables rather than a simple forecast.
Responsiveness to market events and corporate valuation: when the issue of capping stablecoin interest income emerged in March 2026, prediction markets immediately priced a 97.6 percent probability of a decline in Coinbase’s stock price, functioning as a real-time risk indicator rather than retrospective analysis and demonstrating how sensitively participants respond when their own capital is at stake. Academic research has reached similar conclusions: a 2015 study examining internal prediction markets at companies including Google and Ford found that they reduced forecasting error by as much as 25 percent compared to official forecasting models, indicating that predictive accuracy improves when insider knowledge is combined with capital at risk.
Information asymmetry remains a limitation. A January 2026 case involving Venezuela showed an insider trading on confidential information, exposing a real weakness in these markets. The fact that this attempt to exploit privileged information to distort prices was identified and prosecuted as a crime, however, also demonstrates that the market is designed to operate on a transparent and accountable basis.
In areas where information is broadly distributed, prediction markets function as a precise analytical tool. In areas where information is concentrated among a few participants, they function as a monitoring mechanism capable of identifying that concentration itself. Because participant capital is genuinely at stake, the prices these markets generate constitute objective information relevant to assessing the value of financial assets.
5. Prediction Markets Absent From the Asian Policy Conversation
The character and trajectory of prediction markets differ sharply depending on each country’s regulatory framework. The United States has brought prediction markets into the regulated financial system through judicial rulings, while major Asian jurisdictions have largely continued to treat them as a category of traditional gambling.
In the United States, litigation resolved much of the regulatory uncertainty. The Commodity Futures Trading Commission attempted to classify Kalshi’s election prediction contracts as gambling and sanction the platform, but the court ruled that election prediction is not a game of chance and that the regulator lacked authority to prohibit it. That ruling shifted the regulatory posture and became a decisive catalyst for traditional financial institutions, including ICE, Robinhood, and CME, to enter the market.
In major Asian jurisdictions, by contrast, the prevailing view treats the binary settle-on-outcome structure of prediction markets as equivalent to traditional gambling. The dominant regulatory lens is gambling control and public order rather than financial policy, and while individual countries have taken different approaches, prediction markets remain largely outside formal policy discussion in the region, with India and Indonesia as exceptions.
The divide in how prediction markets are treated ultimately comes down to whether regulators view the market as financial innovation or as a matter for social control.
6. Prediction Markets at the Crossroads of Regulatory Dilemma and Institutionalization
Prediction markets have already become a core part of global financial and information infrastructure. A significant gap has opened between this global trend and the rigid posture of Asian regulators. At a point when the boundary between technology and finance has largely dissolved, attempts to constrain a new market within older regulatory frameworks face inherent limits. The regulatory approach currently followed by major Asian jurisdictions carries three significant problems.
The first is the paradox of regulatory arbitrage.
Prediction markets operate on borderless digital networks, so blocking a platform or restricting users in one country does not eliminate underlying demand. Users instead migrate to offshore platforms outside any regulatory reach, accepting greater risk in doing so. Capital flows out of the jurisdiction, and regulators lose both oversight of the market and the tax revenue associated with it, a dynamic that weakens financial competitiveness in the region over time.
The second is the loss of sovereignty over national information infrastructure.
Prediction markets function as a sophisticated information infrastructure that converts complex social issues into precise numerical estimates, not merely as a venue for betting. Recent elections across Asia have shown prediction markets reading public sentiment faster and more accurately than traditional polling. While these markets are excluded under the banner of regulation, the data that most directly reflects a given society’s sentiment accumulates instead on servers located abroad. The result is an imbalance in which foreign media and institutions gain a clearer picture of a domestic society than domestic analysts do.
The third is the abandonment of user protection.
Users are left in a blind spot with no institutional safeguards. A policy that simply denies the market without sufficient prior discussion only exposes users to risk and pushes them outside the system.
The center of this discussion needs to shift entirely.
The question is no longer how to block this market but how to make healthy use of this data within the formal system. This shift in perspective requires dedicated research, but discussion in this area remains limited so far.
In this space, Limitless Research is filling that gap by processing prediction data from Asian markets such as Korea and Japan into information assets. More participants will need to take on this role of building a healthy data ecosystem going forward.
Regulation should function not as a dam that blocks the flow but as a channel that directs it correctly.
What Asia needs now is not stricter enforcement but the start of a forward-looking discussion that responds to this shift. Pushing transactions that are already taking place into the shadows is the worst possible policy. Bringing this activity into the formal system through constructive discussion, establishing a transparent oversight system, and returning the data generated in the process as a national and social asset will require sustained effort
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This report was partially funded by Limitless. It was independently produced by our researchers using credible sources. The findings, recommendations, and opinions are based on information available at publication time and may change without notice. We disclaim liability for any losses from using this report or its contents and do not warrant its accuracy or completeness. The information may differ from others’ views. This report is for informational purposes only and is not legal, business, investment, or tax advice. References to securities or digital assets are for illustration only, not investment advice or offers. This material is not intended for investors.
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