Author: Tiger research
Compiled by: AididiaoJP, Foresight News
Key Points
Prediction markets have matured into a mainstream industry, with monthly trading volumes reaching $140 billion, and Meta's own "Arena" project advancement demonstrates recognition from major tech companies.
Its mechanism is simple: if an event occurs, the contract settles at $1; if not, at $0. Therefore, its trading price reflects the real-time probability, with the outcome confirmed by an oracle after the event concludes.
This is all built on the principle of "skin in the game": participants incur losses if their judgment is wrong, which makes their information credible.
Western markets have incorporated prediction markets into the formal financial system, while limited participation in Asia is leading to capital outflow, loss of information sovereignty, and a lack of user protection.
Asia's current task is not to block these markets, but to consider how to responsibly utilize this data within a formal system. Because avoiding discussion has essentially ceded leadership to foreign entities.
Prediction Markets Have Found Product-Market Fit
Prediction markets spent years mostly in the conceptual stage. Around 2020, things changed. A few small projects began accumulating significant trading volume and successively broke through regulatory barriers, marking the formal establishment of prediction markets as an industry.
Growth accelerated thereafter. Current monthly trading volume now exceeds $140 billion, with the main platforms having a combined valuation of approximately $400 billion.
Meta's entry further proves it has moved beyond the early stage. A recent New York Times report stated that Mark Zuckerberg is personally leading a team to develop a prediction market app named Arena. Such resource investment by a major tech company indicates this industry has exited the experimental phase and established a proven business model.
Where Did Prediction Markets Originate?
Prediction markets are not new. They were used informally in academia and finance for decades before blockchain technology brought them to the masses and helped them become an industry.
Informal Use
The term "prediction market" itself is more recent than its history. By the 1980s, the concept went by various names like information markets, decision markets, until a 2004 economics paper solidified it as "prediction markets."
But its underlying practice predates the name. Its earliest form was political betting on election outcomes. In 18th-century London coffee houses, people placed bets on parliamentary scandals and prime ministerial changes, and the resulting odds sometimes appeared in newspapers. In 19th-century New York, informal futures markets predicting presidential election results thrived in off-exchange markets near Wall Street.
Academic Use
The academic starting point was in 1988 with three economists at the University of Iowa. Perplexed by polls failing to predict Jesse Jackson's win in the Michigan primary, they designed a market where people could directly trade election outcomes. This later became the Iowa Electronic Market (IEM).
In 1992 and 1993, the IEM obtained approval from the Commodity Futures Trading Commission (CFTC) for research purposes. Anyone could participate by contributing $5. From 1988 to 2004, the IEM outperformed traditional polls in about three-quarters of instances, serving as a laboratory for aggregating collective judgment into a price. However, a regulatory framework enabling its operation as a public market did not exist at the time.
Binary Options
These early prediction markets closely resembled binary options in financial markets: contracts based on yes-or-no bets on whether a price would break a certain threshold within a specified time. Their structure—settling at $1 if an event occurs, otherwise $0—is entirely consistent with the logic of prediction markets.
Binary options also entered regulated exchanges. Examples include the Fixed Return Options by the American Stock Exchange in 2007 and the S&P 500-based binary options by the Chicago Board Options Exchange in 2008. However, frequent fraud by offshore platforms led to bans on retail sales of such products in several major jurisdictions between 2017 and 2021. Nonetheless, this yes-or-no binary betting structure remains the logical foundation of how prediction markets operate today.
How Are Prediction Markets Traded Today?
Today, prediction markets cover almost any imaginable event.
Sports events account for the largest trading volume, benefiting from continuous schedules of leagues and global tournaments, with ongoing events like the World Cup further boosting popularity. Politics, geopolitics, and macroeconomics have expanded from indicators like inflation data to predictions of private company valuations, turning information itself into a tradable asset. Cryptocurrency and stock prices, along with some gossip-driven events, together form a complete spectrum from mass interest to professional information demand.
Each contract settles in a binary yes-or-no manner. Take the example of whether the 2028 Republican presidential nominee is J.D. Vance: if Vance is confirmed as the nominee, the "Yes" contract pays $1; otherwise, the "No" contract pays $1.
The simplest way to understand this structure is to think of $1 as 100%. The contract pays $1 (100%) if the event occurs, and $0 otherwise. Therefore, the intermediate trading price naturally reflects a probability. A 40-cent contract represents 40% of that dollar, meaning the market deems the event has a 40% chance of occurring. The cent value can be directly read as a percentage (ignoring bid-ask spreads and transaction costs).
Prices are formed through order books, not decided by any central party. Buy orders (e.g., buy at 39 cents) and sell orders (e.g., sell at 40 cents) accumulate at various price levels, with trades executed where both sides match. The price (and thus implied probability) is generated in real-time through the interplay of funds from numerous participants. Traders can also sell their positions before expiration to lock in profits or cut losses, essentially converting their view on an event into cash.
Outcomes are recorded by oracles. No matter how precise the contract price, someone still needs to determine "yes" or "no" after the event ends. The oracle is the mechanism responsible for this determination.
Oracles operate in two ways:
- Decentralized Oracle: Proposers post collateral and submit a proposed outcome. If unchallenged within a set timeframe, it becomes final. If challenged, a re-proposal process begins, with voting only occurring after further challenges.
- Centralized: Judgment criteria are preset, and after the event concludes, the exchange directly applies official results and immediately settles the market. This method entrusts judgment entirely to a single exchange.
For example, on the Limitless platform, once a deadline passes, results are finalized according to preset rules. This is done by oracle services that report real-world outcomes to the blockchain: most markets tracking crypto prices or stocks report automatically via the Pyth Network, while custom markets for sports or politics are judged manually by the operations team within 24 to 72 hours.
At its core, a prediction market is an information system that compresses the views of many participants into a single number reflected in price, and judges after the event whether the prediction was correct based on preset rules.
The Evolution from Gaming to Information Finance
Prediction markets have evolved beyond simple betting platforms to become core infrastructure for information finance—converting future uncertainty into real-time price information. Their fundamental difference from traditional polls or expert forecasts lies in the "skin in the game" mechanism, where participants back their positions with their own capital.
In traditional methods, expert errors carry little reputational cost, and polls cannot filter out respondent indifference or strategic misrepresentation. Prediction market prices impose a real cost for errors—incorrect positions lose money—forcing participants to validate their beliefs with the most objective, up-to-date information. This willingness to bear cost translates directly into market reliability.
This mechanism's performance is evident in actual data across multiple areas:
Accuracy in Financial and Monetary Policy Predictions: Research by a Federal Reserve economist in February 2026 explained the reason. Since 2022, prediction market expectations for interest rates ahead of FOMC meetings have shown high statistical consistency with actual outcomes, outperforming federal funds futures and Bloomberg consensus. The reason is that participants, who would immediately lose money if wrong, analyze available information more rigorously and price it accordingly.
Transparent Probability Estimates for Politics and Elections: In the June 2026 South Korean local elections, Polymarket correctly predicted the winners in 14 out of 16 major cities and provinces. Where exit polls could only say "too close to call," prediction markets provided real-time probabilities backed by participants' real money, representing the synthesis of many participants weighing multiple variables, not a simple prediction.
Response to Market Events and Company Valuations: When the topic of a stablecoin interest income cap arose in March 2026, prediction markets immediately priced the probability of a Coinbase stock price drop at 97.6%, serving as a real-time risk indicator rather than post-hoc analysis, demonstrating participants' sensitive response when their own funds are at risk. Academic research corroborates this: a 2015 study on internal prediction markets at companies like Google and Ford found a reduction in forecast errors by up to 25% compared to official forecast models, showing that prediction accuracy improves when insider knowledge combines with risk capital.
Information asymmetry remains a limitation. The January 2026 Venezuela case, where someone used confidential information for insider trading, exposed a real weakness. However, this attempt to distort the price was identified and prosecuted as a crime, also proving the market aims to operate with transparency and accountability.
In areas where information is widely distributed, prediction markets are precision analytical tools; in areas where information is concentrated in few hands, they are monitoring mechanisms capable of identifying that concentration. Because participants' capital is truly at risk, the prices generated by these markets constitute objective information for assessing the value of financial assets.
The Absence of Prediction Markets in Asian Policy Discussions
The nature and trajectory of prediction markets differ greatly depending on national regulatory frameworks. The U.S. incorporated them into the regulated financial system through judicial rulings, while major Asian jurisdictions largely still categorize them under traditional gambling.
In the U.S., litigation resolved much of the regulatory uncertainty. The CFTC 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 regulators had no authority to ban it. This ruling shifted the regulatory posture, serving as a decisive catalyst for the entry of traditional financial institutions like ICE, Robinhood, and CME.
In contrast, in major Asian jurisdictions, the mainstream view still equates the binary settlement structure of prediction markets with traditional gambling. The dominant regulatory perspective is gambling control and public order, not financial policy. Although approaches differ by country, prediction markets largely remain outside formal policy discussions in the region, with India and Indonesia being exceptions.
This divergence in treatment ultimately boils down to whether regulators view the markets as financial innovation or a social control issue.
Prediction Markets at the Crossroads of Regulatory Dilemma and Institutionalization
Prediction markets have become central to global finance and information infrastructure. A significant gap has emerged between the global trend and the rigid stance of Asian regulators. In an era where technological and financial boundaries have largely disappeared, attempts to confine new markets within old regulatory frameworks have inherent limitations. The current regulatory approach in major Asian jurisdictions faces three major problems.
The first is the paradox of regulatory arbitrage.
Prediction markets operate on borderless digital networks. Blocking platforms or restricting users in one country does not eliminate underlying demand. Users shift to unregulated offshore platforms, assuming greater risks. This leads to capital outflow from the jurisdiction, with regulators losing both market oversight and related tax revenue, weakening the region's financial competitiveness in the long run.
The second is the loss of sovereignty over national information infrastructure.
Prediction markets are advanced information infrastructure that transforms complex social issues into precise numerical estimates, not mere betting venues. Recent elections in Asia showed prediction markets reading public sentiment faster and more accurately than traditional polls. By excluding them in the name of regulation, the data that best reflects a society's sentiment accumulates on foreign servers. The result is that foreign media and institutions understand local societies more clearly than domestic analysts.
The third is the abandonment of user protection.
Users are in a blind spot, with no institutional safeguards. Policies that simply deny the market's existence without adequate prior discussion only expose users to risks and push them outside the system.
The focus of discussion needs a fundamental shift.
The question is no longer how to block this market, but how to healthily utilize this data within the formal system. This change in perspective requires specialized research, but related discussion remains very limited at present.
In this field, Limitless Research is filling the gap, processing prediction data from Asian markets like South Korea and Japan into informational assets. More participants are needed in the future to take on the role of building a healthy data ecosystem.
Regulation should not be a dam blocking the flow of water, but a channel to guide it correctly.
What Asia needs now is not stricter enforcement, but the initiation of forward-looking discussions to respond to this shift. Pushing already-occurring transactions into the shadows is the worst policy. Bringing them into the formal system through constructive discussion, establishing transparent oversight mechanisms, and returning the data generated in the process as national and social assets requires sustained effort.
