Author: Tiger research
Compiled by: AididiaoJP, Foresight News
Core Points
Prediction markets have grown into a mainstream industry, with monthly trading volume reaching $14 billion. The advancement of Meta's own "Arena" project also demonstrates large tech companies' recognition of it.
The mechanism is simple: if an event occurs, the contract settles at $1; if it does not occur, it settles at $0. Therefore, its trading price is the real-time probability, with the outcome confirmed by an oracle after the event concludes.
This is all built on the foundation of "skin in the game": participants lose money if their judgment is wrong, making 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 the discussion has effectively ceded leadership to foreign entities.
Prediction Markets Have Found Product-Market Fit
Prediction markets mostly remained conceptual for years. Around 2020, things changed. A few small projects began accumulating significant trading volume and broke through regulatory barriers one by one, marking the formal establishment of prediction markets as an industry.
Growth accelerated thereafter. Current monthly trading volume exceeds $14 billion, and the combined valuation of major platforms is approximately $40 billion.
Meta's entry further proves it has moved beyond the early stage. A recent New York Times report stated that Mark Zuckerberg personally leads a team developing a prediction market app called Arena. Such resource investment from a major tech company indicates the industry has moved out of the experimental phase and established a validated business model.
Where Did Prediction Markets Originate?
Prediction markets are not new. They had been 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 younger than its history. By the 1980s, the concept had various names like information markets, decision markets, until a 2004 economics paper settled on "prediction markets."
But the underlying practice predates the name. Its earliest form was political betting on election outcomes. In 18th-century London coffeehouses, people wagered 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 outcomes were active 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 primary win in Michigan, they designed a market where people directly traded election outcomes. This became the Iowa Electronic Markets (IEM).
In 1992 and 1993, the IEM received Commodity Futures Trading Commission (CFTC) approval for research. Anyone who put in $5 could participate. From 1988 to 2004, the IEM outperformed traditional polls about three-quarters of the time, serving as a laboratory for aggregating collective judgment into a price. However, no regulatory framework existed then to allow it to operate as a public market.
Binary Options
These early prediction markets closely resembled binary options in financial markets: contracts that bet yes or no on whether a price would break a certain threshold within a specified time. Their structure—settling at $1 if the event occurs, $0 otherwise—is identical to 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 CBOE's S&P 500-based binary options in 2008. However, rampant fraud by offshore platforms led to bans on selling such products to retail investors in multiple major jurisdictions between 2017 and 2021. Nonetheless, this yes-or-no binary betting structure remains the fundamental logic upon which prediction markets operate today.
How Are Prediction Markets Traded Today?
Today, prediction markets cover topics encompassing almost any imaginable event.
Sports events account for the largest trading volume, benefiting from continuous schedules of leagues and global tournaments, with ongoing World Cups further fueling interest. 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 rumor-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 J.D. Vance will be the Republican presidential nominee in 2028: if Vance is confirmed as the nominee, contracts betting "yes" pay $1; otherwise, contracts betting "no" pay $1.
The simplest way to understand this structure is to think of $1 as 100%. The contract pays $1 (100%) if the event occurs, $0 otherwise. Therefore, the trading price in between naturally reflects probability. A contract at 40 cents represents 40% of that dollar, meaning the market assigns a 40% probability of the event 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 determined 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, and trades execute where both sides match. The price (and implied probability) is generated in real-time by the financial interplay of numerous participants. Traders can also sell their positions before expiry to lock in profits or cut losses, essentially exchanging their view on an event for cash.
Outcomes are recorded by oracles. No matter how precise the contract price is, someone must determine "yes" or "no" after the event concludes. The oracle is the mechanism responsible for this judgment.
Oracles operate in two ways:
- Decentralized Oracles: Proposers post collateral and submit a proposed outcome. If unchallenged within a set period, it becomes final. If challenged, a new proposal process begins, with voting only if further challenges arise.
- Centralized: Judgment criteria are set in advance. After the event, the exchange directly applies the official result and immediately settles the market. This method entirely entrusts judgment to a single exchange.
For example, on the Limitless platform, once the deadline passes, the result is 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 are automatically reported via the Pyth Network, while custom markets for sports or politics are manually judged by the operations team within 24 to 72 hours.
Prediction markets are essentially information systems. They compress the views of numerous participants into a single number reflected in the price and judge whether the prediction was correct after the event based on preset rules.
The Evolution of Gaming and Information Finance
Prediction markets have evolved beyond simple betting platforms into core infrastructure for information finance—converting future uncertainty into real-time price information. Their fundamental distinction from traditional polls or expert predictions lies in the "skin in the game" mechanism, where participants back their positions with their own capital.
With traditional methods, experts face little reputational cost for wrong judgments, and polls cannot filter out respondents' indifference or strategic misreporting. Prediction market prices have a real cost for being wrong—erroneous positions lose money. This forces participants to validate their beliefs with the most objective and up-to-date information. This willingness to bear a cost translates directly into market reliability.
This mechanism's performance is evident in actual data across various fields:
Accuracy in Financial and Monetary Policy Predictions: A February 2026 study by a Federal Reserve economist explained why. Since 2022, prediction market expectations for interest rates just before FOMC meetings have been statistically highly consistent with actual outcomes, outperforming federal funds futures and Bloomberg consensus. The reason is that participants, who would immediately lose money if wrong, more rigorously analyze available information and price 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 aggregated judgment of many participants synthesizing multiple variables, not a simple forecast.
Responsiveness to Market Events and Company Valuations: When the issue 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 capital is at risk. Academic research corroborates this: a 2015 study on internal prediction markets at companies like Google and Ford found prediction errors reduced by up to 25% compared to official forecasting models, showing prediction accuracy improves when insider knowledge is combined 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 prices was identified and prosecuted as a crime, also proving the markets aim to operate transparently and accountably.
In fields where information is widely distributed, prediction markets are precision analytical tools; in fields where information is concentrated in few hands, they are monitoring mechanisms capable of identifying such concentration. Because participants' capital is genuinely 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 significantly depending on national regulatory frameworks. The U.S. has incorporated them into the regulated financial system through judicial rulings, while most major Asian jurisdictions still largely categorize them under traditional gambling.
In the U.S., litigation resolved much regulatory uncertainty. The CFTC attempted to classify Kalshi's election prediction contracts as gambling and sanction the platform, but the court ruled election prediction is not a game of chance and regulators lack the authority to ban it. This ruling changed the regulatory stance, becoming a decisive catalyst for entry by traditional financial institutions including ICE, Robinhood, and CME.
In contrast, the mainstream view in major Asian jurisdictions 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. While approaches vary by country, prediction markets largely remain outside formal policy discussion 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 dissolved, attempts to confine new markets within old regulatory frameworks have inherent limitations. The current regulatory approach in major Asian jurisdictions presents 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 simultaneously losing market oversight and related tax revenue, weakening regional financial competitiveness in the long term.
The second is the loss of national information infrastructure sovereignty.
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. When excluded 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 gain clearer insight into local societies 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 markets without adequate prior discussion only expose users to risk and push them outside the system.
The focus of discussion needs a fundamental shift.
The question is no longer how to block these markets, but how to healthily utilize this data within the formal system. This shift in perspective requires specialized study, but relevant discussion remains very limited.
In this field, Limitless Research is filling the gap, processing prediction data from Asian markets like South Korea and Japan into information 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 guiding it correctly.
What Asia needs now is not stricter enforcement, but initiating forward-looking discussions to respond to this shift. Pushing already occurring transactions into the shadows is the worst policy. Constructive discussions to incorporate them into the formal system, establishing transparent oversight mechanisms, and returning the data generated in the process as national and social assets requires sustained effort.
