Key Points

• This article is written by Tiger Research. The prediction market has grown into a mainstream industry, with monthly trading volume reaching $14 billion, and the progress of Meta's own "Arena" project shows that large tech companies recognize its significance.
• Its mechanism is straightforward: if an event occurs, the contract settles at $1; if it does not occur, it settles at $0. Therefore, its trading price represents the real-time probability, and the result is confirmed by an oracle after the event concludes.
• All of this is built on the foundation of having "skin in the game": participants lose money if they make incorrect judgments, which gives credibility to their information.
• Western markets have integrated prediction markets into the formal financial system, while limited participation in Asia is leading to capital outflows, loss of information sovereignty, and a lack of user protection.
• The current task for Asia is not to block these markets, but to consider how to responsibly utilize this data within a formal system. By avoiding discussions, the leadership has effectively been ceded to foreign entities.

Prediction Markets Have Found Their Product - Market Fit

For many years, prediction markets mostly remained at the conceptual stage. This changed around 2020, as a few small projects started to accumulate significant trading volumes and break through regulatory barriers one by one, marking the official formation of prediction markets as an industry.

Thereafter, growth accelerated. Currently, monthly trading volume exceeds $14 billion, with the total valuation of major platforms estimated at around $40 billion.

Meta's entry further proves that it has moved beyond the early stage. The New York Times recently reported that Mark Zuckerberg is personally leading a team to develop a prediction market application named Arena. The investment of such resources by a large tech company signifies that this industry has moved out of the experimental stage and established a validated business model.

Where Did Prediction Markets Originate?

Prediction markets are not a new phenomenon. They were informally used in academia and finance for decades before blockchain technology brought them to the public and helped form an industry.

Informal Use

The term "prediction market" itself appeared later than its history. By the 1980s, the concept had various names, such as information markets and decision markets, until a 2004 economic paper fixed it as "prediction markets."

However, the underlying practice predates this name. The earliest forms involved political betting on election results. In 18th-century London coffeehouses, people placed bets on parliamentary scandals and prime ministerial changes, and the odds generated would sometimes appear in newspapers. In 19th-century New York, informal futures markets predicting presidential election outcomes were very active in the over-the-counter markets near Wall Street.

Academic Use

The academic starting point was in 1988 with three economists from the University of Iowa. They were puzzled by polls that failed to predict Jesse Jackson's success in the Michigan primary, so they designed a market where people could trade on election outcomes. This later became the Iowa Electronic Markets (IEM).

In 1992 and 1993, IEM received approval from the Commodity Futures Trading Commission (CFTC) for research purposes. Anyone who invested $5 could participate. From 1988 to 2004, IEM outperformed traditional polls about three-quarters of the time, becoming a laboratory for aggregating collective judgments into prices. Despite this, there was no regulatory framework at the time that allowed it to operate as a public market.

Binary Options

These early prediction markets were very similar to binary options in financial markets: contracts that involved yes or no bets based on whether a price would cross a certain threshold within a specified time. Their structure—settling at 1 if the event occurs, otherwise at 0—completely aligns with the logic of prediction markets.

Binary options also entered regulated exchanges. The 2007 fixed-return options on the American Stock Exchange and the 2008 binary options based on the S&P 500 on the Chicago Board Options Exchange are examples. However, frequent fraud on offshore platforms led several major jurisdictions to ban the sale of such products to retail investors between 2017 and 2021. Nevertheless, this basic structure of yes or no binary betting remains the logical foundation for the operation of prediction markets today.

How Do Prediction Markets Trade Today?

Today, prediction markets cover topics that encompass almost any imaginable event.

Sports events account for the largest trading volume, bolstered by a consecutive schedule of leagues and global events; the ongoing World Cup further heightens the enthusiasm. Political, geopolitical, and macroeconomic topics range from inflation data to private company valuation predictions, transforming information itself into a tradable asset. Cryptocurrency and stock prices, along with events driven by some gossip, together form a complete spectrum from mass interest to professional information needs.

Each contract settles in a binary yes or no manner. For example, regarding whether the Republican presidential nominee in 2028 will be J.D. Vance: if Vance is confirmed as the nominee, the "yes" bet contract pays $1; otherwise, the "no" bet contract pays $1.

The simplest way to understand this structure is to view $1 as 100%. The contract pays $1 (100%) when the event occurs; otherwise, it pays $0, so the intermediate trading price naturally reflects the probability. A contract priced at $0.40 represents 40% of that dollar, meaning that the market believes the probability of the event occurring is 40%. The cent value can be directly read as a percentage (ignoring spread and transaction costs).

Prices are formed through order books rather than being decided by any central entity. Buy orders (like buying at $0.39) and sell orders (like selling at $0.40) accumulate at various price points, with trades executed at the point where both sides match. The price (and implied probability) is generated in real-time through the interactions of numerous participants' funds. Traders can also sell positions before expiration to lock in profits or limit losses, essentially converting their views on the event into money.

Results are recorded by an oracle. Regardless of how precise the contract price is, someone must determine whether it is a "yes" or "no" after the event concludes, and the oracle is the mechanism responsible for this judgment.

Oracles operate in two ways:

• Decentralized Oracles: proposers stake collateral and submit proposed results. If no one raises objections within a specified timeframe, it becomes the final result. If objections arise, it enters a re-proposal process, and only after further objections does it go to a vote.
• Centralized: predetermined judgment criteria are set, and after the event ends, the exchange directly applies the official result and settles the market immediately. This method completely hands over the judgment power to a single exchange.

For instance, on the Limitless platform, once the deadline passes, the result is finalized according to preset rules. The oracle service, which reports real-world results to the blockchain, completes the reporting: most markets tracking cryptocurrency prices or stocks automatically report through the Pyth Network, while custom markets like sports or politics are judged manually by the operating team within 24 to 72 hours.

Prediction markets are essentially an information system that compresses the views of a large number of participants into a single number reflected in prices and judges the accuracy of predictions based on preset rules after the event concludes.

The Evolution of Games and Information Finance

Prediction markets have transcended simple betting platforms, evolving into a core infrastructure of information finance—turning future uncertainties into real-time price information. The fundamental difference from traditional polling or expert predictions lies in the "skin in the game" mechanism, meaning participants are responsible for their positions with their own funds.

In traditional methods, experts face little reputational cost for incorrect judgments, and polls cannot filter out respondents' indifference or strategic misreporting. Prediction market prices carry real consequences for errors—incorrect positions incur losses, compelling participants to validate their beliefs with the most objective, up-to-date information. This willingness to bear costs translates directly into market reliability.

The performance of this mechanism in actual data is evident across various fields:

The accuracy of financial and monetary policy predictions: A study by a Federal Reserve economist in February 2026 explained the reasons. Since 2022, prediction markets have shown a statistically high correlation between interest rate expectations before Federal Open Market Committee meetings and actual outcomes, outperforming fed funds futures and Bloomberg consensus. The reason lies in the fact that participants face immediate financial losses if they are wrong, leading to a stricter analysis of available information and subsequent pricing.

Transparent probability estimation for politics and elections: In the June 2026 local elections in South Korea, Polymarket correctly predicted the winners of 14 out of 16 key cities and provinces. Where exit polls could only say "too close to call," prediction markets provided real-time probabilities based on participants wagering real money, reflecting the comprehensive judgments of numerous participants on various variables, rather than a simple prediction.

Responses to market events and company valuations: In March 2026, when the topic of capping interest income on stablecoins emerged, prediction markets immediately priced the probability of a drop in Coinbase stock at 97.6%, serving as a real-time risk indicator rather than post-analysis, demonstrating how sensitively participants respond when their own funds are at risk. Academic studies have also reached similar conclusions: a 2015 study on internal prediction markets at companies like Google and Ford found that predictive errors decreased by up to 25% compared to official predictive models, indicating that prediction accuracy improves when insider knowledge is coupled with risk capital.

Information asymmetry remains a limitation. In the case of Venezuela in January 2026, someone exploited confidential information for insider trading, revealing real weaknesses. However, this attempt to distort prices was identified and prosecuted as a crime, proving that the market aims to operate transparently and accountably.

In fields where information is widely distributed, prediction markets are precision analysis tools; where information is concentrated in a few hands, they serve as monitoring mechanisms capable of identifying such concentrations. Because participants' funds genuinely face risks, the prices generated by these markets constitute objective information for assessing the values of financial assets.

The Absence of Prediction Markets in Asian Policy Discussions

The nature and trajectory of prediction markets vary significantly due to different regulatory frameworks in each country. The U.S. has incorporated them into a regulated financial system through judicial rulings, while major jurisdictions in Asia mostly still classify them as traditional gambling categories.

In the U.S., litigation has resolved most regulatory uncertainties. 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 predictions are not games of chance, and regulators lack the authority to prohibit them. This ruling changed the regulatory posture and became a decisive catalyst for the entry of traditional financial institutions including ICE, Robinhood, and CME.

In contrast, major jurisdictions in Asia still equate the binary settlement structure of prediction markets with traditional gambling in the mainstream view. The dominant regulatory perspective is focused on gambling control and public order, rather than financial policy. Although countries approach it differently, prediction markets in the region mostly remain outside formal policy discussions, with only India and Indonesia being exceptions.

This divergence in treatment ultimately boils down to whether regulators view the market as a financial innovation or a matter of social control.

Prediction Markets at a Crossroads of Regulatory Dilemma and Institutionalization

Prediction markets have become a core element of global financial and information infrastructure. There is a significant gap between global trends and the rigid stance of Asian regulators. In a time when the boundaries between technology and finance have essentially disappeared, attempts to confine new markets within old regulatory frameworks are inherently limited. The current regulatory practices in major Asian jurisdictions face three major issues.

The first is the paradox of regulatory arbitrage

Prediction markets operate in a borderless digital network, and blocking platforms or restricting users in one country cannot eliminate the underlying demand. Users will turn to unregulated offshore platforms, taking on greater risks. This results in capital flowing out of jurisdictions and regulators simultaneously losing market oversight and associated tax revenue, weakening the long-term financial competitiveness of the region.

The second is the loss of national sovereignty over information infrastructure

Prediction markets are a sophisticated information infrastructure that translates complex social issues into precise numerical estimates, rather than merely betting places. Recent elections in Asia have shown that prediction markets can read public sentiment faster and more accurately than traditional polls. When they are excluded under the guise of regulation, the data that best reflects certain social sentiments accumulates on foreign servers. The result is that foreign media and institutions have a clearer understanding of local society than domestic analysts.

The third is the abandonment of user protection

Users exist in a blind spot, without institutional guarantees. Simply denying the market without adequate prior discussion will only expose users to risks and push them outside the system.

The focus of discussions needs a complete shift.

The question is no longer how to block this market but how to healthily utilize this data within a formal system. This perspective shift requires dedicated research, yet relevant discussions currently remain quite limited.

In this field, Limitless Research is filling the gap by processing prediction data from Asian markets like South Korea and Japan into information assets. More participants need to take on the role of building a healthy data ecosystem in the future.

Regulation should not be a dam blocking the flow of water, but a channel guiding it properly.

What Asia needs now is not stricter enforcement but a proactive discussion to respond to this shift. Pushing already occurring transactions into the shadows is the worst policy. Continuous efforts are needed to integrate them into the formal system through constructive discussions, establish transparent oversight mechanisms, and return the data generated in the process as national and social assets.

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