From elections to crypto, from polls to real money: how prediction markets have become a global information infrastructure.

Written by: Tiger research

Translated by: AididiaoJP, Foresight News

Key Points

Prediction markets have grown into a mainstream industry, with monthly trading volume reaching $14 billion, and the advancement of Meta’s own "Arena" project shows large tech companies' recognition of it.

The mechanism is simple: if the event occurs, the contract settles at $1; if not, then it is $0. Thus, its trading price reflects real-time probability, with the outcome confirmed by an oracle after the event ends.

Everything is built on the foundation of having “skin in the game”: participants incur losses if their judgment is incorrect, making their information credible.

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 lack of user protection.

The current task for Asia is not to block these markets but to think about how to responsibly utilize this data within the formal system. By avoiding discussions, the leadership has essentially been handed over to foreign entities.

Prediction Markets Have Found Their Products - Market Matching

For many years, prediction markets mostly remained in the conceptual stage. Around 2020, this changed, as a few small projects began to accumulate significant trading volume and overcame regulatory barriers one by one, marking the formal establishment of prediction markets as an industry.

Subsequently, growth accelerated. Currently, the monthly trading volume has exceeded $14 billion, with the total valuation of major platforms around $40 billion.

Meta's entry further proves that it has surpassed the early stages. The New York Times recently reported that Mark Zuckerberg is personally leading a team to develop a prediction market application called Arena. The investment from such a major tech company indicates that the industry has moved beyond the experimental phase and established a validated business model.

Where Do Prediction Markets Originate?

Prediction markets are not a new phenomenon. Before blockchain technology brought them to the public and helped form an industry, they had been informally used in academia and finance for decades.

Informal Use

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

However, the underlying practices predate the name itself. The earliest forms involved political betting on election outcomes. In 18th century London coffeehouses, people bet on parliamentary scandals and prime minister changes, with the resulting odds sometimes appearing in newspapers. In 19th century New York, informal futures markets predicting presidential election outcomes were very active near Wall Street.

Academic Use

The academic starting point was three economists at the University of Iowa in 1988. They were puzzled that polls failed to predict Jesse Jackson’s victory in the Michigan primary, so they designed a market that allowed people to trade directly on election outcomes. This became the later Iowa Electronic Markets (IEM).

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

Binary Options

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

Binary options also entered regulated exchanges. For example, fixed return options in the U.S. stock exchange in 2007 and S&P 500 based binary options from the Chicago Board Options Exchange in 2008 are instances. However, frequent fraud on offshore platforms led several major jurisdictions to ban retail sales of such products from 2017 to 2021. Nevertheless, the basic structure of this yes or no binary betting remains the logical foundation for the operation of prediction markets today.

How Are Prediction Markets Traded Today?

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

Sports events account for the largest trading volume, benefiting from the continuous schedules of leagues and global events, with the ongoing World Cup further boosting interest. Political, geopolitical, and macroeconomic topics expand from indicators such as inflation data to private company valuation predictions, turning information itself into a tradeable asset. Cryptocurrency and stock prices, along with events driven by rumors, collectively form a complete spectrum from public interest to specialized information demand.

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

The simplest way to understand this structure is to view $1 as 100%. The contract pays $1 (100%) when the event occurs, or $0 otherwise, thus the middle trading price naturally reflects probability. A 40 cent contract represents 40% of that dollar, meaning the market sees a 40% probability of the event occurring, and cents can be read directly as a percentage (ignoring the bid-ask spread and transaction costs).

Prices form through an order book, rather than being determined by any central party. Buy orders (such as buying at 39 cents) and sell orders (such as selling at 40 cents) accumulate at various price levels, and trades are executed at matches between the two parties. Prices (as well as implied probabilities) are generated in real-time through the interplay of the funds of numerous participants. Traders can also sell positions before expiration to lock in profits or cut losses, effectively turning their views on an event into money.

Outcomes are recorded by oracles. No matter how precise the contract prices are, someone still needs to determine “yes” or “no” after the event ends, and the oracle is the mechanism responsible for this judgment.

Oracles operate in two ways:

• Decentralized Oracles: Proposers stake collateral and submit proposed outcomes. If no one disputes within the specified time, it becomes the final result. If a dispute arises, it enters a re-proposal process, and only after further disputes will it go to vote.
• Centralized: Standards for judgment are set in advance, and after the event ends, the exchange directly applies the official result and settles the market immediately. This approach gives complete decision-making power to a single exchange.

For example, on the Limitless platform, once the deadline passes, it will determine the result according to predefined rules. This is reported by oracles that submit real-world results to the blockchain: most markets tracking cryptocurrency prices or stocks automatically report through the Pyth Network, while custom markets like sports or politics are manually judged by the operations 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 numeric reflection represented by prices and judges the correctness of predictions based on pre-set rules after the event ends.

The Evolution of Games and Information Finance

Prediction markets have gone beyond simple betting platforms and evolved into core infrastructure of information finance—turning future uncertainties into real-time price information. Its fundamental distinction from traditional polls or expert predictions is the “skin in the game” mechanism, wherein participants use their own funds to back their positions.

In traditional methods, experts incur almost no reputational costs for incorrect judgments, and polls cannot filter out respondents' indifference or strategic misreporting. The prices in prediction markets have real costs for errors—incorrect positions lead to losses, forcing participants to validate their beliefs with the most objective and latest information. This willingness to bear costs directly translates to market reliability.

This mechanism's performance in actual data can be seen in multiple fields:

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 been statistically highly consistent with actual results in interest rate expectations before Federal Open Market Committee meetings, outperforming federal fund futures and Bloomberg consensus. The reason lies in participants incurring immediate losses if they are wrong, leading to stricter analysis of available information and pricing accordingly.

Transparent probability estimation for politics and elections: In South Korea’s local elections in June 2026, Polymarket correctly predicted 14 out of 16 winners in major cities and provinces. In places where exit polls could only indicate “too close to call,” prediction markets provided real-time probabilities from participants who bet real money, reflecting a combination of judgments of various variables rather than simple predictions.

Responses to market events and company valuations: When the topic of capping interest income on stablecoins arose in March 2026, prediction markets immediately priced the probability of Coinbase’s stock price declining at 97.6%, serving as a real-time risk indicator rather than a post-analysis and demonstrating participants’ sensitive responses when their own funds are at risk. Academic research has also reached similar conclusions: a 2015 study on internal prediction markets at companies like Google and Ford found that prediction errors were reduced by up to 25% compared to official forecasting models, indicating that when insider knowledge combines with risk capital, forecasting accuracy improves.

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

In areas with widespread information distribution, prediction markets act as precise analytical tools; in areas where information is concentrated in the hands of a few, they identify mechanisms for monitoring that concentration. Because participants’ funds genuinely face risks, the prices generated in these markets constitute objective information for evaluating the value of financial assets.

The Absence of Prediction Markets in Asian Policy Discussions

The nature and trajectory of prediction markets vary significantly due to regulatory frameworks across countries. The United States has incorporated them into the regulated financial system through judicial rulings, while major jurisdictions in Asia still largely regard them as traditional gambling categories.

In the United States, litigation has resolved most regulatory uncertainties. The Commodity Futures Trading Commission attempted to classify Kalshi’s election prediction contracts as gambling and sanctioned the platform, but the court ruled that election predictions are not games of chance, and the regulators have no authority to prohibit them. This ruling changed the regulatory stance and became a decisive catalyst for traditional financial institutions, including ICE, Robinhood, and CME, to enter the market.

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, rather than financial policy. Although practices differ among countries, prediction markets generally remain outside formal policy discussions in the region, with only India and Indonesia as exceptions.

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

Prediction Markets at a Regulatory Crossroads and Institutionalization

Prediction markets have become core to global financial and information infrastructure. A significant gap has emerged between global trends and the rigid stance of Asian regulators. In a context where technological and financial boundaries have essentially dissolved, attempts to confine new markets within old regulatory frameworks face inherent limitations. Current regulatory practices in major Asian jurisdictions have three major issues.

The first is the paradox of regulatory arbitrage.

Prediction markets operate on a borderless digital network, and blocking platforms or restricting users in one country does not eliminate the underlying demand. Users will turn to unregulated offshore platforms, taking on greater risks. This leads to capital flowing out of jurisdictions, and regulators simultaneously lose market oversight and associated tax revenues, undermining regional financial competitiveness in the long term.

The second is the loss of sovereignty over national information infrastructure.

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

The third is the abandonment of user protection.

Users find themselves in a blind spot, devoid of institutional guarantees. Merely denying the market without engaging in sufficient prior discussions will expose users to risks and push them out of 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 the formal system. This perspective shift requires specialized research, but discussions on this topic remain limited for now.

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

Regulation should not be a dam blocking the flow of water but a channel for guiding the flow correctly.

What Asia needs now is not stricter enforcement but the initiation of forward-thinking discussions to respond to this shift. Pushing transactions that have already occurred into the shadows is the worst policy. Continued efforts are needed to incorporate 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.