Do You Really Understand Prediction Markets? - Tiger Research

• The prediction market has established itself as a mature industry recognized by big tech, with a monthly trading volume of $14 billion and news of Zuckerberg's development of 'Arena' overlapping with it.
• The principle of the prediction market is simple. Contracts are traded on the order book that settle at either $1 or $0 when an event occurs, and that price becomes the real-time probability, with an oracle determining the final result after expiration.
• In other words, it is based on 'skin in the game', where participants lose money if they are wrong, adding trust to information by putting in their own capital.
• While the Western world has already incorporated prediction markets into the institutional framework, discussions about prediction markets in Asia are lacking, resulting in three costs: capital outflow, loss of information sovereignty, and neglect of users.
• The question that Asia needs to ask now is not "How do we stop this?" but rather "How do we use this data healthily within the institutional framework?" While avoiding discussions about prediction markets, the initiative is shifting overseas.

1. Prediction Markets That Have Found Clear PMF {#rps-1}

Prediction markets have long remained at a conceptual level. However, since around 2020, several small projects have accumulated meaningful trading volumes and have begun to break through regulatory barriers one by one, finally establishing themselves as an industry.

Recently, the growth trend of the market has become even steeper. The monthly trading volume has exceeded $14 billion, and the corporate value of the leading players in the market has reached around $40 billion.

The fact that prediction markets have moved beyond the initial stage has become even clearer with Meta's entry. Recently, the NYT reported that Mark Zuckerberg is personally leading a team to develop the prediction market app 'Arena'. The involvement of big tech indicates that this market has established itself as a mature business model validated by a clear PMF (Product-Market Fit).

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2. Where Did Prediction Markets Start? {#rps-2}

Prediction markets are not an invention that appeared suddenly one day. They have been informally utilized for a long time, primarily in academia and finance, but have only recently been made public and established as an industry through the combination with blockchain technology.

2.1. Informal Use {#rps-3}

The term 'prediction market' has only recently become officially established. In the 1980s, it was referred to by various names such as information market and decision market, but the current name solidified through an economic paper in 2004.

In fact, this concept had been realized through actual betting long before. The most classic form was political betting on election outcomes. In 18th century London coffeehouses, bets were placed on parliamentary scandals or changes in prime ministers, and the odds were even reported in newspapers. In the 19th century, informal futures markets predicting presidential election outcomes were actively operated near Wall Street in New York.

2.2. Academic Use {#rps-4}

In the academic realm, the attempt at the University of Iowa in 1988 marked the starting point. At that time, three economists questioned why polls failed to predict Jackson's victory in the Michigan caucus and designed a market for buying and selling election outcomes. This led to the birth of the Iowa Electronic Markets (IEM).

In 1992 and 1993, they were granted permission from the CFTC for research purposes. The IEM, which anyone could participate in for $5, outperformed traditional polls by about 75% from 1988 to 2004, effectively serving as a 'laboratory for aggregating collective intelligence into prices'. However, there was still no institutional foundation to operate it for the public.

2.3. Binary Options Products {#rps-5}

These prediction markets are very similar to 'binary options' in the financial market. They operate on a structure where bets are placed on whether the price will exceed a baseline at a specific point in time, perfectly aligning with the principle of prediction markets that settle at 1 or 0 when an event occurs.

This product has also been actively introduced in institutional exchanges. Examples include the Fixed Rate Options (FRO) of the American Stock Exchange (Amex) in 2007 and the S&P 500-based binary options of the Chicago Board Options Exchange (CBOE) in 2008. However, as fraudulent trading became frequent on offshore platforms, major countries experienced ups and downs, banning personal sales between 2017 and 2021. Nevertheless, the essence of the 'yes or no' contract remains aligned with today's prediction markets.

3. How Are Today's Prediction Markets Traded? {#rps-6}

Currently, the topics covered in prediction markets are close to virtually everything in the world.

The sports sector, which attracts the most funds, boasts overwhelming trading volumes thanks to various leagues and global events throughout the year. It is currently receiving even greater attention due to the ongoing World Cup. Additionally, in politics, geopolitics, and macroeconomics, it has expanded its scope beyond indicators like price indices to include predictions of the value of unlisted companies, providing value through data. Including prices of cryptocurrencies and stocks, as well as gossipy micro-events, prediction markets showcase a broad spectrum that encompasses both popular interests and professional information demands.

All contracts in these prediction markets fundamentally follow a 'yes/no (YES/NO)' binary settlement method. For example, consider the market asking, "Will the Republican presidential candidate in 2028 be J.D. Vance?" If Vance is indeed confirmed as the Republican candidate, those who bet on YES will receive $1, while those who bet on NO will receive nothing if it does not materialize.

The intuitive way to understand this structure is to view one dollar as 100%. When an event occurs, you receive one dollar (100%), and if it does not occur, you receive zero dollars (0%). Therefore, the price traded in between naturally becomes a number that represents probability. A contract trading at 40 cents means that the market perceives the probability of that event occurring as 40% out of 100%. In other words, you can read the cents directly as a percentage. (However, this assumes that spreads and transaction costs are ignored.)

Price determination occurs through the 'Order Book' without central intervention. Buy orders stating "I will buy at 39 cents" and sell orders stating "I will sell at 40 cents" stack up at different price levels, and transactions are executed at the points where they match. Thus, prices and probabilities are the real-time calculations resulting from numerous participants betting their capital, allowing participants to resell contracts before expiration to secure profits or minimize losses, effectively exchanging opinions about specific events in monetary terms.

The results are recorded through an oracle. No matter how sophisticated the price is, someone must ultimately confirm "So, is it YES or NO" once the event concludes. The device responsible for this determination is the oracle. In the previous example, the final procedure to determine whether Vance was actually confirmed as the Republican candidate takes place here.

Oracles operate in two main ways:

1. Decentralized Oracle: If someone stakes a deposit and proposes a result, it is confirmed as long as there are no objections within a certain period. If an objection is raised, it goes through a re-proposal process, and if there are still objections, it is finally resolved through voting.
2. Centralized: The criteria for judgment are pre-written, and once the event concludes, the central authority applies the official announcement directly for immediate settlement. Instead, it completely entrusts the judgment authority to a single exchange.

For example, Limitless confirms results according to predetermined rules once the deadline passes. At this point, the oracle reports the results, with most markets like crypto or stocks automatically reporting through the Pyth Network, while custom markets such as sports or politics are manually determined by the operating team within 24 to 72 hours.

In this way, prediction markets, including Limitless, compress the opinions of numerous participants into a single number reflected in the price, and furthermore, determine the accuracy of that prediction according to established rules after the event concludes, creating a system of information.

4. The Evolution Towards 'Information Finance' Created by Skin in the Game
Prediction markets have evolved beyond simple gambling platforms to become the core infrastructure of 'Information Finance,' which transforms future uncertainties into real-time price information. The fundamental point that differentiates this market from traditional opinion polls or expert forecasts is the 'Skin in the Game' mechanism, where participants stake their capital to make decisions.

In traditional methods, experts may be wrong without significant damage to their reputation, and opinion polls fail to filter out respondent indifference or strategic lies. However, the price in prediction markets incurs a definite cost of 'losing money if wrong,' prompting participants to reflect the most objective and up-to-date information to validate their convictions. This voluntary expenditure of costs directly correlates with the market's credibility.

To see how the principle of 'Skin in the Game' is implemented in actual data, consider the following:

• Accuracy of Financial and Monetary Policy Predictions: A study by economists from the Federal Reserve published in February 2026 demonstrates why prediction markets are powerful. Just before Federal Reserve meetings since 2022, the interest rate forecasts from prediction markets statistically aligned with actual outcomes. This was more precise than existing financial sector federal funds futures or Bloomberg consensus, as market participants face immediate asset losses when wrong, leading them to analyze and reflect any information more rigorously in prices.
• Transparent Probability Calculation in Politics and Elections: During the June 2026 local elections in South Korea, Polymarket accurately predicted the winners of 14 out of 16 regional mayoral elections. While traditional exit polls only provided ambiguous results of 'contested,' prediction markets presented the 'probabilities' evaluated by participants betting their money in real-time prices. This transcends simple predictions, resulting from the collective intelligence of the market incorporating numerous variables into prices.
• Reflection of Market Events and Corporate Value: In March 2026, during the issue of limiting interest earnings on stablecoins, prediction markets immediately reflected a 97.6% probability of Coinbase's stock price decline. This serves as a real-time crisis detection indicator rather than a retrospective analysis, demonstrating how sensitively market participants respond to protect their assets. Academia has also shown through cases of major companies like Google and Ford in 2015 that internal prediction markets can reduce forecast errors by up to 25% compared to official forecasting models, proving that the combination of insider knowledge and capital maximizes predictive power.

Of course, information asymmetry always exists. The betting case of an insider with confidential information during the Venezuelan crisis in January 2026 illustrated the market's limitations. However, paradoxically, the fact that actions attempting to distort prices by monopolizing information were detected as crimes proves that this market is designed to operate based on transparent and credible grounds.

In conclusion, prediction markets serve as the most precise analytical tool in areas where information is evenly distributed, and as a monitoring device that identifies monopolies in areas where information is concentrated. As long as participants' capital is at stake, the prices presented by the market are the most objective information that cannot be ignored and an essential indicator for assessing the value of financial assets.

5. Prediction Markets Not Yet on the Asian Discussion Table
The nature and development of prediction markets vary significantly according to the regulatory frameworks of different countries. While the United States has incorporated prediction markets into the realm of institutions through judicial rulings, major Asian countries still tend to regulate them within the category of traditional gambling.

The United States resolved regulatory uncertainties through legal disputes. The Commodity Futures Trading Commission (CFTC) attempted to sanction Kalshi's election prediction contracts as gambling activities, but the court ruled that "election predictions are not gambling games, and regulatory authorities do not have the authority to prevent them." This judicial ruling prompted a change in the attitude of regulatory authorities and ultimately became a decisive catalyst for traditional financial capital such as ICE, Robinhood, and CME to enter the market.

In contrast, major Asian countries predominantly equate the binary structure of prediction markets, which settles upon event realization, with traditional gambling. They approach it from the perspective of gambling regulation and public safety rather than financial policy, adopting different responses by country, but still perceiving it as an industry that has not even reached the discussion table, except for India and Indonesia.

Ultimately, the attitude towards prediction markets sharply divides based on whether regulatory authorities view this market as 'financial innovation' or as a 'subject of social control.'

6. The Dilemma of Regulation and the Crossroads of Institutionalization for Prediction Markets

The predictive market has already become a core pillar of global finance and information infrastructure. However, there exists a serious gap between this global trend and the rigid responses of Asian regulatory authorities. In an era where the boundaries between technology and finance have collapsed, attempts to confine new markets within outdated regulatory frameworks are bound to face limitations. Currently, the regulatory approaches maintained by major Asian countries are committing critical errors on three fronts.

First, there is the paradox of regulatory arbitrage.

Predictive markets operate on a borderless digital network. Just because a specific country or region blocks a platform or cracks down on users does not mean that demand itself disappears. Users simply migrate to platforms in regulatory blind spots abroad, taking on greater risks. Ultimately, capital flows out of the country, and regulatory authorities find themselves trapped in a 'regulatory paradox,' losing not only their supervisory authority over the market but also their tax sovereignty. This leads to a self-inflicted erosion of financial competitiveness within Asia.

Second, there is the loss of sovereignty over national information infrastructure.

Predictive markets are not merely betting spaces; they are sophisticated 'information infrastructures' that convert complex social issues into precise numbers. As recent election results across Asia have shown, predictive markets can read public sentiment much faster and more accurately than traditional polls. While we reject this under the guise of regulation, the core data that best represents our society accumulates on foreign servers. Consequently, we surrender the initiative for analyzing data about our society to foreign media and institutions, resulting in an imbalance of information that causes us to lose insight domestically.

Third, there is the neglect of users.

Users are left in a blind spot without any institutional safeguards. Policies that simply deny the market without sufficient discussion expose users to risks and push them out of the system.

It is now time to completely shift the focus of the discussion.

Rather than asking, "How can we block this market?" we should be contemplating, "How can we healthily utilize this data within the institutional framework?" A professional study is essential for this shift in perspective, but discussions in this area are still lacking.

In this domain, 'Limitless Research' is filling this gap by processing predictive data as information assets in Asian markets such as Korea and Japan. In the future, there should be more entities leading a healthy data ecosystem like this.

Regulation should not be a dam that blocks the flow but a channel that guides the water correctly.

What Asia needs now is not repression but the beginning of forward-looking discussions that respond to the trends of the times. Pushing already occurring transactions into the shadows is the worst policy. It is necessary to incorporate them into the institutional framework through healthy discussions, establish a transparent supervisory system, and return the data generated here as national and social assets.

This article is a feature from Tiger Research, a global Web3 research institution partnered with Block Media, titled 'Do You Really Understand Predictive Markets?' The report can also be found on the official site of .

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