Tiger Research: Zuckerberg Begins Betting on Prediction Markets, While Asian Countries Still View Them as Gambling

Key Takeaways

• This article is written by Tiger Research. Prediction markets have grown into a mainstream industry, with monthly trading volumes reaching $14 billion. The advancement of Meta's own "Arena" project also demonstrates recognition from major tech companies.
• The mechanism is simple: if an event occurs, the contract settles at $1; if not, it settles at $0. Therefore, its trading price represents the real-time probability, and the outcome is confirmed by an oracle after the event concludes.
• This is all built on the principle of "skin in the game": participants lose money if their predictions are wrong, which lends 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.
• Asia's current task is not to block these markets, but to think about how to responsibly utilize this data within a formal framework. Avoiding the discussion essentially cedes leadership to foreign entities.

Prediction Markets Have Found Product-Market Fit

Prediction markets mostly remained a conceptual stage for many years. Around 2020, the situation changed as a few small projects began accumulating significant trading volumes and overcoming regulatory hurdles one by one, marking the formal emergence of prediction markets as an industry.

Growth accelerated thereafter. Monthly trading volumes now exceed $14 billion, and the combined valuation of major platforms is around $40 billion.

Meta's entry further proves it has moved beyond the early stage. The New York Times recently reported that Mark Zuckerberg personally leads a team developing a prediction market application called Arena. A major tech company investing such resources indicates this industry has exited the experimental phase and established a proven business model.

Where Did Prediction Markets Originate?

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

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 economics paper solidified the term "prediction market."

However, the underlying practice predates the name by far. The earliest form was political betting on election outcomes. In 18th-century London coffeehouses, people placed bets on parliamentary scandals and changes of prime ministers, with the resulting odds sometimes appearing in newspapers. In 19th-century New York, informal futures markets predicting presidential election results were active in over-the-counter markets near Wall Street.

Academic Use

The academic starting point was in 1988 with three economists at the University of Iowa. Puzzled by polls failing to predict Jesse Jackson's victory in the Michigan primary, they designed a market where people could directly trade election outcomes. This became the Iowa Electronic Markets (IEM).

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

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 the event occurs, 0 otherwise—is perfectly consistent with the logic of prediction markets.

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

How Do Prediction Markets Trade Today?

Today, prediction markets cover topics spanning almost any imaginable event.

Sports events account for the largest trading volume, fueled by continuous league and global event schedules, with the ongoing World Cup further boosting activity. Politics, geopolitics, and macroeconomics have expanded from indicators like inflation data to private company valuation predictions, turning information itself into a tradable asset. Cryptocurrency and stock prices, along with some rumor-driven events, collectively form a complete spectrum from mass interest to specialized information needs.

Each contract settles on a binary yes-or-no basis. Take the example of whether the 2028 Republican presidential nominee will be J.D. Vance: If Vance is confirmed as the nominee, a "yes" contract pays $1; otherwise, a "no" contract pays $1.

The simplest way to understand this structure is to treat $1 as 100%. The contract pays $1 (100%) if the event occurs and $0 otherwise, so the intermediate trading price naturally reflects the probability. A contract trading at 40 cents represents 40% of that dollar, meaning the market perceives a 40% probability of the event occurring; the cent value can be read directly as a percentage (ignoring bid-ask spreads and transaction costs).

Prices are formed through the order book, 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 the two match. The price (and implied probability) is generated in real-time through the interplay of capital from numerous participants. Traders can also sell their positions before expiration to lock in profits or cut losses, essentially converting their view on the event into money.

Outcomes are recorded by oracles. No matter how precise the contract price, someone still needs to determine "yes" or "no" after the event concludes; the oracle is the mechanism responsible for this judgment.

Oracles operate in two ways:

• Decentralized Oracle: A proposer stakes collateral and submits a proposed outcome. If no one challenges it within a specified timeframe, it becomes the final result. If challenged, it enters a re-proposal process, and voting occurs only after further challenges.
• Centralized: Judgment criteria are set in advance. After the event concludes, the exchange directly applies the official result and settles the market immediately. This approach places the power of judgment entirely with a single exchange.

For example, on the Limitless platform, once the deadline passes, the result is finalized according to preset rules. Reporting is done by oracle services that relay real-world outcomes to the blockchain: most markets tracking crypto or stock prices are reported automatically via the Pyth Network, while custom markets like sports or politics are judged manually by the operations team within 24 to 72 hours.

Essentially, a prediction market is an information system. It compresses the views of numerous participants into a single number reflected by the price, and after the event concludes, it checks whether the prediction was correct based on preset rules.

The Evolution of Gaming and Information Finance

Prediction markets have transcended simple betting platforms, evolving into the core infrastructure of information finance—converting future uncertainty into real-time price information. Their fundamental difference from traditional polls or expert predictions lies in the "skin in the game" mechanism, where participants use their own capital to back their positions.

In traditional methods, experts face little reputational cost for being wrong, and polls cannot filter out respondents' indifference or strategic misreporting. Prediction market prices have a real cost for being wrong—incorrect positions lose money, forcing participants to validate their beliefs with the most objective and up-to-date information. This willingness to bear cost directly translates into market reliability.

The performance of this mechanism in actual data can be seen across several areas:

Accuracy of Financial and Monetary Policy Predictions: A February 2026 study by a Federal Reserve economist explained why. Since 2022, interest rate expectations from prediction markets before FOMC meetings have shown statistically high consistency with actual outcomes, outperforming federal funds futures and the Bloomberg consensus. The reason is that participants immediately lose money if they are wrong, compelling them to analyze available information more rigorously and price it accordingly.

Transparent Probability Estimates in 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. This results from numerous participants synthesizing multiple variables, rather than a simple forecast.

Responsiveness to Market Events and Company Valuations: When the topic of a stablecoin interest income cap emerged 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 a post-hoc analysis. This demonstrates the sensitive response of participants when their own capital is at risk. Academic research reaches similar conclusions: a 2015 study of internal prediction markets at companies like Google and Ford found prediction errors reduced by up to 25% compared to official forecasting models, indicating that prediction accuracy improves when inside knowledge is combined with risk capital.

Information asymmetry remains a limitation. In the January 2026 Venezuela case, someone used confidential information for insider trading, exposing a real vulnerability. However, the attempt to distort prices was identified and prosecuted as a crime, proving the market aims to operate with transparency and accountability.

In areas where information is widely distributed, prediction markets are precise analytical tools. In areas where information is concentrated in few hands, they act as monitoring mechanisms capable of identifying that 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 vary greatly depending on each country's regulatory framework. The United States has integrated them into the regulated financial system through judicial rulings, while major Asian jurisdictions mostly still classify them under traditional gambling categories.

In the U.S., litigation resolved most 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 predictions are not games of chance and the regulator lacked the authority to ban them. This ruling shifted the regulatory posture and became 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, rather than financial policy. Although approaches vary by country, prediction markets in this region largely remain outside formal policy discussions, with India and Indonesia being exceptions.

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

Prediction Markets at the Crossroads of Regulatory Dilemma and Institutionalization

Prediction markets have become central to global financial and information infrastructure. A significant gap has emerged between global trends and the rigid stance of Asian regulators. In an era where technological and financial borders have largely disappeared, attempts to confine new markets within old regulatory frameworks face inherent limitations. The current regulatory approach in major Asian jurisdictions presents three major problems.

First is the Paradox of Regulatory Arbitrage

Prediction markets operate on borderless digital networks. Blocking a platform or restricting users in one country cannot eliminate the underlying demand. Users will turn to unregulated offshore platforms, taking on greater risks. This leads to capital outflows from the jurisdiction, while regulators simultaneously lose market oversight and related tax revenue, weakening the region's financial competitiveness in the long run.

Second is the Loss of National Information Infrastructure Sovereignty

Prediction markets are an advanced information infrastructure that transforms complex social issues into precise numerical estimates, not merely a place for betting. Recent elections in Asia showed that prediction markets read public sentiment faster and more accurately than traditional polls. When excluded under the guise of regulation, the data best reflecting a society's sentiment accumulates on foreign servers. The result is that foreign media and institutions understand local society more clearly than local analysts.

Third is the Abandonment of User Protection

Users are left in a blind spot without institutional safeguards. A policy of simply denying markets without adequate prior discussion only exposes users to risk and pushes them outside the system.

The focus of the discussion needs a fundamental shift.

The question is no longer how to block this market, but how to healthily utilize this data within a formal framework. This shift in perspective requires dedicated research, but related discussions remain 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 water's flow, but a channel guiding 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 possible policy. Sustained effort is needed to bring them into the formal system through constructive dialogue, establish transparent oversight mechanisms, and return the data generated in the process as a national and social asset.