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

Key Takeaways

• This article is authored by Tiger Research. Prediction markets have grown into a mainstream industry, reaching a monthly trading volume of $14 billion, with Meta's push for its own "Arena" project signaling recognition from big tech.
• The mechanism is straightforward: if an event occurs, the contract settles at $1; if not, it settles at $0. Consequently, its trading price reflects real-time probability, with oracles confirming the outcome after the event.
• This is built on the principle of "skin in the game": participants lose money if they are wrong, lending 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, a 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 framework. Avoiding the discussion effectively cedes leadership to foreign entities.

Prediction Markets Have Found Product-Market Fit

For many years, prediction markets largely remained a concept. Around 2020, things began to change. A few small projects started accumulating significant trading volume and overcame regulatory hurdles one by one, marking the formal emergence of prediction markets as an industry.

Growth has accelerated since then. Monthly trading volume now exceeds $14 billion, and the combined valuation of major platforms is around $40 billion.

Meta's entry further proves it has moved beyond its early stages. The New York Times recently reported that Mark Zuckerberg is personally leading a team to develop a prediction market app called Arena. A large tech company investing such resources indicates the industry has moved past the experimental phase and established a proven business model.

Where Did Prediction Markets Originate?

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

Informal Use

The term "prediction market" itself emerged later than its history. By the 1980s, the concept had various names, such as information markets or decision markets, until a 2004 economics paper standardized it as "prediction markets."

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

Academic Use

The academic starting point was 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 allowing people to trade election outcomes directly. 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 willing to put up $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 prices. Nonetheless, there was no regulatory framework at the time that would allow it to operate as a public market.

Binary Options

These early prediction markets closely resemble binary options in financial markets: contracts for yes/no bets based on whether a price breaks a certain threshold within a specified time. Their structure—settling at 1 if the event occurs, otherwise 0—is perfectly aligned 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 retail sales of these products between 2017 and 2021. Nevertheless, this basic yes/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 nearly any imaginable event.

Sports events account for the largest trading volume, fueled by the continuous schedule of leagues and global tournaments, with the ongoing World Cup further boosting activity. Politics, geopolitics, and macroeconomics extend from indicators like inflation data to valuations of private companies, turning information itself into a tradeable asset. Cryptocurrency and stock prices, along with some rumor-driven events, together form a complete spectrum from mass interest to specialized information needs.

Each contract settles on a binary yes/no basis. Take the example: "Will the 2028 Republican presidential nominee 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 view $1 as 100%. A contract pays $1 (100%) if the event occurs and $0 otherwise, so the intermediate trading price naturally reflects probability. A contract trading at $0.40 represents 40% of that dollar, meaning the market implies 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 form through the order book, not by any central authority. Buy orders (e.g., buy at $0.39) and sell orders (e.g., sell at $0.40) accumulate at various price levels, and trades execute where bids and asks match. The price (and implied probability) is generated in real-time by the interplay of funds from numerous participants. Traders can also sell their positions before expiration to lock in profits or cut losses, essentially swapping their view on the event for money.

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: The proposer posts collateral and submits a proposed outcome. If no one challenges it within a specified timeframe, it becomes the final outcome. If challenged, a re-proposal process begins, only proceeding to a vote after further challenges.
• Centralized: Judgment criteria are set in advance. After the event, the exchange directly applies the official result and immediately settles the market. This approach entrusts the judgment entirely to a single exchange.

For example, on the Limitless platform, once the deadline passes, the outcome is finalized according to preset rules. Reporting is done by an oracle service that relays real-world results to the blockchain: most markets tracking crypto or stock prices report automatically via the Pyth Network, while custom markets like sports or politics are manually adjudicated by the operations team within 24 to 72 hours.

Essentially, a prediction market is an information system that compresses the opinions of a large number of participants into a single number reflected by the price, and after the event, judges whether the prediction was correct based on preset rules.

The Evolution of Gaming and Information Finance

Prediction markets have evolved beyond simple betting platforms into the core infrastructure of information finance—transforming 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 back their stance with their own money.

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

Evidence of this mechanism in actual data appears across several domains:

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 ahead of FOMC meetings have shown statistically high alignment with actual outcomes, outperforming federal funds futures and the Bloomberg consensus. The reason: participants lose money immediately if wrong, forcing 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 winner 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 collective judgment of numerous participants synthesizing multiple variables, rather than a simple forecast.

Responsiveness to Market Events and Company Valuations: In March 2026, when the issue of stablecoin interest income caps emerged, prediction markets immediately priced the probability of a Coinbase stock price decline at 97.6%, functioning as a real-time risk indicator rather than a post-hoc analysis. This demonstrated the sensitive responsiveness of participants when their own capital is at stake. 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, suggesting that combining inside knowledge with risk capital enhances prediction accuracy.

Information asymmetry remains a limitation. The January 2026 case in Venezuela, where someone used confidential information for insider trading, exposed a genuine weakness. However, this attempt to distort prices was identified and prosecuted as a crime, demonstrating that the market aims to operate with transparency and accountability.

In domains where information is widely distributed, prediction markets are precise analytical tools. In domains where information is concentrated in few hands, they act as monitoring mechanisms capable of identifying that concentration. Because participants' funds are 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 significantly across countries due to differing regulatory frameworks. The United States has integrated them into the regulated financial system through judicial rulings, while most major Asian jurisdictions still largely treat them as a form of traditional gambling.

In the US, litigation resolved much 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 that the regulator lacked the authority to prohibit them. This ruling shifted the regulatory posture and served as a decisive catalyst for entry by traditional financial institutions, including ICE, Robinhood, and CME.

In contrast, within 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 between countries, prediction markets in the region mostly 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 a Crossroads: Regulatory Dilemma vs. Institutionalization

Prediction markets have become central to global financial and information infrastructure. A significant gap has emerged between the global trend and the rigid stances of Asian regulators. In an era where technological and financial borders have largely vanished, attempts to confine new markets within old regulatory frameworks have inherent limitations. Three major issues arise from the current regulatory approaches in major Asian jurisdictions.

First: The Paradox of Regulatory Arbitrage

Prediction markets operate on borderless digital networks. Blocking platforms or restricting users within a single country cannot eliminate the underlying demand. Users will turn to unregulated offshore platforms, assuming greater risk. This leads to capital outflows from the jurisdiction, regulators simultaneously losing market oversight and related tax revenue, and a long-term weakening of the region's financial competitiveness.

Second: 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 have shown that prediction markets read public sentiment faster and more accurately than traditional polls. When excluded in the name of regulation, the data best reflecting that society's sentiment accumulates on foreign servers. The result is that foreign media and institutions have a clearer understanding of local society than local analysts.

Third: The Abandonment of User Protection

Users are left in a blind spot without institutional safeguards. Policies that simply deny the market without adequate prior discussion only expose users to risk and push 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 currently, the relevant discussion remains limited.

In this area, 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 to initiate forward-looking discussions to respond to this shift. Pushing transactions that are already happening 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.