Predictive Markets vs Traditional Forecasting: Why Does Money Voting Outperform Opinion Expression in Accuracy?

In November 2024, the moment the results of the U.S. election were announced, many people’s expressions shifted from anticipation to shock.

Before the election, almost all mainstream polls told the same story: it was a closely matched contest. FiveThirtyEight’s polling aggregation showed Harris leading Trump by only 1.2 percentage points nationwide; the New York Times polls even narrowed the gaps in 7 key swing states to within 1 point. The media dubbed it “the closest election in history,” with commentators repeatedly stressing that the race was tight and the outcome was uncertain.

Reality played out a completely different script. Trump ultimately won with 312 electoral votes to Harris’s 226, sweeping all 7 swing states and leading by more than 3 million votes in the popular vote. This was not a narrow win—it was an outright landslide.

Polls were off again. This was the third consecutive presidential election in which traditional polls collectively underestimated support for the same side.

At the same time, the Polymarket prediction market painted a different picture. On the eve of the election, Trump’s win probability on Polymarket held around 60%, far higher than what the polls implied—the so-called “even split.” More notably, traditional polls barely reflected this gap—many media polls still showed Harris leading before the vote. Independent academic research after the fact quantified the discrepancy: Polymarket outperformed traditional polls in predicting the 2024 presidential election, with especially strong performance in swing states.

Two faces, two entirely different judgments. Which one is closer to the truth?

Money Voting vs. Opinion Expression: The Fundamental Difference Between Two Mechanisms

To answer this question, we need to go back to the core mechanisms behind these two prediction approaches.

Traditional polls rely on “opinion expression”—respondents are asked, “Who do you plan to vote for?” and provide an answer verbally. In this process, respondents face no cost for being wrong. Answers are free, and bias is free. A respondent may hide their true intent due to social pressure, choose differently on election day after a last-minute change of mind, or may not have thought through the question at all—because, in the end, no one knows. The lesson from the 2024 election is that polls underestimated support for the same side three elections in a row, revealing long-standing systemic difficulties for traditional polls in identifying and reaching specific voter groups.

The logic of prediction markets is completely different. On platforms such as Polymarket or Kalshi, participants put real money on the line. If their judgment is correct, they exit with profit; if it is wrong, the loss is reflected directly in their account balance. Every trade is a “vote” using real assets, and the cost of an incorrect call shows up immediately.

This money-voting mechanism creates two key effects.

First, financial incentives force beliefs to become honest. In polls, respondents can say whatever they want without consequences. But in prediction markets, a wrong judgment means a real loss of money. This asymmetric risk structure forces participants to handle every piece of information cautiously, repeatedly validating their judgments. Researchers point out that prediction markets use financial incentives to reveal private information quickly, and use trading to correct biases; their historical performance has already been better than traditional polls or expert forecasts in elections and other events.

Second, price discovery is continuous and real-time. Polls are snapshots—surveys take days or weeks to complete, and by the time they are published, the state at a particular moment is already frozen. Prediction market prices change every second. A breaking news item, a leaked document, or a spontaneous press conference can be transmitted to market prices within seconds, faster than the dissemination speed of traditional media reports. During the 2024 election, price fluctuations on platforms such as Kalshi responded to some subtle changes faster than aggregated polls.

The Source of Efficiency: Not Crowd Wisdom, But 3% of Informed Traders

The high accuracy of prediction markets is often attributed to “crowd wisdom”—information aggregated from a large number of participants, with biases canceling each other out, ultimately converging on the correct answer. This explanation sounds intuitive, but the latest top-journal research from SSRN—based on complete Polymarket trading data—offers an answer that overturns this view.

After analyzing millions of trading accounts, the study found that prediction markets’ price-discovery efficiency does not come from the crowd, but is driven by about 3% of informed traders. Across all categories, only about 3.14% of accounts have statistically significant, sustained profitability; they contribute the vast majority of the price-discovery function, and their order flow can significantly predict future prices and final outcomes. Meanwhile, the remaining roughly 97% of ordinary traders, despite generating most of the trading volume, do not provide meaningful informational value. In settings with high information sensitivity—such as FOMC decisions or the release of corporate earnings—research confirms that it is only these informed traders who trade in the direction of the information at the moment it is released.

The significance of this finding is that the high accuracy of prediction markets is not simply due to “the power of many,” but because a small minority with informational advantages and professional skills continuously inject information into prices, and the broader market provides liquidity. This pattern suggests that market efficiency does not necessarily equal widespread rationality among participants. Understanding this helps clarify the quality of the signals that prediction markets provide.

When Prediction Markets Outperform Traditional Information Sources: A Mirror Experience in Crypto

The application of prediction markets in finance, to a certain extent, mirrors the operating logic of cryptocurrency markets.

The 24/7 trading rhythm and global liquidity of crypto assets naturally make them an ideal training ground for prediction markets. On Polymarket, crypto-related categories account for about 40% of activity from the smallest-scale traders; Bitcoin attracted about 593,000 users to participate in trading. This pattern is not accidental: continuous trading and familiar price volatility make crypto markets an ideal entry point for new users into prediction markets.

More importantly, prediction markets are evolving from “election betting tools” into an information-aggregation infrastructure across multiple categories. In 2026 Q1, sports prediction markets on Polymarket generated about $10.1 billion in trading volume; political categories contributed about $5 billion. User active days increased from 2.5 to 9.9, and the average number of categories participated in expanded from 1.45 to 2.34. This shift indicates that user participation is moving from “event-driven” to “continuous behavior”—people no longer only open the platform just before major events like elections or the Super Bowl, but use it as an everyday tool to track news, macro trends, and asset prices.

As an exchange deeply focused on the crypto industry, Gate has always been paying attention to this composite track where information pricing and asset trading are fused. Since 2026, Gate has aligned with multiple prediction market ecosystems across several dimensions, helping users access a broader range of prediction assets through familiar crypto trading interfaces.

The Unignorable Dark Side: Three Major Challenges for Prediction Markets

High accuracy does not mean prediction markets have no flaws. In fact, this fast-growing sector has also exposed multiple risks during rapid expansion.

First, regulatory pressure on insider trading and market manipulation. In May 2026, the U.S. House Committee on Oversight officially launched an investigation into Polymarket and Kalshi, because it was concerned that government employees might trade using non-public information on policy and national security events. Previously, cases had already confirmed that people made profits of tens of thousands of dollars based on insider information in specific sensitive events. The CFTC has officially added insider trading to its top five enforcement priorities for prediction markets, and the Department of Justice has begun investigating multiple related cases.

Second, the “fat-tail” problem in liquidity distribution. The markets for top-tier events are extremely liquid, but long-tail prediction topics often lack depth. When building positions on niche events, slippage costs can be as high as 10% or even more. This uneven distribution limits the usefulness of prediction markets as a general-purpose information aggregator—only price signals from high-attention events have sufficient reference value.

Third, uncertainty in regulatory compliance. Although the CFTC, through multiple court actions in 2026, clarified its exclusive jurisdiction over event contracts, the jurisdictional struggle between federal and state governments is still ongoing. Some states still treat prediction market contracts as illegal gambling and have brought criminal prosecutions, leaving platforms facing resistance at the local level to operate in compliance.

These challenges show that while prediction markets outperform traditional polls in certain dimensions, they are far from perfect. Their advantages are reflected in specific scenarios—when trading volume is high, liquidity is strong, and events are transparent—where price-discovery efficiency is higher. In situations with insufficient liquidity or severe information asymmetry, the quality of signals can be significantly diminished.

Summary

Returning to the core question of this article: prediction markets vs. traditional polling—who is more reliable? The answer is not a simple “who wins and who loses.”

At the mechanism level, prediction markets have inherent advantages. Financial incentives make participants responsible for their judgments; continuous trading allows prices to reflect new information immediately; global participation gives the information pool far greater depth and breadth than any single institutional poll. Academic research also supports this: in U.S. presidential elections since 1988, prediction markets have been closer to the final outcome than polls in 74% of cases.

However, prediction markets also face risks such as capital manipulation, legal compliance obstacles, and low liquidity for certain topics.

Whether something is reliable ultimately depends on the use case. For events with sufficient liquidity and transparent information, prediction markets often provide more accurate probability estimates than traditional polling. But in areas where participation is insufficient and information asymmetry is severe, traditional research methods may still have irreplaceable value.

For ordinary users, the key is to understand the limitations of both tools—to neither blindly trust the accuracy of prediction markets, nor completely dismiss the reference value of polls because of past failures. The best approach is to cross-verify the real-time probabilities from prediction markets with traditional information sources, and use multiple perspectives to get closer to the truth—rather than placing all your bets on any single tool.

As for the 2024 general election, prediction markets provided answers closer to reality than polls. This may not be a coincidence, but rather the structural advantages shown by the money-voting mechanism when facing two major challenges: dispersed information and participation incentives.