In November 2024, as the results of the U.S. presidential election were announced, anticipation quickly turned to shock for many observers.
Leading up to the vote, nearly all major polls told the same story: this was a neck-and-neck race. According to FiveThirtyEight’s polling aggregate, Harris led Trump by just 1.2 percentage points nationwide. The New York Times’ polling put the gap in seven key swing states at less than one percentage point. Media outlets dubbed it "the closest election in history," with commentators repeatedly emphasizing the deadlock and unpredictability of the outcome.
Reality, however, played out very differently. Trump ultimately won with 312 electoral votes to Harris’s 226, sweeping all seven swing states and leading the popular vote by over 3 million. This wasn’t a narrow win—it was an unequivocal landslide.
Once again, the polls missed the mark. For the third consecutive presidential election, traditional polling significantly underestimated support for the same political camp.
Meanwhile, the Polymarket prediction market painted a different picture. On the eve of the election, Trump’s odds on Polymarket hovered around 60%, far higher than the "toss-up" suggested by the polls. Even more striking, traditional polls barely reflected this divergence—many media surveys still showed Harris in the lead right before Election Day. Subsequent independent academic research quantified this gap: Polymarket outperformed traditional polls in forecasting the 2024 presidential outcome, especially in swing states.
Two faces, two starkly different judgments. Which one was closer to the truth?
Money Votes vs. Expressed Opinions: The Fundamental Difference Between Two Mechanisms
To answer this, we need to examine the core mechanisms behind these two forecasting approaches.
Traditional polls rely on "opinion expression"—respondents are asked, "Who do you plan to vote for?" and answer verbally. In this process, there’s no cost to being wrong. Responses are free, and so are biases. A respondent might hide their true intentions due to social pressure, change their mind before Election Day, or not have seriously considered the question at all—after all, no one will know. The lesson from the 2024 election is clear: for three consecutive cycles, polls have systematically underestimated support for the same political camp, exposing long-standing challenges in identifying and reaching certain voter groups.
Prediction markets operate on an entirely different logic. On platforms like Polymarket or Kalshi, participants put real money on the line. If their prediction is correct, they profit; if not, losses are immediately reflected in their account balance. Every trade is a "vote" backed by real assets, and the cost of being wrong is instantly apparent.
This money-based voting mechanism creates two key effects.
First, financial incentives enforce honest beliefs. In polls, respondents face no consequences for any answer. In prediction markets, a wrong call means real monetary loss. This asymmetric risk structure forces participants to scrutinize every piece of information and rigorously test their own views. Researchers have noted that prediction markets, through financial incentives, quickly reveal private information and correct biases via trading. Historically, they’ve outperformed both traditional polls and expert forecasts in elections and other events.
Second, price discovery is continuous and real-time. Polls are snapshots—they take days or weeks to conduct, and by the time they’re published, they capture only a frozen moment. In prediction markets, prices change every second. Breaking news, leaked documents, or impromptu press conferences can move market prices within seconds, often faster than traditional media can report. During the 2024 election, platforms like Kalshi responded to subtle developments more rapidly than aggregated polls.
The Source of Efficiency: Not "Wisdom of the Crowd," but 3% Informed Traders
Prediction markets’ high accuracy is often attributed to the "wisdom of the crowd"—the idea that aggregating many participants’ information cancels out biases and converges on the right answer. While intuitive, the latest top-tier SSRN research using Polymarket’s complete trading data offers a disruptive insight.
After analyzing millions of trading accounts, researchers found that price discovery in prediction markets isn’t driven by the masses, but by a small group—about 3%—of informed traders. Only around 3.14% of accounts consistently showed statistically significant profits and contributed the vast majority of price discovery. Their order flow could meaningfully predict future prices and outcomes. The remaining 97% of ordinary traders, while accounting for most of the trading volume, added little informational value. In highly information-sensitive scenarios—such as FOMC decisions or corporate earnings releases—only these informed traders traded in the direction of new information the moment it was released.
This finding is significant: the high accuracy of prediction markets isn’t simply a case of "the more, the merrier." Instead, it’s the result of a small, information-advantaged, and skilled group continuously injecting information into prices, with the broader public providing liquidity. This model shows that price efficiency doesn’t require universal rationality among participants. Understanding this helps clarify the quality of signals that prediction markets provide.
When Prediction Markets Outpace Traditional Information Sources: A Mirror in Crypto
The application of prediction markets in finance, in many ways, mirrors the logic of cryptocurrency markets.
Crypto assets trade 24/7 with global liquidity, making them a natural testing ground for prediction markets. On Polymarket, the crypto category accounts for about 40% of activity from the smallest traders, and Bitcoin alone has attracted around 593,000 users to participate. This isn’t a coincidence: the nonstop trading and familiar price volatility of crypto make it an ideal entry point for new users exploring prediction markets.
More importantly, prediction markets are evolving from "election betting tools" to multi-category information aggregation infrastructure. In Q1 2026, sports predictions on Polymarket saw about $10.1 billion in volume, while politics contributed around $5 billion. User active days rose from 2.5 to 9.9, and the average number of categories per user increased from 1.45 to 2.34. This shift shows that participation is moving from "event-driven" to "ongoing behavior"—people are no longer just logging in for major events like elections or the Super Bowl, but are using these platforms as daily tools to track news, macro trends, and asset prices.
As a leading crypto exchange, Gate has always kept a close eye on prediction markets, which blend information pricing with asset trading. Since 2026, Gate has integrated with the prediction market ecosystem across multiple dimensions, helping users access a wider range of prediction assets through familiar crypto trading interfaces.
The Dark Side: Three Major Challenges Facing Prediction Markets
High accuracy doesn’t mean prediction markets are without flaws. In fact, rapid growth has exposed several significant risks.
First, regulatory pressure over insider trading and market manipulation. In May 2026, the U.S. House Oversight Committee launched formal investigations into Polymarket and Kalshi, citing concerns that government employees could trade on policy and national security events using nonpublic information. There have been confirmed cases of individuals profiting hundreds of thousands of dollars from trades based on inside information about sensitive events. The CFTC has officially named insider trading as one of its top five enforcement priorities for prediction markets, and the Department of Justice has begun investigating multiple related cases.
Second, the "fat tail" problem of liquidity distribution. Major events enjoy abundant liquidity, but niche prediction topics often suffer from shallow markets. Building a position in a low-interest event can incur slippage costs of 10% or more. This uneven distribution limits the utility of prediction markets as general information aggregators—only high-profile events offer sufficiently reliable price signals.
Third, regulatory uncertainty. Although the CFTC clarified its exclusive jurisdiction over event contracts through several court actions in 2026, federal and state governments continue to battle over regulatory authority. Some states still classify prediction market contracts as illegal gambling and pursue criminal charges, meaning platforms face ongoing local compliance challenges.
These challenges demonstrate that while prediction markets outperform traditional polls in some respects, they are far from perfect. Their advantages are most pronounced in specific scenarios—high trading volume, deep liquidity, and transparent events—where price discovery is most efficient. In illiquid or highly asymmetric information environments, signal quality can deteriorate significantly.
Conclusion
Returning to the central question: Prediction markets vs. traditional forecasting—which is more reliable? The answer isn’t a simple "winner takes all."
Mechanistically, prediction markets have inherent strengths. Financial incentives force participants to stand by their judgments, continuous trading ensures prices reflect new information instantly, and global participation creates a deeper and broader information pool than any single polling organization. Academic research backs this up: in 74% of U.S. presidential elections since 1988, prediction markets have come closer to the final result than polls.
Yet prediction markets also face risks of capital manipulation, legal and regulatory hurdles, and liquidity challenges in less popular topics.
Reliability ultimately depends on the use case. For events with ample liquidity and transparent information, prediction markets often provide more accurate probability estimates than traditional polls. In areas with low participation or severe information asymmetry, traditional research methods may still have irreplaceable value.
For everyday users, the key is to understand the limitations of both tools—don’t blindly trust the accuracy of prediction markets, nor dismiss the value of polls due to their failures. The best approach is to cross-validate real-time probabilities from prediction markets with traditional information sources, using multiple perspectives to get closer to the truth—instead of betting everything on a single tool.
As for the 2024 election, prediction markets offered an answer much closer to reality than the polls did. This was likely no accident, but rather a structural advantage of money-based voting when facing the twin challenges of dispersed information and participant incentives.
