Hidden Risks in Market Prediction: From 84% Loss Rate to Insider Trading

The prediction market is growing at an unprecedented pace. As of April 2026, Polymarket’s annualized trading volume has surpassed $100 billion, monthly active users have exceeded 700,000, March’s single-month trading volume is about $80 billion, and, in February, the combined monthly peak with Kalshi reached roughly $168 billion. Yet behind the frenzy of trading volume lies a series of risks that most participants have never seriously examined. When 84% of traders lose money, insider trading begins to be prosecuted criminally, and a single hairdryer can easily manipulate market settlement—have you truly seen the traps right beneath your feet?

User Traps: Why 84.1% of Traders Lose Money

The harshest truth about prediction markets is hidden in the distribution of returns. According to a study published by analyst Andrey Sergeenkov in April 2026, as of April 2026, 84.1% of traders on Polymarket are in a non-profitable state, with losses covering nearly five-sixths of traders—a dramatic drop from the situation about two years ago, when roughly 40% of traders were profitable.

Even more striking is wealth concentration. On-chain analysis shows that fewer than 0.04% of wallet addresses on Polymarket capture more than 70% of all realized profits, totaling about $3.7 billion. Out of 2.5 million wallets, only 2% have accumulated profits exceeding $1,000, and only 840 wallets (0.033%) earn more than $100,000. This means that in a prediction market, you are not merely wagering against probability—you are wagering against an elite group that controls information and capital, and your win rate is far lower than in a traditional casino.

The risk warning is not alarmist. Arizona Democratic U.S. Representative Yassamin Ansari said bluntly that Polymarket and Kalshi are “casinos—rich and powerful people are the house, and everyone else is the chips.” If you think prediction markets are an easy way to make money, then the 84% loss rate has already answered your question.

Insider Trading: When “Insiders” Become the Biggest Winners

The core logic of prediction markets is “aggregating collective wisdom,” but when some participants possess confidential information that others can’t access, this logic collapses completely.

On April 23, 2026, the U.S. Department of Justice arrested a soldier from a U.S. military special forces unit. He had participated in the operation to capture former Venezuelan President Maduro, and hours before the operation he placed bets of about $33,000 on Polymarket. He ultimately profited more than $400,000, achieving returns of over 1,200%. This was the first criminal enforcement action by the U.S. Department of Justice targeting insider trading in prediction markets. Polymarket said that after discovering anomalies, it proactively transferred the matter to the U.S. Department of Justice and cooperated with the investigation.

This is far from an isolated case. In March 2026, a Polymarket user accumulated profits of about $550,000 by betting on events such as “U.S. strikes Iran” and “Will Iran’s Supreme Leader Khamenei be removed?” Meanwhile, on February 25, 2026, the CFTC enforcement division issued enforcement guidance for prediction markets, exposing two insider trading cases that occurred on Kalshi: one where a YouTube channel editor profited by using non-public information from prior access to video content, and another where a political candidate traded their own campaign outcome on Kalshi.

Even more concerning is the qualitative shift in regulatory attitude. On March 31, 2026, David I. Miller, the newly appointed head of the enforcement bureau at the CFTC, explicitly announced at NYU Law School that insider trading laws also apply to prediction markets, emphasizing that there is a “misconception in mainstream media and social media” that “insider trading laws do not apply to prediction markets”—“that is wrong.” The CFTC has ranked insider trading as the top of its five enforcement priorities and said it will “actively detect, investigate, and prosecute insider trading in prediction markets at the appropriate time.”

What is even more thought-provoking is that Miller also clearly stated that the CFTC only prosecutes traders who “use stolen information,” not those who make trading decisions “legally using their own knowledge and analysis.” What does this mean for ordinary users? If you have no source of internal information, your informational disadvantage in prediction markets is structural and will not be covered by law enforcement.

Manipulation: From Physical Interventions to Oracle Attacks

If “profiting from an information advantage” is a gray area, then physical interventions and mechanism manipulation directly undermine the foundation of trust across the entire prediction market.

“Hairdryer Manipulation Incident”: On April 6 and April 15, 2026, the weather sensors at Paris Charles de Gaulle Airport were heated at close range by a battery-powered hairdryer, and the temperature readings rose by about 4℃ within just 12 minutes. This temporarily triggered the settlement of a low-probability option on Polymarket: “Paris highest temperature exceeds 21℃.” Two accounts, each with a few dozen dollars in principal, collectively profited about $34,000. One of the accounts was only created 48 hours before the first manipulation. ( ) The French meteorological agency later conducted an on-site inspection of the sensors, found evidence of human interference, and filed a criminal complaint with the gendarmerie. Vitalik Buterin commented that this case proves that a single-source data settlement model “should be required to use the intermediate values from at least three independent sources.”

“UMA Oracle Voting Attack”: In March 2025, a major trader on Polymarket betting on the contract event “whether the U.S.-Iran mineral agreement is signed” used their high voting weight in the settlement oracle UMA. At the final moment, they forced the market—which was set to be “not signed”—to be settled as “signed,” reversing the outcome and profiting against the odds. The community strongly protested, and the platform refused to issue refunds. Similar absurd rulings have even included one where the decision claimed the outcome was “not wearing a suit” because the person did not wear a tie.

In addition, the CFTC found that the manipulators influenced prices through coordinated actions on social media; they forged multiple identities to access KYC platforms and used mixers to transfer funds, increasing the difficulty of law-enforcement tracking.

Structural Risks: Liquidity Traps and Mechanism Flaws

Even without manipulation and insider trading, the infrastructure and mechanisms of prediction markets themselves have fatal flaws.

Liquidity traps: Newly created markets face a vicious cycle of “no traders → no liquidity → no pricing efficiency → no traders.” Long-tail markets have almost no chance of survival. Polymarket and Kalshi each invested about $10 million and $9 million, respectively, to subsidize market makers’ liquidity. But this money-burning model is, in essence, using capital to buy a dominant position—so small projects can hardly break this cycle.

Very low capital efficiency: Prediction markets require full collateralization, making capital efficiency 10 to 20 times lower than in perpetual contract markets. Capital is locked with zero returns during the contract period, and after settlement, liquidity resets to zero.

Lack of natural hedgers: Prediction markets lack “natural counterparties” from the real economy, and market makers face pure adverse selection. An estimated 90% of prediction market projects may fail before the end of 2026.

How to Protect Yourself: Four Suggestions for Traders

In this high-risk game, your opponents have informational advantages, capital advantages, and trading-technology advantages. The following suggestions may help you avoid becoming “one of the 84% who get harvested”:

1. Fully understand the truth behind the 84% losses: Don’t be misled by a handful of cases with extraordinary profits. The return distribution in prediction markets is extremely concentrated, and ordinary traders are almost certain to lose money.
2. Break down your opponents: When you trade on Polymarket or any prediction market, recognize your counterparty’s role—possibly a member of a special forces unit holding internal information, possibly someone who manipulates sensors with a hairdryer, possibly a large trader with high UMA voting power, or possibly a professional market maker. Always ask yourself: what advantage do I have in this trade?
3. Keep a retail perspective and identify low-probability combinations: Don’t try to profit from information gaps. Instead of chasing short-term hot topics, wait patiently for “emotion mismatches in rational markets.”
4. Manage your positions well—never go all-in: Volatility in prediction markets is often driven by discrete, abrupt, event-based factors; a single piece of news can move prices from 0.5 to 0.1 or 0.9. Only ever participate with a very small portion of funds you can afford to risk.

Summary

Prediction markets are growing at an astonishing speed: annualized trading volume has surpassed $100 billion, and the valuation of top players is approaching tens of billions or even hundreds of billions of dollars. They carry a grand vision of information-driven price discovery, and are even regarded by institutions as real-time pricing tools for geopolitical and macroeconomic risk.

But beneath the growth myth, there are brutal realities that are easy to overlook: more than 84% of traders lose money, insider trading is being criminally prosecuted, real-world cases of physical manipulation and oracle attacks keep appearing, and liquidity traps and mechanism flaws are deeper structural problems at the core. Amid the hype, staying clear-headed—identifying risks, facing your opponents squarely, and participating rationally—is the only way to stand firm long term. Even when using the prediction market entry integrated with Gate for convenient participation, please remember: keep in awe of the 84% loss rate, and strictly control each position. Avoiding these hidden risk traps is the correct starting point on the path to stability.