The capture of Venezuelan President Nicolas Maduro by US forces in the early hours of January 3 was framed by the Trump administration as a decisive step against alleged narco-terrorism and a turning point in US-Venezuela relations.
But the episode has put prediction markets under the lens after a little-known trader walked away with profits exceeding $400,000 by correctly betting on Maduro’s removal just hours before it occurred.
When the wager was placed
In the hours before US President Donald Trump announced that Maduro had been captured, unusual activity was already unfolding on Polymarket, an online prediction market that allows users to bet on real-world outcomes.
One trader placed a large wager — more than $32,000 — on a specific proposition: that
Maduro would no longer be president of Venezuela by January 31, 2026.
When Trump confirmed the capture at around 4:30 am Eastern Time on January 3, the bet resolved almost immediately in the trader’s favour.
The result was a profit of more than $400,000, with some figures placing the gain at approximately $436,759.61.
The trader had joined Polymarket only weeks earlier, in December, and initially used the handle “Burdensome-Mix” before changing the display name to a random string of letters and numbers.
Despite extensive efforts by online users to identify the individual, the trader’s real-world identity remains unknown.
Prediction markets such as Polymarket operate by allowing users to trade contracts that resolve to either “yes” or “no” outcomes.
Prices fluctuate based on perceived probabilities, meaning that those who buy contracts early at low prices can earn large returns if the outcome later appears almost certain.
What market data showed before the capture
At 3 pm GMT on January 2, the implied probability that Maduro would be out of office by the end of the month stood at just 5.5 per cent.
By 6 am GMT on January 3, that figure had doubled to 11 per cent. Thirty minutes later, it had jumped to 28.5 per cent, and by 7:30 am it had climbed to 56.5 per cent.
By 9:30 am, following Trump’s announcement, the market was trading at close to 99 per cent, effectively treating Maduro’s removal as a certainty.
A related Polymarket contract — asking whether Maduro would be in US custody by January 31 — also saw early wagers placed at prices implying an 11 per cent and 22 per cent chance. Those odds rose sharply within hours, reaching above 99 per cent
once the capture was confirmed.
Across Polymarket alone, $56.6 million was wagered on propositions related to Maduro’s ouster. When bets placed on Kalshi, a rival prediction market platform, are included, the total amount staked rose to $64.3 million.
The big question
The extraordinary profitability of the Maduro wager has raised an unavoidable question: was the trader simply lucky, or did they have access to nonpublic information about an impending US military operation?
So far, there is no evidence that the trader had inside knowledge.
Most participants on prediction markets use pseudonyms rather than real names, and while transactions are recorded on blockchains, identifying individuals behind wallets can be difficult.
Chainalysis, a firm that specialises in tracking cryptocurrency activity and theft, told NPR that it was unable to determine who controlled the account. However, the company noted that the trader appeared to be using several US-based cryptocurrency exchanges to cash out winnings.
According to Chainalysis, this behaviour does not resemble typical crypto fraud schemes, which often involve routing funds through opaque overseas exchanges to conceal identity.
Daniel Taylor, a professor at the University of Pennsylvania’s Wharton School who studies insider trading and corporate fraud, cautioned against drawing conclusions based solely on outcomes.
“Was it insider trading? Hard to say,” he told NPR. “It’s easier in hindsight to pick out things that look suspicious than to pick them out in real time.”
Taylor also raised questions about enforcement, even if nonpublic information had been used.
“How would the US government be harmed by someone trading on advanced warning of the Maduro operation?” he asked. “If you can’t show that you’re depriving someone of value, it’s going to be a very difficult case.”
Prediction markets in the US fall under the oversight of the Commodity Futures Trading Commission, rather than the Securities and Exchange Commission, which regulates stock markets and aggressively pursues insider trading cases.
The CFTC has the authority to enforce anti-fraud rules, including cases involving manipulation or insider trading.
However, it operates with far fewer resources. Despite Kalshi alone processing more than $2 billion in trades in a single week, the CFTC has roughly one-eighth the staff of the SEC.
Kalshi said in a statement that it prohibits insider trading, “including government employees trading on prediction markets related to government activity.”
Polymarket’s rules similarly bar market manipulation.
Under US President Joe Biden, the federal government pursued a more aggressive stance toward prediction markets, challenging them in court over election-related betting and pushing back against sports betting, which remains illegal in nearly 20 US states.
Since Trump returned to office, that approach has changed.
The Justice Department and the CFTC have dropped investigations into prediction markets, and Trump’s own social media platform, TruthSocial, has announced plans to launch a prediction market of its own.
Donald Trump Jr, the US president’s eldest son, serves as an adviser to both Polymarket and Kalshi.
Supporters of the industry argue that prediction markets provide valuable information by aggregating beliefs and expectations.
Critics counter that they create incentives for exploiting sensitive information, particularly when contracts are tied to national security or law enforcement actions.
How Congress is planning to tackle the issue
On Monday (January 5, 2026), US Representative Ritchie Torres, a Democrat from New York, introduced legislation aimed at limiting the participation of government employees in prediction markets.
The bill, titled the “Public Integrity in Financial Prediction Markets Act of 2026,” would prohibit government employees from engaging in prediction market contracts if they possess “material nonpublic information relevant to such covered transaction.”
The restrictions would also apply to officials who may reasonably obtain such information in the course of their duties, even if they do not yet possess it.
The bill explicitly covers elected officials, political appointees, and executive branch employees.
How prediction markets have grown in general
Once a niche corner of online gambling, prediction markets have grown rapidly in recent years.
According to a report by crypto firms Dune and Keyrock, the total volume of bets placed on such platforms in the United States rose from under $100 million in early 2024 to more than $13 billion.
These platforms allow users to bet on a wide range of outcomes, from political developments to financial market movements. Typical contracts are binary in nature, asking yes-or-no questions or offering higher-or-lower outcomes.
Recent markets have included wagers on
the identity of the next Republican presidential nominee — such as JD Vance or Marco Rubio — as well as bets on whether the S&P 500 index will rise or fall on a given trading day.
Although the concept may feel new in the US, prediction markets have existed for decades, reported The Guardian.
In the late 1990s, a UK-based platform called Flutter.com allowed small-scale betting between users but failed to gain traction and was later acquired by Betfair.
What distinguishes today’s platforms is scale, speed, and integration with cryptocurrency systems, which allow near-instant settlement and global participation.
The Maduro betting saga is not the first time prediction markets have faced allegations of suspiciously well-timed trades.
In a previous case on Polymarket, a user reportedly made nearly $1 million by correctly predicting 22 out of 23 of Google’s most-searched terms in a single year.
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With inputs from agencies
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