A trader profited close to $500,000 by predicting the removal of the Venezuelan leader shortly prior to it was officially announced, sparking debate about the possibility of profiting from inside knowledge of American actions.
Predictions made on the forecasting site, a blockchain-based service, that the Venezuelan president would be out of power by the close of the month rose in the hours before former President Trump stated on Saturday that the Venezuelan leader had been taken into custody.
A single trader, which became a member in December and took four positions, all on the Venezuelan situation, profited a total of $nearly half a million from a modest bet of $32.5K.
It remains unclear. The user had only a string of letters and numbers for identification.
Platform data shows that users put the odds of the president's removal at just under 7% in the late afternoon of the prior Friday.
However the odds had climbed to eleven percent by the end of the day and skyrocketed in the first hours of Saturday, pointing to a rapid movement in positions right before the public announcement was made.
"This specific wager has all the signs of a bet based on inside information," commented an industry expert.
Several of other individuals also earned tens of thousands of dollars from bets on the same outcome.
Some lawmakers are starting to take note.
A bill put forward on recently seeks to ban federal workers from placing bets on prediction markets if they have "insider details" related to a market.
Event-driven betting sites have surged in popularity in the past few years, with users able to wager on everything from sports to political events.
The industry faced scrutiny under the last presidential term. Yet it has received a warmer welcome during the current presidency.
Using confidential knowledge is illegal in the stock market, but there are less oversight in the forecasting industry.
A company executive for Kalshi said their site "explicitly prohibits trading on insider information of any form."
A tech journalist specializing in UK digital policy and startup ecosystems, with a background in computer science.