Er verdiente viel Geld, wurde aber erwischt, als ein Mitarbeiter des Weißen Hauses auf Trumps Reden setzte.

A former White House employee has been ordered to return over $107,000 in profits and pay a $65,000 fine after using confidential information about U.S. President Donald Trump’s speeches to place bets on prediction markets. The Commodity Futures Trading Commission (CFTC) announced Friday that Gabriel Perez must repay the funds and face a three-year trading ban due to his breach of trust and confidentiality obligations. Perez, who worked in the White House managing projection equipment for speeches, placed bets between December 2025 and February 2026 based on insider knowledge of Trump’s upcoming remarks. The CFTC detailed in its enforcement action that Perez exploited his access to sensitive information regarding the content of presidential speeches. He used this knowledge to predict specific terms or references, such as country names or particular phrases, that would appear in Trump’s public addresses. His actions violated both ethical standards and federal laws governing insider trading and market manipulation. The agency reduced the penalty slightly due to Perez’s cooperation during the investigation. Perez was identified through unusual activity detected by Kalshi, a prediction market platform where users bet on real-world outcomes. Analysts at Kalshi noticed irregular transactions on markets related to speech content predictions in March 2026. By analyzing account data, they determined that one user was a federal employee responsible for projecting speeches within the White House. This led them to flag the account and report it to regulatory authorities. Kalshi’s chief legal counsel, Bobby DeNault, welcomed the decision in a post on social media, stating that anyone violating their rules or federal law would face consequences. “It doesn’t matter who you are,” he wrote. “If you break our rules or federal law, you will bear the consequences.” The White House has yet to officially comment on the case. Earlier this year, press secretary Karoline Leavitt confirmed that Perez had been placed on unpaid leave and would not return to his position. In July 2026, the White House management office sent a memo to staff prohibiting betting on prediction markets. Employees confirmed this policy to CBS News. Kalshi had already warned earlier in the year that political leaders' statements could trigger massive movements in financial markets. In a public statement, the company noted that words from figures such as the president or Federal Reserve officials could influence billions of dollars in foreign exchange, oil, and stock markets. The case highlights growing concerns about the intersection of insider information and financial markets. Prediction platforms have become increasingly popular for forecasting everything from sports events to political developments. However, the potential for abuse by individuals with privileged access to information has raised alarms among regulators and market participants alike. Perez’s actions were uncovered months after Kalshi flagged suspicious behavior. The platform’s analysts conducted a thorough review of transaction patterns and cross-referenced them with internal records. Once they identified the connection to a White House employee, they alerted the appropriate authorities. This led to the formal investigation by the CFTC, which ultimately resulted in the enforcement action against Perez. The incident has prompted renewed scrutiny of how government employees handle confidential information. While the White House has taken steps to address the issue internally, questions remain about the adequacy of existing safeguards. The CFTC’s ruling serves as a warning to others who might consider exploiting non-public information for personal gain. Perez is required to return all illicit gains and pay the imposed fine by a specified deadline. Additionally, he faces a three-year ban from participating in any form of trading. The outcome underscores the serious consequences of misusing confidential information, even in contexts that may seem relatively benign compared to traditional forms of insider trading.