Trump teleprompter operator made $100K betting on Kalshi markets tied to speeches: ABCAccording to ABC News, federal regulators are investigating whether the longtime White House staffer used nonpublic information to profit from event contracts tied to President Donald Trump’s speeches.
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Gabriel Perez, who has operated President Donald Trump’s teleprompter since 2016, is under federal investigation for allegedly using nonpublic information to profit from betting on Kalshi prediction markets tied to Trump’s speeches. According to ABC News, Perez made over $100,000 in profits by placing wagers on whether certain words or phrases would appear during more than a dozen speeches, including high-profile events like the State of the Union and the World Economic Forum. Kalshi’s surveillance systems detected this suspicious trading activity and reported it to the Commodity Futures Trading Commission (CFTC).
The investigation found that Perez sometimes exited his market positions in real-time if Trump deviated from his prepared remarks, suggesting he had access to inside information. Following the report, the White House placed Perez on unpaid administrative leave, with Trump reportedly condemning the conduct as a “disgrace.” This incident highlights concerns about insider trading risks within prediction markets, which have boomed recently alongside increased trading volumes.
Prediction markets have come under heightened regulatory attention because of several high-profile cases where traders profited from information not yet public. For example, some Polymarket users earned roughly $1 million betting on a U.S. strike against Iran before the event was publicly reported, and other traders made significant gains betting on geopolitical developments and blockchain investigations shortly before news broke. Federal lawmakers have responded by proposing legislation to restrict political insiders from trading on such platforms.
This case underscores the challenges regulators face in monitoring and preventing insider trading in emerging markets that trade on real-world events. It also raises questions about the governance and surveillance mechanisms of decentralized and centralized prediction platforms. The outcome of the investigation and any potential regulatory actions could impact how prediction markets operate and how insiders are regulated in the future.