French Trader Gains Millions from Trump Bets Amid Polymarket Controversy

French Trader Gains Millions from Trump Bets Amid Polymarket Controversy

By: Eliza Bennet

The recent US elections not only witnessed a startling outcome but also highlighted extraordinary gains made in the decentralized betting universe. A French trader, reportedly known as Theo4, has made headlines for securing nearly $79 million by wagering on the re-election of Donald Trump on Polymarket, a platform renowned for its prediction market capabilities. Polymarket allows users to speculate on interpretations with real money, and Theo4's substantial success came from Trump's electoral victory.

According to Chainalysis, a blockchain intelligence firm, there were initial rumors about Theo4 maintaining four accounts on the platform with significant bets on Trump. However, upon further investigation into funding methods, transaction timings, and particular exchange cash-outs, Chainalysis noted nine accounts that appear to be managed by the same persona. Together, these accounts amassed close to $78.7 million in net profits betting on the outcome of the US elections.

This surge in winnings, however, raised alarms as Theo's trading behavior prompted attention given the volumes and stakes involved. Theo, whom WSJ tags as a seasoned European financial investor, stood firm on his speculative choices, stating they stemmed from his unwavering conviction about the electoral results.

Meanwhile, this event coincides with growing scrutiny of Polymarket itself in France. The country's gambling authority is reportedly poised to ban the platform, as per local media sources, following revelations of the substantial $3.5B turnover associated with the US election bets alone. Any potential ban would dramatically limit French citizens' participation in such decentralized prediction markets in the future. This situation underscores the need for balanced regulatory frameworks that address both the innovative potential and financial risks present in crypto-based betting and prediction environments.

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