Investors are never lacking for data on Wall Street. We’re currently in the heart of earnings season, and economic data continues to be reported on a near-daily basis. But arguably the most important data release of the quarter is set to occur this Wednesday, May 15. This marks the deadline for institutional money managers to file Form 13F with the Securities and Exchange Commission.
A 13F provides an over-the-shoulder snapshot of what Wall Street’s brightest minds, with at least $100 million in assets under management, have been buying, selling, and holding in the most recent quarter — in this instance, the March-ended quarter. It’s not uncommon for professional and everyday investors to follow in the footsteps of Wall Street’s most-successful asset managers.
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But you don’t have to wait until this coming Wednesday to get a sneak peak at what some of Wall Street’s most-prominent asset managers have been up to. Thanks to interviews, annual meetings, and earnings releases, select billionaire investors have revealed selling activity in three ultra-popular stocks during the first quarter.
Stanley Druckenmiller sells shares of Nvidia
The first widely owned stock that was given at least a partial heave-ho during the March-ended quarter is semiconductor titan Nvidia (NASDAQ: NVDA). In a recent interview on CNBC’s Squawk Box, Druckenmiller admitted that his fund, Duquesne Family Office, cut its position in Nvidia in “late March.” For context, Duquesne Family Office held 617,494 shares of Nvidia, along with 489,500 Nvidia call options, as of Dec. 31, 2023.
Although Druckenmiller believes the value of artificial intelligence (AI) for corporate America may be undervalued looking five years into the future, he admits that near-term hype may have gotten ahead of itself for AI stocks, including Nvidia.
For the moment, Nvidia’s A100 and H100 graphics processing units (GPUs) are the undisputed top choice by businesses operating AI-accelerated data centers. In particular, the H100 is powering generative AI solutions and helping to train large language…
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