Analyst Sets $400 Nvidia Target, Calling NVDA 'Extremely Cheap'
New Street's Pierre Ferragu argues Nvidia is deeply undervalued and forecasts a potential 78% rally within 18 months.
Nvidia's meteoric rise over the past two years has left many investors wondering whether the stock still has room to run — and at least one prominent analyst thinks the answer is an emphatic yes. Pierre Ferragu of New Street Research has labeled Nvidia shares "extremely cheap," setting a price target north of $400 and projecting that level could be reached within the next 18 months. From current trading levels, that would represent a gain of roughly 78%, a bold call on a company already carrying one of the largest market capitalizations in the world.
The framing of Nvidia as a value play may seem counterintuitive given the stock's extraordinary appreciation since generative AI entered the mainstream conversation in 2023. Yet analysts like Ferragu argue that conventional valuation metrics can be misleading when applied to a company whose earnings trajectory is still accelerating. The core thesis is that the market continues to underestimate the durability and scale of AI-driven infrastructure spending, with Nvidia's chips remaining the dominant hardware of choice for training and running large language models.
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The call arrives at a moment of genuine uncertainty for technology investors. Concerns about export restrictions, customer concentration, and eventual competition from custom silicon designed by cloud hyperscalers have weighed on sentiment, even as Nvidia has repeatedly delivered earnings that exceed Wall Street expectations. Ferragu's optimistic stance implicitly pushes back against those headwinds, suggesting the demand runway is long enough to overwhelm near-term risks.
For retail and institutional investors alike, a price target of this magnitude from a credible research house reframes the risk-reward calculus around NVDA at a time when many have grown cautious after the stock's historic run. Whether the 18-month timeline proves accurate will depend heavily on sustained enterprise and hyperscaler AI capital expenditure — a variable that remains robust for now but is not guaranteed. Continue reading at Benzinga.
