Nvidia valuation based on expectations that it can do no wrong, says NYU's Aswath Damodaran
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Published 2024-05-24
All Comments (21)
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I think it's important to stick to stocks that are immune to economic policies. I'm looking at NVIDIA and other AI stocks that have the potential to power and transform future technologies. It seems AI is the trajectory most companies are taking, including even established FAANG companies. Maybe there are other recommendations?
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Strong BUY. Still early innings. NVIDIA is the dominant leader in AI and the preferred technology partner globally. Even w new competition on the horizon, NVIDIA is far ahead of the competition. 85% market share. 76% margin. Unrivaled demand for new Blackwell chip. Demand far exceeds production for Blackwell through to 2025 and beyond. No competitor has anything close to Blackwell. And forward P/E is about 33 (cheap for a high growth stock). Buy this stock and wait. You will be rewarded.
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AI stocks will dominate 2024. Why I prefer NVIDIA is that they are better placed to maintain long term growth potential, and provide a platform for other AI companies. I know someone who has made more than 200% from NVIDIA. I'll also take these other recommendations you made.
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I recently sold some of my Nvidia stocks to secure profits, but I'm retaining a portion for the long term, its growth potential is robust. I'm also considering diversifying my $400K stock portfolio, but I'm uncertain about managing risks in my next move.
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One question please! Is NVIDIA a safe buy to outperform the market this year? I'm tired of these new buys every week, just to make up some assets with low percentage on my $236k portfolio and try to keep everything around 10%.
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8 months ago he said fair value for Nvidia is $240. It’s $1050 today. Mr. Dean of valuation, it’s actually cheaper since earnings.
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My spouse and I are adding a variety of stocks/ETF to my present holdings for the long term, We've set aside $250k to start following inflation-indexed bonds and stocks of companies with solid cash flows, I believe it is a good time to capitalize on the market for long-term gains, but it wouldn't hurt to know means of actualizing short term.
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There is a reason why he is a professor not a hedge fund billionaire. CNBC is stupid to invite him to talk about Nvidia
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Let's not forget this was a $110 stock just 19 months ago. Trading at only 4x FY 2026 earnings (FY 2026 ends in Jan 2026, only 20 months from now). You can argue the stock has routinely been terribly underpriced because the Wall St analysts cannot see around the corner to the true earnings potential of the company.
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I am so embarrassed for him whenever Aswath Damodaran opens his mouth commenting a knowledge domain he knows absolutely zilch about. Calling it a "chip market" proves the point.
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This guy was saying 300 is definitely too high back then - what happened 😂
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This guy is never right about Nvidia. Just a permabear.
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The force is strong with NVDA. - Yoda
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"It can do no wrong?" Nvidia by Huang's own account has made big mistakes. They'll no doubt make more, but they've proven to be an adaptable company and Huang is good a anticipating where the markets will be and fulfilling them.
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This whole valuation thing is purely based on history and future projection based on historic trendline. If you invest based on valuation your portfolio will consist of the most boring stocks that simply fluctuates between 1 or 1.5 standard deviations.
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Even I would not short NVDA, always profits, an American based company, includes storage and even gambling. It is a mini ETF?
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it is momentum? do you study fundamental when valuing a company
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This guy comes across as a smug know it all who doesn’t know it all.
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1 year ago he said NVDA is expensive at 400 dollar
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he is right, some people forget how massive the valuation by now is, and growing from here is much, much harder, growing another 10% means now adding the worth of GM, McDonalds and JPMorgan.... also there won't be many players that train very large models because this is damn hard, hyperscalers are in a panic arm race right now to train the biggest models but if that turns out to be a dead end and/or smaller local models will get better less GPUs are needed
