The Nasdaq Composite's brutal 3.6% sell-off on Oct. 10 was a painful reminder of how quickly growth stocks can sell off when doubt creeps in. Friday's tumble marked the worst session since April during the height of trade tensions between the U.S. and China.
The sell-off was a reaction to the U.S. threatening an additional 100% tariff on Chinese imports as a retaliation for China's stricter export controls on rare-earth minerals and magnets. These materials and products are used across economic sectors, including semiconductors and technological equipment with artificial intelligence (AI) applications.
On Oct. 12, reports indicated that China would not back down against escalated tariff threats from the U.S.
Investors often talk about buying opportunities when the market is selling off. But it can be just as helpful to be aware of potential mistakes and prevent them before they do damage to your portfolio. Here are three that apply to AI growth stock investors who are preparing for next year.
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1. Having an overly concentrated AI portfolio
A common mistake is to overly focus on one facet of a value chain.
For example, an investor may own Nvidia (NVDA 1.96%), Broadcom, and Advanced Micro Devices as a way to diversify across different AI chip designers. The issue is that many of these companies have the same customers. For example, OpenAI is buying chips from all three companies to build out 10 gigawatts of data centers. If OpenAI were to cut its spending, it could affect the earnings of all three companies.
Similarly, equipment suppliers like Applied Materials, Lam Research, and ASML all share the same largest customers -- which are semiconductor manufacturers like Taiwan Semiconductor, Samsung Electronics, and Intel. So if Taiwan Semi cuts its spending, it would reduce earnings across the semiconductor equipment supplier industry.
Further down the value chain are the cloud computing giants like Amazon Web Services, Microsoft Azure, Alphabet-owned Google Cloud, and Oracle. These companies benefit from increased AI spending, but they also serve general computing and storage needs. A slowdown in AI spending, or a widespread economic downturn, could reduce demand for additional cloud computing usage across major corporations.
By building out an AI portfolio across the value chain rather than focusing on one segment, you can help reduce volatility and limit the damage of an industry-specific pullback.
2. Ignoring position sizing
Portfolio sizing and allocation are just as important as the stocks and exchange-traded funds owned. You don't want to be so diversified that your best ideas don't make a big impact, but you also don't want to be overly concentrated to the point where a handful of stocks can damage your financial health.
There's no one-size-fits-all solution to diversification. But factors to consider include investment goals, investment time horizon, and risk tolerance.
A risk-averse investor would probably want to limit the size of a single stock in their financial portfolio, whereas an investor with a high risk tolerance and a multi-decade time horizon may not mind betting big on a handful of stocks, especially if they are still making new contributions to their investment accounts.
3. Buying stocks and not companies
Building a diversified portfolio isn't enough. In fact, it's not even the most important factor.
Arguably, the greatest mistake investors can make when approaching AI is to invest in stocks rather than companies. In other words, focusing too much on price action and potential gains rather than on what a company does and where it could be headed.
Peter Lynch's investment advice to "know what you own, and why you own it," still rings true today. Without conviction, a concoction of emotion and volatility can corrode the foundations of even the strongest portfolios. An investor may hold positions in stocks just because they are going up, even if those gains are temporary, because they don't have to do with the underlying investment thesis.
The best investments are the ones you can put a decent amount of your portfolio into and be confident in owning, even if they suffer an extreme sell-off -- like we saw in April during the height of trade tensions. If someone bought Nvidia just to make a quick buck, they may have been tempted to sell it when it fell by over 37% from its high in early April. Or when it dropped over 55% from its high in 2022. But someone investing in Nvidia for its multi-decade potential in AI data centers would have had an easier time holding the stock throughout these volatile periods.
NASDAQ: NVDA
Key Data Points
Unlocking lasting success in the stock market
Diversifying across the AI value chain in companies you understand and with an awareness of portfolio sizing can help you build a portfolio that's built to last, rather than one that can get hot only if the conditions are right.
Long-term investors know that success is more about making consistently good decisions over an extended period, rather than a few great ideas wedged between mediocrity and mistakes.
AI stocks have generated monster returns for patient investors, and many have the potential to create lasting generational wealth going forward. But those gains could take time, with many bumps along the way.
No one knows when the next major stock market sell-off will occur. Instead of guessing the timing and severity of a sell-off, it's better to put your effort into following great companies and limiting mistakes.
In sum, diversification, conviction, and good companies are components that can help you build an investment suspension system capable of absorbing sell-off shocks.
