Wealth
Four experts highlight popular investment areas today that may be treacherous.
By Suzanne Woolley
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The mood in markets this year is far from celebratory, even with the S&P 500 up roughly 14%.
The market has been propped up by a narrow tech rally. Signs of a recession are starting to mount. And geopolitical tensions are adding to anxiety among investors.
It can be tempting to resort to Treasuries and money market funds, which are offering no- or low-risk yields above 5%. But taking on some risk is essential to building a portfolio. The key is understanding where there is too much risk.
That's why Bloomberg asked four investment pros to pinpoint areas where they see inflated valuations and vulnerable business models. They pointed to stocks linked to artificial intelligence, private credit products, and pandemic-boom companies that may take a hit from the restart of student loan payments and consumer belt-tightening.
Here's what else they had to say, with comments lightly edited for length and clarity.
See also: Where to Invest $1 Million, Where to Invest $100,000, Where to Invest $10,000
Autos Versus AI
The situation: The rise of AI and the fall of China are lining up to be the investment stories of 2023, prompting many to predict that we are revisiting the IT-dominated cycle of the 1990s. This would suggest that investors should plow money into US tech stocks, the dollar, and then ride out the disinflationary wave by hedging via bonds. I’ll take the other side of that trade.
Reason for caution: First, it is rare that a new technology clearly articulates itself in an easily investible thesis, particularly in public markets. The true winners of the AI revolution are likely out of the reach of retail investors, hidden away in privately held companies that mere mortals have not heard of yet. FAANG valuations are already “priced for perfection,” with AI-enabled earnings likely to disappoint. If anything, AI will help unheralded sectors such as industrials, materials, and energy that never really gained much productivity from the IT revolutions in the 1990s and 2000s.
The big picture: This is a world that does not need another SAAS [software as a service] business. It needs factories, ports, roads, trains, planes, and automobiles (preferably running on batteries). Strong consumer spending is encouraging corporates to invest in the redesign of global supply chain, and this capex cycle will not be good for stock portfolios laden with tech. It will be good for global stocks, commodities, and non-USD currencies. Instead of the 1990s, we are likely to live through another early 2000s. But you could still query ChatGPT on how to invest accordingly...
What I've learned
The most painful lesson I’ve learned as an investor is to not bet against or short a mania. While second- and third-order thinking often generates a ton of alpha, first-order thinking can sometimes be all you need to make money. A good example is the performance of Indian stocks in 2021. Every macro indicator worth any predictive salt was telling you to short India in 2021 – particularly its negative correlation with commodities. But the flight of investors out of China, combined with a domestic enthusiasm for financial assets, led to a massive rally in Indian stocks.
Food-Delivery Dynamics
The situation: The subsector in the stock market that I’m cautious about are the food delivery apps used with restaurants. These are highly innovative companies that provide great convenience to consumers, but being innovative and convenient doesn’t necessarily translate into having a strong business model.
Reason for caution: These companies have experienced sizable growth, but there are several factors that warn investors to proceed with caution. There was a lot of pulling forward of demand during Covid, which did create some new sticky habits, and the second piece of growth has been price increases. The question is, how sustainable are the increases, and with the pull-forward of growth, does it get harder to find natural sources of growth?
If you look at restaurant delivery demographics and student loans, there’s a pretty good overlap, and loan repayments restarted Oct. 1. The economics have gotten to the point where 40% to 50% of the food delivery price tag to consumers could be saved if they didn’t do delivery, so it feels vulnerable to belt-tightening behavior. There are new sources of revenue, like ad revenue, but if their corpus of customers is plateauing and we get into a tougher economic situation, that gives me pause on how much growth there can be.
Additionally, the downside of the gig economy here is that drivers have to pay for their car, insurance and gas. Gas is going the wrong way and we’re seeing some companies raise minimum wages and focus more on their benefits packages. At some point, if there are more stable and lucrative job offerings elsewhere, drivers will demand more compensation or do something else.
The big picture: Food delivery stocks are all down from the peak of Covid but there are still some rich valuations, especially if you add back stock-based compensation. Many are barely making money, and if you treat stock-based compensation as a cash cost, the companies would not be generating free cash flow. Also, stock-based compensation has the effect of diluting public shareholders — the three to four public companies I studied are diluting shareholders to the tune of 5% to 7% a year.
What I've learned
When investing in consumer companies, I’ve learned to actively discount my own experience as a consumer when analyzing the investment merit of a given company. Instead, I delve into the business model to see if it stands up to a high test of durability and quality. For example, I am an avid Peloton user and love the bike and the app, but I am really happy that I never bought the stock.
Be Wary of Chasing Yield
The situation: For the first time in about 15 years, you can get meaningful low-risk yields on securities like short-term Treasuries. And yet, because of the rise of alternative investments, or perhaps the pulling back in the banking industry or all of the cash the government and Federal Reserve pumped into system, there seems to be a great desire to manufacture a lot of financial products, particularly in private credit, with the allure of a juicy yield.
Reason for caution: In my experience, people tend to make worse mistakes and more mistakes stretching for yield than they do when investing in stocks and not looking for yield. Most of the yields these alternative products offer range from about 8% to 12%. When you can basically get half of that without taking much risk in form of a short-term Treasury, it makes you think twice. Historically a lot of financial planning models are built around 4% withdrawal rates. If you can get more than 4% on Treasuries, that makes you think twice about a lot of risky investments.
The big picture: I’m not universally saying these products are bad or good but my concern is the false sense of security and safety people might get from high-yielding private products. The appeal of the steadiness of the income payment along with the perceived lack of volatility due to the illiquid nature of private products might mask underlying risks.
What I've learned
One lesson I have learned is pretty basic — cheaper doesn’t always mean good value. But to me, the biggest lesson I’ve learned is, when designing a portfolio, you will be right sometimes and wrong sometimes, but you should be buying investments that you have the financial and psychological fortitude to hold for a long enough term to see the results through fruition. You don’t want to buy long-term investments with a short-term horizon, because that often ends poorly.
Valuations Always Matter
The situation: The AI revolution spurred an equity market frenzy as investors looked to capitalize on the potential boost to global productivity that suggests stronger economic growth with more modest inflation. We don’t doubt that AI will deliver great gains for the global economy in the future; it is just important to recognize that, as investors, there is a price for everything. Elevated valuations and earnings expectations mean that these AI-driven stocks are something we would be keen “not to buy.”
Reason for caution: The AI frenzy has resulted in a dramatic divergence between the performance of a narrow group of tech stocks — Apple, Microsoft, Alphabet, Amazon, Nvidia, Tesla and Meta — and the rest of the markets. If you look at the weighted change in market capitalization across the seven names, it’s a 59.6% jump since Jan. 1, while the remaining 493 names in the S&P 500 gained just 1.4% — less than you could make in a cash mutual fund.
We see many similarities to the 1998-2000 dot-com bubble. Back then, the underlying narrative was borne out — we now live in a digitally driven world. However, even some of the stocks that have become global tech leaders, and wonderful investments, saw share prices fall dramatically in 2000-2003 as investors reassessed underlying valuations. Even eventual winners such as Microsoft and Apple lost around 60% and 70% through 2000 and took years or decades to regain peak 2000 levels.
We worry that the same thing could happen with many of the leading stocks benefiting from the AI boom. In 2000, US tech stocks had a price/sales multiple of close to 18x, with Microsoft starting the year around 30x and Apple around 19x. The rest of the market had a multiple of just 2.5x. Now, Nvidia has a price/sales ratio of about 34 compared to US tech stocks at 6x price/sales and US non-tech stocks at just 2x.
The big picture: One lesson from the tech bubble was that even though there was a strong structural narrative, if the companies that you are selling your products to are cyclical, and sales slow, that will limit your ability to sell your products, challenging your valuations and share price. Where not to invest? In areas where valuations have become unsustainable, because, in the end valuations always matter – no matter how strong the underlying structural story might be.
What I've learned
The key lesson every professional investor learns in financial markets is humility. Markets typically work to hurt the greatest number of investors, and it’s hard to avoid this even when your investment thesis turns out to be correct in the long run. The second lesson is that derivatives are best left to professionals. Their volatility can be huge and a moment of inattention can be very expensive. One day early in my career, I went to do an in-person interview with a TV station. When I left the office, I had a healthy profit on a nice futures trade due to expire the next day, but while I was out an unanticipated event meant I lost all those profits and much of my initial stake. Avoiding those situations is much easier than having to explain such losses to your family.
(Corrects first paragraph for Ian Harnett)
