Storytelling is a powerful way of engaging with people and connecting ideas. One of the most captivating stories in financial markets today is based on the transformative potential of artificial intelligence (AI) and the companies that are best positioned to benefit from the technology. The real challenge for investors, given the current levels of market exuberance, is separating fact from fiction in that story and establishing what a fair price is to pay for uncertainty
Some recent narrative examples include:
- Jeff Bezos, Amazon CEO – “We’re at the beginning of a golden age of AI. Recent advancements have already led to invention that previously lived in the realm of science fiction.”
- Sundar Pichai, Alphabet CEO – “AI is one of the most important things humanity is working on. It is more profound than, I dunno, electricity or fire.”
- Dan Loeb, Third Point CIO – “To be an investor is to live constantly at the intersection of story and uncertainty.”
The Magnificent Seven
US equities have delivered exceptional returns since the start of the year – fuelled by the growth prospects of companies focusing on the development and integration of AI technologies. The ramp-up in performance has led to a new bull market, which can loosely be defined as a return of 20% or more from the market lows experienced during a particularly volatile 2022.
The market has coined the phrase, The Magnificent Seven, to reference a narrow group of disruptive, high-growth, capital-light companies leading the world towards the fourth industrial revolution. That story is especially compelling and resonates with the market since these are recognisable brands run by entrepreneurial CEOs who also happen to be charismatic storytellers, as the above quotes from Jeff Bezos and Sundar Pichai would suggest.
Separating fact from fiction is not easy as the incumbent market leaders are established companies with strong competitive advantages, but whose valuations appear to be discounting especially linear growth prospects. Alphabet (Google), Amazon, Apple, Meta (Facebook), Microsoft, Nvidia and Tesla may all very well be magnificent companies. However, they may not necessarily be magnificent investments if investors are forced to pay too high a price to buy into a story that is likely to have multiple chapters with a few unexpected plot twists.
The ‘greater fool’ theory
Putting aside overly complicated valuation metrics, the fair price of any asset is determined by the principle of “willing buyer, willing seller”. Asset prices generally converge towards fundamental fair value in the long term but investor sentiment often contributes to extreme price fluctuations in the short term. Unfortunately, markets do not compensate investors for getting it wrong, nor do they calibrate for investor skill or experience.
The democratisation of financial markets should theoretically be a good thing in that it allows access to a more diverse pool of potential investors. The caveat, however, is that less experienced investors often succumb to market euphoria and are more likely to continuously bid up prices for companies with especially intriguing stories.
This is colloquially referred to as the “greater fool” theory. Investment returns are an outcome of less informed (or more perversely incentivised) investors paying continuously inflated prices to participate in market rallies. The effect is that valuations become detached from fundamentals as too much money ends up chasing too few good ideas. Left unchecked this behaviour can result in particularly severe short and long-term outcomes for investors. It’s the stuff market bubbles and boom-bust cycles are made of.
Price does not equate to value
The pre-pandemic meme stock frenzy fuelled by social media and online forums (like Twitter and Reddit)contributed to investors suffering permanent capital losses in companies like Gamestop, Blackberry and AMC. The post-pandemic surge in new listings backed by private equity (PE) and venture capital (VC) firms has also yielded especially poor outcomes for investors buying into the hype of high-growth, technology-led companies disrupting companies with outdated business models.
Coursera and Udemy were two Edtech companies that led the initial public offering (IPO) mania in 2021 as competitors to traditional brick-and-mortar learning institutions. Both failed to deliver results in line with the exceedingly lofty expectations that were priced into their valuations.
The graphs above show that returns on PE and VC-backed IPOs were deeply negative after deal activity slowed dramatically following the mania that saw sizeable amounts of capital allocated to these companies in 2021. The story framing their growth prospects turned out to have substantially less substance than market participants had anticipated.
While these companies are much lower quality businesses compared to the leading US tech incumbents, current investor behaviour shows similar levels of risk-taking and speculation that ultimately contributed to their deep drawdowns.
This time is different…. or is it?
The current US tech sector leaders are in a significantly better position than the internet companies that failed after the dot-com bubble burst in early 2000. The current cohort is made up of established companies with durable business models supported by strong network effects and high switching costs. Their ability to capture users in their ecosystem and integrate technology across different business segments allows them to leverage significant economies of scale and develop sustainable competitive advantages over potential market entrants.
There is, however, no guarantee that established incumbents will emerge as the market leaders in the AI race. The nature of innovation is that it’s not uncommon for the disruptors to be disrupted. Google was a relatively unknown company during the dot-com bubble and emerged as a market leader, while companies like Pets.com, Alta Vista, Ask Jeeves and Infoseek have all faded into distant memories.
Technology companies also operate in a highly complex environment where firms are forced to innovate repeatedly. These companies don’t get growth for free. They need to identify the most feasible projects and spend substantial amounts of money on research and development (R&D). The opportunity cost of that R&D expenditure in an environment where the cost of capital has increased substantially is particularly high. The compounded effect of allocating expensive capital to the wrong projects can significantly impair the ability of these businesses to simply maintain, never mind grow, market share.
The business of running these companies is far more complex than the linear assumptions the market is making about their growth prospects. At current valuation levels, investors need to price for a wider range of potential outcomes to allow for the uncertainty of developing, integrating and scaling AI technologies.
Market structure is also currently playing an outsized role in driving market performance. Market cap weighted indices tend to overweight stocks performing exceedingly well which leads to an investment universe that is less representative of the broader market.
5 of the Magnificent Seven Increased index weighting and concentration in the leading US tech companies exposes investors to significant downside risk in the current environment.
The graph below compares the performance of the leading US tech companies using a capweighted versus equal-weighted approach. It shows that the return contribution from these companies is substantially amplified by their higher index weights. Investors need to be mindful that any meaningful change in sentiment will also amplify drawdowns. 2022 provided an opening chapter of how quickly markets can sell off should investor sentiment turn negative.
Separate fact from fiction
Morningstar South Africa’s global portfolios are underweight US equities and US tech. Implied returns are below levels investors should be earning to adequately compensate them for the range of potential outcomes, in a volatile market, hinging on the growth prospects of high quality but exceedingly complex and expensively priced businesses.
Global markets are currently being supported by an especially narrow group of companies, where investors have priced in overly positive outcomes at a point in time when the range of potential outcomes is especially wide.
While most may be high-quality businesses, these companies are trading at expensive valuations and are likely to require significant ongoing investment to develop and integrate AI technologies into their business models.
The concentration of US tech in market indices also suggests that additional caution is warranted as downside risk at the aggregate market level is exaggerated.
While the recent rally in US equities meets the technical definition of a bull market, the market is largely retracting the drawdowns experienced in 2022.
Embedding the AI story into the investment thesis for US tech does have merit but separating fact from fiction and maintaining a valuation framework that caters for uncertainty is especially important.