Why The FAANGs Continue To Defy Gravity

by: ClearBridge Investments

Not only has the technology sector grown to be the largest in the S&P 500, but the FAANGs seem to be gathering strength and competitive dominance as they grow.

The reality of digital scaling economics partially explains the FAANG’s dominance as it results in a few massive winners and a greater number of losers.

The growing wedge in favor of FAANGs paints a picture of massive disruption of traditional sectors.

By Sam Peters, CFA

The technology sector has not only grown to be the largest weight of any sector in the S&P 500 Index at over 22%, but the so-called mega-cap FAANG (Facebook (FB), Apple (AAPL), Amazon (AMZN), Netflix (NFLX) and Alphabet/Google (NASDAQ:GOOG) (NASDAQ:GOOGL)) tech stocks seem to be gathering strength and competitive dominance as they grow in size. As a result, the current run in tech stocks is very different than the 2000 tech bubble, as the stocks in the current cycle are supported by ample and growing cash flows and very high, but not yet bubble-like valuations. These dynamics create a bit of a capitalist puzzle, as most companies show some signs of slowing down and mean reversion to more pedestrian growth rates and returns on capital as they grow. These mega-cap tech companies, however, are defying gravity, and are treating investor capital so amazingly well that investors have naturally fallen in love and crowded into tech. The critical question is whether these companies truly are different, and what could crack the dream and disappoint the crowd?

In tackling this scaling question, our team had the great fortune of reading a brilliant new book, Scale, by Geoffrey West. In the book, West tackles the underlying structure of scale for a broad set of areas, ranging from organisms to cities, and also fortunately for our purposes, companies. His scaling framework assigns a scaling exponent that is either superlinear and above 1, or sublinear and below 1.1 For superlinear categories that scale above 1, like cities, innovation drives increasing returns with scale and an infinite life span. Sublinear categories, such as organisms, scale below 1 and must endure the realities of bounded growth and a finite lifespan. Although the scaling exponent for companies varies, it is broadly around 0.9, subjecting companies to biological-like constraints: bounded growth, decay and eventual death. Perhaps it’s inescapable that the emotion of love is intertwined and attached to mortal entities, whether humans or corporations. To bear this out, West’s research showed that 78% of U.S. companies had “died” since 1950, with only 5% of initial public offerings remaining alive after 30 years, putting the average company’s “half-life”2 at roughly 10.5 years. Another byproduct of sublinear scaling is that you end up with tons of small companies, but very few extremely large companies. This observation led us to our scaling question around tech, and specifically why the extremely large company category is increasingly dominated by tech and the FAANGs in particular?

Exhibit 1: Growth of Revenue with Headcount (1990-2016)

Source: FactSet

To tackle this question we compared the scaling factor for the FAANG's revenue and income growth versus employee headcount growth against that of the S&P 500 overall, from 1990 to 2016. As Exhibits 1 and 2 clearly show, the FAANG's scaling ability over this period has greatly exceeded that of the overall market: FAANG revenue growth enjoyed a scaling factor of 0.94 versus 0.71 for the S&P 500, while net income scaled at a shocking and superlinear 1.24 for the FAANGs versus a very mortal 0.52 for the S&P 500 overall.

Exhibit 2: Growth of Net Income with Headcount (1990-2016) Source: FactSet

We think this massive relative scaling advantage for FAANGs explains quite a bit. The growing wedge in favor of FAANGs paints a picture of massive disruption of traditional sectors, as more and more of the economy’s overall revenues and profits flow to these select few companies, further driving scale advantages. The superscaling of net income relative to employees also highlights that FAANGs don’t need a lot of labor to generate massive profits. The reality of digital scaling economics results in a few massive winners, but with a byproduct of a greater number of losers at both the company and labor level.

This certainly helps explain why so much equity market capitalization has been attached to these few scaling winners, but it also highlights the economic angst that is felt by many voters. This angst also is spreading disruption to traditional politics, with a resulting shift to populism.

Currently it is hard to see what could slow the FAANGs down, which is why the resulting winner-take-all narrative could power a late market cycle shift to exuberance. Outside the direct investor narrative, the massive shift in capital to passive, where flows have gone superlinear, is also chasing the FAANG stocks, given FAANG domination of major U.S. indexes. This is a powerful flow dynamic that could certainly continue to pull the market’s equity risk premium down into record low territory, as investor love is heaped on a select few winners.

The challenge for investors is that love and exuberance are ultimately very vulnerable, because they over-extrapolate the present. This results in excessive dependence on a narrow set of future outcomes that are ultimately crushed by new realities. As long-term investors we must think much more broadly than the market, and realize that more things can happen than will happen. What could easily happen is that FAANG growth expectations will ultimately disappoint inflated stock price levels, and within the next couple of years some of the FAANGs will start to show signs of mortality. It’s inevitable that new technologies and companies will emerge, powering the endless capitalist cycle of disruption. We also think investors should put some probability on a policy response that looks to break up or blunt the economic power of these digital giants, which would echo the anttrust movement that broke up industrial giants during the industrial revolution.

As a last point of caution, the two asset classes and related narratives in the recent past that the crowd fully embraced were U.S. houses that were assumed to never drop in price, and commodities that the world was quickly running out of. Both those narratives were fully supported by data at that time. This is an essential reminder that the most powerful narratives are well supported by real underlying drivers, but they can get taken way too far by the crowd’s flood of capital.

1. A function is said to be superlinear if its slope on a log-log scale is greater than 1, sublinear if its slope is less than 1, and linear if its slope is equal to 1. In economics, these scaling types represent increasing marginal returns, decreasing marginal returns, and constant marginal returns, respectively. In the context of this discussion, a sublinear scaling exponent indicates that the value (in terms of incremental revenue and net income) of an additional employee diminishes at higher headcounts, whereas a superlinear scaling exponent indicates that the value of an additional employee increases at higher headcounts.

2. A term from nuclear physics that describes the time required for any specified property, e.g., the existence of a public corporation, to decrease by half.

Disclosure: I am/we are long AMZN, GOOG. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.

Additional disclosure: All opinions and data included in this commentary are as of June 30, 2017 and are subject to change. The opinions and views expressed herein are of Sam Peters, may differ from the firm as a whole, and are not intended to be a forecast of future events, a guarantee of future results or investment advice. This information should not be used as the sole basis to make any investment decision. The statistics have been obtained from sources believed to be reliable, but the accuracy and completeness of this information cannot be guaranteed. Neither ClearBridge Investments nor its information providers are responsible for any damages or losses arising from any use of this information. Past performance is no guarantee of future results. Performance source: Internal. Benchmark source: Standard & Poor’s. Neither ClearBridge Investments LLC nor its information providers are responsible for any damages or losses arising from any use of this information.