If it’s a slow news day and you’re the financial media, you can always compel a recently-graduated journalism major, a new hire or a junior team member to pen something generic about extreme market concentration.

It’s an easy task. Anything with “Magnificent 7” or Nvidia in the title will probably work, where “work” means generate enough clicks to keep the lights on.

If you’re Bloomberg, and you’re going to publish on the terminal, you do want to be careful not to accidentally blast out the draft version to a few hundred thousand people. See this laugher which went out prematurely on October 10:

The latest ETF flows data for the QQQ ETF show that so far in October it’s suffering the biggest outflow on a monthly basis since January 2023 at a time when Invesco S&P 500 Equal Weight ETF (RSP) saw two straight months of inflows. //ELENA PLS CHECK ME ON THIS//

“Check”! That is indeed how you read a bar chart, Natalia! All set! (I’m insufferable, I know.)

I’m not immune to the Mag 7 placeholder siren song. I’ve penned so many generic market concentration articles over the last three or so years I couldn’t count them all if my life depended on it. This is another one.

Below is a chart (i.e., some eye candy) from Goldman.

//ELENA PLS CHECK ME ON THIS//, but I think it shows S&P market concentration by bucket plotted with forward rVol.

Regular readers will no doubt recognize this is a continuation of the discussion from “100-Year Market Concentration Is Huge Risk To Long-Term Stock Returns,” published earlier this week. In that linked article, I spent a few minutes editorializing around a Goldman note which found David Kostin warning that annualized stock returns may be lackluster over the next decade.

The biggest drag on returns in Kostin’s model came from market concentration, and there’s some interplay with volatility and valuations which I think’s interesting — or as interesting as straightforward things can be. Here’s a passage from Kostin, explaining the chart above:

Although elevated market concentration is not a sign of near-term downside risk, high concentration is associated with lower returns over longer horizons. A highly concentrated index is typically associated with greater volatility in the near term. As a result valuations should be lower amid extreme market concentration to compensate for the risk tied to a more volatile group of stocks. While the univariate relationship between concentration and valuation does not show that investors always price this risk, returns are ultimately higher-than-average following periods when valuations were low and market concentration was high to compensate for this risk.

In other words: If the market admits of very high concentration, you need cheaper valuations commensurate with what history says about the volatility you’re likely to experience over the ensuing 12 months.

The problem, of course, is that today’s leadership — i.e., the mega-caps which comprise an enormous share of SPX market cap — trade very rich, in part on the assumption they can maintain above-average top- and bottom-line growth.

The figure on the left shows the median P/E for the 10 biggest names, as well as the median valuation for the “other 490,” so to speak. The figure on the right illustrates one (potential) problem with the bull case often associated with AI beneficiaries.

“The competitive moats possessed by the largest companies in the S&P 500 have allowed them to maintain high levels of growth and margins,” Kostin wrote, before quickly noting that looking back four decades, “the share of unique S&P 500 companies able to consistently generate 20%+ revenue growth faded sharply after ten years [and] only a small share of firms were able to maintain EBIT margins greater than 50% for more than a few years.”

Who knows. What I do know is that AI’s image-generation capabilities have come a very long way since “Reinventing Wheels.” Just ask that bull reading the paper at the top of this article.