What Happened to Value in 2010?
Last month was another good one for growth stocks, continuing the trend that started in early 2023. Like many investors, however, I am on the lookout for a reversal in favor of value. Jeremy Siegel, based on valuations, expects we will see that some time this year. Josh Brown, based on the euphoria in some growth names, thinks that the Nasdaq has topped for the year, which suggests we will see that some time this quarter.
In the meantime, I’m sticking with my strategy of trend following value versus growth, which continues to work nicely. Instead of trying to pick a bottom for value, then, I’ve also been spending some time looking back at value’s glory days and trying to better understand what changed and when.
As I wrote about in this article for OSAM, I find machine learning (ML) techniques extremely valuable not only for making predictions but also for understanding and visualizing data. Rather than trying to summarize momentum and value as purely linear effects (slopes), we can use ML to describe and summarize more general relationships.
Visualizing Returns From 1988–2009
For example, if we build a random forest to predict returns in the S&P 100 between 1988 and 2009, using O’Shaughnessy’s composite value and momentum scores as input, we get the following picture:
Overall, the returns to momentum (bottom axis) and value (right axis) are fairly linear overall, but a linear model would miss interesting details. In particular, the highest returns came to stocks with high value and low momentum (and vice versa). That is, I think, conventional wisdom among traditional value investors, but that is not what a linear model would predict. Instead, the linear model would predict the highest returns for stocks with high value and high momentum, where you get exposure to both factors. The latter is not what we see in the data from 1988–2009.
To emphasize just how great the glory days were for value investors, let me point out that these are monthly returns! You didn’t even need to hold for 3 months (let alone years) to achieve alpha. You could rebalance your portfolio every single month and pick up surprisingly consistent alpha.
Visualizing Returns From 2010–2020
Anyone with even the faintest belief in the efficiency of markets would expect a source of consistent alpha to be arbitraged away once it is well known, as value and momentum certainly were by 2010.
What would that look like? Well, instead of having a chart that slopes up as you move right or up in the picture, the chart would be completely flat.
What if, despite the fact that the strategy was well known, people continued to pour large amounts of money into it? In that case, we could even start to see some reversal. For example, the highest value stocks could actually underperform or, if there is also lots of shorting, low momentum stocks could actually outperform. In short, the results would be consistent with most of what you see in the following picture, visualizing returns to value and momentum in the S&P 100 from 2010–2023:
The dip at the top of the picture shows the underperformance of high value stocks, while the bump on the left shows the outperformance of low momentum stocks. The right side (high momentum) is broadly flat, but on average underperforms. All of these are reversals from 1988–2009. In short, overall, the results are consistent with the idea that these strategies were not only arbitraged away but, in some areas, overcrowded.
Let me me note that the large spike in returns near the bottom right is essentially just due to Tesla, which had several 40+% return months when in the lowest quintile of value. Without those Tesla months, these returns would look like the rest, so let’s set those aside.
The low value stocks along the bottom are the one place where we see a continuation of the earlier trends. That is, we see generally higher returns as we move up in momentum along the lowest value decile. However, we see lower returns in the highest decile of momentum. That and the fact that moving up just a tiny bit in value, from the lowest decile to the next lowest, increases returns are consistent with overcrowding.
Asset-Heavy and Asset-Light Companies
We can get a slightly clearer picture by splitting up returns by different types of companies. The cleanest way that I found to do this is to split up companies in terms of their Novy-Marx “profitability”, which is defined to be gross profits divided by assets. This number will be lower for asset-heavy companies and higher for asset-light ones.
Returns from 2010–2020 look like the following if we restrict ourselves to just the quartile of asset-heaviest companies, many of which are traditional value-type stocks:
Here, in the bottom-left, we see a continuation of prior trends: low value and low momentum asset-heavy companies did poorly, as expected. Returns generally increase as we add momentum. However, the highest decile of value stocks in this group did worse.
In short, the lessons from 1988–2009 that we should avoid stocks with poor value or poor momentum held up. The only part that changed is that piling into the highest value decile did not deliver outperformance.
When we look at the three quartiles outside of the most asset-heavy, we also get clearer picture:
Here, the picture is a complete reversal of the value trend, with the highest value stocks doing the worst and the lowest value stocks doing the best. Meanwhile, momentum shows little effect outside of the most expensive.
The first picture of the 2010–2020 period that we saw above simply looked like overcrowding in the value and momentum trades. However, when we see that it is the amalgamation of these last two pictures, which are so different from one another, I think we learn that there is more going on. ML tools not only help us see what happened but also see when we are missing something, in this case, that there was a strong difference between asset-heavy and and non-asset-heavy companies.
Conclusions
These last two pictures, I think, tell a more correct story of what happened from 2010–2020. We can see that value did continue to work within traditional, asset-heavy companies. That is backed up by the fact that sector-neutral value strategies also continued to work.
The fact that the highest quintile of value within this group underperformed is consistent with overcrowding, but it is also consistent with the market under-appreciating how fast the world was changing and how fast their business models were deteriorating — a rare case of market under-reaction rather than over-reaction to bad times.
Within the non-asset-heavy companies, the complete reversal of prior trends does not look, to me, like overcrowding. Instead, it looks like a failure of our value metrics to properly capture value. As I wrote about before, the market did not appreciate how cheap Google, Facebook, and the other tech companies were due to how software costs are expensed.
These accounting problems are much more acute for fast-growing companies. With Google et al. growing over 20% per year at this time, their measured P/E ratios could easily be 50% higher than they should have been (e.g., 27 rather than 18) if their expenses were associated with revenues over the next 5 years rather than just the same year.
The fact that we are looking at the S&P 100 in this study, rather than the S&P 500, makes these issues more prominent. We probably did not see many of the 100 largest companies growing 20+% per year prior to 2010. That said, if I had to bet, I’d guess that the 2020s and beyond look more like the 2010s in that respect than earlier decades, so these lessons are important to keep in mind.
While it requires work to dig into the accounting and discover the issues just mentioned, ML tools can help us by discovering for us automatically the fact that these two very different pictures were hidden within the original one. And they can make this information easier to understand, even when the effects are non-linear, by displaying it to us visually. In the modern era, these are tools any data analyst should have in their toolbox.
