Insights into margins, revenue growth, and acceleration/deceleration

Before I dive into the nuts and bolts of this post, I first wanted to give a brief background to explain my motivation behind this analysis. I’m a newer investor, having only begun at the beginning of 2021 and I didn’t discover this board / transition to high growth stocks until late 2021. While Saul’s method and the Knowledge Base really resonated with me, I found over the past year that I needed to continue building my own intuition and conviction of these methods since I didn’t have experience or prior gains to fall back on when prices were cratering.

I understood that revenue growth was the main factor to driving returns, but I didn’t understand the impact of potentially missing out on price appreciation during the hyper-growth phase of our companies because of the macro environment. And if the macro muted those hyper growth returns, could I still generate solid returns after the tides turned and our companies were just in “normal” growth phase? The future is obviously uncertain, but it does appear this scenario has happened with many companies frequently discussed on the board showing growth slowdowns.

So my goal with this analysis was (a) to find some heuristics to guide which companies will out-perform over the medium to long-term (as Saul says, he often buys with intention to hold forever, but more often 2-3 years), (b) to understand the impact of growth deceleration on share value, and (c) to understand whether things like increasing FCF / margins can offset some of the impacts of growth deceleration. Since I’ve seen these topics discussed in various forms recently, I figured it may be helpful to share my findings.

The basis of my analysis comes from this valuation scatterplot (Market Cap / Last 12 Month FCF) vs (Next 12 Month Growth) that I’m sure everyone has seen some version of.

I’ve removed some of the big outliers, but an R^2 near 0.8 for something as complex as valuing companies seems pretty impressive to me. To go one step further though, we can use the underlying linear relationship of this plot to derive some much more interesting insights into company value appreciation.

To start with, we can multiply both sides of the linear regression equation (y=mx+b) by FCF and calculate the current Market Cap of a company at time t (with t+1 signifying one year in the future):

However, as investors what we’re really interested in is share appreciation, which translates approximately to Market Cap appreciation (ignoring dilution for now). Looking one year out, which we’ll call t+1, you can divide next year’s equation by this year’s equation to approximate Market Cap appreciation:

I’ll save going through all of the algebra here, but if you assume b=0 to simplify things (which is reasonable based on the scatter plot chart), you can arrive at the following solution which is surprisingly simple and (mostly) intuitive–we’ve essentially created a FCF growth equation.

Before I continue, I should note that I’m aware things like DCF analyses exist that can calculate present value based on assumed future cash flows and other factors. My goal here isn’t to create a different / more accurate valuation method, it’s simply to find heuristics of what drives share price appreciation and to get a sort of “scaling factor” of their importance. As the common statistics saying goes, “all models are wrong, but some are useful”.

With that caveat out of the way, examining the first 2 terms of the equation are pretty straightforward–the slope ratio of t+1 vs t is essentially the overall multiples applied to these stocks YoY (call it the macro factor). For reference, in another post I noted last year the slope was 330 and this year it’s around 180, so we should expect stocks to have roughly halved in value over the past year based on nothing except multiple compression alone.

The second factor is the YoY FCF margin expansion (or contraction). Obviously as that ratio increases, we’d expect greater value due to higher free cash flows for given revenue numbers, with the inverse also true.

Finally, the third term is the most interesting, consisting of a Growth_t+2 term and then a ratio of t+2/t+1 growth–essentially a future deceleration (or acceleration) term. Here are three simple examples to get some intuition for how that full growth term works (assuming the slope and FCF margin ratios = 1).

Scenario 1: Assume t+1 growth = 35% and t+2 growth = 35%: (0.35 + 0.35/0.35) = 1.35, which means 35% price appreciation for that growth profile.

Scenario 2: Assume t+1 growth = 50% and t+2 growth = 35%: (0.35 + 0.35/0.5) = 1.05, which means 5% price appreciation for that growth profile.

Scenario 3: Assume t+1 growth = 35% and t+2 growth = 40%: (0.4 + 0.4/0.35) = 1.54, which means 54% price appreciation for that growth profile.

This result may seem unintuitive at first glance–the 2nd scenario with faster t+1 growth and equal t+2 growth provides only 5% price appreciation compared to 35% for the constant grower in Scenario 1–but it’s important to remember that this equation assumes all stocks exist perfectly on the line from the scatterplot. Essentially, the ratio term is showing how a stock’s value will move down that line as growth decelerates from one year to the next (and its impact on returns). And obviously an acceleration of future revenue growth (moving up the line) can have huge upside to returns as shown in Scenario 3.

Now of course valuations could be offset to that line, meaning the market may be “pricing in” these types of moves beforehand (amongst other factors), but nevertheless acceleration / deceleration will have huge impacts on price appreciation from a mathematical perspective, not to mention the sentiment as well (think about guidance miss impacts).

So what are some of the takeaways from this analysis? Here are a few that I’d propose:

  1. Saul’s method is justified by the math–revenue growth and margin expansion (essentially FCF growth) are going to be the 2 main driver’s of value appreciation over time.

  2. However, multiple re-pricing during growth deceleration (whether it happens ahead of the deceleration or in more real-time) will have a significant impact on value appreciation, at least on shorter time-scales. There was recent discussions about how exiting before severe growth slowdowns is one of the most important things we can do, which is backed up by the math.

  3. While high growth rates are important, it seems that durable growth rates over many years are what’s actually most important (unless you can exit before the slowdown). Maybe this is why SNOW and NET have commanded such high premiums over time (expectations of continuous high growth compounding many years into the future)?

  4. FCF margin expansion can potentially help amplify returns or ease the impact of growth slowdowns, but playing with the math a bit seems that growth will ultimately be the bigger driver.

Some lingering questions I have:

  1. Once a company has been re-rated for a slowdown, such as CRWD has been recently, is it better to continue to hold a consistent 30-35% compounder or look for the next high growth name? The math says at this point there is upside to holding (assume growth steadies at this lower level), but I’d be curious on other’s opinions.

  2. How much are these growth slowdowns already priced in to stocks ahead of time–is it really the consensus estimates or more so the deviation from expectations? The best fit line is pretty good in the original plot, so I am at least somewhat skeptical of how much is being priced in ahead of time, but the market should in theory adjust multiples ahead of time.

Any other thoughts or discussion as they pertain to our specific high growth companies or high growth companies in general would be welcome. Thanks for reading!


A few more addendums to the original post. To give a more concrete example that the math checks out, here is the result using DDOG numbers. Going back a year, estimates for NTM growth were around 55%, with the following year growth around 35%. Assuming constant FCF margins, the expected share appreciation over the past 12 months (with the benefit of hindsight on the macro impact would be): = (180 / 330) * (1 / 1) * (0.35 + 0.35/0.55) = 0.54 or a 46% drawdown over the course of 2022 compared to a roughly 55% drawdown that it actually experience. Even with neutral macro impact, that type of growth slowdown would expect a -1% price appreciation over the course of the year.

Further, if anyone is interested on the impact of share dilution on returns, we can also very easily incorporate that factor since Market Cap = Price * Share Number:

where higher shares in the t+1 will reduce the price appreciation.