Embrace Moving Goalposts

Back in 2016, I had an “aha moment” while exchanging a couple of emails with the editor of an investment newsletter built around the dividend yield theory. The idea was that reliable dividend-paying stocks tend to offer great value when they hit such a low price that their dividend yield reaches extremely high levels. While this may only hold for a group of select blue chips, the service promised to identify those and reveal the dividend yield levels where they presented historically attractive buying opportunities. This sounded like the Holy Grail of investing! The concept suggested that you can play the investment game with fixed goalposts, such as buying McDonald’s when it offers a 3.6% dividend yield and selling it when the price rises so much that the yield drops to 2.1%, signaling overvaluation. Easy-peasy, except that it doesn’t work like this.

For example, I bought a restaurant stock that was supposed to be on sale with an entry dividend yield of 3.20%. Over time, that undervalued yield threshold published in the newsletter was moved to 3.75%, implying that I might have overpaid by ~15%. This prompted my questions: Are we trying to shoot accurately with the goalposts moving? What are the underlying reasons that can change these yield thresholds? Armed with decades of experience, the editor answered that both company-specific issues and the interest rate environment could impact what yield the market deems irresistibly attractive with a stock. So there went the illusion of fixed goalposts, the Holy Grail, and easy-peasy investing out the window. After all, every company’s business operations are in constant motion, and market yields are also anything but static. Did I really overpay for that restaurant stock? And all this while following expert advice? I was as annoyed as you may have been when first noticing that the entry price recommendations of the FALCON Method can move lower when things change.

As for expert advice, knowing what I know now, I wholeheartedly agree with Scott Adams: “No one has ever followed the advice of another person. People think they follow advice but they don’t. Humans are only capable of receiving information. They create their own advice. If you seek to influence someone, don’t waste time giving advice. You can change only what people know, not what they do.” Based on this observation, let me share some information on what we do at the FALCON Method, how those entry prices are calculated, and what factors influence them.

First of all, analyzing a company means that our team must dissect what the firm is doing to make money. Using Amazon as an example, they have various segments of operations like retail, cloud, and advertising services. Understanding where the revenue comes from, we should assess each field’s profitability along with the underlying markets’ key characteristics (the size of the total addressable market, current penetration, and expected growth rates). Formulating an opinion on how the company’s market position will likely evolve in each segment is also essential when modeling future revenues and margins. This process involves a ton of reading. For example, Alibaba’s margins on third-party selling can be pretty telling about how much money Amazon can make in its corresponding sub-segment. At the same time, Alphabet and Meta can serve as reasonable benchmarks for the profitability of Amazon’s advertising business. (However, one must dive deep into analyzing all the relevant competitors to incorporate these numbers.) When putting together our valuation models, we pay close attention to analysts’ published expectations on the companywide revenue growth. We also consider the underlying markets’ prospected growth and the firm’s position in those segments. (As for Amazon’s ad business, the online advertising market is expected to grow at a CAGR of ~10% over the next five years, while Amazon will most likely gain market share thanks to its first-party transactional data of unique quality. Consequently, modeling a sub-10% revenue growth in the ad segment doesn’t seem sensible at this point.) After a deep examination of all the company’s business areas, we can come up with a range of reasonable revenue expectations for our explicit modeling period (that is five years).

The segment margins may either be disclosed, or peer data might be available to get the ballpark right, so we can venture an educated guess on the companywide profitability and the resulting EVA trajectory. Please note that we always think in ranges, as precise forecasting is impossible. This means we are building an enterprising and a very conservative (dare I say pessimistic) fundamental scenario. For example, in Amazon’s bear case model, we are calculating with zero profitability in the retail segment (which is almost unrealistic) while also tuning down the growth expectations across the company. Once the models of the two scenarios are ready, we must apply the relevant valuation metrics (FGR, in our case) to come up with estimates of enterprise values at the end of our 5-year forecasting period. Next, we convert those to per-share values and calculate the annualized total return potential, including the dividends harvested along the way.

I hope you see that this is more art than science, and plenty of moving pieces can impact a stock’s valuation and total return potential. Revenues, margins, and cost of capital are far from static, just to name a few variables. As for the evolution of the FALCON Method, we used to build only one fundamental model for every company. Then we applied an enterprising and a moderate valuation to that single EVA trajectory to come up with a range of reasonable outcomes. While our one-model approach involved rather conservative numbers, we now have a more realistic and a more pessimistic fundamental scenario, and we apply the corresponding FGRs to both. Since the revenue and margin inputs in our bear case models seem overly restrained, we hope (and probability suggests) that companies will seldom underperform those levels, prompting us to reassess our valuation. Consequently, our punchcard prices should become less volatile, but we refrain from promising fixed goalposts. (As for our entry price recommendations, the higher “start accumulating” level offers a 15% annualized total return potential averaging our two scenarios. The lower “take full position” price provides that 15% return potential in the pessimistic scenario with the corresponding, more depressed valuation multiple.)

We keep learning and aim to earn high returns on our past mistakes. One of 2022’s key lessons was that total return potential doesn’t essentially equal attractiveness. Since getting the qualitative assessment right is overwhelmingly more important with EVA Monsters than the degree of precision on the quantitative modeling side, differences in forecastability and our level of understanding must come into play when ranking investment candidates. A risk-adjusted total return ranking (inevitably involving more subjective inputs) would allow us to present “safer bets” in our Top 10 that can act as anchor positions in a portfolio. Put simply, regardless of how many Chinese companies boast total return potentials above 20%, I would rather go for the likes of Nike, Costco, or Novo Nordisk when the opportunity arises.

Employing this approach can make our Top 10 less static as we may occasionally want to present (and buy) a lower-ranking name that offers enticing risk-adjusted return potential. One thing will never change, though: mistakes remain part of the game. Since we use them to get better, our long-term returns should be more than satisfactory. (Not that our current results weren’t decent.)

Jason Zweig of The Wall Street Journal says there are three ways to earn money as a writer: (1) Lie to people who want to be lied to, and you’ll get rich. (2) Tell the truth to those who want the truth, and you’ll make a living. (3) Tell the truth to those who want to be lied to, and you’ll go broke. As a newsletter provider and born longtermist, I am invariably aiming for option #2, hoping enough investors are out there wanting the truth instead of fancy promises. In this spirit, I will continue sharing well-structured, relevant information so that you can create your own advice and make educated investment decisions.

If you liked this piece, you may also want to take a look at my Confessions of an Ironman book.

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