Rational Investment Methodology (RIM)

To provide additional context for the Portfolio Action [PA]* I recently shared with RIM clients, I wanted to explore a counterintuitive example of how a company’s operating profit can be impacted when (i) there is a sudden increase in Cost of Goods Sold (COGS) and (ii) customer budgets remain unchanged in USD terms. For reference, you can find the link to the PA here.

Below, you’ll find two tables illustrating this concept. The first table outlines the economics of a company selling a specific product (let’s use tennis shoes as an example). The second table shows the scenario after the company experiences a sudden increase in COGS, perhaps due to an import tariff hike.

In the initial case, customers allocate $100 per year for tennis shoes. Each pair costs $200 because the seller applies a 100% markup—buying the shoes for $100 and selling them for $200. This means customers purchase one pair every two years. As shown in the first table, the company generates $50 in operating profit per pair sold.

Now let’s examine the second case: COGS rises from $100 to $150 per pair. If the company maintains its standard markup, the selling price increases to $300 per pair. A quick note on this practice: many companies I follow are remarkably disciplined about preserving gross margins during significant COGS fluctuations, passing on these costs to consumers.

What happens to purchase frequency? If customers stick to their $100-per-year budget (a key assumption since sticker shock could alter behavior), they will now buy a pair every three years instead of every two. Despite this reduced frequency, operating profit per unit could still rise—even with SG&A (Sales, General & Administrative expenses) increasing from $50 to $75 per unit in this scenario. In fact, as shown in the second table, operating profit grows to $75 per unit.

Over six years, when both cases complete their respective cycles, here’s what we observe: In the first case, three units are sold, generating $150 in total operating profit. In the second case, only two units are sold—but total operating profit remains identical at $150. This phenomenon, where absolute USD profits stay consistent despite lower unit sales, often surprises me—and I’ve seen it play out multiple times in real-world scenarios.

The key takeaway here—especially relevant to the PA concerning a transportation company—is that a sudden rise in tariffs typically leads to reduced volumes for products affected by higher COGS and selling prices.

From there, several financial dynamics come into play. For example:

Will typical customers maintain their annual budgets? If their income is negatively impacted, budgets may shrink.

If the product is essential and purchased regularly, budgets might even increase over time in USD terms—potentially boosting profits for sellers.

On the other hand, demand for non-essential products could decline significantly.

In some cases, companies might switch to sourcing locally produced goods. While unlikely for tennis shoes, this scenario could apply to industrial products made with heavy automation where local production costs are competitive with international suppliers. In such cases, neither companies nor customers would experience significant changes.

One thing is certain: a sudden increase in COGS creates shockwaves. Given the potential magnitude of current cost pressures, volume dislocation risks are high—and these effects will not be evenly distributed across companies or industries. This underscores why fundamental analysis remains critical for understanding how these dynamics will impact specific businesses.

For further insights related to this example and its connection to our PA, please refer back to that communication.

(*) A PA is a message sent to RIM clients whenever a position enters or exits our portfolio.

During a recent conversation with an investor, he said: “I’m not concerned about the high valuation of the company I’m buying today because it’s a great company that’s growing and has good margins, and I plan to hold it for a very long time." What is your first instinct when hearing such a statement?

If you have doubts about whether this approach is wise, take a moment to examine the three charts below for $WMT (Walmart), $KO (Coca-Cola), and $HD (Home Depot). These charts show returns (indexed to 100 as of January 1, 2000) through December 2014—a span of 15 years. The blue line represents stock prices, but since these companies pay dividends, the “Total Return” line (red) is the one you should focus on. Two other lines are included for comparison: one in yellow for the Russell 2000 and another in green for the S&P 500. Pay close attention to how much better small, mundane companies (represented by the Russell 2000) performed compared to the broader index that participated in the bubble (the S&P 500).

The US equity market was in a bubble in 2000—a fact that hindsight makes clear. But here’s what’s curious: there was no definitive event that caused the bubble to burst in March 2000. Concerns existed, but no single trigger led to the collapse. This highlights how difficult—if not impossible—it is to predict when a bubble will pop.

What we can observe from the charts below is that each of these companies—despite their success in subsequent years in terms of sales growth, margins, and earnings—delivered poor stock performance during this period. Why? Eventually, investors evaluate their returns on investment, and if future prospects are too low relative to the price paid, share prices adjust. I’ve discussed this tendency—to seek reasonable internal rates of return (IRRs)—in this post.

Consider Walmart: around that time, its price-to-earnings ratio (P/E) ranged from 40 to 50—not unlike its recent peak above 40 times earnings. It took until late 2011—12 years—for investors to recover their money in nominal terms. Adjusted for inflation, it took even longer. Along the way, investors faced a painful drawdown of 37%.

How about Coca-Cola? Surely its stable business made it safer, right? In some ways, yes—but investors still had to wait nine and a half years to break even. While its drawdown was slightly smaller than Walmart’s (34% versus 37%), it was still significant.

And then there’s Home Depot—a case of even greater suffering due to its exposure to the housing crisis during the Great Financial Crisis (GFC). Investors didn’t recover their principal until mid-2012—a grueling wait of 13 and a half years. Worse still, they endured a staggering drawdown of 70%. How many investors could resist panic-selling under such conditions?

What ties all these stories together is one common factor: abnormally high valuations at the time of purchase. Despite these companies’ eventual growth in sales and margins—and even their ability to achieve high valuations again later—paying too much upfront led to years of poor returns and significant volatility. This brings us back to the statement at the beginning: Buy highly priced companies at your own risk. Even if their prospects are excellent, valuation matters—and it always will.

When you buy shares in a company, the price you pay carries an implicit Internal Rate of Return (IRR). Essentially, anyone who purchases shares—and is willing to hold them indefinitely—locks in a certain IRR, determined by two factors: (i) the price paid and (ii) all future cash flows the company will provide to its owners. These returns typically come either as dividends or as a lump sum (usually cash) if the company gets acquired.

This concept applies universally to all companies—even those operating in sectors like technology, where expectations for rapid sales and margin growth are common. However, because shares can be traded easily and frequently, investors often mistakenly focus on market timing. In reality, collectively, investors always end up receiving the IRR described above—someone is always holding shares of each publicly traded company.

From my experience—supported by extensive data and proprietary tools(*)—the Market does attempt to value companies based on their future cash flow prospects. It isn’t just a random walk detached from fundamentals. Yet, the Market suffers from short-term bias: it tends to overly emphasize current earnings, causing prices to fluctuate far more than they would if investors consistently took into account earnings across full economic and industry cycles.

This short-term bias creates opportunities for active portfolio management. By carefully selecting investments based on long-term cash flow potential rather than short-term earnings fluctuations, we aim not only for strong long-term returns but also for the comfort of knowing our investments are fundamentally sound—backed by real cash flows rather than speculative hopes that someone else will pay more later.

Every company entering RIM’s portfolio on the long side has an “above normal” expected IRR, while those on the short side have “below normal” expected IRRs. To illustrate what “normal” looks like within RIM’s Circle of Competence (CofC), see the table below. It shows cumulative real returns—how many times your initial investment would grow—after a given number of years based on the observed IRR at purchase.

I’ve selected the 10-year mark as a practical proxy for “long term.” For instance, if you bought something today with an implied IRR between 10% and 11%, after ten years, your investment would grow—in real terms—to between 2.06x and 2.26x your original investment (highlighted in yellow). This range represents the average observed returns over very long cycles (20+ years) for companies within RIM’s CofC.

The green box highlights the typical IRRs (14% to 15.5%) at which we initiate long positions. Conversely, the red box shows typical IRRs (5.5% to 7%) at which we initiate shorts. In practical terms, when RIM initiates a long position in a company, we expect cumulative dividends (and/or cash payment for the business) equivalent to roughly 3.2x our initial investment; shorts typically offer only about 1.4x—a significant 120% difference in expected returns.

Ultimately, regardless of personal hopes or beliefs, the Market collectively prices companies based on expected future cash flows available to shareholders. Therefore, successfully investing requires both skill and discipline: skill in accurately forecasting long-term earnings prospects and discipline in buying when earnings—and thus prices—are unusually low (and shorting when they’re unusually high). This disciplined approach is precisely what I practice every day at RIM.

(*) My insights are built upon 67 detailed valuation models I’ve meticulously developed and refined over the past two decades. Additionally, I rely heavily on Odysseus—RIM’s custom-built portfolio management, trading, and reporting tool—which I developed using Python and some of today’s fastest computational libraries.