To get a feel for the quality of these decks, check out the companion deck I shared along with my Amadeus deep dive (you can find it here).

I made it for two readers. The first is the person who asks me over coffee what I think about Reddit and has only fifteen minutes rather than an entire afternoon. The second is me, eighteen months from now, when the price has moved in one direction, and I want to know what I actually claimed.

Both of those readers need the same thing. A compressed version of the argument. So the deck opens where I think every case should open, which is on the hypothesis and the conditions that would refute it. No target price. No expected CAGR in bold on the cover just yet. Just the proposition that Reddit is a twenty-year-old asset in the first innings of learning how to charge for itself, and the specific readings that would tell me I had misjudged it.

From there, we cover everything else in compressed from with some stunning visualizations.

This is where it gets interesting!

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As a member, you get:

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I could write a book about discounted cash flow analysis. The variations, the sequence of steps, how you arrive at each input and what each input actually means, why a margin of safety belongs in the process at all, why thinking in ranges beats thinking in points.

I realize that I sometimes struggle to keep my posts concise. There is always more I want to hand over than one post can carry, and the result tends to be bloated enough that nobody gets through it.

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So I am going to be narrow here. One problem. At least I will try.

A DCF is artificial by construction, and that artificiality is spread unevenly across the model. Some of it does no damage at all. Some of it costs you money without ever announcing itself, and the expensive part tends to be the part investors spend the least time on.

If you buy quality and hold for growth, as I do, this matters more to you than to almost anyone else, for reasons I will get to.

In yesterday’s write-up on Wise, I put it this way:

“Investors like straight lines. We build models in spreadsheets where each column is slightly larger than the one to its left, and something in the presentation of that arithmetic persuades us that reality will cooperate. It rarely does. You might forecast a topline CAGR of 13% over the next four years. A 1% margin expansion each year. The reality looks different. Maybe 18% next year, followed by 7% the year after. Real businesses sprint, stall, take a step backward, hit an obstacle nobody modelled, and then sprint again. What separates a temporary setback from a broken thesis is not the size of the stumble. It is whether the destination changed.”

I wrote that about a business, Wise, and not about a theoretical model. Yet, the same complaint applies to DCF models. Most DCF spreadsheets I’ve seen move in straight lines. Three growth stages. Constant Growth. Simple.

As a result, every DCF ever built describes a company that does not exist, growing in a manner no company has ever grown, before settling into a maturity no company has ever settled into on schedule.

That sounds like a devastating criticism. It mostly is not, and working out where the criticism stops being harmless and starts costing you money is the point of this piece.

Before I get there, we need to be clear about what a DCF does at the far end, because there is more than one way to finish one.

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Every model needs an ending …

A DCF says something simple. A business is worth the cash it will hand its owners over its life, discounted back to today at a rate reflecting what else you could do with the money and how uncertain the cash is.

Clean idea, right?

The trouble arrives because businesses do not have a known life, and neither you nor I can forecast a cash flow in year 47. So the model gets split in two. There is an explicit forecast period, where you determine the growth the company will grow at, and there is everything after that, compressed into a single figure called the terminal value.

In my own framework, the explicit period runs ten years, broken into two stages. Years one through five get one growth rate, years six through ten get a lower one. A high-quality business might get 14% in the first stage and 9% in the second. Then the model has to end, and here the conventions diverge.

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The first approach is the perpetuity growth model. You assume that after the explicit period the business grows at a constant rate forever, and you value that stream. Most investors settle somewhere between 2% and 3% for that growth rate, roughly in line with long-run inflation.

The second approach is the terminal multiple, or exit multiple. Rather than assuming perpetual growth, you take the final year’s earnings, EBITDA, or free cash flow and apply a multiple to it, generally drawn from where comparable businesses trade today or from what acquirers have recently paid.

Sell-side models lean on this heavily, and so does most private equity work, for the reasonable reason that those models are usually built around an eventual exit. If you intend to sell the asset in year ten, asking what a buyer will pay is the reasonable question to ask.

For someone buying a listed compounder with no intention of selling, that approach carries a problem baked into it. You are importing a relative valuation into an absolute one. The multiple comes from the market, so you have outsourced the most important assumption in your model to the same market whose pricing you were trying to assess independently. When the market is enthusiastic, your terminal value inflates. When it is miserable, your terminal value collapses, and your DCF obligingly tells you the business is worth less, which inverts what an intrinsic valuation is for.

There is a second, subtler flaw: The multiple you observe today attaches to a business at today’s stage of maturity. Some investors apply it to the same business ten years older, ten years further into its S-curve, with a growth rate that by their own model has already stepped down twice. A business growing 9% does not deserve the multiple of a business growing 20%, and yet analysts routinely lift the current multiple and drop it into year ten unchanged.

Problematic.

None of this makes exit multiples useless. It makes them a different tool answering a different question, and it makes the two methods most valuable when you run them against each other. This is the practical habit worth building, and it takes about ninety seconds.

Whatever perpetuity growth rate you chose, compare it to the value of a reasonable terminal multiple, and consider the multiple you have implicitly assigned to your terminal cash flow.

Then reverse it. Take whatever exit multiple you find defensible, and solve for the perpetual growth rate it implies. If your 3% terminal growth turns out to imply a 14 times free cash flow multiple on a business you have argued will still be earning premium returns in 2036, you have learned something.

If your 20 times exit multiple implies 5% perpetual growth, you have learned something too, something somewhat alarming, because you just asserted that this company outgrows the economy until the end of time.

Questionable.

Both methods embed a claim about forever. The perpetuity model states its claim openly and invites you to argue with it. The exit multiple hides the same claim inside a number that take a second to jot down in your spreadsheet.

Neither is more rigorous. One is more honest about what it is doing, which is why I use it.

It’s all fiction!

Of course, the used compounded growth rate - take the 14% for years one to five mentioned above - does not necessarily imply that the business grows at that rate every year. It claims where the business ends up after five years.

A company need not deliver 14% to the decimal in each of the first five years for a 14% first-stage assumption to describe its trajectory accurately.

A path of 13%, then 25%, then 4%, then 7%, then 23% compounds to 14.09% over five years, and that’s likely a more accurate description of how a business behaves. A strong year, an air pocket, a recovery, then a new product or a new market starts contributing.

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The spreadsheet column reading 14% five times in a row and the messy path arrive at the same place.

So what exactly is the complaint?

Every model is a simplification. The question worth asking is which simplifications cost you money, and this particular one costs you less than its obvious wrongness suggests.

Three flaws are worth highlighting:

• The first is arithmetic. Discounting cash flows cares about the order in which cash arrives – as future cash is less valuable in a DCF due to the time value of money –, so two paths with identical compound growth rates and identical ending cash flows are worth slightly different amounts today. Take a business generating $100 in free cash flow, a 10% discount rate, and two five-year paths. The front-loaded path grows 25%, 20%, 15%, 10%, 5%. The back-loaded path runs the same sequence in reverse. Both turn $100 in earnings power in year zero to $199 in year five, and both compound at an identical rate. Strictly speaking, the front-loaded path is worth more than the back-loaded one, though. It doesn’t make a major difference – somewhere in the vicinity of 3-4% – but it is something worth acknowledging, though, hardly worth agonising over.

  

• The second is that reinvestment does not follow a smooth line either. A business accelerating from 6% to 24% growth is most likely reinvesting (more) aggressively, funding working capital ahead of shipments, and often committing capital a year or two (or more) before the increased earnings power shows up. Its cash conversion in that sprint year will be worse than a smooth DCF model implies, sometimes considerably worse, and its conversion in the stall year afterwards will look better than it deserves. Your model shows a placid ramp, which you rarely encounter in the real world. I’m going to reference the example of Alphabet again, as it serves the purpose of illustration very well right now.

  

  The company lives through a sequence where the cash generated in 2026 bears little resemblance to the cash generated the year before. For a business with a strong internal cash generation and a strong balance sheet this is not that relevant. For a business funding growth externally, the sprint years are when often new equity gets issued.

• Thirdly, the straight line approach of DCFs may train your brain and thinking about your hypothesis in a suboptimal way. An inexperienced investor may look at a column of numbers rising by a consistent percentage, and may start to treat any deviation as evidence that something has broken. The company guides down for a quarter, the organic growth number prints 7% against your 13%, and the model in your head says the thesis is broken. However, in my view, and we discussed this in yesterday’s Wise piece, what separates a temporary setback from a broken thesis is whether the destination changed. A model built out of smooth increments has no room for a temporary stumble. It cannot represent one, so when a stumble arrives you have nothing in your framework to compare it against, and you improvise under pressure.

So the smooth line approach inside the explicit forecasting period is a fiction; theory vs. the real world of business. It’s not a big deal, but worth acknowledging.

However, something else is going on further down the model. The same criticism that lands gently in years one through ten lands hard at the point where the explicit period ends.

Artificial Cliffs

Look at what the illustrative example model above asserts. Years one through five, the business grows at 14%. Year six arrives and it grows at 9%, a step down of five percentage points occurring between two years for no obvious reason connected to the company.

Then year eleven arrives, growth falls to 3%, and stays there until the heat death of the universe.

My own template runs the terminal stage as a thousand years of cash flow, which I find useful to see written down (even though whether you opt for 1,000, 100 or 50 years makes little difference to the actual terminal value; again, the concept of time value of money applies here), because it makes explicit what the perpetuity formula is doing behind a tidy piece of algebra.

Both growth step-downs appear on a calendar boundary existing because I built the model in two stages of five years. Had I built it in three stages of four, the deceleration would have arrived at different moments – and be more gradual –, yet the business would have had no way of knowing the difference.

My point is, the timing and shape of the “fade” is one of the most consequential features of your forecast, and the architecture of the spreadsheet is setting it.

Now put that next to the evidence. McKinsey, in one of its research efforts, sorted companies into quintiles by revenue growth and followed each cohort forward. The pattern is no staircase as in your typical DCF model.

Rather, it is a curve, yet a steep one.

Businesses growing above 20% in year one are down to roughly 8% within three to four years. The 15% to 20% cohort halves inside five. By year ten, every cohort has converged to around 5%, roughly nominal GDP, because no group of companies can outrun the economy indefinitely without becoming the economy.

“Analysts have been persistently over-optimistic for the past 25 years, with ‘earnings’ estimates ranging from 10 to 12 percent a year, compared with actual earnings growth of 6 percent. |...] On average, analysts’ forecasts have been almost 100 percent too high.“

That cuts in an uncomfortable direction for anyone who thinks the standard structure is conservative. Assume 14% for five straight years and you are already claiming your business defies the median path by a wide margin, since the median 15% to 20% grower is nowhere near 14% by year four.

Most of investors’ “fantasy” (read: overconfidence or misjudgment) can be found in the first stage, and it does not feel like fantasy, because it is expressed as a single unremarkable number in a green cell.

A median is not a destiny, though, and this is where tension arises. A quintile is a crowd. Inside it sit the businesses that did compound at 12% or 15% for a decade and beyond, and if you own quality stocks, your proposition is that you can occasionally identify one business defying base rates in advance.

So the structure is too generous to the typical company and potentially too harsh on the exceptional one, and most investors correct for neither.

The textbook DCF model offers two stages and a terminal rate. Whatever you believe about durability has to be forced through those three cells.

More than the growth rate snaps into place at year eleven, too. Cross that boundary and the model asserts that margins have reached their permanent level, that reinvestment needs have normalised, that competitive intensity has settled, and that all of it happened at the same calendar moment.

Unlikely.

Businesses do not mature the way bonds mature. They mature in pieces, unevenly, some parts commoditizing while others are still expanding.

Here is why this artificiality can cost you. Take the illustration from my own framework: $557 of present value from the first stage, $581 from the second, $1,680 from the terminal stage.

That last figure is 59% of the total.

Ten years of judgment gets outvoted by one cell I filled in by convention, in about two seconds, using a number I have never thought more deeply about.

The terminal value dominates

Once you accept that most of the value sits in the terminal stage, it feels logical to conclude that years one through ten barely matter. Spend your effort on the terminal cell, wave through the rest. Right?

No!

It is wrong, because the terminal value inherits everything. Take a business earning $100 in FCF with a 10% cost of equity, assume our growth rates from above and hold terminal growth fixed at 3%. Our intrinsic value estimate was $2,820. The terminal value $1,681.

Now let’s only vary the five-year high growth period and move it up to 18% (from 14%). The terminal value, too, rises to $1,997.

The terminal growth never changed. The terminal value went up significantly.

Everything you assumed about growth, margins, and reinvestment across ten years flows into the base the formula is applied to, and the perpetuity equation faithfully multiplies your error into infinity.

How long does the moat last?

Value gets created only while a business earns more on capital than that capital costs. The moment the spread closes, growth stops mattering. So the durability question has a precise form: how many years does the spread stay positive, and how fast does it narrow?

Mauboussin and Paul Johnson named this in a 1997 paper and the concept has never had the attention it deserves. They called it the competitive advantage period and gave it a measure. The fade rate, f, is the exponential decay at which return on invested capital converges towards the cost of capital, and the competitive advantage period equals one divided by that fade rate. Across US stocks the range typically lands between ten and fifteen years, varying by industry and by company.

Here is what struck me. A ten-year explicit forecast followed by a terminal stage where returns have settled at the cost of capital is a statement that the competitive advantage period is ten years. You never typed that number anywhere. It emerged from a structure you chose because five plus five is tidy. For the median listed company, ten years is roughly right. For a business you selected specifically because you believed its advantages were unusually durable, you have applied the median assumption to a company you do not think is median, then congratulated yourself on being disciplined.

Three ways to express a longer runway, none of them clean. You can extend the explicit period to fifteen or twenty years, though forecasting revenue in year eighteen is a different order of fiction than year six, and if the true excess return period runs thirty years, extending the forecast to cover it defeats the purpose of having a terminal value at all. You have replaced one guess with fourteen more. You can fade gradually rather than stepping, letting growth and returns decay year by year at rates you have to justify, which describes the world far more faithfully but takes work per company and moves the answer less than you would hope. Or you keep the structure and let a positive spread persist into the terminal stage. That is Damodaran’s own practice, and he is explicit that it is discretionary: excess returns move towards zero in stable growth, but how far depends on the business, zero for firms lacking sustainable advantages, positive for firms that have them, negative for badly run companies with entrenched management. His reasoning is the one I landed on above. Excess returns outlive high growth by a wide margin, so the terminal assumption should reflect that asymmetry rather than flattening both to nothing on the same date.

I use the third route, and it does not become a licence to justify whatever price you were hoping to justify. Granting a permanent 15% return rather than a cost-of-capital return lifted that terminal value from 1,000 to 1,143. Granting 20%, heroic for eternity, gets you 1,214. Roughly a fifth of additional value for an assumption most people would call indefensible, set against the more than half that ten years of explicit assumptions moved. The dial carrying the durability argument is a gentle dial. I find that reassuring. It means the honest expression of “this is an exceptional business” adds ten to twenty percent, which matters when I am weighing 30 times against 34, and is useless if I am trying to talk myself into paying 60. If your quality argument needs more than that from the terminal stage, you are making it in the wrong place, and it probably belongs in the explicit period as higher growth or better margins, where reported numbers will check you within a few years.

What determines the fade rate is business analysis rather than modelling. Switching costs embedded in mission-critical workflows erode slowly, because the cost of ripping the system out rises with every year of accumulated data and habit. A brand supported by continuous marketing spend requires that spend forever, so the moat is rented rather than owned, showing up as a permanently higher reinvestment rate and a thinner spread than the headline margin suggests. Anything resting on one regulatory arrangement or one distribution partner has a fade rate that can go vertical without warning, and there I would not extend the spread at all.

One further consideration gets missed and it matters for the businesses I favour. A high return with nowhere left to deploy it is not a compounder stock. The competitive advantage period tells you how long the spread survives, not how much capital the company can push through it, and a business earning 25% on incremental capital while reinvesting only 10% of profits is a different proposition to one reinvesting 60% at 18%.

The second creates far more value over a decade despite the lower return.

The gap between your forecast and the base rate is something you have to earn

It takes one keystroke to type 20% growth for ten years into a model. Fewer than five percent of companies ever deliver it. That asymmetry between asserting something and achieving it is why base rates matter, and I have been turning it over for years without the tension resolving, which is why I keep coming back to it.

Mauboussin flags base rate neglect more than almost any other error. When you forecast growth you hold an inside view, your own judgment, built from work on one specific company. You have read the filings, listened to the calls, mapped the competition. That view feels authoritative because it was “expensive” to acquire.

It cannot stand alone.

You also need an outside view, an appropriate reference class, and the question of what actually happened to its members.

For Dino Polska the class would be grocery retailers at a comparable stage of maturity, and the question is what they delivered over the following five or ten years on the top line, on profit, on margins.

Base rate neglect is pushing that data aside and building a conclusion from conviction alone.

The data is unkind in a specific way. Mauboussin sorts companies by revenue base, since a business at two billion faces different arithmetic than one at forty, then reads across to the growth rate you are contemplating. Applied to Musk’s 2015 claim that Tesla would grow sales 50% annually for a decade from a six billion dollar base, the relevant decile produced no companies at all that had managed it.

Not few. None.

Buffett ran a version of the same test on profits, and my framework has carried it for years. Of the 200 companies with the highest net income in 1990, only 162 still existed by 2000. Fewer than nine percent of those grew net income at 15% or better across the decade. Not one of them repeated it in the ten years to 2009.

“We started by identifying the 200 companies with the highest net income in 1990. By 2000, only 162 of those companies were still around. Of those, less than 9% (14 of 162) grew net income at a rate of 15% or more from 1990-1999. None of those 14 companies grew at higher than a 15% rate for the decade ended in 2009.“

There is a methodological reason the outside view comes first rather than serving as a check afterwards. Sales growth shows very low year-over-year correlation, so last year tells you remarkably little about next year, which is why you start from the base rate and look for reasons to depart from it. Reverse the order and something predictable happens. You build from company-specific reasoning, arrive at 16%, then consult the reference class, at which point the base rate becomes a hurdle to argue around instead of a starting position.

This is where the exercise gets awkward for someone in my seat. If you buy quality and hold for growth, your proposition rests on the belief that this particular business is one of the rare ones, and the base rates are brutal on exactly that point.

They function as a direct statistical challenge to the thing I have built my process around, and there is no clean escape.

I do not think the discipline lies in choosing between conviction and the statistics of the reference class, and I am suspicious of anyone claiming to have resolved it. What is left is holding both at once. Genuine belief that you have found something exceptional, alongside clear-eyed acceptance that the odds say you probably have not.

Practically, the gap requires the defence rather than the forecast itself. If the class delivered 7% and you are underwriting 13%, six points of excess growth need a mechanism attached, specific enough to be wrong. New store formats at a defined pace with defined economics. Pricing power of a stated magnitude, supported by what happened the last three times prices went up. A category shifting online at an observable rate. When you cannot name the mechanism, you are likely blinded or biased; or both.

Justifying the gap properly, qualitatively takes real work, and often the work talks you down. That discomfort is the signal you are doing it correctly.

Base rate thinking applies to the terminal stage too, and almost nobody applies it there. Your terminal assumption contains a claim about how long an advantage survives, and there is a reference class for that as well, clustering between ten and fifteen years. Companies holding returns above their cost of capital for thirty years exist.

They are rare, identifiable and mostly famous.

If your model implies your business joins them, say so explicitly, because that assertion deserves the scrutiny you would give a 20% growth forecast and it usually receives none.

When EXACTLY does a great business become an average one?

Three percent is a defensible number and I am not about to question it per se. No company grows faster than the economy forever. Push the rate to five and you have asserted that this business eventually becomes the economy, which is simply an impossibility.

However, forever and from year eleven onwards (for a few more years) are different claims, though. A difference your textbook perpetuity growth DCF formula does not capture.

That is where the trouble starts for anyone who looks at “moaty” businesses.

Take a company with multiple structural advantages, enabling it to reinvest at high rates for longer than the outside view would suggest. Switching costs rising every year as the network grows. Or a brand name that has become synonymous with a product category. A cost position competitors cannot match.

Let’s just take Hermès as an example. Do I believe that business grows at 3% in year eleven? No. I think it probably grows at six or seven, then at five a few years later, drifting towards the economy sometime later, maybe in year 20.

The textbook model has nowhere to put that. Our example model above offers 9% through year ten and 3% after, so the entire question of how long the advantage keeps working gets compressed into one step change, on a date chosen because five plus five is tidy. Type 3% at year eleven and you have asserted that the competitive advantage period is exactly ten years. You never determined that number by thinking it through qualitatively. It arrived with the template. Which poses a problem.

For the median competitively advantaged listed company, ten years is maybe roughly right. But as McKinsey also showed, returns on invested capital can remain higher for longer. Put differently, structural advantages seem to persist (and only the ability to reinvest a lot of cash at these rates fades).

If you stick to the textbook DCF approach, for a business you selected specifically because you thought its advantages were unusually durable, you have applied the median assumption to a company you do not think is median, then congratulated yourself on being disciplined.

Again, look at the two McKinsey charts. Revenue growth converges across every cohort within a handful of years, fast growers and slow growers alike, funnelling towards 5%. Returns behave differently. The top ROIC quintile is still near 15% fifteen years after portfolio formation, having started around 28%, while the middle cohorts barely move.

Growth is fragile. Advantage is sticky.

If a business is still earning exceptional returns in year fifteen, something is still protecting it, and whatever that something is has not finished working by year eleven, and even if reinvestment opportunities fade, if you only reinvest 30% of your cash at 15% ROIIC, you still grow your topline at 4.5% (and not at 3%) – and maybe your business is inflation-hedged on top of this (take payment processing companies for instance).

So what do you do about it? Three options, none of them clean.

• You can stretch the second stage, running 9% through year twelve or fifteen for instance rather than year ten.

• You can fade gradually, stepping growth down a point or so a year towards 3% rather than dropping it in one move, which describes the world better and takes more work per company.

• Or you keep 3% at year eleven, accept that you are being conservative, and let your margin of safety do correspondingly less work at the other end, since applying both charges the same caution twice.

Nobody ever posts about a compounder they refused to buy

Everything so far pushes one way. Fade harder, respect the base rates, do not flatter your terminal assumptions. That is the conventional counsel and it is largely correct. I’m all for conservatism.

Applied without care, however, it is also a reliable method for missing every great business you will ever encounter.

Consider conservatism when it accumulates. You take 11% rather than the 15% your own work suggested. You fade to 7% in the second stage because the convergence curves say deceleration arrives faster than your inside view would suggest. Terminal growth at 3%, because “nothing outgrows the economy forever.” Then a 50% margin of safety on top.

Every one of those is defensible in isolation and I would sign off on each individually. Stacked, they produce a valuation so far below any plausible price for an ultra-competitively advantaged company that you will never own the business, even though it was maybe priced attractively enough at some point.

This kind of error, an error of omission, often stays invisible. Overpaying leaves a mark. It shows up in your returns, in your portfolio. Underwriting a compounder at half its worth and walking away leaves nothing behind. No entry in the P&L. Nothing to review. Nothing anyone can criticize, and a certain amount of unearned credit for discipline.

The investing world has produced an enormous literature on valuation-agnostic buying and almost nothing on the cost of habitual under-underwriting.

The arithmetic runs in the direction people find counterintuitive. A business earning 25% on incremental capital and reinvesting 60% of its profits grows earnings at 15% a year and funds that internally. Fifteen years turns $1 of earnings into roughly $8. A model built to be safe has the same business decelerating to 7% by year six and 3% by year eleven, arriving at a little over $3.

You have valued a different company.

The entry multiple matters less than it feels like it should for these compounders. Buy that compounder at 35 times, hold fifteen years, and even if the multiple compresses to 20 your annualized return comes to just under 11%.

This is the risk of underappreciating a runway vs. the risk of overpaying.

None of this argues for waving through a high multiple. The point is narrower: conservatism has a cost, too, and treating it as free is itself an analytical error. So I try to be deliberate about where it enters. I keep the forecast as honest as I can make it, neither optimistic nor defensively low, and hold the margin of safety separately as an explicit decision about how uncertain I am, so I can see what it is doing.

So what do I actually do?

I am not offering a superior model here. In fact, I rarely use a DCF these days.

The value of this entire discussion, and the value of attempting your hand at a DCF in the first place, lies in the questions it forces you to tackle rather than the figure it produces.

A DCF handing you a number and nothing else has failed at the only thing it is good for.

The sequence starts outside the company. Before I build anything, I want the reference class and what happened to it, which means finding businesses at a comparable stage in comparable industries and looking at what they delivered over the following decade on revenue, margins and returns on capital. That becomes my anchor, and everything afterwards is an argument for departing from it. The order is not cosmetic. Build the forecast first and the base rate becomes an obstacle you negotiate around, a completely different mental operation to starting from the data and having to justify every point of excess.

Then comes the exercise I find more valuable than anything else in my valuation work at the moment. If you take one practical thing from this piece, take this. Rather than typing a single growth rate into a cell, I decompose the top line into its drivers and assign a contribution range to each.

For a retailer, for instance, that might be new store openings contributing four to seven points, like-for-like growth at one to three, pricing at two to three, and a new category contributing nothing in the bear case and two points if it works. Sum the ranges, and you have a growth band rather than a point estimate, plus a picture of where the growth is supposed to come from.

The assumptions become auditable, and they generate questions I would not otherwise have asked. Assigning a band to new store contribution forces me to ask how many locations remain in the addressable market, what the recent cadence has been, whether new stores cannibalise existing ones, what the payback period looks like. When the company reports I am not checking whether revenue growth was 13%. I am checking whether openings ran at the pace I assumed and whether like-for-like held, which tells me which part of my thinking was wrong rather than merely that something was. By the time I have worked through every driver I understand the business considerably better, and the model is a by-product of that understanding rather than a substitute for it.

The same treatment applies at the far end, the terminal value, where most of the intrinsic value can be found.

• What am I claiming about the competitive advantage period?

• What generates the advantage and what is its natural decay rate?

• Can the company still deploy meaningful capital at that return, or is it a wide spread with nowhere to put the money?

• Are the reinvestment rate and the return I assumed consistent with the growth rate I typed in?

That last check takes thirty seconds and catches an embarrassing number of errors.

Everything gets expressed as a range, and I usually hold at least three scenarios rather than one valuation, so the output stops being a price and becomes a distribution, a range, and the decision becomes a judgment about the scenario distributions and assigned probabilities.

Then I invert the whole thing and ask what the market price already asserts. Solve for the growth rate the market is embedding, given a sensible discount rate and a reasonable view on returns and reinvestment.

A stock at 40 times might be asserting 18% growth for a decade, which the base rates say is a stretch bordering on fantasy. Or it might be asserting 9%, which for a business with a durable advantage is worth taking seriously.

What felt expensive on a multiple often looks different once you see what it requires, in both directions. I find that reframing more useful than any forward model I build, and it deserves its own piece rather than a paragraph here.

Is this as granular as sell-side modelling? Not remotely. Those models run to hundreds of line items, and building one teaches you the business in a way nothing else does. The failure mode is something other than imprecision. It is the man with a hammer, the conviction that four hundred rows make the output knowledge rather than a very elaborate opinion. Precision and accuracy have nothing to do with each other, and a forecast carried to the decimal place in year nine claims a resolution nobody possesses.

What I am describing is less granular and better calibrated to what can be known. Enough structure to catch inconsistencies and force the right questions, not so much that the apparatus starts manufacturing confidence. It is a long way from the alternative, the hasty assumption, which has a formal name. The hasty generalisation fallacy describes drawing a conclusion from a sample too small or unrepresentative to support it. Three years of strong growth extrapolated across a decade. One good quarter treated as a trend. A competitor’s stumble read as a permanent advantage.

I am not accusing you of it. I do it too, generally when I like a business and want the numbers to agree with me.

So where does this leave the DCF? Roughly where it should have been all along. It is a device for thinking, and its highest use is diagnostic. It tells you what you would have to believe. When my model implies a terminal spread putting a company among the two dozen most durable franchises in the world, I have learned something about my own reasoning rather than about the company. When the price embeds growth the base rates say almost nobody achieves, I have learned something about the price.

Neither insight arrives as a number, and both are worth more than the numerical output a DCF provides.

The artificiality never goes away. Growth stages stay arbitrary.

Knowing where your tools distort is what separates using a tool from being used by one.

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Disclaimer:

The analysis presented in this blog may be flawed and/or critical information may have been overlooked. The content provided should be considered an educational resource and should not be construed as individualized investment advice, nor as a recommendation to buy or sell specific securities. I may own some of the securities discussed. The stocks, funds, and assets discussed are examples only and may not be appropriate for your individual circumstances. It is the responsibility of the reader to do their own due diligence before investing in any index fund, ETF, asset, or stock mentioned or before making any sell decisions. Also double-check if the comments made are accurate. You should always consult with a financial advisor before purchasing a specific stock and making decisions regarding your portfolio.

Something you briefly glance at after you’ve fallen in love with a company. Something you tweak until the number looks reassuring – “Yes, that confirms my thesis.”

Yet the longer I invest, the more convinced I am that valuation is still the #1 factor that decides the outcome. It decides who really compounds at high returns – regardless of style (quality, GARP, special situations, etc.) – and who treads water.

It decides whether a brilliant business becomes a brilliant investment or a frustrating one. And most importantly, it decides whether your process is anchored in reality or drifting into a story that sounds good but doesn’t cash-flow its way into your future returns.

Underperformance often hides behind complicated explanations, but the core reasons tend to be simple. Some investors never want “value” in the first place and chase whichever narrative happens to sparkle that year. This year in particular, I see more and more investors give up on “value.” Others never built the skill to value a business. Still others know how to value companies but set hurdle rates that are far too low. In each case, the problem is the same. You can own world-class companies and still fall short if the price you paid assumed too much. Markets have shown this over and over again. Cisco and Microsoft in the early 2000s didn’t suddenly become weak businesses. Their stocks just needed years to grow into the expectations embedded in the price.

That reality shaped how I built my own valuation toolkit. Over time I experimented with different approaches, some simple, some unnecessarily complex, and eventually landed on a system that forces clarity instead of encouraging wishful thinking.

Valuation is imprecise by nature, but that doesn’t make it optional. If anything, it makes structure more important. You need a framework that turns intuition into explicit assumptions and narrative into numbers you can interrogate.

That is why my total shareholder return (TSR) model has become the backbone of how I analyze high-quality growth companies. It breaks returns into the actual mechanics that drive what a shareholder earns over time.

Earnings growth. Multiple changes. Share count dynamics. Dividends.

Once you see these pieces next to each other, valuation stops feeling abstract. It becomes a map. I wrote about this approach a while ago – read this post here:

I recently updated the model in two important ways. In the sections that follow below, I’ll walk you through these updates, show how they deepen the model, and explain why they’ve improve the quality of investment decisions.

This is where it gets interesting.

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There’s something inherently satisfying about stock screeners. They give you the illusion of control in a messy, uncertain world. With just a few clicks, you can filter a universe of over 90,000 stocks down to a manageable handful.

A simple set of criteria – say, high returns on invested capital, strong free cash flow, low leverage, consistent growth – can magically produce a shortlist of names that look like they deserve your attention.

It’s like panning for gold, but instead of sifting through mud, you’re sifting through data.

Over the years, I’ve built quite a few of these filters myself. Some of them have names that sound almost too good to be true – my “high-quality screener,” my “40-30-20 screener,” the “Buffett screener,” and one I once called the “Hagstrom screener.”

Each of them reflected a different phase in how I thought about investing and how I tried to codify what made a great business. I’d tweak the criteria, run them across different geographies, and see what came up. Sometimes it was a company I already knew well. Other times it was a total unknown that sent me down a rabbit hole for days.

And that’s the beauty of screeners: they help you generate ideas. They don’t replace fundamental research, but they can help you stumble upon the ideas worth researching in the first place.

Every serious investor should, in my view, build the habit of screening regularly. It’s one of the cleanest ways to “turn over lots of rocks,” to borrow Peter Lynch’s phrase. I’ve written about the “rock turning” principle in this recent article:

You might not always strike gold – but you train your eye to recognize what gold even looks like.

That said, I have to admit I don’t screen nearly as often these days as I probably should. My idea generation process has shifted more toward following what great investors are buying, cloning portfolios, and watching emerging themes play out in real time.

In some ways, I’ve become less reliant on systematic tools and more reliant on intuition and pattern recognition.

But every time I do return to screening, I’m reminded of just how powerful it can be – when used in the right way.

Because here’s the thing: the very best investment ideas rarely show up on traditional screens.

If you’re filtering for low P/Es, high margins, and stable ROICs, you’ll mostly find companies that are already in their prime. They might be solid compounders, but they’re rarely mispriced. By the time a company checks all those boxes, the market usually knows it – especially in a market dominated by algorithmic trading.

You’ll rarely find a misunderstood emerging compounder through a screen like that. The next Amazon, ASML, or MercadoLibre won’t pop up in your “cheap and high-quality” filter – they’d have failed almost every one of those screens at the time when they were still incredible opportunities.

That paradox is what makes this topic so interesting to me. Screeners are essential tools, yet they’re inherently limited by the very logic that makes them useful. They surface what’s (seemingly) quantitatively attractive, not necessarily what’s qualitatively mispriced.

Which means that if you want to find the best investments – the ones that look expensive at first glance but are actually hiding enormous future value – you need to go a level deeper.

In this post, I want to unpack how I think about this trade-off, why “40x earnings” stocks often make the best investments, and how I’ve adapted my screening process to deal with that reality.

I’ll walk you through two kinds of screeners I use that have completely revolutionized how I look for ideas, and the two ways I apply them – a sort of 2x2 matrix that’s become my mental model for idea generation via screeners.

So let’s start with that central paradox – the fact that the best ideas don’t screen well.

Why the Best Ideas Don’t Screen Well

If you’ve been investing long enough, you’ve probably noticed a pattern: the stocks that become your biggest winners rarely look like winners when you first encounter them. In fact, they usually look expensive, uncomfortable, or just plain illogical. They defy neat classification. They don’t fit any of your screeners.

That’s because the market’s best opportunities are rarely found where the data already looks good – they’re found where the data is about to look good.

Steve Mandel, the founder of Lone Pine Capital, recently said:

“I don’t need an analyst to tell me when a 10 PE stock is cheap. I need an analyst to tell me when a 40 PE stock is cheap.”

That line captures a subtle truth most investors never fully internalize. It’s not hard to recognize a bargain when the numbers already scream “cheap.” What’s hard is recognizing when a company trading at 40 times earnings – or even 80x, or 100x – is actually undervalued because the earnings denominator is temporarily depressed or because future growth will make that backward-looking multiple vanish faster than you think.

A 40x earnings stock might be expensive today because the company is pouring every spare dollar into expansion, new markets, or R&D. On paper, it looks like overvaluation. In reality, it might be a business in the middle of a reinvestment cycle, temporarily suppressing margins while sowing the seeds for explosive profit growth a few years down the line.

Alternatively, it might simply be a company capable of compounding earnings at 20–25 percent for a sustained period – fast enough that what looks like a nosebleed multiple on trailing numbers compresses to a single-digit forward multiple within a few years.

Those are the kind of setups that produce the true home runs: the inflection-point investments where growth, margins, and/or returns on incremental capital (all) start to accelerate.

But how do you screen for that?

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Traditional valuation filters will never catch these opportunities because the numbers look wrong by design. They penalize reinvestment. They ignore optionality. They treat all high multiples as dangerous when, in fact, some are simply a reflection of quality or accounting artifacts that hide immense future value creation.

That’s why I find Christopher Tsai’s concept of “Value Investing 3.0,” which he recently described at a MOI Global Latticework 2025 session, so useful. He describes it as a kind of intellectual evolution within the discipline:

• Value 1.0: Ben Graham’s era of “cigar-butt” investing – buying statistically cheap assets for less than their liquidation value.

• Value 2.0: Charlie Munger’s realization that it’s better to buy a wonderful company at a fair price than a fair company at a wonderful price.

• Value 3.0: A framework for the modern, software-driven economy, where GAAP earnings are intentionally suppressed by visionary, founder-led businesses investing heavily in technology, ecosystems, and customer acquisition.

Tsai argues that these companies – think of them as long-duration compounding machines – are building the dominant ecosystems of the next decade. Their current income statements don’t reflect their true economics because they’re reinvesting at returns that will compound internally for years. Traditional multiples therefore fail to measure what matters: the scale and quality of the opportunity set, the reinvestment runway, and the durability of competitive advantage.

Seen through that lens, “40x earnings” stops being a warning sign. It becomes a clue that the business might be playing a different game.

I recommend watching the entire sequence here (link).

This also explains why catching inflection points – moments when growth, margins, or capital efficiency are about to rise structurally – tends to produce the highest returns. If you can identify those transitions before they show up in the reported numbers, you’re effectively front-running the change in investor perception that will eventually re-rate the stock. That’s not speculation; it’s recognizing the time lag between reality and recognition.

Which brings us back to the practical problem: how do you build a screener that reflects this kind of thinking? How do you construct a quantitative filter that helps you find qualitative change?

That’s where I think most investors get stuck – and where I started rethinking how to use screeners altogether. Instead of trying to find “cheap” stocks, I began looking for contextually mispriced ones. I started asking: relative to its own history, is this business cheap or expensive? Relative to what it could earn in a few years, is the current valuation misleading?

To answer those questions, I needed a way to add context to raw multiples …

The full story starts here:

The rest of this post covers the structure of the brokerage business. If you’re serious about sharpening your investing edge, the full post (and all my previous premium content, including valuation spreadsheets, deep dives (e.g. well-known mid- and large caps such as LVMH, Duolingo, Meta, Edenred as well as more hidden gems such as Tiger Brokers, Digital Ocean, Ashtead Technologies, InPost, Timee, and MANY more) and powerful investing frameworks. is just a click away. Upgrade your subscription, support my work, and keep learning.

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That’s where charting comes in.

For me, Koyfin has been a real game changer – not just because of the sheer breadth of data it covers, but because of how easy it makes it to build, customize, and save chart templates. With one click, I can pull up a view I’ve fine-tuned to answer specific questions, whether it’s about company fundamentals, price drawdowns, or relative performance.

Charting has traditionally been seen as the realm of traders. Think technical analysis, moving averages, and support lines. But I’d argue it can be just as valuable – maybe even more so – for long-term, fundamentally oriented investors.

The ability to look at historical sequences of data, to overlay multiple series, or to compare developments across time and peers brings clarity that a spreadsheet of raw numbers simply doesn’t. For me, it often sparks the kind of insights that feed directly into my investment process.

In this post, I want to walk you through the 14 charting templates I’ve set up on Koyfin and actually use in my day-to-day research. If you have any questions about how to set them up, just drop a comment below.

These aren’t abstract examples – they’re the exact views I return to again and again. To make things more concrete, I’ll illustrate them using Duolingo, a company I’ve written about recently. By the end, you’ll have a sense not only of how I structure my charts but also of how powerful it can be to have the right visualizations ready at your fingertips.

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My 14 Charting Templates

Template #1 Headlines Numbers

The first template I always pull up is what I call my “headline numbers” view. It’s the chart that anchors my understanding of a business at the highest level – revenues, EBIT, net income, and free cash flow. I want to see them all in one place because together they tell me whether topline growth is translating into real economics or if it’s just topline momentum.

Take Duolingo. The company has scaled revenues to over $800 million on a trailing twelve-month basis, and the growth rate has been extraordinary – north of 40% YoY in most years, still 39% recently, and closer to 50% if you take the mean across the last couple of years; I find the “mean” statistical band a true game changer fwiw.

That kind of revenue and profit expansion is impressive on its own, but the real story is what sits beneath it: free cash flow margins running in the mid-30s. For a business still early in its monetization journey, that combination of growth and profitability is exceptional.

What the chart also makes clear is the spread between the different earnings lines. EBIT margins are still in the single digits, reflecting at least partly stock-based compensation-related expenses. Free cash flow (unadjusted for SBC), by contrast, is much stronger, which says a lot about SBC but also the capital-light nature of the business and the negative working capital dynamics at play.

For a fundamental investor, this is exactly the kind of picture you want to have at your fingertips – one chart that condenses growth, profitability, and cash generation into a coherent snapshot so you can decide whether this company deserves a closer look.

Template #2 Free Cash Flow

If the “headline numbers” chart gives me the big picture overview, this free cash flow template zooms in on what really matters for long-term shareholders: the company’s ability to turn accounting profits into real, distributable cash.

Ultimately, you want to combine it with the template focused on the shares outstanding (template #9) – because ultimately, FCF per share growth will determine a stock’s trajectory.

This chart pulls together revenues, cash from operations, capital expenditures, and stock-based compensation – and then ties them into free cash flow levels, margins, and growth rates.

Looking at Duolingo, the story that jumps out is how clean the cash generation profile has become. Revenues are approaching $900 million, but what matters here is that cash from operations sits above $330 million, with capital expenditures of less than $8 million. That light CapEx burden is one of the defining features of Duolingo’s business model. It means that most of the cash flow is truly free, and not tied up in heavy reinvestment just to keep the lights on. The result is free cash flow margins north of 35% – roughly double the company’s own historical mean —> operating leverage at play.

Related to this is the growth trajectory. Over the last year, FCF grew at nearly 50%, and if you stretch the timeframe out to three years, the compound annual growth rate is over 100%. That kind of scaling is hard to find, especially when it comes with high incremental margins.

Of course, stock-based compensation – at more than $120 million – is a real cost for shareholders and one I keep a close eye on. But even accounting for dilution, the underlying economics are very strong.

This is why I like to have this chart saved as a one-click template. It forces me to ground any investment thesis in the cash reality of the business, not just in the revenue excitement or headline earnings figures.

For Duolingo, the chart illustrates exactly why some investors are willing to pay a premium multiple: it’s a business that converts growth into cash at scale and the cash generation machine is growing.

Template #3 Margin Profile, Intangible Investments & ROIC

Margins are where the story of a business model really reveals itself, which is why I keep a dedicated template to look at a company’s profitability profile in more detail, and most importantly, over time.

One of the most common valuation mistakes I see is investors over- OR underestimating “true” (steady-state) profitability of a business. Of course, getting this right requires qualitative work, but looking at the historical profile over time, is a good starting point too.

So in this chart, I can see gross margins, operating expenses as a percentage of revenue, the progression of EBIT and net income margins, as well as free cash flow margins. I also overlay R&D intensity, CapEx levels, SG&A expenses (or investments; this requires qualitative work), and return on invested capital (ROIC) to get a sense of how efficient the company is at turning investments into returns.

For Duolingo, the first thing that stands out is just how strong and stable the gross margins are – north of 70%. That reflects the digital nature of the product: once the platform is built, adding more learners comes at almost no incremental cost. The challenge is further down the P&L. R&D sits at more than 30% of revenues, which isn’t unusual for a young, product-led company, but does hold back operating leverage (for now). But operating leverage has at least started to kick in a little bit as R&D investment scaled down from >40% back in 2023. SG&A runs another 30% of revenues, so while Duolingo is scaling, it’s still early in the margin expansion journey.

ROIC, at around 6%, is only just starting to move into positive territory – again, I’d consider this normal for a business in this stage of the corporate life cycle. Given Duolingo’s limited capital intensity, I’d expect that number to climb as the company grows into its cost base.

This chart is useful because it keeps me honest. It’s easy to get excited about high gross margins, but until SG&A and R&D moderate as a percentage of sales, operating margins will remain compressed. Consider this chart of trivago, for instance, where SG&A expenses represent almost 90% of revenue which is strongly tied to the business model:

Template #4 Valuation Multiples

Valuation is where everything ultimately comes together. No matter how strong the growth or how attractive the margins, the question for investors is always the same: what am I paying for this business?

My valuation multiples template lays out the key multiples – EV/EBIT, P/E, P/FCF, EV/Sales, and PEG – both on a trailing and forward basis. I also track free cash flow yield, dividend yield, and shareholder yield (dividends + buybacks) to get a sense of how much value is actually flowing back to investors.

Looking at Duolingo, there’s no denying the premium the market is demanding. On trailing numbers, the stock trades at more than 100x earnings, nearly 40x free cash flow, and over 13x sales. Of course, you have to tie these numbers to the current vs. historical vs. projected margins. I bought Wix.com at an almost infinite earnings multiple, for example, because I had high conviction in the business becoming structurally extremely profitable once it enters a later stage in its business life cycle.

Forward estimates bring the Duolingo multiples down somewhat, with P/E dropping into the 30s and EV/EBIT into the 70s, but these are still lofty levels. The PEG ratio, at a little over 2, gives some context: growth helps justify part of the valuation, but investors are clearly betting on sustained momentum.

The free cash flow yield tells another part of the story. At under 3%, it’s thin – especially when you consider what else investors could earn in today’s interest rate environment. Shareholder yield, at just a quarter of a percent, reflects the fact that Duolingo is reinvesting most of its cash back into the business rather than paying dividends or buying back stock. If the company were to initiate a dividend, it would show up in the lower part of this chart, alongside buybacks, giving a quick visual of how much capital – relative to the current price – is being returned.

This is one of my favorite templates because it forces me to look at valuation not in isolation but across multiple angles. A stock can look expensive on earnings but reasonable on free cash flow, or cheap on sales but expensive on EBIT. Having them all in one place, plotted against history, makes it much easier to judge whether a business is truly priced for perfection or if there’s hidden value the market is missing.

The full story starts here:

The rest of this post covers the other 10 templates I’ve set up. If you’re serious about sharpening your investing edge, the full post (and all my previous premium content, including valuation spreadsheets, deep dives (e.g. Duolingo, a 12x EBIT Royalty Business, Tiger Brokers, LVMH, Edenred, Digital Ocean, Zalaris, or Ashtead Technologies), and powerful investing frameworks) is just a click away. Upgrade your subscription, support my work, and keep learning.

Just to give you a heads up, I will also raise prices in October.

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Why is forecasting growth so hard? Because it tempts us to trust our intuition. We fall in love with company narratives, impressive management teams, or disruptive technologies and project straight-line trajectories into the future. Yet history tells us that growth almost always slows faster than expected, and the exceptions – Amazon, Apple, Nvidia, Tesla – are exactly that: exceptions.

That doesn’t mean we shouldn’t forecast. It means we need tools that keep us grounded. One of the most powerful is the use of base rates – the statistical record of how similar companies (an appropriate reference class) have grown and matured over time. Base rates don’t kill your optimism, but they put it in context. They show you what’s typical, what’s rare, and what’s nearly impossible.

In this post, I’ll explore how base rates can transform the way you approach forecasting – be it forecasting growth, steady state margins, or ROIIC. I’ll show how base rates act as a reality check against the stories we tell ourselves and how, when combined with qualitative analysis, they can lead to far more balanced and accurate investment decisions.

Here’s what we’ll cover in this 5,000-word piece:

• The challenges of forecasting growth – why investors systematically get it wrong and how optimism creeps into valuations.

• What base rates are – and why they provide the “outside view” that tempers company-specific narratives.

• Applying base rates in investing – how to combine the inside and outside views to build disciplined forecasts.

• Regression to the mean – why extreme outcomes rarely persist, with examples across industries.

• Practical applications in portfolio management – how base rates can guide diversification, asset allocation, and growth

• An overview of key base rates – including sales growth, margins, and returns on capital, drawn from Mauboussin’s work.

• Challenges and limitations – where base rates fall short, especially in fast-changing industries.

• Integrating qualitative analysis – how factors like pricing power, TAM, and management quality allow companies to bend the rules.

• Inside vs. outside views in practice – case studies that show how combining both perspectives leads to better outcomes.

• The bottom line – why base rates are a guardrail, not a crystal ball, and how they help you invest with both humility and conviction.

Part 1: The Challenges of Forecasting & Understanding the Concept of Base Rates

Forecasting corporate earnings growth is one of the most critical – and most difficult – tasks in investing. Growth drives valuation, and getting it even slightly wrong can swing investment outcomes dramatically. Predict too little, and you may miss out on opportunities. Predict too much, and you risk overpaying, often with painful consequences.

In my experience, both professional and private investors tend to lean too heavily toward optimism. They build rosy forecasts, assume straight-line growth, and justify ever-higher multiples. Inevitably, this leads to overpayment and disappointing long-term returns.

Michael Mauboussin has spent much of his career reminding investors of this bias. The chart above is one of his friendly reminders to embrace conservatism.

As Sean Stannard-Stockton of Ensemble Capital framed it well:

“The question at hand for investors is not whether they should forecast the future growth (or lack thereof) of corporate earnings power. Rather it is how should investors go about making those forecasts without being too optimistic, nor by naively ignoring growth potential.”

Traditional value investing has historically sidestepped some of these issues. By focusing on low valuations rooted in current or past financials, value investors implicitly assume the future will look more like the past than the optimistic scenarios baked into many growth stories. That conservative mindset has often been more accurate than the blue-sky forecasts that growth investors rely on.

That doesn’t mean one should never “pay up” for growth, though – in fact, this can be a very costly mistake. The last two decades have shown us just how quickly the future can diverge from the past. The rise of the internet, a global financial crisis, a pandemic, and now the explosion of artificial intelligence have reshaped entire industries in ways most investors failed to anticipate. Forward-looking investors who correctly spotted these divergences were rewarded handsomely.

Still, I believe history offers lessons. Using base rates – the statistical patterns of how similar companies have grown in the past – can help anchor expectations and keep investors from straying too far into fantasy. They don’t replace judgment, but they provide a much-needed reference point in a world that tempts us toward overconfidence.

Understanding Base Rates

At its core, a base rate is just a probability drawn from history. It’s a way of asking: “When situations like this have happened before, how did they usually turn out?”

For investors, base rates act as a statistical guardrail, helping us avoid two classic mistakes – overconfidence and ignoring the tendency of outcomes to drift back toward the average.

Think about weather. If I asked you to predict whether it will more likely rain tomorrow in London or Barcelona, and you had no access to forecasts, you’d probably guess London. And you’d be right: London averages 155 rainy days a year (around 42%), compared to Barcelona’s 78 (21%).

That’s the base rate – a historical record of how often something happens. Of course, if you were betting serious money, you’d also want to look at the sky that day. Dark clouds over Barcelona might outweigh the averages. This distinction between the broader statistical record (the “outside view”) and the specific, immediate conditions (the “inside view”) will come up again later.

Another example: suppose you meet a dentist at a party in New York City and are asked to guess his income. You’d probably anchor your estimate to the average dentist’s salary in New York, around $228,000 per year. If someone told you this dentist earns $29 million, you’d be skeptical – the base rate alone tells you that outcome is wildly improbable.

In investing, base rates serve the same function. Before projecting the future of a promising software startup, you might first study the historical growth rates of similar tech companies. That outside view won’t give you a perfect forecast, but it grounds your expectations. It helps you weigh the company-specific story against the hard reality of how most comparable businesses have actually performed.

Part 2: Applying Base Rates in Investing

When forecasting growth, investors need to blend two perspectives. The inside view digs into the specifics of a company – its products, strategy, competitive position, and management team. The outside view looks at what history tells us about similar companies in similar situations. Base rates are the backbone of this outside view.

The real insights start here:

The rest of this post covers the content outlined above. If you’re serious about sharpening your investing edge, the full post (and all my previous premium content, including valuation spreadsheets, deep dives (e.g. LVMH, Edenred, Digital Ocean, or Ashtead Technologies), and powerful investing frameworks) is just a click away. Upgrade your subscription, support my work, and keep learning.

PS: Using the app on iOS? Apple doesn’t allow in-app subscriptions without a big fee. To keep things fair and pay a lower subscription price, I recommend just heading to the site in your browser (desktop or mobile) to subscribe.

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Most investors stop at the surface: the chart went up, the business grew, maybe there were some dividends. But rarely do they break it down into the actual math of value creation. That’s where the Total Shareholder Return (TSR) framework comes in.

It gives you a structured way to deconstruct performance into its most essential drivers – and more importantly, it lets you flip the lens forward to estimate future returns using the same building blocks.

Whether you're evaluating a portfolio company, pitching an idea, or simply sanity-checking a stock you’re about to buy, this approach helps you go beyond narrative and price action and focus on the mechanics that actually create wealth.

I’ll be honest: this is one of the most practical and underrated tools I’ve come across in my years of investing. It’s simple in theory – but powerful in practice.

I originally published this framework as part of a my mentorship program curriculum, but in this blog post wanted to make it available for paying subscribers too so you can apply it immediately yourself.

This isn’t just another post to skim and forget. You’ll also get access to:

• A downloadable valuation spreadsheet that lets you run the TSR model for any stock

• A step-by-step video tutorial walking you through how the model works

• A set of practical exercises so you can test the framework on companies you already own (or are thinking about buying)

If you’ve ever felt uncertain about what’s driving a stock’s return – or how to structure your thinking when projecting forward – this post will give you the clarity you’ve been missing.

What You’ll Learn Inside the Full Post:

• A simple but challenging quiz to help you intuitively grasp how TSR works – and why most investors get it wrong

• The actual formula for Total Shareholder Return, explained clearly and in detail

• Breakdowns of all four TSR drivers: earnings growth, valuation changes, share buybacks (or dilution), and dividends

• Why some drivers are easier to forecast than others – and how to spot mispriced sentiment

• Six detailed examples, including Apple, Copart, a forward-looking case study, and a classic value trap

• A deep dive on Apple’s 5,818% return between 2006–2022, broken down factor by factor

• Key takeaways for analyzing future investments using the TSR lens

• Access to the full spreadsheet and video walkthrough, so you can apply this to your own portfolio

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The full deep dive is available to paying subscribers only. But if you’re on the fence, just know this: once you start thinking in terms of Total Shareholder Return, it fundamentally changes how you evaluate businesses. You start asking sharper questions. You focus on what actually matters. And you become a far more disciplined investor.

Part 1: Let’s Start with a Quiz

To kick things off, let me challenge you with a simple quiz. It’s designed to warm up your intuition around how TSR works – and trust me, even experienced investors occasionally get tripped up by this one.

Assume that between 1999 and 2024 (a 25-year period), Stock XYZ has delivered the following:

• Earnings grew by a factor of 10.83x, or roughly 10% annually

• The P/E ratio doubled from 10 to 20 (that’s a 2x increase, equivalent to ~2.8% compounded annually)

Now here’s the question:

What do you think the total return of this stock was over that 25-year period?

Take a moment to pause and think it through. Don’t scroll too quickly.

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Ready?

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Can a single multiple (or even several) tell you everything you need to know about a company’s value, or do you need more rigorous methods like discounted cash flow (DCF) analysis to make sound investment decisions?

In conversations with my friend Tiho Brkan, he argues that using any single multiple as your primary gauge is simply wrong and leads to subpar results.

He says, “Shorthand or not, it’s the wrong way to go about it. Most people using multiples don’t even know what they mean. As Oscar Wilde famously said, ‘People know the price of everything and the value of nothing.’”

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Investors worldwide are pouring money into markets that appear highly overvalued, often leading to puzzling questions: Why are people still investing in these markets despite sky-high valuations? And what does this mean for future returns? Do they understand the relationship between valuation and expected future returns?

In this blog post, we’ll take an in-depth look at the current landscape of global stock market valuations. We'll explore valuation metrics like the Shiller-CAPE ratio and the price-to-book ratio, discuss why these metrics can be problematic, and most importantly, understand what this means for investors in the years to come.

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Introduction: Breaking Down Global Valuations

Valuation is critical in understanding stock market investing, especially if you're looking at individual stocks. Arguably, understanding valuation is less relevant if you’re a passive investor investing in entire national markets, but understanding the impact of high (or low) valuation levels on future returns isn’t going to harm you.

The two key metrics to measure stock markets’ valuation that we’ll focus on in this article are the Shiller-CAPE ratio and the price-to-book ratio, both of which provide insights into how richly (or cheaply) different stock markets are priced. While these metrics have been staples for value-oriented investors for decades, we’ll also discuss why one of the two metrics might be losing some of its relevance in modern markets dominated by intangible assets and technology companies.

But first, let’s start by looking at the actual data.

The Data: Insights from Norbert Keimling

The data I’m sharing today comes from Norbert Keimling, a German value investor who works at Taunus Trust, a German investment company. Keimling is known for his detailed analysis of stock market valuations, and he recently shared some fascinating charts on his X (formerly Twitter) profile.

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The charts that I will share below provide a comprehensive view of national stock market valuations using two key metrics: the Shiller-CAPE ratio and the price-to-book ratio.

(Source: Norbert Keimling; Taunus Trust)

The chart structure is fairly straightforward but informative.

The blue dots represent individual national stock markets and the red dots represent various indices like developed markets (DM) and emerging markets (EM).

On the x-axis, we have the Shiller-CAPE ratio, which measures the cyclically adjusted price-to-earnings ratio of various stock markets. It is one of the most widely used valuation metrics, especially for long-term stock market analysis, and was developed by Nobel Prize-winning economist Robert Shiller. This ratio adjusts the standard price-to-earnings (PE) ratio by using inflation-adjusted earnings over the last 10 years. By smoothing out earnings volatility, it provides a clearer picture of whether a stock market is historically overvalued or undervalued.

• A higher Shiller-CAPE ratio indicates a more expensive market, where investors are paying a premium for future earnings.

• A lower Shiller-CAPE ratio suggests a cheaper market, potentially offering better long-term returns.

This axis helps us identify which markets are more expensive or cheaper based on long-term earnings trends.

As shown, historically, the global average Shiller-CAPE ratio has been around 20x, and this serves as a useful benchmark when assessing today’s market conditions.

On the y-axis, we have the price-to-book ratio, a metric that compares a stock market's price to the book value of its companies, where book value equals a company’s assets minus its liabilities. Traditionally, value investors, including legendary figures like Warren Buffett during his early "cigar butt" investing days, looked for companies with a price-to-book ratio below 1. This was considered a key indicator of undervaluation, suggesting the market was pricing the company for less than its net assets.

The y-axis in the chart thus provides another layer of understanding about how richly or cheaply the markets are priced from a value investor’s perspective.

Historically, the global average P/B ratio has been around 1.9x.

However, in today's market, dominated by technology and platform businesses, this metric is becoming less relevant. Companies like Airbnb and Meta don't rely heavily on physical assets but on intangible assets like data, intellectual property, and networks. This shift means that many companies, while having high price-to-book ratios, may not necessarily be overvalued. We’ll explore this idea further later in the post.

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Global Stock Markets: The Data Speaks

Now, let’s turn our attention to the specific markets that are most overvalued today.

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This approach isn't just a suggestion—it's a necessity if you want to achieve consistent, long-term success in the stock market.

Inspiration from Aswath Damodaran: The Dean of Valuation

Recently, I was listening to a podcast episode featuring Aswath Damodaran, often referred to as the "Dean of Valuation," on The Investor’s Podcast Network.

Damodaran is a well-respected figure in the investing community, especially known for his expertise in company valuation. His insights during the podcast inspired me to share my take on this topic with you.

In the podcast, Damodaran discussed the concept of valuing companies "for an audience of one"—himself.

This idea is powerful because it underscores the importance of personal responsibility and independent thinking in investing. While it's valuable to learn from books, fellow investors, and experts and consider their insights, relying solely on someone else's valuation can be a perilous path!

Building Your Own Investment Framework

If you're serious about investing, developing your own investment framework is essential.

In a previous blog post (“Framework First! The Power of an Investment Framework & Why It’s Essential for Long-Term Success“), I emphasized the importance of having a clear, personalized, and well-structured investment strategy – an investment framework that guides you through the world of volatile markets.

A critical component of any robust investment framework is the ability to accurately value the businesses you're considering. It’s a fundamental pillar in my investment framework:

To be able to determine whether a stock is attractively valued requires being equipped with more than just one valuation method but rather having a diverse toolkit of valuation techniques.

Each investment case, each business is unique, and the appropriate valuation method depends on various factors, including:

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