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.

Become a paying subscriber to read the rest of this post and get access to all of my other research, 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.

Annual members also get access to my private WhatsApp groups – daily discussions with like-minded investors, analysis feedback, and direct access to me.

Subscribe now

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.

Read more

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?

Subscribe now

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.

Subscribe now

Annual members also get access to my private WhatsApp groups – daily discussions with like-minded investors, analysis feedback, and direct access to me.

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.

Read more

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.

Read my latest thoughts on here:

Share

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.

Subscribe now

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.

Read more

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.

Subscribe now

Read more

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

Subscribe now

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.

.

.

.

Ready?

Read more

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.’”

Read more

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.

Subscribe now

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.

Subscribe now

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.

Subscribe now

Global Stock Markets: The Data Speaks

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

Read more

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:

Read more