Microsoft scored 72, A Tier but the numbers with context tell me it’s an S Tier business. See the final score section for the reasons.

Also, it’s a buy at $464 with potential double-digit returns. Check the valuation section at the end.

Before we run through the checklist and valuation, here is a mental model for Microsoft so you have a better understanding of the business: 50-30-15.

Think of Microsoft as having three engines. About half of the business is Azure and cloud services, which power businesses behind the scenes. About one third comes from Microsoft Office, including Microsoft 365, Teams, Outlook, and LinkedIn. The remaining about one fifth comes from Windows and Xbox, including Windows licenses, gaming, Surface devices, Bing, and Edge.

Now, let’s go through each step in detail.

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Step 1. Historical Returns: how has the stock done, and how steadily?

Great over a decade. Messy over the last two years.

The bar: a timeframe passes when the stock compounded at 10% or better AND the price line hugs its trend with an R² of 0.85 or higher. Six timeframes get checked. All six passing is a 10. Five is an 8. Four gets a 6.

Every single timeframe cleared the 10% CAGR test. The 20Y is 18.0% and the 10Y is 24.7%. Then the R² row falls over at the short end. 0.29 at three years. 0.67 at five.

Rsq is my steadiness check. It asks whether the price went up in something resembling a line. The stock hit $520, dropped to $390 by the end of June, and sat at $464.

Four of six pass. 6/10.

The market has paid up for this business for twenty years. Recently it stopped. Let’s look under the hood and find out whether that’s the business or the multiple.

Step 2. Historical Growth: is the business actually growing?

Revenue and earnings, yes. Cash, no.

The bar: three metrics tested across six timeframes for eighteen checks total. Revenue needs a 7% CAGR. EPS and FCF each need 10%. Eighteen out of eighteen is a 10. Sixteen or seventeen is an 8. Twelve gets a 6.

Revenue passed all six. The 20Y CAGR is 10.6% and the 3Y is 16.1%. A company doing $332B in annual revenue accelerating into its fifth decade is a genuinely odd thing to see. A monster at a monstrous scale.

EPS passed all six too. 14.5% over twenty years, 22.9% over three. Two dips are worth explaining because they distort the shorter windows if you start the clock in the wrong year. FY2015 EPS fell to $1.48 on a $7.5B impairment of the Phone Hardware business, of which $5.1B was goodwill and $2.2B was intangibles. FY2018 fell to $2.13 because of a $13.7B net charge from the Tax Cuts and Jobs Act, which knocked $1.75 off diluted EPS by itself.

And then free cash flow. One out of six.

3Y at 4.0%. 5Y at 3.6%. FCF was $65.1B in FY2022 and $67.0B in FY2026. Four years of essentially flat cash while revenue went from $198B to $332B.

Capital expenditure went from $28.1B in FY2023 to $44.5B, then $64.6B, then $115.9B in FY2026. It quadrupled in three years. Microsoft is building AI datacentres, and every dollar of that spending lands between operating cash flow and free cash flow.

Thirteen of eighteen checks pass. 6/10.

Step 3. FCF / Net Income: is that growth real cash or just accounting?

Half of it is showing up as cash. This is the category that breaks the score.

The bar works in levels. Take the median FCF/NI ratio in each of the six timeframes. Every median at 100% or better is a 10. All of them at 90% is an 8. All at 80% is a 6. If only two thirds clear 80%, you get a 4.

FY2026 FCF/NI came in at 50.1%. The year before it was 70.3%. Before that, 84.0%.

Five of six medians clear 80%. The 3Y sits at 70.3%. 4/10.

Growth rates for absolute values would be less useful if the shareholders are being diluted. So, let’s checkteh share count.

Step 4. Share Count: is management buying back stock or diluting me?

Buying back. Slowly.

The bar: four windows, 3Y through 10Y. Shares shrinking by 3% a year or more across all four is a 10. Shrinking by 1% or more is an 8. Stable or shrinking, meaning anything at or under +1% a year, is a 6.

Shares outstanding went from 7,808M ten years ago to 7,428M now. That’s 0.50% a year. Over three years it’s 0.02% a year, which is a rounding error away from flat.

All four windows stable or shrinking, none of them by 1%. 6/10.

The 20Y figure is 1.51% a year. Microsoft used to retire a lot more stock than it does now.

Microsoft retired 95 million shares in fiscal 2022 at an average price around $295. In fiscal 2026 they retired roughly 35 million at about $477. The dollars did fall, from $28 billion of program buybacks down to something near $17 billion, because capex hit $115.9 billion last year and the dividend now takes $26.4 billion a year regardless of what else is going on. Price did most of the damage though. When the stock triples, a buyback dollar retires a third of the float it used to and the $12.4 billion of stock comp didn’t help. That flat line since 2022 is what a big buyback looks like when your stock goes up faster than your buyback.

Step 5. Margins: is the business getting better or worse?

Better on two of the three. Worse on the one that’s under pressure everywhere else in this post.

The bar: three margins compared across five windows against their own 20Y median for fifteen checks. A window passes if its median sits within 100bps of the long-run median or above it. Fifteen out of fifteen is a 10. Thirteen or fourteen is an 8.

Gross margin passed all five and sits at 68.8% against a 68.9% reference. Flat as a table for a decade.

Net income margin passed all five by a mile. The 3Y median is 36.1% against a 20Y reference of 29.6%. FY2026 printed 40.3%.

FCF margin failed at 3Y and 5Y. The 3Y median is 25.4% against a 30.5% reference, which is 503bps of contraction. Same story as step 3, showing up in a different place.

Thirteen of fifteen. 8/10.

Step 6. Moat: can anyone else do this?

No. This is the best category in the report and it isn’t close.

The bar: median gross margin of 60% or better AND median ROIC of 25% or better gets a 10, so long as the current year hasn’t fallen more than 200bps below those medians. 50% and 20% is an 8. 40% and 15% is a 6.

The median gross margin over twenty years is 68.9%. Median ROIC is 32.4%. Both clear the top bar with room.

The trend gate is what I actually worried about here, because capex should be crushing returns on capital. FY2026 gross margin came in at 67.9%, which is 93bps under the median. ROIC came in at 30.6%, which is 180bps under. Both inside the 200bps band. Both stable.

10/10.

One dip on that ROIC line needs explaining before someone emails me about it. FY2018 shows 10.2%, the worst reading in the series. FY2018’s effective rate was 54.57% because of the $13.7B Tax Cuts and Jobs Act charge. Operating income that year was $35.1B and rising. The business was fine.

Step 7. Management: is capital being allocated well?

Yes, and this is the category I went back and forth on the longest.

The bar: median ROE of 30% or better AND median ROIC of 25% or better is a 10. 25% and 20% is an 8. 20% and 15% is a 6. If the medians clear the bar but the current year has fallen more than 200bps below them, the score drops to a 4.

Median ROE over twenty years is 34.0%. Median ROIC is 32.4%. Both sail past the 10-point thresholds.

10/10.

Step 8. Balance Sheet: can it survive a storm?

Easily. Next question.

The bar: interest coverage of 25x or better AND net debt to EBITDA under 1x is a 10. 20x and 2x is an 8. 15x and 3x is a 6. Both the long-run median and the current year have to clear, and the score is the lower of the two.

Median interest coverage is 45.0x and the current year is 50.9x. Median net debt to EBITDA is 0.31x and the current year is 0.10x.

Net debt is $19.4B against EBITDA of $193.8B. Interest expense is $3.1B against operating income of $155.2B. Every one of the four readings sits at the 10-point level.

10/10.

The one hump in that line is FY2017 at 2.06x, and it has a name. Microsoft closed the $26.2B all-cash LinkedIn acquisition in December 2016 and financed it largely with new debt. Net debt went from $47.0B to $78.5B in a single year and interest coverage bottomed at 13.2x.

Step 9. Future Growth: does the market expect the compounding to continue?

Yes, and more of it than the business has actually been delivering.

The bar: every forward year gets checked on its own. Revenue at 15% AND EPS at 20% in every single year is a 10. 10% and 15% is an 8. 7% and 10% is a 6. No averaging, no median. One weak year drags the whole category, which is the point of a consistency test.

All five years clear the 6-point bar. Four of the five clear the 8-point bar. FY1E EPS at 11.00% is the one that blocks it, since the 8-point level wants 15%, and under this scoring a single miss is enough.

6/10.

Step 10. Predictability: can analysts even model this thing?

Yes, and they’ve got better at it every decade.

The bar: overall beat rate of 95% or better is a 10. 90% is an 8. 80% is a 6. 70% is a 4. I score the lower of the revenue and EPS rates.

80 quarters, from calendar 2006 to 2026. Nothing excluded, GFC and 2020 included.

Revenue beat in 65 of 80 for 81.2%. EPS beat in 71 of 80 for 88.8%. The lower of the two is revenue, so 81.2% is the scoring number.

6/10.

The era breakdown is where this gets interesting. Revenue beat rate went 69%, then 70%, then 85%, then 93%. Fifteen of the twenty-nine misses in this dataset happened before 2015. Post-2015 the revenue beat rate is 42 of 47, or 89.4%. More subscriptions? Better communication between the management and the analysts? Business becoming more predictable? All?

The Final Score

72 out of 100. A Tier.

Moat, management and balance sheet all scored a perfect 10, and margins scored an 8. On the questions of whether this is a great business with a defensible position, a fortress balance sheet and people who know what to do with a dollar, Microsoft answers as emphatically as anything I’ve ever run through this thing. Revenue passed every growth check. EPS passed every growth check. Gross margin has been flat within 100bps of its long-run level for a decade while net income margin expanded 650bps above its.

Three categories dragged it, and the same thing caused all three. Step 2 lost five of its eighteen checks on free cash flow. Step 3 dropped to a 4 because FY2026 cash conversion was 50.1%. Step 5 lost its two checks on FCF margin. Capex went from $28.1B to $115.9B in three years and it shows up in every cash-based measure in this report.

Strip the FY2024-26 capex step-up back to the FY2023 run rate and add the 4 points in step 2 and 4 points in step 3. That would make the total score 80. Also, adding the 2 points lost in step 5 takes into the S Tier with 82 points.

It’s an investment-cycle problem. Microsoft is deliberately converting free cash flow into fixed assets at a rate nobody in software has attempted, and the QS is built to notice exactly that and mark it down. This checklist measures every business against an ideal business that goes up and to the right at double-digit rates in everything.

Now, let’s check out the valuation.

What the Market Has Paid

One window, twenty years, and it’s a clean one. EPS went from $1.49 in FY07 to $17.28 in FY26. Two down years in that whole stretch. FY09 was -12% and FY13 was -7%. Everything else was up, and the market handed the stock a 22x average multiple for the trouble.

That 1.58 PEG is the number that matters. It’s what buyers have been willing to pay per point of growth across two decades, a financial crisis and three CEOs.

Today the blended multiple is 26.6x.

Now, let’s see what we can make based on what we assume.

Read more

You are probably thinking I should just cancel my Quality Score when you saw 66 for GOOGL. Wait, wait, don’t leave just yet. Hear me out. There is a good reason why.

Remember it’s a checklist, and it did its job pretty well. By reading this post, you’ll get an overall historical picture of GOOGL through numbers. I learned a few things by running this checklist; I hope you do too.

Also, it’s a buy. Check out the potential returns at the end.

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Step 1. Historical Returns: how has the stock done, and how steadily?

Great returns. Terrible line.

The bar per timeframe: CAGR of at least 10% AND an R-squared of at least 0.85. R-squared is just how straight the line is. A compounder should look like a staircase. All six timeframes pass gets you a 10. Five gets 8. Four gets 6. Three gets 4.

Every single timeframe cleared the CAGR bar. 3Y at 34.64%. 20Y at 19.15%. The worst number on the page is a 19% twenty-year compound annual return.

And it scored 4 out of 10.

R-squared failed at 3Y (0.84), 5Y (0.67), and 7Y (0.82). The long windows are pristine. 10Y at 0.92, 15Y at 0.97, 20Y at 0.96.

Here’s the thing. GOOGL is up 66% over the last twelve months and down 20% from its May high of $408. It closed Friday at $319. You cannot post a +66% year and then drop a fifth in under three months and still have a straight five-year line.

Step 2. Historical Growth: is the business actually growing?

Yes. And it’s not close.

The bar: revenue CAGR of 7% or better, EPS and free cash flow at 10% or better, measured at every timeframe. That’s 17 checks here. All of them pass gets a 10. Missing one or two still gets 8.

Revenue is 6 for 6, 13% over three years, and 23% over twenty years. Revenue went from $6.1 billion in FY2005 to $402.8 billion in FY2025.

Twenty years. One down year. FY2022 at -19%. Three years total below the 10% bar: FY2012 at +9%, FY2017 at +3%, FY2022 at -19%. Then +27%, +39%, and +34% coming out of it.

16 of 17 checks. 8 out of 10.

Growth without cash conversion is just accounting. So let’s check the cash.

Step 3. FCF / Net Income: is that growth real cash or just accounting?

It was. Then Google decided to build the world.

The bar works in tiers and every timeframe has to clear it. All six medians at 100% or better gets a 10. All at 90% gets 8. All at 80% gets 6. Two-thirds of them above 80% gets 4.

The 3Y median is 73%, and that one number caps the category. 4 out of 10.

FY2025 converted 55% of net income into free cash flow. That’s the lowest reading since FY2014.

Now, why. Capex went $32.3 billion, $52.5 billion, $91.4 billion across FY2023, FY2024, FY2025. As a share of revenue that’s 10.5% to 15.0% to 22.7%. Net property, plant and equipment on the balance sheet went from $29 billion in FY2015 to $262 billion in FY2025. 8-9x basically, while revenue did 5x.

Operating cash flow never stopped growing. $125.3 billion to $164.7 billion in the last year alone. So this is not an earnings quality problem. Nothing is being faked. Alphabet is simply spending its cash flow on concrete, chips and power instead of handing it to you.

And it gets worse before it gets better. FY2026 capex guidance is $195 to $205 billion, raised on the July 22nd call from $180 to $190 billion a quarter earlier. Q2 2026 was Alphabet’s first negative free cash flow quarter since the 2004 IPO. Quarterly capex of $44.9 billion came in above operating cash flow.

One more flag the rubric makes me raise. Stock-based comp was $24.95 billion in FY2025, or 18.9% of net income, and it averaged 24.9% over five years. SBC is a non-cash charge that gets added back on the way to free cash flow, so it flatters this ratio. Strip it out and FY2025 conversion drops to 36.6%. Cross-reference that with the next step: buybacks are mopping up the dilution rather than returning fresh capital to you.

Step 4. Share Count: is management buying back stock or diluting me?

Buying back. Steadily.

Only four windows get scored here (3Y, 5Y, 7Y, 10Y) because early-stage dilution says nothing about how management behaves today. All four shrinking at 3% a year or faster gets a 10. All four at 1% or faster gets 8. All four flat or shrinking gets 6.

-2.01% at 3Y, -2.19% at 5Y, -1.99% at 7Y, -1.28% at 10Y. Every window is shrinking, and none of them fast enough for the top tier. 8 out of 10.

Share count peaked at 13,911 million in FY2018 and has fallen every year since. 13.1% of the shares retired.

One forward risk that does not affect the score is capital allocation. Alphabet has completed roughly $45 billion of equity financing and established a $40 billion at the market (ATM) program, which management can draw on over time rather than all at once. The historical buyback record still earns the 8. The question now is whether share count continues to shrink while funding a roughly $200 billion capex year.

Step 5. Margins: is the business getting better or worse?

Worse for a decade. Better for five years.

The bar: each margin at each window has to sit above the twenty-year median, or within 100 basis points below it. Three margins, five windows, 15 checks. All pass gets a 10. Missing one or two gets 8. Two-thirds gets 6.

10 of 15. 6 out of 10. Net income margin passes 5 of 5. FCF margin passes 5 of 5. Every single failure is gross margin.

So what actually happened to gross margin? You asked, and it’s a real business story rather than an accounting quirk.

Gross margin peaked at 65.21% in FY2011, bottomed at 53.58% in FY2020, and has climbed back to 59.65%. Cost of revenue went from 34.8% of sales to 46.4% and back to 40.3%. Four things went into that trough, and they are all mix.

Motorola Mobility got consolidated from 2012 to 2014. Handsets carry hardware margins, and hardware margins are nothing like search margins.

Traffic acquisition costs climbed as users moved to mobile. Google pays publishers a cut and pays Apple to be the default in Safari, and mobile TAC (Traffic Acquisition Costs) runs higher than desktop TAC. In 2023 Google spent 21.39% of advertising revenue, roughly $50.9 billion, on traffic acquisition.

Content acquisition costs arrived with YouTube at scale. Paying creators is a cost of revenue. Ads against your own search results are not.

Then Cloud. Google Cloud scaled from nothing into a $24.77 billion quarter, and infrastructure gross margins sit well below advertising gross margins. Add devices. Add the depreciation, energy, and network capacity that live inside the cost of revenue and grew with a nine-fold increase in the PP&E base.

The recovery since 2020 is the same forces running the other way. TAC discipline. Cloud swinging from a money pit to $8.81 billion of quarterly operating income against $2.83 billion a year earlier. Revenue mix tilting back toward Search and subscriptions.

The compression was real and the cause was mix. A company that adds three lower-margin businesses to a 65% margin business will print a lower blended margin. That is arithmetic. The QS flagged it and what it should have.

Margins without a moat are temporary. So can anyone else do this?

Step 6. Moat: can anyone else do this?

Obviously not. And it still only scores a 6.

The bar works in tiers on the long-run medians. Gross margin of 60% or more AND ROIC of 25% or more gets a 10. GM 50% and ROIC 20% gets 8. GM 40% and ROIC 15% gets 6. If the current year is falling below the average, it drops to 4.

Gross margin median is 60.05%. That clears the top bar.

ROIC median is 19.02%. The 8-point tier needs 20%. Missed by 0.98 of a percentage point. 6 out of 10.

Here’s what’s odd. Current ROIC is 24.44%. The median that scores the category is 19.02%. The gap is a nine-year trough: ROIC ran between 9.37% and 18.15% from FY2012 to FY2020.

Why? Alphabet had too much money. Cash and short-term investments were 76.7% of shareholders’ equity in FY2011 and 66.8% in FY2017. That is a colossal pile of capital earning treasury yields sitting inside the denominator of a return-on-capital calculation. Add capex ramping, add Other Bets burning, add the EU fines, add the FY2017 tax charge.

Then cash fell to 44.4% of equity by FY2022 and 30.5% by FY2025, mostly through buybacks. Operating margin round-tripped from 32.3% in FY2011 to 22.6% in FY2020 and back to 32.0% in FY2025. ROIC followed.

So the moat is not in question. Twenty years of mid-teens returns on capital is what caps this category, and the reason those returns were mid-teens is that Alphabet was a fortress with an idle treasury bolted to it. The QS scored that.

A moat with bad capital allocation is wasted potential. So how has management done?

Step 7. Management: is capital being allocated well?

Better lately.

The bar: ROE of 30% and ROIC of 25% gets a 10. ROE 25% and ROIC 20% gets 8. ROE 20% and ROIC 15% gets 6. Below that it does not drop to 4. It drops straight to 2.

ROE median 20.30%. ROIC median 19.02%. That clears the 6-point floor by 0.30 of a percentage point. 6 out of 10.

Alphabet’s first three years as a public company are load-bearing for a management score twenty-three years later.

The ROE trough is the same story as the ROIC. Equity grew 7.1 times from FY2011 to FY2025 while net income grew 13.6 times, but the middle years ran the other way as retained earnings piled up faster than Alphabet could deploy them at high returns. ROE bottomed at 8.66% in FY2017. It’s 35.70% now.

Step 8. Balance Sheet: can it survive a storm?

Easily. Next question.

The bar: interest coverage of 25 times or better AND net debt to EBITDA under 1 time gets a 10. Both the long-run median and the current year have to clear the tier, and the lower of the two wins.

Net cash in every single year from 2004 to 2024. FY2025 flipped to net debt of $15.8 billion, which is 0.11 times EBITDA. Interest coverage is 175 times, and the worst year in twenty-two years of data is 73 times. 10 out of 10.

One thing to keep an eye on. This is the category most likely to move next year. Long-term debt roughly quadrupled to $46.5 billion in FY2025 and interest expense went from $268 million to $736 million. Capex guidance of $195 to $205 billion against operating cash flow of roughly $215 billion leaves the buildout funded at the margin by debt and equity. Watch it.

We know what Alphabet looks like from the inside. The fundamentals mostly support the price performance from Step 1. Now: does the market expect it to continue?

Step 9. Future Growth: does the market expect the compounding to continue?

Yes, aggressively.

The bar: four checks (FY1E, FY2E, FY3E, and the three-year average), and each one passes a tier only if BOTH the revenue line and the earnings line clear it. Revenue of 15% and cash flow of 20% gets a 10. Revenue of 10% and 15% gets 8. Revenue of 7% and 10% gets 6.

All four checks clear the top tier. 10 out of 10. Revenue growth of 24%, 21%, and 18% across the next three years. Operating cash flow growth of 31%, 22%, 25%.

I scored this on cash flow instead of EPS, and the reason is worth explaining because it caught me out.

Here's the thing. I could've scored this whole thing on EPS and called it a day, but EPS for Alphabet right now is basically noise. The company doesn't publish an adjusted number, so consensus just tracks GAAP diluted EPS, and GAAP EPS is currently a hostage to unrealized marks on Anthropic and SpaceX. Q2 alone booked $77 billion of net income from a paper gain, $6.26 of the $9.11 headline. Operating cash flow strips all of it out, because unrealized gains never touch cash.

Step 9 scores 10 out of 10 on a business whose implied free cash flow falls roughly 80% next year. Step 3 scores 4 out of 10 on the same phenomenon seen from the other side. Both are correct. Hold them together.

Next: Have analysts been able to model Alphabet?

Step 10. Predictability: can analysts even model this thing?

Not really. And for a business this dominant, that’s genuinely surprising.

The bar: score on the LOWER of the two overall beat rates. 95% or better gets a 10. 90% gets 8. 80% gets 6. 70% gets 4.

Revenue: 67 beats, 21 misses. 76.14%. EPS: 63 beats, 23 misses, 2 unreadable. 73.26%.

4 out of 10.

So why does a company with a search monopoly miss its numbers a quarter of the time? You asked and I think there are five reasons, in rough order of how much they matter.

Alphabet provides virtually no financial guidance. No revenue target, no EPS guidance and no operating margin outlook. That’s the big one. No revenue range, no EPS range, no margin target. On the July 22nd call the only forward number management volunteered was more capex. Analysts are modelling a $400 billion revenue business with very little help from management. The misses are not a business failure. They’re the predictable result of a company that refuses to hand out a ruler.

The business keeps changing shape. The 2010 to 2015 era is the worst stretch on the chart (58.3% revenue, 54.5% EPS) and it maps exactly onto the mobile transition, when paid clicks were rising while cost-per-click was falling and nobody could model the mix. Motorola got consolidated and then deconsolidated inside three years. Nest, Waze and a dozen others landed in between.

Other Bets is unmodellable by design. Q2 2026: $382 million of revenue against a $1.80 billion operating loss. Waymo passed 500,000 autonomous rides a week and it still barely moves the consolidated revenue line. Alphabet-level activities produced another multi-billion dollar operating loss that's inherently difficult for analysts to forecast. That’s a billion-dollar miss in a line item nobody outside the building can forecast. So yes, to your question: starting new businesses constantly is part of the answer.

Foreign exchange. More than half of revenue is non-US. Q1 2026 hedging swung to a $180 million loss from a $260 million gain a year earlier, on a $31.47 billion EMEA base.

Lumpy legal and accounting items. The Android decision became final this month, illustrating how legal outcomes can create difficult-to-model swings when they occur. Since 2018, GAAP requires unrealized gains and losses on Alphabet's equity investments to flow through earnings. Most quarters the effect is modest, but in Q2 2026 it overwhelmed the operating results, producing headline EPS growth of more than 200% even though the underlying business only narrowly missed expectations.

One genuinely encouraging pattern. Since Q1 2023 the EPS panel shows 14 beats and zero misses. The 2010 to 2015 disaster is fifteen years old. The rubric scores the whole record though, and the whole record is a mid-70s beat rate.

The Final Score

66 out of 100. B Tier. Four points below A Tier.

The balance sheet and the forward cash flow ramp carry this score. 10 and 10. Growth and buybacks add another 8 each.

Four categories drag it, and they split cleanly into two kinds of problem.

Step 1 at 4 is a price problem, not a business problem or a narrative problem. A 19% twenty-year CAGR with a broken five-year R-squared means the market is repricing this thing violently.

Step 3 at 4, Step 6 at 6 and Step 7 at 6 are business problems, and they’re all the same business problem wearing different clothes. Alphabet has spent two decades generating more cash than it could deploy at high returns. For ten years that showed up as an idle treasury dragging ROIC into the mid-teens. Now it shows up as $200 billion a year of capex dragging free cash flow conversion to 55%. Rich problems.

Step 10 at 4 is neither. It’s a disclosure choice. Alphabet doesn’t guide, so analysts miss.

My take: this is the widest gap I’ve seen in a QS run between what the scorecard says and what your gut says. Your gut is not wrong about the business. The scorecard is not wrong about the record.

The whole thing now turns on one question the QS deliberately refuses to answer. Does roughly $200 billion a year of concrete and silicon earn a return? If it does, Step 3 recovers, ROIC pushes through 25%, and the categories holding this to 66 flip within three years. If it doesn’t, you own a capital-intensive advertising business at a software multiple.

Valuation is next.

What the Market Has Paid

I’ll be using OCF instead of EPS or FCF due to equity gains, depreciation, and ongoing AI CAPEX.

Two clean windows, and I’m reading the multiple and the growth rate off the same timeframe each time so the ratio actually means something.

The first window is the one I care about most. That’s Google growing cash flow at 18% in a normal interest rate world before free money warped every multiple on the screen. The market paid 18xish multiple for it.

Over the full history cash flow compounded at 23% and the market paid 20x.

Notice what that means. Faster growth, roughly the same PEG. The market has never fallen in love with this stock the way it fell in love with Costco. Google has spent twenty years being priced at slightly less than one times its growth rate.

Today the blended multiple is 21x. On next year’s cash flow though it’s about 15x, and on 2028 it’s 12x.

The dividend is 0.28%. Rounding error. This is a cash flow growth story and nothing else.

Now let’s see what we can make depending on what multiple we assume.

What You Actually Make

Read more

Netflix‘s quality score of 44 surprised me. This is one of the best growth stories of the last twenty years, and it screens as a C Tier.

Quality Score is built for analyzing compounders, more mature companies. It did its job by telling us that Netflix was a growth story that is now maturing. If you’re a strict quality-first, consistency-first buyer, Netflix has always looked “not quite a compounder,” and this 44 explains why in numbers.

It’s fairly valued. See the valuation section below for potential returns.

Read on if you want to know why it scored 44 and the assumptions behind the fair value calculations and returns.

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Step 1. Historical Returns: how has the stock done, and how steadily?

Great returns. Ugly line.

The bar: I score six timeframes (3, 5, 7, 10, 15, 20 years). A timeframe passes only if the stock compounded at 10% or better AND the price line was smooth, measured by R-squared of 0.85 or higher. R-squared is just how close the price hugs a straight trend. High R-squared means a calm, steady climb. Low means a rollercoaster. Ten points if all six pass, and it steps down from there.

Look at that footer. The CAGR row is almost all green. 17% over 3 years, 23% over 10, 31% over 20. The compounding is there. It’s the R-squared row that guts the score. Only the 15Y (0.86) and 20Y (0.93) windows are smooth enough to clear the bar. The 3, 5, 7 and 10 year windows all fail on linearity even though the returns are fine.

Why? Because the last five years have been a demolition derby. The stock ran to a blow-off top in late 2021, then cratered in Q1 2022 when Netflix reported its first subscriber decline in a decade and guided to more losses. It then tripled off the lows, ran up through 2025, and has been sliding again into 2026. Down ~44% over the past year, ~24% YTD, and another ~9% leg down after the Q2 ‘26 print on July 16. Fantastic if you timed it. A mess if you’re measuring smoothness. So 2 of 6 pass. That’s a 2 on Step 1. And this is a price problem, not a business problem. Hold that thought; it comes up again.

Step 2. Historical Growth: is the business actually growing?

Yes. And it’s not close.

The bar: I check revenue, diluted EPS and free cash flow across all six timeframes. Revenue needs a 7% CAGR to pass. EPS and FCF need 10%. Any window where EPS or FCF started negative gets marked N/A and dropped. Score is how many of the available checks clear.

This is a clean sweep. Revenue compounded 13% over three years and 23% over twenty.

EPS compounded between 32% and 56% depending on the window.

FCF passes every window where it can even be measured. Sixteen of sixteen available checks pass. That’s a 10.

Two windows show N/A on the FCF row. Those are the 7Y and 10Y, where free cash flow started deeply negative (2018 FCF was minus $2.9B, and 2015 was minus $919M), so a growth rate is meaningless. We’ll get to why the cash was negative in the next step. For now, the machine is unambiguously growing. Revenue went from $6.8B ten years ago to $45B last year. Nobody argues with Step 2.

Step 3. FCF / Net Income: is that growth real cash or just accounting?

For a full decade, it wasn’t cash at all. This is the ugliest step in the whole report.

The bar: for each timeframe I take the median FCF-to-net-income ratio and find the highest tier where every window clears it. All windows at 100% or better is a 10. All at 90% is an 8. All at 80% is a 6. Below that, it falls off a cliff fast.

Read the yearly ratios and you’ll see the story. 2016 free cash flow was negative 887% of net income. 2017 was negative 360%. 2018 negative 239%. Netflix paid cash for content years before that expense flowed through the income statement, so it reported accounting profits while free cash flow stayed deeply negative.

So the medians are all over the place. The 3Y median is a healthy 86%. But the 5Y is 79%, the 7Y is 70%, the 10Y is 17%, and the 15Y is actually negative 20%. Only one window out of six clears the 80% bar. Per the rubric that’s a 0.

Here’s the context read though, and it matters. The cash turned. Netflix went FCF positive in 2020 and has stayed there, printing $9.5B of free cash flow in 2025. The 3Y median at 86% is the real Netflix now. The business model flipped from “borrow to build the library” to “the library funds itself.” One more thing worth saying: stock comp is only 3.4% of net income here, so the recent FCF/NI is genuinely clean. The raw score stays 0 because the QS is measuring consistency and Netflix simply wasn’t consistent. But if you’re underwriting the future, the recent trend is the tell, not the 15Y median.

Step 4. Share Count: is management buying back stock or diluting me?

Slowly buying back. No games here.

The bar: I score four windows (3, 5, 7, 10 years) on annualized share change. Shrinking 3%+ a year is a 10. Shrinking 1%+ across all four is an 8. Stable or slightly shrinking, within plus 1% a year, is a 6.

Shares outstanding peaked around 4,453M in 2022 and have drifted down to 4,222M. The 3Y annualized change is minus 1.76%, the 5Y is minus 0.95%, and the longer windows are basically flat. All four windows are stable or shrinking, so it clears the 6 tier. It misses the 8 because the 5, 7 and 10 year windows don’t shrink a full 1% a year.

My read: Netflix started returning cash through buybacks in earnest once it went FCF positive, and it’s been steady about it. Worth knowing: after briefly pausing buybacks for the Warner Bros. bid it later abandoned, Netflix just ran its biggest buyback quarter ever. The 6 scores the history; the present is more aggressive.

Step 5. Margins: is the business getting better or worse?

Better. Meaningfully better.

The bar: I average gross, net and FCF margin over each window and compare to the 20-year average. A margin passes if it’s within 100bps of, or above, that long-run average. Fifteen checks total. Ten points if all fifteen expand.

Gross margin runs from a 36% long-run average up to 45% over the last three years. Net margin went from an 8.8% average to 21% recently. Both of those pass every window. The only misses are on the FCF margin, where the 15Y and 10Y windows sit below the long-run average, again because of the burn years. Thirteen of fifteen checks pass. That’s an 8.

The margin story is the bull case in one chart. Operating margin hit 29.5% in 2025, up from 26.7% the year before, and management is guiding to 31.5% for 2026. The business is getting better, not worse, and the QS agrees.

Step 6. Moat: can anyone else do this?

The QS says “meh.” I disagree, and here’s the honest wrinkle.

The bar: this one’s tiered on historical average gross margin AND ROIC together. A 10 needs 60% gross margin and 25% ROIC. A 6 needs 40% gross margin and 15% ROIC. If gross margin can’t clear 40%, the best you can do is a 2.

Netflix’s historical average gross margin is 36.5%. That’s below the 40% gate. So even though average ROIC clears 15% with room to spare (10Y ROIC is 20.7%, current is 35.6%), the low gross margin caps this at a 2. The rubric is built for asset-light software and it punishes a business that runs content amortization through cost of goods. That’s what’s happening here.

So take the 2 with a grain of salt. Morningstar rates Netflix a narrow moat, built on intangibles and a network effect, and treats it as the clear standout among the streamers they cover. The current gross margin is 48% and rising as the library scales. I’d argue the true moat is wider than a 36.5% gross margin suggests, and the expanding margin trend is the evidence. But the raw score reads what the historical average reads. It’s a 2 on the page and a “narrow moat and widening” in my head.

Step 7. Management: is capital being allocated well?

Pretty well, and getting better.

The bar: tiered on average ROE and ROIC. A 10 is 30% ROE and 25% ROIC. An 8 is 25% and 20%. A 6 is 20% and 15%.

On the longest-available average, ROE is 20.8% and ROIC clears 15%, which lands it at a 6. But look at the trend. On a 10-year basis, ROE is 25.2% and ROIC is 20.7%, which would score an 8. The current-year ROE is 41% and ROIC is 36%. So the honest read is “6 on the full history, 8 on the modern business, and the arrow is pointing up.” I scored the 6 because that’s what the rubric’s longest-average rule produces. The capital allocation quality is clearly improving as the burn years roll off the average.

Step 8. Balance Sheet: can it survive a storm?

Easily now. It was hairier a few years ago.

The bar: I score interest coverage and net-debt-to-EBITDA together, taking the lower of the historical average and the current year. A 10 needs 25x coverage and under 1x leverage. A 6 needs 15x and under 3x.

The net-debt-to-EBITDA picture is pristine. It’s 0.18x today and has averaged around 0.2x. Netflix is basically net-cash-neutral. The thing dragging this to a 4 is the interest coverage average. Back through the 2012–2020 stretch, as Netflix borrowed its way to a debt load that peaked above $16B, coverage sat as low as 2 to 4x. That pulls the historical average down to 12x, which caps the score at the 4 tier, even though current coverage is a comfortable 17x.

My take: this is a backward-looking penalty. Netflix deleveraged hard once the cash turned, and today the balance sheet is a non-issue. The 4 is the ghost of the debt-funded content era. Real, but fading.

Step 9. Future Growth: does the market expect the compounding to continue?

Yes, but the EPS path is lumpy.

We just established what Netflix looks like from the inside. Steady revenue, expanding margins, cash that finally shows up. Now the question is whether the street expects that to keep going. Here’s consensus.

The bar: I check revenue and EPS estimates for the next three fiscal years plus their average. Each check passes the base tier if revenue is 7%+ AND EPS is 10%+. All four checks at 15% revenue and 20% EPS is a 10. Three of four at the base tier is a 4.

Revenue estimates are solid all the way out: 13%, 11%, 10% for the next three years, decelerating gently. EPS is where it gets bumpy. FY1 is a huge 40%, then FY2 drops to just 7.5%, then FY3 back up to 20%. That FY2 EPS number is the problem. At 7.5% it’s below the 10% bar, so one of the four checks fails. Three of four pass. That’s a 4.

Is the FY2 dip real or noise? It reads like a margin-investment year sitting between two operating-leverage years, the kind of lumpiness you get when a company steps up content or ad-tech spend. Compare it to Step 2, where Netflix actually delivered 30%+ EPS growth for years. The street is modeling slower and lumpier EPS than the business has historically produced. Whether that’s conservatism or realism is the whole valuation question. Netflix’s own FY2026 guide is 13-14% revenue and $12.5B of FCF, which lines up with the revenue estimates here.

Step 10. Predictability: can analysts even model this thing?

Yes, but it isn’t clear from the score.

Quick framing first. Consensus estimates cluster because analysts herd. There’s a whole matrix of being right or wrong alone versus right or wrong with the crowd, and most analysts sit in the “with the crowd” boxes. For a compounder, clustering is a feature. If the business is genuinely modelable, analysts should cluster tight, and they should be right a lot. So the question is simple: have they been able to model Netflix?

The bar: I count every beat and miss on the surprise chart and score on the lower of the revenue and EPS beat rates. 95%+ is a 10. 90% is an 8. 80% is a 6. 70% is a 4. 60% is a 2. Below 60% is a zero.

Across roughly 95 quarters, Netflix missed on revenue 33 times and on EPS 17 times. That’s a 65% beat rate on revenue and 82% on EPS. Since the score takes the lower of the two, revenue’s 65% sets the grade, and 65% only clears the 60% band. That’s a 2.

Revenue misses are very small, with the beat and miss range being 0.5-1% on average. So, this score is a little bit harsh.

The Final Score

44 out of 100. C Tier.

Here’s the honest one-line version: Netflix is a genuinely great growth business that the QS grades harshly because it spent a decade not looking like a clean compounder. Two categories carry the score. Growth is a perfect 10 and margins are an 8, and both are still improving. Share count and management sit at 6, solid and trending up.

The damage is spread across four places, and they’re worth separating.

FCF/NI at 0 and the balance sheet at 4 are scars from the 2015-2019 content-borrowing era. That cash burn was real, and the QS refuses to forget it, but the business flipped to $9.5B of free cash flow and a net-cash balance sheet. Those are backward-looking. Eventually this one should trend closer to 10.

Moat at 2 is a rubric-fit problem: the framework wants a 40% gross margin and Netflix runs content through COGS, so a narrow-moat streaming leader screens like a no-moat company. Maybe a 6, using the Morningstar rating as a proxy.

Returns at 2 is a price problem, not a business problem. The compounding is there, the path was just volatile.

Predictability at 2. Revenue misses were tiny, closer to 0.5%, and beats were 1% on average. Maybe 6-8 going forward.

Not very low like the four above, but the management and balance scores are 6 and 4. Both are trending upwards towards a score of 8 and 10 based on the rubric shared in their respective steps.

So, adjusting for the trends and adding the net number to the current score:

10 (FCF/NI) + 4 (Moat) + 6 (Predictability) + 2 (Management) + 6 (Balance sheet) = 28. Then 44 + 28 = 72. B Tier. This looks more like it.

Now, let’s take a look at valuation.

What the Market Has Paid

That 90x number is not a valuation anchor. One thing I want to be honest about. I do not have a clean 20-year or 10-year normal multiples as usual to build a proper PEG range from. The window I can read is polluted by the early years. So the three cases below are built off multiples I picked by hand rather than PEGs the market handed me. Treat them as my assumptions, not as history.

Now, let’s see what we can make based on what multiple we assume.

Read more

After scoring Mastercard in the previous post, it made sense to score Visa next.

It scored 80 vs 86 for MA. Investors know that Visa is a slower version of Mastercard, and my Quality Score picked up on that, hence the points difference. Still, that makes it an A Tier business.

I think Visa is overvalued at $362 with about a 28x blended P/E (second half of 2025 and the first half of 2026). Over five years that math spits out roughly 12% a year if the multiple just holds. If it doesn’t, you are in for 5-12%.

Let’s find out why it scored 80 and is overvalued compared to MA.

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Step 1. Historical Returns: how has the stock done, and how steadily?

Short answer: great over the long haul, choppy lately.

The bar: I check six windows (3, 5, 7, 10, 15, 20 years) and each one passes only if the stock compounded at 10% or better AND did it steadily, meaning an R-squared of 0.85 or higher. R-squared is just how close the price line hugs a straight trend. High R-squared means smooth compounding. Low means a rollercoaster. Ten points if every window passes. Eight if all but one. Six needs two-thirds. Below that you start bleeding points fast.

Visa only has price history back to the March 2008 IPO, so there’s no 20-year window. Of the five I can measure, three pass: 7Y (11.6% CAGR, R-squared 0.86), 10Y (17.7%, 0.93) and 15Y (21.3%, 0.96). Beautiful, smooth compounding over the long windows.

The two that fail are recent. The 5-year CAGR is 9.6%, which misses the 10% bar by a hair, and its R-squared is 0.82. The 3-year R-squared is 0.72, which is the rollercoaster showing up. Three of five passing gets 4 out of 10.

Now, a 4 on a business this good looks wrong, so let me confront it. This isn’t the business breaking. Look at Steps 2 through 8 and you’ll see the machine underneath is humming. This is a textbook example of stock price getting ahead of the business, then multiples compressing. We will see it clearly in the valuation section.

Vs Mastercard: dead heat at 4/10. Both stalled the last three years. MA has the longer record (20-year CAGR of 26.7% since its 2006 IPO) and Visa only goes back to 2008, but the recent chop hit both the same way. Visa’s 5-year CAGR of 9.6% actually held up a shade better than MA’s 5.6%.

The market has rewarded this business enormously over 15 years. But price alone doesn’t tell us why. Let’s look under the hood.

Step 2. Historical Growth: is the business actually growing?

Short answer: yes, and it barely takes a breath.

The bar: three metrics (revenue, diluted EPS, free cash flow) across all six windows. Revenue passes at 7% CAGR or better. EPS and FCF need 10%. That’s up to 18 checks. Ten points if all 18 pass. Eight if you miss only one or two.

Revenue is 6 for 6. Every window from 3 to 20 years compounds between 10% and 15%.

EPS is even better, 6 for 6, running 14% to 26% depending on the window. That leaves FCF, which passes 4 of 6.

Here’s the context about Visa’s lumpy-looking free cash flow. Three things actually move the cash. Client-incentive payments, which spike when a batch of big deals renews at once. Income-tax timing, exactly as thrilling as it sounds. And the litigation escrow, the pile Visa keeps feeding to settle the interchange fight with merchants.

That escrow explains the FY2024 dip. On September 26, 2024, four days before the fiscal year closed, Visa dropped $1.5 billion into it. That one deposit is most of why FY2024 FCF landed at $18.7B while 2023 printed $19.7B. The business was fine. Just a calendar quirk.

16 of 18 checks pass. That’s an 8 out of 10.

Vs Mastercard: MA takes this one, 10 to 8. That’s the first of three places MA pulls ahead. MA’s free cash flow cleared 10% in all six windows. Revenue and EPS growth are basically neck and neck between the two. MA just had cleaner FCF across the board.

Growth is nice. But growth without cash is just accounting. So let’s check the cash.

Step 3. FCF / Net Income: is that growth real cash or just accounting?

Short answer: real cash. Every dollar of profit becomes a dollar of cash, and then some.

The bar: I take the median FCF-to-net-income ratio in each window and find the highest tier they all clear. All windows at 100% or more gets a 10. All at 90% gets an 8. All at 80% gets a 6. The score caps at 100%, so there’s no bonus for going higher.

Every single window sits above 100%. The medians run from 101.8% (20-year) up to 110.8% (recent). Full marks, 10 out of 10.

And before you ask the obvious question. No, this isn’t stock comp inflating the cash flow. SBC is only 4.5% of net income and has averaged 4.3% over five years. That’s tiny. The reason FCF runs above net income is boring and good: depreciation runs a bit ahead of capex on an asset-light payment network, plus favorable working capital. This is about as clean as cash conversion gets.

Vs Mastercard: tie at 10/10. Both turn every dollar of profit into more than a dollar of cash. Two toll bridges doing the same clean thing.

Real cash. But cash conversion with a leaky share count is a treadmill. Is management diluting it away?

Step 4. Share Count: is management buying back stock or diluting me?

Short answer: buying back. Steadily.

The bar: annualized share change across four windows (3, 5, 7, 10 years). I don’t score 15 or 20 years because the early post-IPO share math is noisy. All four shrinking 3% a year or more is a 10. All four shrinking 1% or more is an 8. All four flat-to-down is a 6.

Shares outstanding are down every window: 2.5% a year over 3 years, 1.7% over 5, 1.8% over 7 and 2.2% over 10. Class A shares outstanding, the clean count, from 3,377 million in 2008 to 1,933 million today. All four windows shrink more than 1% a year, so this clears the 8 tier. None reaches the 3% a year needed for a 10. Call it a steady 8 out of 10.

Vs Mastercard: tie at 8/10. Both shrink the count every year at roughly the same clip, around 2% a year, and neither hits the 3% bar for a 10. On a clean shares-outstanding basis they’re basically twins here.

Step 5. Margins: is the business getting better or worse?

Short answer: better. And it wasn’t bad to begin with.

The bar: gross, net and FCF margin, each measured as a window average against the longest-available average. Stable (within 100bps) or expanding passes. That’s up to 15 checks. All 15 stable or expanding is a 10.

All 15 pass. Start with gross margin, and here’s a correction worth making. Visa’s real cost of revenue is one small line: network and processing, the direct cost of running VisaNet, about 2 to 3% of revenue. Strip that out and gross margin runs ~96 to 98%. More important than the level is the direction. It has climbed from 95.7% in FY2011 to 97.8% in FY2025, a steady grind higher as the network scales and each incremental transaction costs almost nothing to carry. That’s real operating leverage at the very top of the P&L.

Net margin is the other eye-opener. Recent windows average 52% against a 32% long-run number. FCF margin, same story, 56% recent versus 39%.

Vs Mastercard: tie at 10/10, and it’s the same shape. Both carry only a tiny direct network cost, so both run gross margins in the high-90s with net and FCF margins expanding underneath. Two nearly identical P&L structures. Dead tie.

Expanding margins are great. But margins without a moat are temporary. So can anyone actually replicate this?

Step 6. Moat: can anyone else do this?

Short answer: no. This is one of the widest moats in the market.

The bar works in tiers. A 10 needs a long-run gross margin of 60% or better AND ROIC of 25% or better, with the current trend stable or rising. Step down from there.

Gross margin is ~97% and rising (net of Visa’s small network and processing cost, as covered in Step 5) and current ROIC is 47.9%, way above the long-run average and climbing. The long-run ROIC average is around 30% (or 25% if I strip out one artifact I’ll mention in a sec). Either way it clears the 25% bar with the current trend pointing up. That’s a 10 out of 10.

Vs Mastercard: tie at 10/10. Two halves of the same duopoly. MA’s ROIC is even higher (~48% average, 60% now, against Visa’s ~30% average and 48% now), but both are miles past the bar. Nobody is building a third global network.

A quick note on ROIC calculation without going into detail: I checked 4 different data sources, and depending on how it’s calculated, the data range can be 25-50%. Yeah, that’s a big range, but for our purposes the score goes from 8 to 10.

A moat is great. But a moat with bad capital allocation is wasted. So how’s management deploying?

Step 7. Management: is capital being allocated well?

Short answer: very well, and getting better.

The bar: long-run ROE and ROIC plus the current trend. A 10 needs ROE of 30% or more AND ROIC of 25% or more. An 8 needs ROE of 25% and ROIC of 20%, current stable or rising.

Current ROE is 52.9% and marching straight up the chart. The catch is the long-run average ROE, which lands around 26 to 28%. That’s below the 30% bar for a 10. So this is an 8 out of 10.

Why is the average dragged down? Two reasons, both explainable. First, the 2008 IPO dramatically expanded Visa's equity base. ROE started at just 3.6% because earnings were being measured against a much larger equity base, then climbed steadily as aggressive share repurchases shrank equity over time. Second, FY2012 net income was depressed by a $4.1 billion pretax litigation provision (roughly $2.9 billion after tax) related to the interchange antitrust settlement. That’s the visible dip in the middle of the chart. The trajectory here is a 10. The 20-year average is an 8. The rubric scores the history, so 8 it is.

Vs Mastercard: MA wins, 10 to 8. And here’s the honest asterisk. MA’s average ROE runs around 71% partly because it has bought back so much stock its equity base is tiny (tangible book is actually negative). Visa still carries the big equity pile from its 2008 IPO, so its ROE looks lower on paper. On ROIC, the cleaner read of return on capital, both are elite. MA scores higher on the letter of the rubric.

Capital allocation is strong. But can the business survive a storm?

Step 8. Balance Sheet: can it survive a storm?

Short answer: easily. This is a fortress, basically a needless exercise.

The bar: interest coverage and net-debt-to-EBITDA, both on a historical median and current basis, scored on the lower of the two. A 10 needs coverage of 25x or better AND net debt under 1x EBITDA.

Current interest coverage is 45x. Median is 31x. Net debt to EBITDA is 0.29x today, 0.09x median. Both metrics sit in the top tier. 10 out of 10.

One standout on the chart. Interest expense jumps from $3M in FY2015 to $427M in FY2016, and net debt swings from negative $3.5B to positive $10.3B. That’s the Visa Europe acquisition, which closed June 2016 and was funded with $16B of senior notes issued the prior December. Even after taking on that debt, interest coverage never fell below roughly 23×, and net leverage remained comfortably below 1× EBITDA.

Vs Mastercard: tie at 10/10. Both are fortresses.

We’ve established what Visa looks like from the inside. The fundamentals are elite and they fully support the price performance from Step 1. Now the question becomes: does the market expect this to continue? Here’s what the analyst consensus says.

Step 9. Future Growth: does the market expect the compounding to continue?

Short answer: yes, steady double-digits, just not screaming.

The bar: consensus revenue and EPS growth for the forward years plus the average. Each check passes a tier only if BOTH revenue and EPS clear it. A 10 needs 15% revenue and 20% EPS. An 8 needs 10% and 15%. A 6 needs 7% and 10%.

Revenue estimates: 13.9%, 10.5% and 10.2% for the next three years. EPS estimates: 14.6%, 13.1% and 13.2%. All four scored checks clear the 6 tier comfortably. What stops them at 8 is the EPS side. Those forward EPS numbers cluster around 13 to 15%, and the 8 tier wants 15% or better across the board. So close. 6 out of 10.

Stack it against Step 2, where EPS actually grew 13 to 26% historically. The estimates are in line to a touch below what Visa has delivered. The out years are interesting too: FY4 estimates look strong (Rev 12%, EPS 16%), FY5 softens (Rev 5.5%). The near-term picture is healthy, dependable, mid-teens growth.

Vs Mastercard: MA wins, 8 to 6. The third and final spot where it edges ahead. Revenue outlook is basically the same, low-teens for both. The difference is forward EPS. Analysts have MA around 16% and Visa around 13 to 15%. MA clears the 15% EPS bar for an 8. Visa lands just under it.

Analyst estimates are useful. But are they reliable for this specific name? That’s the last question.

Step 10. Predictability: can analysts even model this thing?

Short answer: mostly, yes. On earnings especially.

The bar: I count every quarterly surprise bar, beats versus misses, for both revenue and EPS, then score the lower of the two overall beat rates. 95% or better is a 10. 90% is an 8. 80% is a 6.

On EPS, Visa beat or met in about 67 of 71 quarters. That’s a 94% beat rate. The four misses were tiny and clustered in 2013 to 2016 plus the 2020 COVID quarter. On revenue, the beat rate is lower at 88.7%, eight misses out of 71 quarters, because revenue surprises are noisier than EPS.

Score the lower of the two, which is revenue at 88.7%. That clears 80% but sits just under 90%. So 6 out of 10. If you scored EPS alone, this would be an 8. Revenue is the binding constraint here.

Vs Mastercard: tie at 6/10. Both get gated by a sales beat rate under 90% (Visa 88.7%, MA 85%). Both nail EPS (Visa 94%, MA 96%). Visa’s revenue prints are actually a touch more reliable than MA’s. Same score anyway.

The Final Score

80 out of 100. A Tier.

The business categories are basically flawless: 10s on cash conversion, margins, moat and balance sheet, plus 8s on growth, buybacks and management. That’s a genuine compounder with one of the widest moats I score.

So what’s holding it out of S Tier? Two things, and they’re different in kind. Steps 9 and 10 (future growth at 6, predictability at 6) are the honest, boring truth that Visa is a mid-teens grower whose revenue prints are a little noisier than its earnings. That’s fine. It’s still elite.

The one that stings is Step 1 at 4. And I want to be clear about what that 4 is. It’s a price problem. The assumption for Step 1 is that compounders should just keep compounding and the price should reflect that but the price sometimes gets ahead of the business and then “waits” for the business to catch up. How do I know, steps 2-10 confirm that it’s business as usual.

Now the head-to-head, since I ran the same scorecard on Mastercard. MA came out at 86, S Tier. Visa at 80, A Tier. Here’s where the six points went:

Seven of ten categories are a dead tie. The whole gap is three boxes: Growth, Management and Future Growth, two points each. And I’d put an asterisk on one of them. MA’s Management 10 leans on a 71% average ROE that’s juiced by a bought-down, negative-tangible-book equity base. On ROIC, the two are neck and neck. So on pure business quality these are closer than 80 versus 86 makes them look. MA genuinely grows FCF a touch more evenly and analysts expect slightly faster EPS from it. The rest is a coin flip between the two best toll bridges in the market.

Which is the one question the QS deliberately does not answer: is it cheap today? The score tells me Visa is a A Tier compounder worth owning. It does not tell me the price to pay. That’s what we will check next.

What the Market Has Paid

Here is what Visa’s earnings have done and what the market handed over for them, across three windows compared to Mastercard.

In the early years Visa grew earnings at 22% and the market paid a 24x. Over the last decade the growth cooled to 16.6%, yet the multiple the market was willing to pay actually went UP to 30x. Slower growth, higher price tag. That is the market falling in love with the quality of the rails.

Today the blended multiple is 28x, so the stock sits right in the middle of its own history. Cheaper than the 30x froth of the last decade. Pricier than the 24x it paid in the early days when it was growing faster.

The dividend is almost a rounding error here at 0.74%, so make no mistake. This is an earnings-growth story.

Now, let’s see what we can make based on what multiple we assume.

What You Actually Make

The consensus forward growth rate is running around 14% for the next 5 years, but Visa has never grown at that rate before.

Using the historical periods below, we get 1 to 1.9 PEG ratios:

If the consensus growth had matched one of these periods, we could’ve assumed one of the historical multiples, but it is lower.

So, should we adjust it for growth? Using 1 PEG would give us a 14x multiple. The problem is, Visa has essentially never traded there. So using 14x as my base case would be assuming the market is about to re-rate a wide-moat monopoly. So, no durability premium. Still, we could use it as the bear case.

How about 1.3 PEG? 1.3x14=18 multiple. This is the base case. MA traded at 1.3 PEG up until 2016. It respects what the market has always been willing to pay for Visa’s quality, and it pulls the multiple down honestly to account for the slower growth.

And using the 1.84 PEG with 14% yields a 26x multiple.

Now the returns. And here is why I’m showing two different holding periods instead of one.

The further out you forecast, the shakier the estimate gets. There are usually 10-30 analysts providing estimates for the next 3 years, while five years out there are 1-5.

On top of that, the shorter your hold, the more your return depends on where the multiple happens to land on the day you sell. Stretch the hold longer, and the earnings growth starts to dominate, and the multiple noise washes out. So I show a near horizon and a far one. You get to see how much of the outcome rides on the multiple versus the compounding.

Shorter hold, to the end of FY2028. Not looking good in the short term:

Longer hold, to the end of FY2031. Now this looks better at our base case of 18x.

I feel like the multiples assumed were a little bit harsh, but it helps us see the returns with potential multiple contraction.

So the fair value band. On NTM EPS of roughly $13.90:

Again, the longer you hold a compounder, the more the business does the heavy lifting and the less the exit multiple matters.

Would you buy now or wait for the market to hand you a better price?

It’s an S-tier business, and my quality scoring system confirmed it with an 86-point score. It’s a buy at $484. See the valuation section at the bottom for potential returns.

Let me go step by step.

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Step 1. Historical Returns: how has the stock done, and how steadily?

Done well, like a straight line.

The bar: for each timeframe I check (3, 5, 7, 10, 15, 20 years) the stock has to compound at 10% or better with an R-squared above 0.85. R-squared is just how straight the line is. Clear all six and it’s a 10. Clear half and you get a 4.

Since the 2006 IPO, Mastercard has compounded at 26.7% a year. On an R-squared of 0.97. For the non-nerds, R-squared near 1.0 means that line went up in an almost straight diagonal for twenty years. That’s about as clean a compounding chart as exists.

So why did this only score 4/10?

MA clears three. The 10, 15 and 20-year windows are gorgeous, 19% to 27% a year on a 0.94-plus R-squared. The recent ones are ugly. The 5-year CAGR is 5.6%. The 3-year is 9%. The stock has basically gone sideways since 2021 while the business kept printing low double-digit growth.

The business is fine while the multiple compressed. MA was a premium-priced growth name; the air came out of that whole bucket after 2021, and the price spent three years digesting it while earnings caught up.

Step 2. Historical Growth: is the business actually growing?

Yes. And it’s not close.

The bar: revenue compounding at 7% or better, EPS and FCF at 10% or better, across all six timeframes. Three metrics times six windows is eighteen checks. You need to pass basically all of them for a 10.

Revenue grew 13% a year over the last decade. EPS grew 17%. Free cash flow grew 16%. Pick any window. 3, 5, 7, 10, 15, 20 years. Every single one clears the bar. That’s a 10/10, and it’s rare.

One honest note. The 5-year growth numbers look extra juicy because they start in 2020, when revenue actually fell 9% and net income dropped 21%. Cross-border volume cratered 29% when nobody was flying. Low base, fat-looking CAGR. The 7 and 10-year numbers are the clean read, and they’re still excellent.

Step 3. FCF / Net Income: is that growth real cash or just accounting?

It’s real cash.

The bar works in tiers. Convert at 100%+ across every window and it’s a 10. 80% gets an 8. 60% gets a 6. MA sits around 106% everywhere, so it’s a clean 10. Every dollar of reported earnings shows up as a dollar of cash, and then some.

Free cash flow beats net income basically every year. Median FCF to net income sits around 106% across every timeframe.

This is what asset-light looks like. They spend about half a billion on capex to produce thirty-three billion in revenue. The rest falls to cash.

Step 4. Share Count: is management buying back stock or diluting me?

Buying back.

The bar: shrink the count and you score. Buy back 3%+ a year and it’s a 10, 1%+ is an 8, dead flat is a 6, dilution is a fail.

Shares shrink about 2% a year, every year, across the 3, 5, 7 and 10-year windows, so an 8. The buybacks comfortably outrun the stock comp, so owners get a bigger slice each year without lifting a finger.

Step 5. Margins: is the business getting better or worse?

Better.

The bar here isn’t about the level, it’s about the trend. Each margin, at each timeframe, has to be flat or expanding versus its own long-run average. Gross, net and FCF across five windows, fifteen checks.

Gross is pinned at 100%, and net and FCF are both climbing, so all fifteen pass. Clean 10. Net margins went from about 40% to 45-46%. Free cash flow margins went from the mid-30s to over 52%. A 100% gross margin business that keeps half of every revenue dollar as free cash.

Step 6. Moat: can anyone else do this?

Not many.

The bar: average gross margin above 60% and average ROIC above 25%, with the current readings holding or rising, earns a 10. Step down to 50% and 20% and it’s an 8. 40% and 15% gets you a 6.

MA has a 100% gross margin and ROIC averaging 48% over fifteen years, currently at an all-time high near 60%. It clears the top bar with room to spare. A two-sided network with switching costs and a brand on every card and terminal on earth. 10/10.

Step 7. Management: is capital being allocated well?

Very well.

The bar mirrors the moat step. Average ROE above 30% and average ROIC above 25%, current readings holding or rising, is a 10.

MA’s average ROE is 71% and ROIC is around 48%, both climbing.

Here’s why ROE is so high. Mastercard buys back so much stock that there’s barely any equity left on the books. Tangible book equity is actually negative. When the denominator shrinks toward zero, the ratio explodes. So a 200% ROE isn’t some accounting trick. It’s what happens when a cash machine returns almost everything to shareholders.

Step 8. Balance Sheet: can it survive a storm?

Easily.

The bar: interest coverage above 25x and net debt under 1x EBITDA, and it has to hold on both the long-run average and today. Coverage above 20x or debt under 2x drops you to an 8.

MA earns 27x its interest bill and carries net debt of 0.4x EBITDA. They went from net cash to lightly levered to fund buybacks, but this is still a fortress. 10/10.

Step 9. Future Growth: does the market expect the compounding to continue?

It does.

The bar runs on the forward estimates for the next three years plus their average. Revenue above 15% and EPS above 20% across all of them is a 10. 10% revenue and 15% EPS is an 8. 7% and 10% is a 6.

Consensus has revenue growing 12-13% and EPS around 16%. That clears the 8 tier comfortably but falls short of the 15/20 needed for a 10. So an 8.

Step 10. Predictability: can analysts even model this thing?

Mostly.

The bar is the beat rate, and I take the lower of sales and EPS so one good line can’t carry it. 95%+ is a 10, 90% an 8, 80% a 6.

There are 80 quarters of miss and beat data since the IPO. EPS: 77 beats, 3 misses, 96%. Sales: 68 beats and 12 misses, so 85%. The misses bunch up in the 2010 to 2020 stretch, where both five-year windows ran beat rates of 75%.

The lower of the two is sales at 85%, so a 6. Still, for a compounder, analysts clustering tight is a good thing, and on EPS they nail it. The business is modelable.

The Final Score

An 86, which lands it squarely in S Tier.

I can defend every point. The only category still dragging is returns, and that’s the price stall. The growth, the cash conversion, the margins, the moat, and the balance sheet are all 8s and 10s. There is no quality question here.

The only real question is price. The QS confirmed the business. It says nothing about whether the stock is cheap today. I’ll take a look at that next.

What the Market Has Paid

Here is what the market actually pays for this thing over time:

MA compounded earnings at about 23% a year since IPO, with its multiple expanding in the last 10 years.

Today the stock trades at 26.5x blended earnings or a 24x forward P/E. Above where the cold formula (growth=P/E) says it belongs but very close to the 2010-2016 era with 19% growth.

What You Actually Make

Read more

1. What made it into the portfolio and why

2. What I threw out and why

3. How I picked the portfolio of 20 compounders

4. Recap of how I created the watchlist

Twenty names out of 250.

Equal weight, 5% each, kind of similar to how I started in 2020. Sorted by 3-year returns, ranging from 34% to 12% annually.

A blended return of around 18% a year. 17 of the twenty carry a wide moat or exemplary capital allocation ratings.

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What made it into the portfolio and why

Here is the final portfolio (THIS PART IS VISIBLE TO PAID SUBS ONLY):

What I threw out and why

Here’s why the big ones didn’t make it.

Wonderful, just too expensive. Finally, a long list of great companies that failed on one thing: price. These aren’t rejects so much as a watchlist for the next correction. Costco at a 49% premium to fair value. Walmart at 96% over. Apple at 68%. Deere at 91%. Caterpillar at 111%. Goldman Sachs at 47%. Cisco at 97%. Alphabet at 32x vs the historical 24-27x multiples. The semiconductor equipment names, ASML, Lam, KLA, Applied Materials, all trading at huge premiums after their run. Every one a business I’d happily own. None at today’s price. When these come back to earth, we’ll talk.

Unrated, so I trust the system instead of my gut. A whole group of genuinely great businesses got cut for one reason: Morningstar doesn’t rate them, so including them would mean overriding my own system on a hunch. KKR and the other alternative-asset managers. The Mexican airport monopolies, OMAB and PAC. Corpay in payments. The gold royalty names. The advisory boutiques like Houlihan Lokey.

Skipped because I already own the better version. A few names got cut simply because their slot was filled by something cheaper or stronger..

The financial-data cluster, where I kept the cheap ones. This corner is full of wonderful tollbooths, and they all do roughly the same thing, so I only need a couple. They are both great businesses, with 7% revenue and 12% earnings growth, both top-rated. But one trades at 29x versus 22x for the other. Easy pass. There is another one that is even richer at 32x for similar growth. Two more have narrow moat ratings while trading at 25x and 30x for similar growth rates.

A tale of two multiples. Both are wonderful software businesses with top-rated management. The first one grows faster, 20% revenue and 21% EPS against the other’s 12% and 15%. So why did the slow grower make the book? The multiples. The first one trades around 33x, a full multiple already. For it to really pay off, it needs to grow as projected without any multiple compression. The other trades at 13x, a low multiple for its quality and growth. Its upside comes from its multiple rerating to around 20x, which the fundamentals justify on their own, not back toward its own history of above 30x. Me being a value investor at heart, decided to go for a lower multiple. And I’m usually surprised when a high multiple goes even higher. I’m guessing this will be one of those cases.

Or maybe I’ll just find an excuse to include both later on.

How I picked the portfolio of 20 compounders

Step 1: Moat And Management

In this step I layered on Morningstar’s moat rating and their management rating.

Wide moat first. Not Narrow. If I’m not watching, the moat is the thing keeping competitors out while I’m gone. On management, I leaned exemplary.

Morningstar builds those ratings mostly on hard numbers.

I’m a numbers guy myself and a believer that things boil down to numbers, especially in the long run. Culture? In the numbers. Talent? It’s in there. Innovation? Captured. Maybe not fully for everything, but “mostly” is good enough for investing, which is a probabilistic endeavor.

When assigning economic moat ratings, Morningstar analysts and the moat committee take both quantitative and qualitative considerations into account. Moat ratings for all initiations and any proposed changes must go through the global moat committee, which comprises senior members of Morningstar’s equity research team.

So, this rating has a consensus of experienced professionals. Definitely carries a meaningful weight.

Capital allocation ratings are assigned using analysts’ assessments of balance sheets, investments, and shareholder distributions and the capital allocation flow chart.

Two filters down. A giant list is now down to 51.

Step 2: What The Street Expects

Then filtered further by discount, estimated 3-year returns above 10%, and revenue growth above 10%. I picked the revenue above 10% because this means the companies don’t need to rely on margin expansion or buybacks to get the EPS or FCF/share above 10%. So, this is a conservative filter.

After this filter, there were 15 left. This is Tier 1.

12 made it into the portfolio, while 2 of them didn’t make it to avoid concentration. One was skipped because I had to assume a multiple higher than 30x for around 30% growth.

Step 3: Loosen And Repeat

Then I loosened the revenue below 10% and kept the wide moat and exemplary rated companies.

8 Tier 2 companies.

5 made it to the portfolio. The other 3 were skipped due to their premium valuation, around 30x or higher for similar growth rates of 11-13% EPS.

Now we have a total of 17 companies, 3 slots left.

Then I went for a narrow moat and revenue that’s higher than 10%. Filtered further by a discount of 10% or more as before.

10 Tier 3 companies. 3 made it to the portfolio. The other 7 were mostly dropped to avoid duplication with the chosen 17.

I want to expand on the details of which great businesses I threw out and why.

Recap of how I created the watchlist

Now, let me share a quick recap of how I created the watchlist of 250 companies, and then I’ll share the steps that I followed to get to this portfolio of 20.

I walked through this screen last time. If you missed it, start there. Today’s universe comes straight out of it.

Just as a quick recap, I started with about 8,200 stocks and screened for long-term price growth (CAGR) and the consistency of that growth.

I ended up with 648 companies. That’s about 7.8% of all companies!

Next, I pulled consensus estimates for the next three years. Revenue and earnings.

Any company projected to grow revenues by less than 5% and EPS by less than 10% over the next three years was removed.

About 250 survived (3%) as a watchlist.

Then I prepared the watchlist for portfolio building by adding Morningstar moat and capital allocation ratings as a second opinion.

Finally, I added historical multiples so the fair value and forward returns can be calculated.

For each name, I assigned a fair value P/E based on what that stock actually traded at over the last 10, 15, or 20 years. I picked the lowest and assumed no multiple expansion.

Then I capped every multiple at 30x. No exceptions.

Why the cap? Because multiples come back to earth. Even the best ones. The 30x ceiling keeps the math conservative. Because that is a 3.3% earnings yield (1/30), which requires three years of 10% growth to match the current 10-year treasury yield of around 4.5%. All of these companies are projected to grow faster than that and, based on their moat, longer than 3 years.

Just like any group of items, I knew that not all companies were equal on my watchlist.

So, I divided the results into three tiers based on moat, capital allocation, and future returns. Since the future returns math is EPS growth plus dividend yield plus multiple change (no multiple expansion, mostly contraction assumed), I had to consider growth rates and multiples too.

Tier 1 is a wide moat, and still growing fast. Consensus revenue and EPS are double digits, and returns are in the high teens and up. This is the engine.

Tier 2 is wide moat, exemplary, but slower. Consensus revenue is 6 to 9% while EPS is above 10%. The monopolies and toll booths that compound on buybacks instead of growth.

Tier 3 is a narrow moat. Still quality, still growing, just less protected for the long haul. A few earn a seat for the return and the diversification.

Mostly Tier 1. Some Tier 2. A few Tier 3.

So tell me. Which name would you have left out, and which one am I going to regret cutting? Drop a comment.

If you are reading this for the first time, here is a brief intro:

I’m using stock price annual returns (CAGR) and how straight or exponential the stock price is to find compounders. The assumption is that the stock price and its smoothness reflect the quality of the underlying business, especially in the long run (10 plus years).

Here are two of my posts to get you started:

Let’s take a look at the dataset.

What’s in the dataset?

There are about 1500 companies in there. Sweden has the most listings, with about 880 of the total, followed by Norway. Iceland has the smallest exchange.

Nordic markets aren’t that different from the US in terms of market cap distribution. In both markets small caps lead in terms of numbers, followed by nano, mid, large, and mega in that order.

Small caps ($300mln to $2bn) make up most of the market with about 56% vs. 43% for the US.

In terms of sectors, Nordics are dominated by industrials, tech, and healthcare, while the US is dominated by financial services, healthcare, and tech in that order.

Energy is the smallest sector in both, with about 4% of the total market.

Now, here is the fun part of finding long-term compounders.

Finding Compounders

Filters applied: Trend Quality, Growth Strength, Combined score above 80, and First Year below 2015.

Only 44 out of 1532. That is 2.9% of all companies.

There are 13 large, 17 mid, 13 small, and 1 nano caps.

This is how the CAGR and linearity data look under the hood. Will they keep compounding and grow into large caps? They’re more likely to do so than a random company that is not a compounder. Winners keep winning.

Some familiar names top the list, such as ABB, Investor AB, and Lifco AB:

Now, I’m going to show you some companies from the database that had crazy returns in the last 10-20 years, but there will be no data except the total returns. Can you guess the company? Here we go.

A smooth compounder, large-cap, in an industry most people don't associate with 85x returns. I’m guessing the Nordic investors will recognize this one.

A big winner after the GFC, a mid-cap, 27-bagger.

A small cap, 128-bagger!

And finally, an 11-bagger nanocap that isn’t covered by Google Finance. That’s about 20% CAGR for about 15 years if you were wondering.

Any luck?

If you couldn't name any of them, that's the point.

There are 1,532 companies listed across the Nordic exchanges. Most investors outside Scandinavia can name maybe five. The full dataset of 1532 tickers with CAGR, linearity scores, and combined rankings across every time period is available to paid subscribers, along with the US dataset.

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1. The watchlist

2. Using the Tool to find compounders

3. The Idea Generation Tool that I built

4. How I came up with the Tool

5. Thinking behind it

Here’s what the final list looks like under the hood.

250 companies made it through. 85 have wide moats. 88 are trading below my estimated fair value right now (May 25, 2026). 32 have a PEG under 1.0.

Yes, the usual suspects are here. All of the Mag 7 made the cut. So did the semiconductor heavyweights: AVGO, ASML, TSM, and AMD. Visa and Mastercard. Costco. Eli Lilly. If the screen didn’t catch those, I’d question the methodology.

Industrials dominate with 74 names. Tech and financials are second and third. There are three Mexican airport operators on here. A Canadian gold miner. 26 international names total across 10 countries.

The list skews large cap (169 names), but 48 mid-caps made the cut. Those tend to be the most interesting.

The cheapest name trades 47% below my estimated fair value. It’s a large-cap biotech with a PEG of 0.8. The highest estimated 3-year return on the list is 51%, a mid-cap pharma with a PEG of 0.9. And there’s a mid-cap broadcaster trading at a PEG of 0.3 with a 47% estimated 3-year return. I almost didn’t believe that one.

Every company on the list comes with a fair value estimate, PEG ratio, and estimated 3-year total return.

Here is the final watchlist for my paid readers with estimated fair values and 3-year returns and moat/management ratings.

One more thing. This methodology isn’t perfect. No methodology is. Consensus estimates are wrong all the time. Historical multiples shift. Moat ratings are opinions, not facts.

This is just one way to fish. It narrows 8,000+ names down to 250 and gives you a starting point. The real work starts after the screen.

Bonus

Here is free access to the spreadsheet with about 4,000 tickers across NYSE, OTC, and international listings. Just click “request access” and then make your own copy to use the filters.

Upgrade for the latest list that includes the NASDAQ.

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Using the tool to find compounders

Welcome to the fun part. Here is our first batch of winners.

Filters used: Combined score higher than 90, first year (IPO) before 2010.

It looks like TDG took a breather this year from being a compounder with -44% CAGR this year (-10% YTD).

Another one is RBC; we click through the chart column. What a nice chart! A 35-bagger.

This is why I created this tool; it is a great way to quickly discover companies.

We loosen the First Year a little by filtering for companies that IPOed between 2010 and 2015.

More interesting ideas: KEN, AXSM, and FTAI.

I see some Singaporean and Canadian companies with market capitalizations as low as $5 billion in industries ranging from biotech to defense.

So far we have 26 companies in our watchlist.

You can also filter by each column instead of the Combined Score to have better control of the time periods.

I went ahead and cast the widest possible net by loosening the filters to a CAGR above 10% and filtering for 10, 15, and 20-year periods separately for the linearity columns above 0.8.

I ended up with 648 companies out of 8264! That’s about 7.8% of all companies!

If you’re saying, “Hold on, what kind of watchlist has 648 companies?” I’m aware of that.

So I reduced further.

Remember when I mentioned combining this data with consensus estimates? Any company projected to grow revenues by less than 5% and EPS by less than 10% over the next three years was removed.

After that, there were 250 companies left. That’s about 3% of all companies!

That’s an interesting number. Bessembinder’s research on 26,000 U.S. stocks since 1926 found that roughly 4% of all stocks created all the net wealth in the market. The other 96% collectively added nothing above T-bills. My screen landed on 3% using completely different inputs. Different method, same neighborhood.

These companies are historical winners with at least 10 years of performance and are projected to grow their revenue at least 5% and EPS more than 10% in the next 3 years.

I didn’t stop there, of course, as I compared this watchlist to Morningstar’s database of about 970 companies. About 23% out of those are rated wide moat.

It turns out the watchlist has about 85 or 34% wide moat companies vs. the 23% in the database.

It means this method has 1.5x as many wide-moat companies compared to the Morningstar database. That’s good evidence that the “reverse-engineer from price” approach is systematically surfacing quality businesses.

You can cut further. 51 companies remain out of 250 if you only pick the ones with Exemplary management ratings.

This is another way to cut down the list by adding a qualitative filter to it.

So, 8,265 companies. Filtered for 10%+ CAGR and linearity across 10, 15, and 20-year periods, higher than 0.8. Down to 648. Removed anything below 5% projected revenue growth and 10% EPS growth. Down to 250. Overlaid Morningstar wide moat and Exemplary management. Down to about 50.

That’s the top 0.6%.

The Tool that I built

Every stock has a Combined Score which is the average of Trend Quality and Growth Strength.

For instance, ALSN below has a Combined Score of 84 while having a Trend and Growth score of 88 and 80, respectively.

Blue means a score above 90, while green is a score between 80 and 90. They were color-coded so you could use them to filter quickly, as shown below:

Trend Quality is derived from the linearity data below and is how straight or exponential the stock price is. It is a number from 0 to 1, with higher being better.

Time periods for CAGR and linearity data go from 1 to 20 years.

Each stock is analyzed for a best-fit model and categorized into Exponential (think Nvidia) and Linear (think Visa).

Growth Strength is derived from the CAGR rate (annualized returns). And of course higher is better.

And there is a quick information section, such as an industry and business description, to get us up to speed from the get-go.

Also, the links in the chart column take you directly to Google Finance for a visual check of the CAGR and linearity.

The first-year column is the IPO date, while the cutoff is 2001. I think 20 years of stock performance is good enough to gauge the performance.

How I came up with it

First of all, a wide net needs to be cast to catch them all. There are a few ways to go about it.

I could plug in some filters and let a screener spit out a list. That works. But it makes you pick the reasons (business fundamentals) for the value creation in the market (stock performance).

How about we reverse-engineer the business performance with stock price?

Why? Because the market is a voting machine in the short term and a weighing machine in the long term. A stock can’t go up consistently for a decade by accident. The business has to be doing something right for it to go up in a straight or exponential line, such as below.

If you’ve been following my work, you know that I’ve built a tool that scans every US-listed stock (including OTC, over 8,000 tickers) and measures two things: how fast the price compounded (CAGR) and how straight (linearity) that compounding was.

It is called Generate Ideas and lives inside my paid Compounders spreadsheet.

That’s what we are going to use today and create this watchlist together.

Thinking behind it

Updating my watchlist has been in my backlog for a while now, but I’ve been putting it off because I wanted to look at everything that is listed on the US exchanges.

So, I’m going to walk you through the methodology that I followed to create this ultimate watchlist of compounders.

Before we kick off, there are a few questions that need to be answered.

What is a compounder anyway?

Compounder is a company that consistently, and over the long run (10-plus years at least for me), creates value (cash) for its shareholders.

Or to describe it quantitatively, it’s a business with fundamentals that go to the right and up over time with ROIC higher than the cost of capital.

How do we recognize one?

They usually have kind of a moat and good capital allocators that result in ever-increasing free cash flows per share for very long time periods.

Will it continue being a compounder?

The past is a good proxy for the future.

The longer a company has compounded, the more likely it is to keep compounding. Not because the past guarantees anything. But 15 years of consistent value creation is evidence of something durable underneath. A moat, a culture, a reinvestment engine. Something real. This is the Lindy Effect applied to stocks.

Mauboussin’s research on ROIC persistence backs this up. He tracked 1,000 companies across ROIC quintiles over a decade. Top-quintile companies stayed in the top quintile at rates well above what chance alone would predict.

I combine that with consensus analyst estimates to check if the compounding has legs going forward.

If you scrolled/read all the way here, please leave a like. Thank you!

Markets are shaped by group dynamics, not individual psychology.

Mauboussin:

A lot of the literature on behavioral finance deals with individual mistakes that you and I make... The nature of collective behavior is different from individual behavior... The aggregation of even suboptimal individuals can lead to optimal results. Part of the point is that we’re not really talking about psychology when we think about markets. We’re talking about sociology, which is how groups behave in a group setting.

This connects to the Wisdom of Crowds theory and how it applies to market efficiency. The core idea is that even if individuals have biases or make mistakes, markets can still be efficient if three conditions are met.

1. Diverse perspectives.

Firstly, ...diverse points of view. Some people are optimistic; some are pessimistic. There are technical traders, fundamental traders, short-term, long-term, etc.

2. A properly functioning aggregation mechanism.

The second condition is that there’s a properly functioning aggregation mechanism. The information that is out in the world is reflected, in this case in particular, in prices. Exchanges do that very well, but I would note that sometimes aggregation falls down because people simply do not participate.

3. Incentives for accuracy.

The third is incentives, which are rewards for being right and penalties for being wrong.

So when do markets get interesting? When one or more of those conditions break down.

Mauboussin says diversity is the one that breaks most often. And when it does, humans go to extremes and create bubbles:

By far, the most likely to be violated is diversity. Rather than each of us operating independently or with our own views, we correlate our behaviors. Humans are social, and investing is inherently a social exercise, so from time to time, it happens that people’s views collapse upon one another, and people all start to believe the same thing.

Here’s the thing. There’s a quote from Seth Klarman that Mauboussin loves, and I think it captures the playbook perfectly:

Value investing is, at its core, the marriage of a contrarian streak and a calculator.

Two parts to that. First, the contrarian streak:

The contrarian streak says when others are bullish or bearish, examine the other side of the case. That need not be a simple way to make money, because sometimes the consensus is correct, and by the way, positive feedback is something that we see a lot in nature in order to survive.

Being contrarian for its own sake isn’t enough. You also need the calculator:

So it’s not just being contrarian... Because everyone is uniformly bullish or uniformly bearish, that’s led us to a set of expectations that are unduly high or unduly low, and that company or that industry simply cannot satisfy that set of expectations that’s priced into that particular stock. As a consequence, there’s going to be a reversal... It seems like everybody has a uniform point of view, which seems to be what’s expressed in the security price, and through an expectations approach, I’m going to reverse engineer and say it’s a good bet to be on the other side of the argument.

So what does all of this mean? Herd behavior leads to bubbles and crashes because everyone starts believing the same story and reinforcing price movements in one direction. Maybe this applies to the current AI narrative. Only time will tell. Because a wise man said:

“The learned fool writes his nonsense in better language than the unlearned, but still ‘tis nonsense.” — Ben Franklin

I might end up being that fool. And I might not even be learned.

You can read and watch the full interview here.

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Yeah. I had to read that three times too.

Turns out there’s a scientific name for this. The Dunning-Kruger Effect.

Let’s take a closer look.

Stage 1: Peak of Mount Stupid

You bought Nvidia at $12. You’re up 10x and you’re telling everyone at Thanksgiving dinner about your “strategy.” Your uncle asks what a P/E ratio is and you say “doesn’t matter, just buy what you know.” You’re basically Warren Buffett. Except Warren spent 70 years reading annual reports and you spent 70 minutes on Reddit.

Of course I know everything!

Stage 2: The Oh No

Six months later your portfolio looks like a crime scene. You bought Peloton at $140 because “everyone’s working out at home now.” You loaded up on some crypto coin your coworker recommended at lunch. Your buddy who bought an S&P index fund is beating you by 40 points.

Oh snap. This is hard.

You start Googling “Is investing just gambling?” at 2am. Your confidence is in the basement. You’re ready to sell everything and put it in a savings account earning 0.5%. Your spouse is giving you that look.

Stage 3: I May Never Understand This

Rock bottom. You’ve lost real money. Not paper losses. Real money. The kind where you open the brokerage app and physically wince. You’re convinced the market is rigged and only insiders win.

But something weird happens here. You start reading. Not stock tips on Twitter. Actual books. Maybe it’s Peter Lynch. You stumble across the Substack FinStack community and realize there are people who actually think about this stuff systematically.

You can’t read and learn fast enough.

Stage 4: It’s Starting to Make Sense

You finally build a real portfolio. Not 47 random tickers you heard about on X. An actual diversified set of businesses you understand. You know what ROIC means. You know why free cash flow matters more than earnings. You start reading 10-Ks for fun (okay maybe not fun but you do it).

You stop checking your portfolio every 20 minutes. You start thinking in years instead of days. Your confidence is coming back but it’s a different kind of confidence. The kind built on competence instead of luck.

Stage 5: Sustainable Confidence

Was it easy? No. Complicated? Yes.

Your competence and confidence finally align. You understand your weaknesses. You know your circle of competence, and you actually stay inside it (most of the time). You have a process. You still make mistakes, but you know why you made them.

Not bad for a self-taught investor.

One important side note. This research gets completely butchered online. The original study didn’t just say “dumb people think they’re smart.” It showed that everyone misjudges their abilities because of blind spots in self-awareness. Skilled investors underestimate themselves too. They assume everyone else can also read a cash flow statement. (They can’t.)

Some researchers have even critiqued the study’s methodology and pointed out alternative explanations. So the most popular framework about people being wrong about their abilities might itself be wrong.

Mother of all ironies.

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I'd like to believe I'm somewhere between 4 and 5 right now. Where do you think you are in your investment journey?

First, the Numbers

132 companies. Seven-dimensional scoring framework. Here is what came out:

22 wide-moat downgrades (20 to narrow, 2 all the way to no moat). Adobe, Salesforce, ServiceNow, Oracle, Workday, Accenture, Infosys, ADP. Big names.

18 narrow-moat downgrades (to no moat). Sabre, FactSet, The Trade Desk.

2 upgrades to wide moat: Cloudflare and CrowdStrike. Both cybersecurity. That’s it. Two out of 132.

The full Morningstar report is free. Here is the link. 48 pages. Worth reading if you have the time. If you don’t, keep reading.

Where the Damage Hit

IT Services took the worst beating. Nine downgrades in one sector.

The logic is straightforward. These firms monetize billable hours, labor arbitrage, and simple workflow automation. AI takes a chunk out of all three. Accenture, Infosys, Tata, Wipro, Capgemini, EPAM, Endava, Globant all got cut.

Enterprise software was second. Salesforce, Workday, ServiceNow, Oracle, Adobe, OBIC. Common thread: application layer, per-seat pricing, AI can replicate the workflow.

Payroll processing got hit as a whole sector. ADP, Paychex, Paycom. Per-seat again. Plus the risk AI-native startups skip the incumbents entirely.

The resilient sectors tell you where the market is wrong:

Cybersecurity. More attack surface, more data, more demand. AI is a tailwind here, not a headwind.

Engineering and design software. 30+ years of proprietary data. Workflows with zero room for error. Certification barriers that take years to clear.

Financial exchanges and data providers. Regulated. Benchmark status. Transaction data that cannot be scraped.

Content and music rights. Cannot be replicated. Cannot be trained away.

Payment networks. Network effects, not code.

How about the moat types? Network effects beat switching costs in an AI world.

Network effects had the fewest downgrades by far. Switching costs had the most. That’s the single most important takeaway from the whole report.

Why? Network effects are not based on the product’s technology. They are based on the strength of the network. Technologies change. Networks persist. Payment networks, exchanges, travel platforms. AI does not solve the cold-start problem of building a network from scratch.

Switching costs? Different story. AI can automate the technical processes that created those switching costs in the first place. Data migrations, format conversions, system integrations. All getting easier. Close to half of the downgraded companies had switching-cost moats. Morningstar still cut them.

The 7 Dimensions

Morningstar built a seven-dimensional scoring system to evaluate AI impact on moats. Most AI frameworks I’ve seen are high-level. This one is practical. Each dimension has a “better positioned” and “worse positioned” side. You score your company on each.

I’ve been using it on my own holdings. Here’s the framework with the examples that matter.

1. Layer Positioning: Infrastructure or application?

This is the single most important dimension. Infrastructure companies (cloud platforms, chip design tools, cybersecurity, databases) get more business when AI scales. Application-layer companies (basic CRM, scheduling tools, productivity apps) get replaced by it.

Think about it this way. AI agents need infrastructure to run on. They don’t need a separate scheduling app.

Microsoft scores well because Azure IS the infrastructure. Salesforce scores poorly because CRM sits at the application layer where an AI agent can do most of the job. The tricky ones are hybrids. ServiceNow orchestrates IT workflows (infrastructure-ish) but monetizes per-seat (application-ish). That’s exactly why it was a 3-2 vote to downgrade. Close calls live in the hybrid zone.

2. Data Moat: Unique or scrape-able?

Not all data is equal. Cadence and Synopsys have 30+ years of semiconductor design data certified to specific foundry processes. You cannot scrape that. You cannot assemble it from public sources. Each new chip generation adds more proprietary data that competitors will never touch.

Compare that to a company whose data is mostly customer-owned or can be rebuilt from public datasets. If an AI model can train on freely available information and replicate what you sell, your data moat is thin.

The test is simple. Could a well-funded AI lab recreate your dataset in 2 years? If yes, score low. AppLovin passes this test. Its advertising optimizer trains on proprietary auction win/loss data from a vertically integrated supply and demand network. The Trade Desk does not. It orchestrates client data and collects fees on third-party data. Neither is proprietary. That’s why AppLovin kept its moat and TTD lost it.

3. AI Demand: Does AI grow or shrink the TAM?

Cybersecurity gets MORE demand from AI. More agents running 24/7 means a bigger attack surface. More endpoints. More data to secure. CrowdStrike and Palo Alto benefit every time someone spins up a new AI workload.

Chip design tools also win. AI chips are among the most complex semiconductors ever built. More complexity means more EDA software. The AI boom is a direct tailwind for Cadence and Synopsys.

On the other side: basic CRM, contact management, scheduling, payroll. If AI reduces headcount or automates routine coordination, demand for those products shrinks. Fewer employees means fewer seats, fewer paychecks, fewer licenses. Score low.

4. Pricing Model: Per-seat is the kill zone.

This one is almost binary. Per-transaction and usage-based pricing survive because they are headcount-agnostic. If AI agents generate more transactions, you make MORE money. Adyen and the payment networks win here.

Per-seat pricing is exposed. If one person with AI tools does the work of two, you just lost half your seats. Workday, Salesforce, the payroll companies. All per-seat. All downgraded.

The smart incumbents will try to shift to value-based or usage-based pricing. Some will pull it off. Most will take a margin hit in the transition. And here’s the thing: even if they manage the transition, if they were previously earning economic rents above what AI-priced work costs, there could be compression anyway.

5. Workflow Complexity: Dashboards die. Regulated multi-step workflows survive.

Simple tasks get automated first. If your software mostly generates reports or organizes basic data, an AI agent can do that now. Score low.

Complex multi-step workflows embedded in regulatory frameworks are a different story. Semiconductor design (one wrong transistor layout and the chip doesn’t work). Insurance underwriting (Guidewire’s P&C workflows touch dozens of regulatory regimes). Multi-country payroll tax compliance. These workflows have too many domain-specific edge cases for a generic AI to handle reliably.

Dassault Systemes scores well here. CATIA is the “system of truth” for physical products. In aerospace, the model IS the airplane before it gets built. Regulatory certification references the Dassault model as the authoritative source. Switching CAD in aerospace would require recertification across 50+ year aircraft lifecycles. Good luck disrupting that with a chatbot.

6. Switching Costs: Still real. Just weaker than before.

Almost every software company has switching costs. That was not enough. Close to half of the downgraded companies had switching-cost moats at one point. Morningstar still cut them.

AI can automate the technical processes that CREATED those switching costs. Data migrations, system integrations, format conversions. All getting easier. The switching costs that survive are non-technological. Regulatory barriers (can’t switch your EDA tool mid-certification cycle). Network effects where the switching cost is leaving the network, not moving the data. Multi-decade product model libraries with no migration path.

If your company’s stickiness comes from “it’s annoying to move your data,” that moat just got thinner. If the stickiness comes from “switching could crash a $200M chip design program,” you’re fine.

7. Competitive Position: Distribution still matters. Maybe more than ever.

Getting people to adopt your product was always hard. Executing a sales motion, managing enterprise relationships, scaling across an existing client base. AI does not solve any of that.

Incumbents with big installed bases and competent go-to-market teams have more than a fighting chance, especially Microsoft with distribution into every enterprise on the planet. That advantage persists even if the product itself faces competition.

The risk is fragmented markets where AI-native startups can gain traction with lower prices and better tech. Small companies with no switching costs and no distribution moat are vulnerable.

Running This On My Portfolio

I scored my own holdings against the framework.

A few things to note here. My largest position, 40%, held its moat.

None of my core holdings got downgraded. My other 5 are not even on the 132-company list. Morningstar did not flag them as AI-susceptible. Good.

The AI framework’s verdict: nothing to act on. No downgrades, no fair value cuts, no exposure to contested sectors. The portfolio passed the test.

Weighted portfolio score: 27.9 / 35 sits one tick under “very resilient” (28+ threshold). Here is the result:

Run This On Your Portfolio

I built a Claude skill that automates the scoring. Drop in a CSV or paste your holdings. It runs each name through the 7 dimensions, cross-references Morningstar’s moat ratings with external sources, flags overlap with the top picks list, and gives you a weighted portfolio score.

The skill file is free:

Ai Moat Framework

148KB ∙ PDF file

Download

Download

Here’s how to use it:

1. Open any AI chat (I used Claude Opus 4.6 Extended)

2. Upload the AI_Moat_Framework.pdf file

3. Paste your portfolio in the same message

4. Type: “Run the AI moat framework on my portfolio”

Did your portfolio beat my score of 28? Share in the comments.

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Here’s how I TRY to avoid stupid mistakes.

1. Invest in companies you already know.

Massive time saver. I ripped through Adobe’s Digital Media segment (learned some Photoshop back in school) in one sitting. The Digital Experience segment (enterprise marketing tools I’ve never touched; I’m an engineer) took me a while to wrap my head around.

You still have to read the filings. latest 10-K, 10-Q, transcripts. Non-negotiable. Search SEC EDGAR or google “ticker IR” (example: ADBE IR). But starting with a product you actually use gives you a head start before you open the 10-K.

2. Stick to widely covered names.

I focus on companies with analysts on them. Here’s why: those analysts have channel checks, industry contacts, unit-level data, and access to management. My model isn’t going to beat theirs. My job is to understand the why behind the model.

So I use the consensus. Koyfin has it for free.

3. Pull all the earnings surprises.

Koyfin has them for free. The companies I own almost never miss consensus, especially on full-year numbers (visually ad up quarters into years).

Below are quarterly beats and misses for ADBE sales and EPS.

I believe this data says more about business predictability than analyst skill. If a name beats or misses by 40% every other quarter, it’s not that the analysts are bad. It’s that the business is unpredictable or too cyclical.

4. Keep valuation simple.

Historical multiples with context. Reverse DCF. That’s it. It just has to be reasonable.

Get the historical multiple and the growth. Find the PEG by dividing the average P/E by the growth rate. Then multiply that P/E by the next 5 years’ consensus future growth rate.

For instance, ADBE traded at median NTM P/E of 31x since switching to subscription model while growing 26% annually. So, 31/26=1.2 PEG.

ADBE is projected to grow about 14% in the next 5 years while trading at 8.6 NTM P/E.

So, 1.2x14=16.8 (PEG times the next 5 years’ growth rate equals P/E). ADBE should be trading around 17x P/E with that growth based on the historical multiples and growth rate.

Its fair value would be NTM EPS times the fair multiple, so 25.93 x 17 = 440. That’s a double from $222 (07 July, 2026).

Koyfin gives away this data for free.

Here is a link to my free reverse DCF calculator.

It looks like Adobe needs to grow about 8% to return 15% annually in the next 10 years.

5. Start small. Add on conviction.

I open new positions at 1-3% of the portfolio. Conviction comes with time as I keep digging through filings and read what other investors think.

Just wrote about this in my last post.

6. Hold it.

Help yourself to hold it. How? It especially helps if you have a diversified portfolio (aka multiple small positions).

I keep even at 20% drawdowns (even though it hurts). I’m changing this attitude a little because a 20% drop requires a 25% gain to break even.

If the drop is company specific, then it’s definitely time to dig deeper.

How do you avoid mistakes? Drop it in the comments. I want to hear it.

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Maybe you didn’t read the filings. You trusted a Substack writer who turned out to be wrong. Hey, could be me.

The stock drops 50%. It was a 10% position. I’m pretty sure it has happened to all of us.

That’s a mismatch between how much you knew and how much you bet.

What Honest Sizing Actually Looks Like

You can do shallow research and still be ok if you size accordingly. You can build a 50-stock portfolio on checklists and quick write-ups and do just fine (I did just that as a busy person). Diversification is the last free lunch in investing.

The mistake isn’t doing light research. The mistake is doing light research and then betting 10% of your portfolio on the outcome.

Think about what you actually know before you buy. Not what you meant to read. Not the tab you have open. What you actually know, right now, today.

Most positions fall into one of three buckets.

You read a tweet or a headline. Maybe you caught a tip on Reddit. Someone on Substack wrote something compelling. You skimmed it, and it sounded good. That’s it. Position size: 0% (okay, maybe a tiny tracker position below 1%). You don’t own this yet. You have a hunch, not a thesis.

You ran a checklist and read a couple of write-ups. You cross-referenced a bull case and a bear case. You have a rough sense of valuation. You understand what needs to go right. This is actually a legitimate basis for owning something. Position size: 2-3%. At 2% across 50 names, a 50% drawdown costs you 1% of your portfolio. That’s a mosquito bite. Sleep is fine at this level.

You read the 10-Ks, transcripts, slides, and proxies. You understand the unit economics. You’ve recognized the patterns in management decisions. You’ve understood the KPIs that matter. You’ve thought about what would break the thesis. This is when you’ve earned the right to size up. Position size: up to 10%.

The Averaging Down Trap

Here’s where mismatched sizing gets really dangerous.

Stock drops 20%. You didn’t know enough to hold with conviction, but you sized it at 8% anyway. Now it feels wrong to sell. So you average down. It drops another 20%. You average down again. Now it’s your largest position, and you still haven’t read the filings.

The antidote is sizing correctly at entry. If it’s a 2% position and it drops 50%, it’s not going to be difficult to hold. The math is manageable (2%x50%=1% drop in your portfolio), and you have room to be wrong. If it’s a 10% position and it drops 50%, averaging down is how small mistakes become portfolio-defining ones (5% drop from a single position).

My Take

So, here’s the rule. Write this down.

Read a tweet or a headline: the position is 0%.

Ran a checklist and read a couple of write-ups: 2-3%. This is actually fun. Diversified, low-stress, and you’ll sleep fine.

Read the filings and can explain the business to a 10-year-old: up to 10%.

If you are busy with family and work commitments and unable to do enough work before buying, most of your portfolio should be in the first two buckets.

What’s the biggest position you’ve held where you knew, deep down, you hadn’t done the work? Drop it below. I’ll share mine.

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Scary numbers. But here is the thing. Every single piece of data I am looking at says that the current level is the bottom. And if you are a long-term investor, the current market is one of the better entry points you have had in years.

Let me walk you through it.

Volatility Spikes = Opportunity

Now here is where it gets interesting. Because while the macro picture is ugly, the data on what happens after volatility spikes is overwhelmingly bullish for long-term investors.

When the VIX is in its top 10% of readings (above 28, which is where we are now), the S&P 500 averages a 22% return over the next year. That is double the average.

And 85% total returns in five years. Not bad.

It gets better. When the VIX closes above 40 or 50, the average one-year forward return is above 30%. We are not there yet. But a year ago, the VIX closed above 50 during the tariff tantrum. Every single time that has happened historically, the S&P 500 was positive one year later. Every time.

And this is potentially the bottom. That’s what the data is telling me. Every time the put volume on SPY was above 8 million since 2022, that was the exact market bottom. It happened on April 5th.

If that was not enough, every time the Daily Sentiment index dropped below 15, that was the bottom.

And everyone’s favorite, Fear and Green Index, just bounced back from Extreme Fear.

What I'm Doing

I see two things at the same time. A genuinely uncertain macro picture and the cheapest stock prices in years for the highest-quality businesses, but the data is telling me that this is the likely bottom for now.

I am using this selloff to high-grade my portfolio. I made four moves today. Two exits, two adds: rotating into the compounders I want to own for the longer term.

What I Sold

I sold Salesforce (CRM) and Adobe (ADBE) in full. While they were closer to 5% positions initially, they were 1–3% positions when I sold them due to fresh cash.

If ADBE maintains the current 11-12x P/E multiple, we can expect total returns of 45% or a 15% annual return going forward.

If we see a multiple expansion towards 20x, then the expected return would be 140% total or 38% annual in the next 3 years. I believe it is possible if they maintain their current results in the coming years. Not accelerating growth, but simply maintaining the status quo. You know, put the AI worries to rest.

And for CRM, it is 45% total and 14% annual with the current P/E multiple of 15 in 3 years.

If there is a multiple expansion to 20x, then the returns would be 85% total or 25% annual.

I think they are solid choices if one wants to pick a side in this SaaSacre.

Why did I sell?

Two reasons. First, the valuations are attractive, but I feel uncomfortable adding to them regardless while I have a 40% high-conviction position from SaaSacre. So, I see no point in keeping additional positions. Second, I’ll be honest, I kind of started losing interest because they were small positions. More like the excitement had faded and the positions had become passengers. That’s usually a signal worth listening to. There is a good chance someone is going to make a killing holding those two in the next 3 years: 140% and 85% total returns if they rerate to 20x multiples.

What I Bought

I added to two of my high-conviction names. Made each 10% position after they had dropped to around 5% positions due to new cash.

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If I told you a 27-year-old from Oklahoma moved to Moscow in 1994 and bought 35% of Russia’s Google for $5 million, you’d think I was making it up. If I told you that $5 million turned into a 450x return, you’d walk away. Now add a 98% stock market crash he survived, an arrest by Russia’s secret service, and a stint in the same prison where a man died under mysterious circumstances. Still made billions for his investors.

All true. And it gets weirder.

The Name on the Door

Before we get to Mike Calvey we need to talk about the name. Baring Vostok. “Vostok” is Russian for “east.” The “Baring” part? That comes from Barings Bank. One of the most storied financial institutions in history. And one of the most spectacular failures.

Barings Bank was founded in 1762. For over two centuries it was royalty. Literally. The Queen of England banked there. At the Congress of Vienna in 1815, five nations reportedly called Barings “the sixth great European power.” This was a bank that financed the Louisiana Purchase in 1803. The largest land deal in history. Doubled the size of the United States. It funded the purchase of Alaska from Russia. It bailed out the Bank of England in 1839 and the Banque de France in 1847.

Barings also had deep ties to Russia going back centuries. It served as the Russian government’s financial agent in London. Managed their bank accounts. Arranged payments. Issued bonds. So when Baring Asset Management backed a team of twenty-somethings to raise a PE fund focused on Russia in 1994, there was a poetic logic to it.

Mike Calvey was part of that team. The Barings brand gave them instant credibility. A bunch of kids in their mid-twenties with no track record don’t raise $160 million from the World Bank, EBRD, TIAA-CREF, and Fidelity without a four-hundred-year-old name on the door. Right?

The Day the Name Almost Died

One year later the Barings name became a punchline.

On February 26, 1995, Barings Bank was declared insolvent. Destroyed. A 233-year-old institution gone overnight. Sold to the Dutch bank ING for one pound. Not a typo. One pound.

The man responsible was Nick Leeson. A 28-year-old derivatives trader in Barings’ Singapore office. Leeson grew up in Watford, a suburb of London. His father was a plasterer. He didn’t go to university. He failed his math exams. And somehow he ended up controlling both the trading desk and the back office at Barings’ Singapore operation. Nobody was watching the guy writing and grading his own tests. (This would be funny if it didn’t destroy a 233-year-old bank.)

Leeson started hiding losses in a secret error account numbered 88888 in 1992. A new employee under his watch had lost a small amount. Rather than report it and risk his reputation, Leeson buried it. Then he kept trading. When he lost more he doubled down. Classic gambler’s spiral. By the end of 1992 the hidden losses were £2 million. By late 1993, £23 million. By the end of 1994, £208 million.

On January 17, 1995, an earthquake hit Kobe, Japan. The Nikkei index cratered. Leeson had bet heavily that it wouldn’t fall below 19,000 points. It blew right through. He kept doubling down. Bought more than 20,000 futures contracts worth $180,000 each. Each bet made the hole deeper.

On February 23 Leeson left a note that read “I’m sorry” and fled Singapore with his wife. Total losses: £827 million. More than twice the bank’s entire capital. The Bank of England tried and failed to organize a rescue. Barings was dead.

Leeson made it to Malaysia, then Thailand, then Germany. Arrested in Frankfurt. Extradited to Singapore. Pled guilty to fraud and forgery. Six and a half years in Changi Prison. He was diagnosed with colon cancer while locked up. He survived. Released in 1999 for good behavior.

He wrote an autobiography called Rogue Trader. In 1999 it became a movie starring Ewan McGregor and Anna Friel. The New York Times called the book “dreary” and described Leeson as “a young man very taken with himself.” He later got a psychology degree and hit the after-dinner speaking circuit. In 2019 he said “two events a month is enough to keep me in the manner to which I’m accustomed.” He still trades stocks. Only with his own money now.

Sounds crazy right? Not so fast. The story’s just getting started.

Meanwhile, in Moscow

While Barings was imploding in London, Mike Calvey was in Moscow building the fund that would become legendary.

Born in 1967 in Wisconsin. Raised in Oklahoma. Studied computer science and business at the University of Oklahoma. Got an MS in finance from the London School of Economics. Started at Salomon Brothers in New York in 1989 doing energy M&A. His ambition at the time? To become the governor of Oklahoma. Wall Street was supposed to be a two-year pit stop.

His boss left to join the newly created EBRD in London. Calvey followed. He started traveling to Russia in 1991 at age 24. Moscow in those days was the Wild East. A country that needed everything from a business perspective. He fell in love with the process of working alongside entrepreneurs building something from nothing.

When Barings collapsed ING swept in and acquired the parent. But the private equity arms eventually completed management buyouts. Calvey and his mostly Russian colleagues kept the Barings brand and kept building.

Here’s the thing. Calvey grew up in Oklahoma. The oil industry there was built around the carried interest concept. A geologist who believed in a discovery would find three investors to put up a third of the capital for a quarter of the equity. They called these “third for a quarter” deals. Plenty of bullshitters. But the ones with real substance created fortunes. And the ones who got wiped out? Almost always the ones who borrowed too much.

Calvey carried that lesson into everything he built. No debt at the fund level. Never borrow in dollars and invest in local currency. It sounds obvious but it’s the cardinal sin of emerging markets investing. It works in the short term and you always lose it in the long term.

The 98% Crash That Made Everything Possible

In 1998 disaster hit. Russia defaulted on its sovereign debt. The ruble devalued by 4x. The stock market collapsed by 98%. Not 20%. Not 50%. Ninety-eight percent.

Most investors would have written it off. Calvey didn’t. Baring Vostok carried no debt and held cash reserves. So he doubled down. Increased stakes in his best companies from minority to controlling positions. The cash he injected gave those companies a massive edge over their struggling peers.

That first fund could have been a total wipeout. It ended up returning 4x to investors. $160 million in, $620 million out. Not bad for a fund that lived through a 98% drawdown.

More importantly it made Calvey the last man standing. The only foreign PE fund raising and investing in Russia between 1999 and 2003. Monopoly access to the best deals and the best talent.

One of those deals was Yandex.

$5 Million to $2+ Billion

In 2000 Baring Vostok led a $5 million seed round in Yandex, the Russian search engine. That bought roughly 35% of the company. It was the only equity funding Yandex received before its IPO.

Google came knocking. First offering $100 million. That would have been a 20x return. Calvey’s team listened to Arkady Volozh, the founder, who believed the company was worth billions. They passed. Google came back a year later with $250 million. Other shareholders wanted to sell. Calvey exercised a veto right to block the deal.

Six years later Yandex went public at an $8 billion market cap. Later grew past $20 billion.

So, Baring Vostok’s $5 million stake ended up worth well over $2 billion. Some sources cite a return in excess of 800x on the original investment. On a blended fund-level basis the number that circulates is 450x. Either way it’s one of the greatest venture returns ever recorded.

The lesson? Warren Buffett’s concept of watering the flowers and cutting the weeds. Calvey learned early (through some painful exits he made too soon) that the real money in PE is made by holding your best companies longest. Many firms sell their winners first under pressure to generate DPI (distribution to paid-in capital) for LPs. They end up holding the mediocre stuff for years. I’ve made this exact mistake in my own portfolio in the form of adding to my losers.

After that 450x return everyone who had wanted to sell at $250 million developed amnesia. They were appearing on Bloomberg and CNBC boasting about being early investors in Yandex. But they were the ones who wanted to sell when Google tried to buy it.

The FSB, the Prison, and the Way Out

By 2019 Baring Vostok had roughly $10 billion invested in Russia. Five portfolio companies had exceeded $10 billion market caps. Mike Calvey had become a legend in the Russian market.

Then in February 2019 Calvey was arrested in Moscow on fraud charges.

The case was linked to a shareholder dispute at Vostochny Bank where Baring Vostok held a majority stake. The charges alleged embezzlement of 2.5 billion rubles (about $38 million). Calvey said the accusations were fabricated by Artem Avetisyan, a minority shareholder with FSB connections. Avetisyan stood to gain tens of millions if Calvey was convicted. And Calvey had been suing Avetisyan in London. And winning. The arrest happened right in the middle of that lawsuit.

He was thrown into Matrosskaya Tishina (”Seaman’s Silence Prison”). Same facility where lawyer Sergei Magnitsky died under mysterious circumstances. Same judge assigned to Calvey’s case.

Russia’s former finance minister called the arrest “a shock” that triggered $40 billion in capital outflows. The U.S. Ambassador boycotted Russia’s premier economic forum in protest. Both the Trump and Biden administrations said it was a commercial dispute that shouldn’t have been handled through criminal courts.

After months of detention and house arrest a settlement was reached. In August 2021 Calvey received a suspended sentence. He left Russia in January 2022 just before the invasion of Ukraine. In April 2024 his conviction was formally vacated.

Remarkably the Baring Vostok funds had their most profitable years during this entire ordeal. 2019, 2020, and 2021.

After Russia

When the war started everything changed. Holding Russian assets would toxify the firm’s international portfolio. The exit model for Russian companies on international exchanges was gone for good.

The firm split in two. Vostok Investments took the Russian assets (owned by the former Russian managers). Baring Ventures took the international portfolio: Kaspi (now on NASDAQ, $20 billion+ market cap), Revolut, BlaBlaCar, and a Mexican digital bank called Plata. Baring Ventures has no Russian shareholders. No Russian employees. No Russian assets.

Calvey is now Chairman of Baring Ventures managing roughly $5.5 billion. He’s not raising institutional funds anymore. He’d rather do fewer things better. Investing his own money alongside co-investors on a deal-by-deal basis. (There’s a Lenin quote he likes: “Better, fewer but better.” He disagrees with almost everything else Lenin said.)

His biggest bet after Russia is Plata. A digital bank in Mexico built by roughly 200 former Tinkoff specialists who relocated to Mexico City after the war. It took 18 months and 40-50 iterations on the risk model before unit economics worked. Now it’s scaling fast. Reached 3 million customers in about half the time it took Tinkoff.

And he’s still bullish on Kaspi. The company is expanding into Turkey. Calvey draws a parallel to MercadoLibre’s logistics investment several years ago. The market punished MELI for investing in last-mile infrastructure. Turned out to be exactly the right strategic move. He believes Kaspi’s Turkey expansion will look the same way in three years. That is a post for another day.

My Take

The Barings connection is almost too perfect as a parable. One arm of the Barings empire was destroyed by a 28-year-old with no oversight who hid his losses until they swallowed the whole bank. Another arm of the same empire, under a 27-year-old with no track record in a country that barely had functioning markets, generated one of the greatest investment returns in PE history.

Same name on the door. Wildly different outcomes. Risk management and knowing when to double down versus when to cut your losses and some luck. That’s the whole game. (I’m still learning.)

Have you heard of Mike Calvey before? Comment below.

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I’m going to share exactly what I sold and why. What did I buy?

Read on to find out.

What I Sold

ASML ~100% gain

I started building this position in November 2024 and kept adding through early 2025, between $668 and $750 at around 33x P/E. It was about a 5% position. At the time, ASML had sold off on weaker-than-expected orders and fears around China export restrictions.

Fast forward to Q4 2025: record revenue, 16% annual growth, the strongest booking quarter ever, and a shiny new buyback announced. The stock ripped back.

Here’s the thing. After that run, the forward P/E sat at ~41x. That’s about a 20% premium over the 15-year average of ~35x.

Using the FASTgraphs chart below: 20% consensus growth in the next 3-5 years, just like the historical growth but at a higher multiple. Slightly overvalued.

So, I sold exactly at the peak of $1470 (lucky). The easy money has been made. Could it keep running? Sure. If we get a market correction, I’ll buy it back, as the potential returns are 150% total or 16% annual in the next 5 years even with some multiple compression from 41x to 35x.

But valuation isn't the only reason; I believe my new position offers a better opportunity, and I'm hesitant to hold 100% gains in an AI play.

GOOGL ~80% gain

I first bought a tiny position back in mid-2022, when the stock was getting hammered alongside the rest of tech. Then I built the real position (as clients added more cash) in August through December 2024, adding more in early 2025, ending up with a ~5% position, mostly between $163 and $205 per share, around a 19x multiple. The stock had pulled back on a cocktail of fear: investors worried that AI chatbots like ChatGPT and SearchGPT could siphon users and ad dollars away from Google Search, while Alphabet’s announcement of $75 billion in 2025 capital expenditures raised alarms about margin compression. A DOJ antitrust ruling finding Google had illegally monopolized search added a regulatory overhang, though the market largely shrugged at the ruling itself; it was the AI disruption narrative doing the real damage.

The bears were wrong. Google Search didn’t collapse; AI Overviews actually drove search usage to record highs. Google Cloud became the headline story, with revenue surging 48% year-over-year to $17.7 billion in Q4, fueled by enterprise AI demand. Gemini crossed 750 million monthly active users, and the launch of Gemini 3 gave the “Google is behind in AI” narrative a quiet burial. All of a sudden (not really, it took years), Alphabet is the only company that owns the entire AI stack.

At ~26x forward earnings, Alphabet is no longer the “cheap mega-cap” it was a year ago. FASTgraphs tells the same story. Fully valued. So, I sold all of it at ~$315 for an 80% gain.

It is trading around 26x forward P/E with a 19% consensus growth in the next 5 years.

Potential returns through 2031 are 150% total, or 17% annually, using the historical 24x as the exit multiple. I think that is a reasonable assumption for a similar growth to the historical 18%.

Crazy for selling? Maybe.

What I Bought and My Current Portfolio

I used the proceeds from two sells and existing cash to fund a 40% position in a new company.

This stock has the potential to return 30-40% CAGR.

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ADSK: FV $243, fairly valued, 64% total, or 10% annual returns.

FICO: FV $1066, 28% overvalued, crazy returns.

The market has not been kind to software names. It was “sell first, ask questions later.” When that happens, opportunities present themselves, and I go fishing.

We've done ten companies so far. Today, I'll go through three more companies that are some of the largest and the best ones in the industry.

As a group, they are projected to grow sales at 14%, EPS, CFO, and FCF at 20% rates.

Also, their ROIC is at 33%, Net/Debt is 1.2, SBC is 10% of revenue, and CAPEX is 0.5% of sales while they are buying back shares with a buyback yield of 3.5%.

Filter used on Finviz: around 52-week lows, higher than 0% revenue growth next year, tech only.

This is a quick visual valuation test with FASTgraphs, the way I evaluate ideas.

If they pass, they are added to the watchlist.

INTUIT INC ()

Business Description:

Intuit, Inc. engages in the provision of business and financial management solutions. It operates through the following segments: Small Business and Self-Employed, Consumer, Credit Karma, and ProTax. The Small Business and Self-Employed segment offers QuickBooks financial and business management online services and desktop software, payroll solutions, payment processing solutions, and financing for small businesses. The Consumer segment includes do-it-yourself and assisted TurboTax income tax preparation products and services. The Credit Karma segment serves consumers with a personal finance platform that provides personalized recommendations of credit cards, home, auto and personal loans, and insurance products, and online savings and checking accounts. The ProTax segment refers to the professional accountants in the U.S. and Canada, who are essential to both small business success and tax preparation and filing. The company was founded by Scott D. Cook and Thomas A. Proulx in March 1983 and is headquartered in Mountain View, CA.

Comments:

Intuit has been trading at a premium valuation in the last 20 years with a P/E of 30 while growing 16% annually.

The valuation multiples expanded, especially after 2014, trading at 37x while growing 17%.

INTU had been trading around 20 P/E with 15% growth up until that point, dropping down to 15 P/E during the GFC.

Many businesses dropped down to single-digit multiples during that time, while INTU held up pretty well because it grew about 17% during that period.

It is hard to believe they are the same business, with almost the same growth of 16% while trading at 20x vs 37x multiples.

Now, that is a lesson in valuation. That is why I don’t like to pay high multiples, even for quality.

Potential return: The consensus estimate is 13% growth for the next 5 years, which is lower than the historical 15-17% range.

So, what multiple should be assumed?

To be more conservative, let’s use the 20 P/E for the 15% growth period, which means 1.3 PEG (20/15).

Using 1.3 PEG for 13% growth means 17 P/E.

Using 17 P/E means assuming multiple contraction from the current 22ish LTM P/E.

In that case, fair value would be 391 (17 P/E x 23 NTM EPS). That is about 12% overvalued from the current price of $437.

In that case, our expected return in 5 years would be 72% total, or 11% annually.

Verdict: Quality. Fairly valued. Could be a good entry point to start a position.

As we’ve been observing in this series, lots of mistakes that are related to valuation can be easily avoided.

1. Visual valuation tools like FASTGraphs make it easier to do just that.

2. That is why it is my favorite tool.

3. If you end up subscribing, please use my referral by clicking the link here. Use AFFILIATE25 for a 25% discount.

AUTODESK INC ()

Business Description:

Autodesk, Inc. engages in the design of software and services. Its products include AutoCAD, BIM Collaborate Pro, Revit, Civil 3D, Fusion 360, InfraWorks, Inventor, Maya, PlanGrid, Revit, Shotgun, and 3ds Max. The firm also offers product development and manufacturing software, which provides manufacturers in the automotive, transportation, industrial machinery, consumer products, and building product industries with digital design, engineering, and production solutions. The firm is also involved in digital media and entertainment, which consists of tools for digital sculpting, modeling, animation, effects, rendering, and compositing for design visualization, visual effects, and games production. The company was founded by John Walker in April 1982 and is headquartered in San Francisco, CA.

Comments: That is some premium valuation: trading at 37 P/E while growing 12% in the last 20 years.

That trough in earnings is due to switching from perpetual licenses to subscriptions.

Today, ADSK trades around TTM 24 P/E or 21 FWD P/E.

Potential return: ADSK is projected to grow at 12% in the next 5 years. Well, I’m not ready to pay the historical 37 P/E for that kind of growth even for God’s business.

Well, the good news is we don’t have to. I’m just going to slap a multiple that is in between the LTM and FWD P/E, which is 22x.

In that case, fair value would be 243 (22 P/E x 11.09 NTM EPS). This means it is fairly valued versus the current price of $243.

Using the 22x multiple, our returns would be 64% total or 10% annually.

Verdict: Quality company that is growing slowly. The market believes these things have longevity, which means they will continue to grow at a rate of around 10% for a long time. Could be a good entry point to start a position.

FAIR ISAAC CORP ()

Business Description:

Fair Isaac Corp. engages in the provision of decision management solutions. It operates through the Software and Scores segments. The Software segment includes pre-configured analytic and decision management solutions designed for a specific type of business need or process. The Scores segment focuses on business-to-business scoring solutions and services, business-to-consumer scoring solutions and services including myFICO solutions for consumers and associated professional services. The company was founded by Bill Fair and Earl Isaac in 1956 and is headquartered in San Jose, CA.

Comments: Dang, look at that chart! That is some multiple expansion. Does that make FICO overvalued straight away? That depends; let’s see.

29 P/E for 16% growth in the last 20 years.

Their EPS grew around 15%, while the price compounded at 18% due to multiple expansion.

FICO didn’t always have these multiples, as it traded at 17x for 15% growth coming out of the GFC between 2009 and 2014. That was a completely different FICO with perpetual licensing than what we have today.

Then the multiples exploded once they started switching to the cloud around 2014, with 25% and 38 P/E. It got crazy during COVID, reaching a peak of 95 P/E.

This means 1.5 PEG (38/25).

Potential return: Currently, FICO trades at 45 LTM P/E or 33 FWD P/E, while it is projected to grow at 26% in the next 5 years.

If we assume PEG 1, that means 26 P/E for 25% growth. In that case, we are giving up about 2 years of earnings because the current P/E already prices that in. You can see it below; our total return is basically zero until 2027.

Fair value would be 1066 using PEG 1 (26 P/E x 41 NTM EPS).

This means it is currently 28% overvalued (current price/fair value = 1372/1066).

If FICO grows as projected in the next 5 years, we are looking at 140% or 17% returns.

That is about 60% multiple compression (45 to 26) yet fantastic returns. Sometimes valuation doesn’t matter that much, but only if there is growth and the market awards it with a proper multiple.

I think it is rather conservative to use PEG 1 for a high-quality grower such as FICO. So, let’s just give FICO a 10% to 20% premium.

In that case, our fair values would be 1172 (1066x1.1) and 1290 (1066x1.2), indicating 17% and 6% overvaluation, respectively.

Potentially, FICO can trade up to 1.5 PEG, so I’m going to show 1.3 PEG (1.3x26=34 P/E) and 1.5 PEG (1.5x26=39).

34 P/E: 213% total, or 22% annual returns.

39 P/E: 260% total or 25% annual returns.

Verdict: Quality, growth, potentially slightly overvalued to fairly valued. It is definitely better to be conservative, especially with high-growth names, for a good margin of safety. Regardless, this could be a good time to buy a starter/small.

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Filter used on Finviz: 0-10% above 52-week low, higher than 10% EPS growth next year, tech only.

The broader goal of this series is to give you a tool to sharpen your investing process. In many ways, these are lessons in valuation.

COMMERCE COM INC (CMRC:US)

Business Description:

Commerce.com, Inc. engages in the development of software-as-a-service (SaaS) technology solutions. It offers SaaS platform for launching and scaling an ecommerce operation, including store design, catalog management, hosting, checkout, order management, reporting, and pre-integrations into third-party services like payments, shipping and fulfillment, point of sale, marketing, and accounting. The company was founded in 2009 and is headquartered in Austin, TX.

Comments: This microcap SaaS grew their EPS 13%, while their prices have come down significantly with an 11.8 P/E. Their LT Debt/Capital is 78%, as can be seen on the lower right side of the image below.

Potential return: Assuming almost no multiple change and 26% annual growth in the next 3 years, our returns would be 122% total, or 31% annual.

Surprisingly, even with the negative sentiment surrounding the SaaS names, analysts haven’t adjusted their EPS estimates for 2026 to 2028:

Verdict: 26% growth with 11.5 P/E. Interesting but a pass because they are too small for me. Someone needs to dig under the hood.

PC CONNECTION INC (CNXN:US)

Business Description:

PC Connection Inc is a provider of information technology solutions. It provides products such as computer systems, software and peripheral equipment, networking communications, and other products and accessories that company purchase from manufacturers and distributors. The company also offers services involving design, configuration, and implementation of IT solutions. It conducts business operations through three business segments namely Connection Business Solutions, Connection Enterprise Solutions, and Connection Public Sector Solutions. The company generates maximum revenue Connection Enterprise Solutions segment.

Comments: This is a mature small-cap company with at least 20 years of earnings history, though there is cyclicality. They grew at 10% while trading at 16 P/E during this period.

In addition, they paid dividends for a while (yellow line), then cut and restored them. Normally a red flag, but it could be beneficial if the dividend is put to better use, such as reinvesting in the business.

I see LT Debt/Capital at almost 0%. It looks like they are flush with cash.

OCF is more cyclical with booms and busts.

Same for the FCF growth.

Potential return: 25% total or 12% annual returns if we assume no multiple change from the current 17 P/E and the company grows 14% as projected.

Verdict: 16 P/E with 14% growth and cyclical. I see nothing to be excited about here. Easy pass.

COMMVAULT SYSTEMS INC (CVLT:US)

Business Description:

CommVault Systems Inc provides data and information management software applications and services. The firm sells software licenses and services to large global enterprises, small- and midsize businesses, and government agencies through both its salesforce and its network of reseller partners. Its software solutions include Cleanroom Recovery, HyperScale X, Air Gap Protect, Compliance, Cloud Rewind, and Clumio Backtrack. The company operates in the United States and exports to many other countries.

Comments: Check this out. This company went up 210% from early 2023 to late 2025.

And then it dropped 60% within a few months in late 2025 and early 2026.

Investors that held the stock through this period made about a 45% total return. I’m pretty sure they regret not selling it when it ran up.

The reason I'm drawing attention to this is not to make fun of the investors who held it during that run-up or, worse, those who purchased at the peak.

Now, let’s go back to the valuation. CVLT traded at 39 P/E while growing 19% in the last 20 years. That’s a rich valuation for a midcap.

The market definitely has a reason for valuing the company that way.

Potential return: This company trades at 22 P/E today with a 15% projected growth.

Assuming no multiple changes, a total return of 34% or 14% annually in the next 2 years.

Verdict: Rich multiple for a mid-teens growth rate. The market thinks this company is special in some way that I don’t know yet.

I will simply pass because there are large caps (a sign of quality) at comparable multiples and projected growth rates.

DOCEBO INC (DCBO:CA)

Business Description:

Docebo Inc is a provider of cloud-based learning management systems. The solutions provided by the company allow learning administrators to deliver scalable and flexible personalized learning experiences, from formal training to social learning to multiple internal, external, and blended audiences. The group provides an easy-to-use, configurable, and affordable learning platform with the end-to-end capabilities and critical functionality needed to train both internal and external workforces, partners, and customers. The majority of the revenue is derived from customers based in North America.

Comments: A Canadian small-cap that’s become profitable in the last few years.

Based on the TEV (Total Enterprise Value) and LT Debt/Capital, they appear to have plenty of cash.

Potential return: Assuming no multiple change from the current 14.5 P/E and if they achieve the 21% projected growth, we are looking at 76% total, or 21% annual returns, in the next 3 years.

Another thing to note, again, is that the EPS estimate has been going up while the stock price tanks.

Verdict: 15 P/E for 21% growth. Not an easy pass. Interesting. Need to look closer.

SALESFORCE INC (CRM:US)

Business Description:

Salesforce provides enterprise cloud computing solutions. The company offers customer relationship management technology that brings companies and customers together. Its Customer 360 platform helps the group deliver a single source of truth, connecting customer data across systems, apps, and devices to help companies sell, service, market, and conduct commerce. It also offers Service Cloud for customer support, Marketing Cloud for digital marketing campaigns, Commerce Cloud as an e-commerce engine, the Salesforce Platform, which allows enterprises to build applications, and other solutions, such as MuleSoft for data integration.

Comments: This is an A+ rated megacap company.

EPS and OCF estimates actually went up in the last month while the stock price went down. Strange. How to make sense of it?

The adjusted EPS chart isn’t that useful for CRM with a historical 95 P/E.

OCF, on the other hand, has grown by 25% over the last 20 years, reaching 32x.

We’ll use this as a reference for our return calculations.

Potential return: Assuming no multiple change from the 15ish P/OCF, our return would be 30% total, 14% annual, by 2028 if they can keep up a 12% growth rate.

It seems kind of logical to assume half the multiple (32x/2~15x) for half the growth of the historical rate (25%/2~12%).

Or in other words, we are likely to double our money in 5 years:

If you are more comfortable using the P/E, I got you covered.

Assuming no multiple change from the current 19.5 P/E, our returns would be 30% total or 14% annually if they achieve about 13% growth for the next 2 years.

Verdict: Since CRM is slowing down, they should be given a lower multiple than in the past. Analysts predict that it is business as usual without SaaSmegadon. Maybe they haven’t woken up and updated their models yet.

Perhaps this is an excellent opportunity to include CRM as a core holding in our portfolio.

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This is a quick pass; it’s how I generate ideas and filter names before doing deeper research.

I thought you might find a few of these useful.

This is the filter used to generate the list: 0-10% above the 52-week low, higher than 10% EPS growth next year, and tech only.

AUDIOEYE INC (AEYE:US)

Business Description:

AudioEye Inc is a software solution provider delivering immediate ADA and WCAG accessibility compliance at scale. Through patented technology, subject matter expertise and proprietary processes, it is eradicating all barriers to digital accessibility, helping creators get accessible and supporting them with ongoing advisory and automated upkeep. Trusted by the ADP, Tommy Hilfiger, A360 Media, Samsung, Landry’s and others. It helps everyone identify and resolve issues of accessibility and enhance user experiences, automating digital accessibility for the widest audiences.

Observation: This is a microcap with analyst estimates. That is a 27% growth with a 12.5 P/E.

Potential return: 77% total or 34% annually in 3 years, assuming no multiple change.

Verdict: Interesting but too small for my universe.

AVEPOINT INC (AVPT:US)

Business Description:

AvePoint, Inc. provides cloud-native data management software platform in North America, Europe, the Middle East, Africa, and the Asia Pacific. It also offers platform-as-a-service architecture to address critical operational challenges and the management of data to organizations that leverage third-party cloud vendors, including Microsoft, Salesforce, Google, AWS, Box, DropBox, and others; license and support; and maintenance services. AvePoint, Inc. was incorporated in 2001 and is headquartered in Jersey City, New Jersey.

Observation: 36 P/E for 20% growth. The description looks interesting, though.

Potential return: using growth = P/E, negative.

Verdict: Pass.

BLACKBAUD INC (BLKB:US)

Business Description:

Founded in 1981, Blackbaud provides software solutions designed to serve the “social good” community, including nonprofits, foundations, corporations, educational institutions, healthcare institutions, and individual change agents. Through M&A and organic product development efforts, the company has also moved into related areas outside core fundraising, notably into K-12 schools. The firm enables more than $100 billion in donations annually across a customer base in excess of 40,000 customers in over 100 countries.

Observation: PEG=1 stock, interesting description.

Potential return: assuming no multiple change, 20% total, and 9% annual.

Verdict: pass.

BADGER METER INC (BMI:US)

Business Description:

Badger Meter is a provider of water management solutions. The company’s solutions encompass measurement and control hardware, connectivity and communication, data visualization and software. Badger Meter has global operations, with the vast majority of revenue derived from the US.

Observation: One of those market darlings at high multiples. This wasn’t a fast grower with historical 13% EPS growth, so the market believes this thing has longevity.

Fun fact: The company has a more than 100-year history in water meters and is one of three primary competitors in a largely oligopolistic market structure. We estimate the three largest US water meter firms—Sensus (Xylem), Neptune (Roper), and Badger Meter—represent approximately 75%-80% of meter sales. Badger’s long history and track record of innovation and product quality lead to favorable brand equity, in our view. ~ Morningstar

Potential return: 43% total or 13% annualized, assuming no multiple change, which I’m not comfortable with. A multiple compression to 25x reduces the return to 20% in 3 years.

Verdict: Seems like quality, but pass.

BENTLEY SYSTEMS INC (BSY:US)

Business Description:

Bentley Systems is a software vendor that caters to civil engineers, constructors, and geospatial professionals by enabling design, simulation, and data management of infrastructure assets such as roads and bridges. While Bentley is smaller than many other design software companies, it is a market leader across a handful of specialty areas, including public works, electricity grids, and transit.

Observation: another market darling trading at a high multiple. This is a CAD (computer-aided design) software company like Autodesk and Dassault Systemes. Morningstar has a wide moat rating on them. This one is a potential forever stock growing at a slower rate.

Fun fact: Bentley Systems' best-in-class client retention shows very strong evidence of switching costs. Its annual revenue retention of above 98% and net revenue retention of around 110% are on par, or better than, many other wide-moat software companies under our coverage. ~ Morningstar

Potential return: 30% total, 14% annual.

Verdict: Pass. I want higher growth for that multiple.

Some of the ideas that I passed, such as Bentley Systems, might fit your portfolio. Or it might get you interested in CAD software names such as ADSK and DSY.

That was the goal, as one man’s pass can be another man’s portfolio holding.

Also, I have to say that the AudioEye company caught my eye (pun intended): 27% growth for 13 P/E.

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I was like, what the…

Then I looked up the day’s 52-week lows list on Finviz for the S&P 500:

Because of concerns about AI agents doing more of the work in the background and leading to fewer seats, the market no longer loves SaaS companies as much as it once did. Here is a solid take on this topic by Elliot.

SaaS multiples are compressing for almost all:

When I see something like that, I fire up my Koyfin and Fastgraphs for a quick valuation.

I am aware that some are not tech-related, like CHTR, or unrelated to concerns about AI, like TTD, which has its own set of problems.

Growth

Revenue growth closer to 10% is solid because this sets the floor for the potential returns without margin expansion or buybacks.

NOW and TTD stand out with growth rates above 15% when sorted by three-year consensus sales projections.

Again, 10% EPS or higher is better.

This time, INTU, TYL, TTD, and CRM stand out with rates near or above 15%, while WDAY and NOW lead the pack with rates above 20%.

Valuation

The table below is sorted by PEG because it provides a starting point for valuation while combining growth and valuation into a single figure.

While WDAY, TTD, PYPL, ADBE, and CRM have PEGs closer to 1.

Returns

Let’s kick off the returns list with the company that is projected to grow its topline the fastest: NOW.

Some explainers for the FASTgraphs image below.

The blue line is the historical multiple given to the company by the market: 88 P/E for a 46% CAGR growth rate in the last 10 years.

The P/E equals the growth rate, or P/E/G = 1, and is represented by the orange line with green shading beneath it. It is the Peter Lynch fair value line.

And of course, the black wiggly line is the daily closing price.

ServiceNow (NOW)

Intro by Buyside Digest:

ServiceNow is the indispensable backbone of enterprise workflow automation, commanding a premium valuation because it's the only platform fully capitalizing on the AI-driven shift in corporate efficiency.
The core investment thesis is built on an unbreakable ecosystem moat derived from extremely high switching costs and a near-perfect $120%+ Net Revenue Retention (NRR) rate. This means every customer not only stays but consistently spends more, funding the company's compounding growth. The true engine of the firm's future is its Generative AI suite, Now Assist, which is being integrated across every product line (ITSM, HR, Customer Service) to offer autonomous, human-level task completion. This AI layer is not a feature, it's a massive labor arbitrage tool that drives immediate, measurable Return on Intelligence (ROI) for clients by automating routine work. This has positioned ServiceNow to achieve a robust "Rule of 40" score (combining growth and profitability) and maintain 81% gross margins. While the 50x-60x forward P/E multiple appears lofty, it is justified by the company's superior profitability, $24 billion total backlog (RPO), and strategic partnerships with giants like NVIDIA and Microsoft that guarantee its role as an AI infrastructure leader for the next decade.

The historical multiple doesn’t help us that much because that is a very high P/E and growth rate, while the company trades around a 35 LTM P/E and is projected to grow EPS at around 20% in the next 5 years.

So, I’m going to have to slap a few multiples based on the consensus growth rate.

The 5-year consensus EPS growth rate is 20%, as can be seen below:

So, just going PEG=1 means 20 P/E for 20% growth:

In that case, returns would be 38% total or 7% annualized.

If we slap a Microsoft type of P/E of 30 on it, then we are looking at 107% total or 16% annual returns. NOW probably deserves a multiple like that since their clientele is exclusively large enterprises.

I’m usually not comfortable paying above a 30 P/E multiple, especially when growth is slowing down. I hate multiple compressions.

Workday (WDAY)

Intro by RGA Investment Advisors:

Workday offers mission-critical HR software for Fortune 500 companies and is steadily expanding into finance workflows. Revenue continues compounding double digits through both new logo wins and land-and-expand momentum. Historically, valuation and margin concerns kept us cautious, but new CEO Carl Eschenbach is driving meaningful operational streamlining. Early 2025 restructuring and ongoing margin progress demonstrate commitment to converting revenue growth into earnings growth. SaaS multiples have compressed materially in the AI era, creating attractive entry valuations. At purchase, shares traded at 25x current-year EPS, 18x EBITDA, and 17x FCF—all growing nearly 20%. Returns should track FCF growth with potential multiple expansion as Workday proves resilient to AI disruption. Upside is higher if growth reaccelerates, which we view as more likely than not.

The historical valuation chart, which shows 68 P/E for 31% growth over the preceding seven years, is again meaningless because the company is expected to grow by 20% over the next five years.

The good news is that WDAY currently trades at 20 P/E for that growth.

With a PEG=1 (20 P/E divided by 20% growth rate), this one has made things simple for us.

So, returns would be 150% total and 20% annualized for the next 5 years.

Again, I‘m not comfortable with this one due to the potential for multiple contractions due to slowing growth.

Takeaways

SaaS multiples are compressing across the board.

NOW is a fast grower with not-so-crazy multiples.

WDAY is very reasonably priced.

That’s it for today. See you on another one.

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