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Actionable insights — Best Buys March 2026

The toll-bridge test, the "own the data the models need" defence, and the discipline of writing a full bull case and still passing on price.
2026-MAR-01 · Compounding Quality (Substack) · Pieter Slegers · read ↗ · full analysis · transcript
How to read this page: each insight is a method used in this issue, written so it can be rerun on other names. Written post, so no timestamps.

1. Hunt the toll bridge — get paid a small fee on someone else's transaction

The repeatable method
  1. Look for a business that collects a small, mandatory fee whenever an activity happens, without carrying the risk or the capital of that activity.
  2. Confirm the "mandatory" part comes from something durable — regulation, a standard, a network everyone already uses — rather than from a superior product.
  3. Check that the fee is trivial to the payer relative to the value received: that is what allows price rises without churn.
  4. Then confirm the volume driver is structural, so the toll grows without new decisions being made.
Here: FICO ("a tollbridge on the American credit system" — a fee on every loan application), SPGI (paid on every bond issued and every S&P 500 ETF bought) and MSCI (a fee on $2.3trn of index-linked assets) are all described with the same phrase — "a classic toll-bridge business."
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2. Against an AI-disruption narrative, ask who owns the data

The repeatable method
  1. Separate the model layer from the data layer: models are commoditising, proprietary data is not.
  2. Identify data that cannot be recreated — decades of history, private commercial records, exclusive collection rights.
  3. Check the company is deploying AI on its own data to raise the value of that data, rather than defending against AI.
  4. Invert the conclusion where it holds: the data owner is a beneficiary of the AI build-out, not a victim of it.
Here: SPGI — a century of proprietary market data, private Platts commodity prices and CARFAX records, with Kensho applied on top. "They own the data that LLMs need to be accurate."
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3. Write the full bull case, then be willing to pass on price

The repeatable method
  1. Complete the moat and quality work even for names you expect to reject — the analysis is reusable when the price moves.
  2. Anchor the valuation on where the multiple came from, not just how far the price has fallen: a −30% move from >100x earnings is still expensive.
  3. Push the earnings base out to a year where the growth is already in the numbers and check the forward multiple there.
  4. State the pass explicitly, with the reason, and rank it against the alternatives: "there are more attractively priced Quality businesses elsewhere."
Here: FICO gets the full write-up — 90% of top US lenders, ROIC >50%, the AI fear dismissed as "ridiculous" — and is still passed over at 21.6x expected 2028 EPS. One month later, after a further fall, it enters the top five.
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4. Diagnose a growth slowdown: deliberate mix shift or lost competitiveness?

The repeatable method
  1. When growth decelerates sharply, ask first whether management chose it — walking away from low-margin business is a different fact from losing customers.
  2. Test the claim in the margin and mix data: profitability holding or rising while volume slows supports the deliberate reading.
  3. Look for the reinvestment the slowdown is funding (a new product line growing off a small base).
  4. Weigh founder ownership: an owner-operator can absorb a bad quarter to protect long-run economics in a way a hired manager often cannot.
Here: ADYEN.AS fell nearly 40% on 12% volume growth versus 30%+ historically — "Adyen is choosing Quality over Volume… walking away from low-margin transactions," with embedded finance growing 8x and the co-founder still running it with 3%.
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5. Buy the collateral damage — a name sold for its exposure to someone else's problem

The repeatable method
  1. When a sector derates, find the businesses sold off for partial exposure to it and quantify that exposure as a share of assets or earnings.
  2. Compare the price move with the exposure. A ~50% decline for a 7% exposure plus a two-cent miss is a mismatch worth investigating.
  3. Check the core drivers are intact and, ideally, at record levels.
  4. Ask whether the dislocation makes the business better off — a buyer with cash benefits when asset prices fall.
Here: KKR — down ~50% because "about 7% of its portfolio [is] in software" and Q4 EPS missed at $1.24 vs $1.26, while 2025 was a record fundraising year ($129bn) with $126bn of cash waiting to be deployed.
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6. Treat open-market insider buying as the confirming signal on a fallen quality name

The repeatable method
  1. After the quality and valuation work points to a buy, check whether insiders are buying with their own money in the open market.
  2. Give weight to clusters — several insiders buying, or buying that continues over months — rather than one token purchase.
  3. Read it as confirmation, never as the thesis: it corroborates that the people with the best information disagree with the market's fear.
Here: the same sentence appears twice — KKR: "Insiders are buying shares as we speak"; MSCI: "Just like for KKR, insiders are heavily buying shares today."
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7. In a cyclical trough, value the installed base rather than the current earnings

The repeatable method
  1. Split the business into new-unit sales (cyclical) and aftermarket/consumables (recurring).
  2. Note that a demand boom permanently enlarges the aftermarket, even as new-unit sales collapse afterwards.
  3. Buy when the market extrapolates trough new-unit earnings across the whole business and the moat is untouched.
Here: POOL — "All the new pools installed during the demand spike will need chemicals and maintenance products for the next few decades… Short term investors are seeing the earnings decline as permanent."
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Methods distilled from the archived Compounding Quality post for personal study. Not investment advice.