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Actionable insights — Adding to Three High-Quality Stocks

Adding to what you already own, valuing a derating by the multiple rather than the drawdown, and separating a policy headline from the business it is supposed to hurt.
2026-FEB-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. When conviction rises, add to what you own before you add a name

The repeatable method
  1. Treat a sector-wide derating as a signal to re-check position sizes, not to widen the list. Every dollar here went into an existing holding.
  2. Rank the existing holdings by the gap between what has changed in the price and what has changed in the business.
  3. Size the add to what the conviction supports, and publish the amount, quantity and limit price before the order goes in — so the decision cannot be revised after the fill.
  4. Accept that this concentrates the book. Two of the three adds here are the same underlying business model (Constellation and its spin-off Topicus).
Here: $50,000 across three names already owned — CSU.TO $15,000 at CAD 2,600 (Q 8), V $20,000 at $325 (Q 60), TOI.V $15,000 at CAD 105 (Q 180). Three weeks later the 22 February issue adds another $25,000 to Constellation.
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2. Measure a derating by the multiple, not by the size of the fall

The repeatable method
  1. Write down the multiple at the prior peak and the multiple today; the ratio between them is the derating.
  2. Separately check what earnings or cash flow did over the same window — a multiple halving on rising cash flow is a different fact from one on falling cash flow.
  3. Only then look at the drawdown. "Largest drawdown ever" is a fact about the chart; the multiple is the fact about the price you pay.
  4. State it in units a reader can check without a model — times cash flow, or price-per-dollar-of-cash.
Here: CSU.TO — "In 2024, investors were willing to pay 35 (!) times the cash flow… And today? Just 16.2x." TOI.V — same price as 2021, free cash flow doubled, so "the valuation halved… like buying the same house that cost $200,000 five years ago for $100,000 today."
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3. Test a "cheap to build" disruption thesis against what customers actually pay for

The repeatable method
  1. Write the bear case in one sentence — here: AI makes software nearly free to build, so niche software has no protection.
  2. List the components of the customer's switching decision: the data held, the regulations encoded, the workflow integration, the cost and risk of migration.
  3. Ask which of those the new technology actually removes. If it only removes development cost, and development cost was never the barrier, the thesis fails.
  4. Then invert: does the same technology help the incumbent? Faster development, higher margins, and cheaper acquisition targets are all upside from the same shock.
Here: Michael Gielkens (Tresor Capital), quoted in full: "It is not the development costs, but mission-critical data, complex regulations, deep integration into work processes and high switching costs that form the moat… AI therefore does not pose an existential threat, but rather a productivity lever that can increase margins and, through lower market valuations, even create new acquisition opportunities."
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4. When rebutting a consensus fear, cite named others who have done the work

The repeatable method
  1. State your own position in one line so the reader knows where you stand.
  2. Then hand the argument to identifiable third parties with different vantage points — an operator, a practitioner, a fellow investor — rather than restating your own view at length.
  3. Keep their reasoning verbatim, so the reader can weigh the argument rather than your confidence in it.
  4. Use disagreement between the sources as a feature: if independent people reach the same conclusion by different routes, that is evidence.
Here: on CSU.TO Slegers writes only "I don't believe this," then quotes Kosta Ristovski (reliability and data-format arguments), Arne Ulland, and Michael Gielkens (moat-economics argument) at length.
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5. Separate a policy headline from the business it is supposed to hurt

The repeatable method
  1. Identify precisely which line of the income statement the proposed rule touches, and for whom.
  2. Ask whether the company in question sits on that line at all — a network that takes a fee per transaction is not the lender earning the interest being capped.
  3. Assess the probability the rule survives, and say on what grounds (legislative, legal, constitutional) — not just "it won't happen."
  4. If both answers point the same way, treat the fall as a discount rather than an impairment.
Here: V and MA fell on a proposed 10% cap on credit-card interest rates. Slegers' answer is legal, not economic: "I think Trump will never be able to push this through in court."
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6. Build the expected return out of disclosed parts, not a target price

The repeatable method
  1. Start with what the company already hands you: the dividend yield plus the percentage of shares retired each year — the shareholder yield.
  2. Add the earnings growth you are willing to underwrite.
  3. Do not add a multiple assumption. If the sum already clears your hurdle, re-rating is optional upside rather than the thesis.
  4. Sanity-check with a doubling time so the number is intuitive.
Here: V — 0.8% dividend + 2.3% buyback = 3.1% shareholder yield, plus 12% expected earnings growth: "you could expect the stock to double every 5 years." The model's expected return is 15.9% a year.
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Methods distilled from the archived Compounding Quality post for personal study. Not investment advice.