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Actionable insights — How We're Going Forward

Running Buffett's ten-year test as a written exercise, auditing your own losses for a repeated cause, and turning the finding into a filter you can actually apply.
2026-APR-28 · Compounding Quality (Substack) · Pieter Slegers / Team Compounding Quality · read ↗ · full analysis · transcript
How to read this page: this is the most process-dense issue of the April series — the one where a review turns into a rule. The best material is the ten-year test applied name by name, the mistake-pattern audit, and the linearity filter. The last two insights are consistency checks: a rule that was announced and then not applied to one of its own candidates, and a sell decision that took months to execute. Written post, so no timestamps.

1. Run Buffett's ten-year-closure test as a written exercise, name by name

The repeatable method
  1. List every holding.
  2. Ask one question of each: if the market closed for ten years and you could not sell, would you still want to own this?
  3. Write the answer down. Allow "not sure" — it is the answer that carries information.
  4. Do it in one sitting so the standard is consistent across names.
  5. Compare the result to your existing conviction ranking. Agreement validates both; disagreement tells you which one you actually believe.
Here: all 18 holdings, answers published. 15 Yes; 3 "Not sure" — EVO.ST, JDG.L, NVO — which are precisely the three Medium convictions from the 19 April review. Two frameworks, same three names.
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2. Audit your losses for a repeated cause, not for individual errors

The repeatable method
  1. List the positions that went wrong, including ones you still hold.
  2. For each, write down the reason you bought it rather than the reason it fell.
  3. Look for a repeated purchase rationale across the list. That is the systematic error; the rest are noise.
  4. Convert the finding into a rule that would have blocked all of them.
  5. Re-read it before every subsequent purchase.
Here: "Every single time I bought a company not because I thought it was the highest quality, but because it was cheap, it ended up being a mistake (so far). Think about: TXT.WA, OTCM, NVO." The rule that follows: "We don't want good companies at cheap prices. We want wonderful companies at fair prices."
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3. Filter on the shape of growth, not just its rate

The repeatable method
  1. For each candidate, look at ten years of revenue and earnings growth as a series, not as a CAGR.
  2. Score the variability: how many down years, how wide the swings, whether the trend is smooth.
  3. Prefer the smoother of two businesses growing at the same average rate — it is worth more because the future is more forecastable.
  4. Apply the same test to the business model, not only the accounts: recurring revenue, contracted volumes, non-discretionary demand.
  5. Be explicit when you make an exception, and say what you are accepting in return.
Here: "Two companies both grow their earnings at 10% per year. Company A: steady and reliable. Company B: +30% one year, -15% the next, +20% the year after. Is Company A or B the most valuable? It's always Company A. The linearity of growth matters a lot." Operationalised as three tests — consistent revenue growth, smooth compounding, a resilient model. It explains four of the five buy candidates: fee businesses and toll-booths.
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4. Name in advance the three fundamentals you will track instead of the price

The repeatable method
  1. Choose two or three portfolio-level fundamental series and commit to them: look-through free cash flow, an aggregated quality scorecard, owner's earnings.
  2. Rebase each to 100 at a fixed start date and update annually, so the chart is comparable over time.
  3. Report them whether or not the share prices have followed.
  4. Use price only at the two moments it matters — buying and selling.
  5. If the fundamentals stall, the thesis is broken; if only the price stalls, it is an opportunity or a style drawdown.
Here: the three are named — the portfolio's free cash flow, the fundamentals scorecard, and owner's earnings growth — with the numbers behind them: look-through FCF compounding at 18.1% a year for a decade, and owner's earnings rebased to 100 in 2015 reaching ~605 by 2025 (a 19.7% CAGR). The justification: "stock prices always follow the evolution of the intrinsic value over time… we only worry about stock prices when we're looking to buy or sell."
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5. State the size of your investable universe, and read your own buy count against it

The repeatable method
  1. Count the names that have passed your quality screen and are eligible to be bought.
  2. Express it as a share of the global listed universe, so the filter's strength is visible.
  3. Then track how many of those eligible names are currently rated a buy.
  4. A high proportion is a statement about valuations across the whole quality cohort, not about your stock picking.
Here: "Our investable universe consists of 153 stocks. That's 0.4% of the 40,000+ publicly traded stocks worldwide." Nine days later, 49 of them are rated Buy — "this number has never been higher", i.e. roughly a third of the pre-filtered universe. Read together, those two numbers say the quality cohort as a whole has de-rated.
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6. Distinguish "not marked daily" as a benefit to the manager's attention from a benefit to the asset

The repeatable method
  1. When a business is praised for owning unquoted assets, separate two claims: the underlying assets are better, or the absence of a quote improves behaviour.
  2. Only the second is usually true, and it is a claim about psychology, not value.
  3. Ask who does the marking and how often, and what an independent transaction would show.
  4. Treat a smooth NAV as unverified, not as low risk.
Here: the same argument is made twice — KKR: "the fact that it owns private assets means that there's no daily price to obsess over… that's exactly how we think too"; and III.L: "3i's holdings aren't publicly quoted day-to-day. That lets them focus on what's really important: store openings, margins, long-term value creation." Note the tension with the 26 April KKR write-up, which spends a paragraph rebutting exactly the market's worry about privately-marked credit.
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7. Commit to announcing a switch before you execute it, and then check whether you did

The repeatable method
  1. State the rule: any new position that requires funding will be flagged, with the sale, before it happens.
  2. Keep the shortlist short enough that the reader can anticipate the pair.
  3. When the transaction comes, publish the size, the limit and the reasoning.
  4. Afterwards, compare the executed trade with the shortlist. If they do not match, say why.
Here: "If we would add one or more of these companies, we might have to sell a position to make room. When this would be the case, you'll be notified in advance." Two days later the 30 April transaction deployed $50,000 into three existing holdings (TOI.V, HGT.L, BRO) rather than any of the five candidates — so no sale was needed and the commitment was never tested. The candidates arrived later: III.L and GOOGL onto the watchlist on 7 May, FFH.TO bought on 16 August.
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8. Separate a ranking from a decision, and date both

The repeatable method
  1. When you name a "most likely sell candidate", record the date and the conditions that would trigger the sale.
  2. Revisit on a schedule. A verdict without a trigger has no expiry and will drift.
  3. If the position is still held three months later, either the conditions were never met or the verdict was soft — decide which.
  4. Publish the rating in the interim so the drift is visible rather than silent.
Here: "The most likely sell candidate: Judges Scientific." The supporting analysis is copied verbatim from 19 April, so no new information arrived in nine days — only a stated conclusion. JDG.L is then rated HOLD on the 7 May portfolio table and is still in the book in August.
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Methods distilled from the archived Compounding Quality post for personal study. Not investment advice.