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Actionable insights — ETF Portfolio Update: June 2026

Stacking two documented premia in one fund, classifying a moat before believing in it, and timing a factor by a relative-valuation ratio rather than by a forecast.
2026-JUN-25 · Compounding Quality (Substack) · Pieter Slegers / Team Compounding Quality · read ↗ · full analysis · transcript
How to read this page: the ETF issues are where the archive's method is at its most mechanical and therefore easiest to copy — a factor stack, an entry-point ratio, a fixed monthly amount, and a fully published transaction log. The last insight is the caveat: the fund's own screen does not produce the businesses the moat framing implies. Written post, so no timestamps.

1. Stack premia that are documented separately, and require both

The repeatable method
  1. Identify two return premia with independent long-run evidence and different causes — here a competitive-advantage premium and a size premium.
  2. Check the causes really are different; two factors driven by the same thing are one bet.
  3. Find (or build) an instrument that requires both conditions rather than blending them.
  4. Quantify each premium and note the source, so the claim can be re-tested later.
  5. Accept that requiring two conditions shrinks the universe — and check the universe left is still investable.
Here: "Since 2008, the Morningstar Wide Moat Index has beaten the US Market by 4% a year" and "small companies outperform the market by 3% per year." SMOT requires both: the Morningstar US Small-Mid Cap Index, filtered to wide or narrow moats, then to 115 names on momentum and price.
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2. Classify the moat by source before you accept that it exists

The repeatable method
  1. For any company you call durable, name which of the five sources applies: switching costs, intangible assets, network effects, cost advantage, or efficient scale.
  2. Write the specific mechanism in one sentence, naming the customer and what it would cost them to leave.
  3. If no source fits, the advantage is probably execution — which is real but not durable.
  4. Re-test the mechanism when the industry changes; a switching-cost moat can be dissolved by a new integration standard.
Here: one example each — FICO (switching costs: "banks have built their automated loan approval systems around them"), RMS.PA (intangible: "they'll never have the prestige"), V (network: the two-sided loop), COST (cost advantage: bulk buying → loyalty → volume → lower prices), UNP (efficient scale: "a market too small for it to make sense for competitors to enter").
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3. Test a growth forecast against the absolute money it requires

The repeatable method
  1. Convert the growth rate into an absolute amount of new revenue or profit.
  2. Compare that amount to something concrete the company already owns — a division, a product line, a competitor.
  3. Ask what would have to be invented or captured to produce it.
  4. Apply the same test in reverse to a small company: the amount needed is often trivially achievable, which is the size premium in one calculation.
Here: a $50m business needs $10m for a 20% year — "a few new enterprise contracts." AAPL needs $80bn, and "iPads and Macs combined generated $62 billion in revenue last year," so it "would have to invent an entirely new category bigger than both." Buffett in 1995: "now we need good big ideas."
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4. Time a factor with a relative-valuation ratio, not a forecast

The repeatable method
  1. Build one ratio: the valuation of the factor you want divided by the valuation of the thing it is measured against.
  2. Plot it over the longest history you can get and mark the mean.
  3. Act when the ratio is at an extreme, and size the action by how extreme it is — not by a view on when it reverts.
  4. Add a structural reason the mispricing persists, so you know what would end it.
  5. Keep buying on the schedule regardless; the ratio decides emphasis, not participation.
Here: SMID-cap forward PE relative to large-cap forward PE at 0.87 against a 1.15 average since 2004, the lowest of the whole series. The structural reason offered is coverage: 11 analysts per SMID security against 26 for an S&P 500 constituent, on a universe where small caps are 51% of listed securities by count. The action taken is small and scheduled: $500 into VB, €500 into IUSN.DE.
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5. Keep a transaction log that shows every purchase, including the losing one

The repeatable method
  1. Record date, instrument, quantity, price and amount for every purchase — never just the current weights.
  2. Mark each line to market so the entry price and the result sit side by side.
  3. Report the portfolio CAGR alongside, so the log and the headline number can be reconciled.
  4. Leave the losers in the table. A log with no red line is a marketing document.
Here: eighteen American transactions (final value $11,481.46, CAGR 14.8%) and nineteen non-American ones (€12,771.13, CAGR 18.7%), each line showing quantity and entry price. Exactly one is negative: the March 2026 minimum-volatility purchase at €75.72, now €72.74, −3.94%.
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6. Look through a fund's label to its actual holdings

The repeatable method
  1. Read the top holdings and the sector split before accepting the fund's description of itself.
  2. Ask which screen produced them — a value filter applied after a quality filter will systematically surface cyclicals.
  3. Check the concentration: what share of the fund is the top ten?
  4. Decide whether the exposure you are getting is the exposure you wanted, and say so if it is not.
Here: SMOT's top ten — Carnival, Acuity Brands, Masco, Gentex, Block, Bio-Techne, Royalty Pharma, Airbnb, Norwegian Cruise Line, Biogen, each 1.35-1.51%, so under 15% of the fund in total. Two cruise lines and a payments company at the top of a moat fund is the visible consequence of accepting narrow moats and then buying "the best-priced" survivors.
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Methods distilled from the archived Compounding Quality post for personal study. Not investment advice.