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Actionable insights — Buying more of this stock

Scaling into a position in equal tranches, reading a whole-book expected-return sheet against the weights, checking a model's inputs are like-for-like, and holding a portfolio through a style drawdown.
2026-SEP-20 · Compounding Quality (Substack) · Pieter Slegers / Team Compounding Quality · read ↗ · full analysis · transcript
How to read this page: this is a short transaction issue, but it prints two whole-portfolio images (expected returns and current weights) next to each other. Most of the reusable method comes from reading those two against each other. Insight 3 is a criticism of the post's own model. Written post, so no timestamps.

1. Open a new position at half size, then complete it with an identical second order

The repeatable method
  1. Decide the full target weight before the first purchase, then buy half of it.
  2. Write down the conditions for the second half: time held, further work done, and the thesis unchanged.
  3. Place the second order on the same terms as the first (same share count, same limit), so the add is not a reaction to price.
  4. Announce the add as the completion of a plan, and point back to where the plan was made.
Here: FFH.TO was opened on 16 August with 30 shares at a CAD 2,300 limit. It was named for an increase on 1 September, and this issue adds 30 more at CAD 2,300, taking the weight from ~2.7% to ~5.3%.
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2. Put the expected-return sheet next to the weights, and explain every mismatch

The repeatable method
  1. Rank the holdings by modelled expected return, and separately by current weight.
  2. Flag the large positions with low expected returns and the small positions with high ones.
  3. For each flag, write one line: why the size is right despite the number (quality, certainty, liquidity, tax), or why it should change.
  4. Direct new money to the high-return, small-weight positions first, unless a flag's written reason says otherwise.
Here: new money goes to FFH.TO, top of the sheet (18.6%) and 19th of 20 by weight. The mismatches left unexplained: EVO.ST is 3rd-largest (~7.95%) at 8.6%, GAW.L is 10th (~5.5%) at the book's lowest 6.7%, and III.L (18.1%, ~3.4%) and KPG.AX (30.5%, ~5.95%) are not added to.
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3. Check that every row of a comparative model uses the same metric

The repeatable method
  1. Read the footnotes on any model table before reading the ranking.
  2. List every row that uses a substitute metric (NPATA, revenue, book value, distributable earnings).
  3. For each substitute, ask whether it grows at the same rate as per-share earnings for that business. If not, take the row out of the ranking.
  4. Look for identical values across unrelated rows, which usually means a plug value rather than a forecast.
Here: "** For Fairfax Revenue was used." The top real-money idea (18.57%) is ranked on revenue growth, 26,825 → 43,708 (+63% in three years), while the neighbouring rows use EPS. Fairfax's own target is 15% growth in book value per share. CSU.TO, BN, III.L and KKR all show exactly 52.09% three-year growth.
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4. Treat heavy buybacks as a management valuation signal, but check the size and the price paid

The repeatable method
  1. Pull the share-count history: the multi-year CAGR plus the most recent month or quarter.
  2. Read an acceleration in buybacks as management saying the shares are cheap. Confirm it by comparing the average price paid with book value or intrinsic value.
  3. Check the buyback is funded by surplus capital, not by leverage or at the cost of the core business (for an insurer, reserve strength).
Here: Fairfax's share count fell from 28.6m (2017) to 21.4m (LTM), −25% or 3.4% a year, and 2.4% of shares were retired in June 2026 alone. "Fairfax is currently cheap (management believes this too as they are heavily buying back shares)."
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5. In a style drawdown, report the cash yield and restate conviction instead of changing style

The repeatable method
  1. Say plainly that results are below expectations.
  2. Replace the price chart with a cash measure: the portfolio's FCF yield against its own history.
  3. Only buy names you could explain yourself. Conviction you have borrowed from someone else will not last through a drawdown.
Here: "The current results are below our expectations. Quality has had a rough time recently." The book's FCF yield is ~5.9% "right now", against ~3.3% at the 2021 low and ~4.2% in 2015. "You can borrow someone's stock idea, but you can never borrow their conviction."
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Methods distilled from the archived Compounding Quality post for personal study. Not investment advice.