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Actionable insights — Our Top Picks: 2025 Results

How to act on a year in which the value went up and the price went down: measure the gap, confirm it with other people's money rather than your own model, and separate a slowing metric from a slowing business.
2026-JAN-08 · Compounding Quality (Substack) · Pieter Slegers · read ↗ · full analysis · transcript
How to read this page: a performance recap, but four of the ten entries carry a real argument, and those arguments are the reusable part. The last insight is about the survey itself. Written post, so no timestamps.

1. Compute the value-versus-price gap explicitly, in percent

The repeatable method
  1. Measure the change in intrinsic value over the year using a per-share operating figure — owner's earnings, FCF per share, NPATA — not reported EPS.
  2. Measure the change in the share price over the same period.
  3. Subtract: the difference is how much cheaper (or dearer) the business became, independent of what it is worth in absolute terms.
  4. Only then ask whether the derating is explained by something real. If nothing operational broke, the gap is the opportunity.
Here: KPG.AX "grew its intrinsic value (Owner's Earnings) by 22.6% last year. Yet the stock moved in the opposite direction, falling -22.9%The result? The company became 45% cheaper!" The same arithmetic runs quietly under TOI.V (record acquisition spend, share price flat) and MELI (30%+ growth, +14% price, "became cheaper last year").
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2. Confirm a cheapness call with capital, not commentary

The repeatable method
  1. When you think a quality name is oversold, look for three independent parties putting money in at today's price.
  2. Rank them by information: an executive buying on the open market, then the company itself repurchasing, then a specialist investor with a matching style adding.
  3. Require the purchases to be open-market and recent — grants, options exercises and scheduled plans carry no signal.
  4. Treat the absence of any such buying as the more informative case.
Here: for KNSL — director Gregory M. Share bought $1.05 million; the board authorised a $250 million buyback, "equal to 2.7% of their Market Cap"; and "François Rochon, one of the best quality investors in the world, recently increased his stake. How many buy signals do you want?" The same test is applied in reverse to KPG.AX, where the lack of buybacks is investigated rather than assumed bearish — and turns out to be a capital constraint, in the founder's own words.
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3. When a cheap company is not buying back stock, find out why before concluding anything

The repeatable method
  1. Notice the absence — a founder-run business that calls its own shares undervalued and repurchases none is a contradiction worth resolving.
  2. Look for management's stated reason in a transcript or meeting, rather than inferring one.
  3. Distinguish the three possibilities: no capital, better uses for the capital, or no real conviction.
  4. "Better uses" is only credible if the alternative deployment is visible in the accounts — acquisitions completed, not a pipeline described.
Here: Brett Kelly, quoted directly — "We are currently limited by the capital available to take on opportunities to bring new firms into the group. We're overwhelmed with opportunities, so we haven't done any buybacks… If we had extra capital, we would be buying back shares enthusiastically and on a large scale." Slegers' inference is stated as an inference: "Brett Kelly clearly thinks the stock is undervalued at today's price."
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4. Separate a slowing metric from a slowing business — then check the aggregate

The repeatable method
  1. Identify the single metric the market is reacting to, and define exactly what it excludes.
  2. Find the aggregate figure that includes what the metric leaves out, and see whether the total is still growing.
  3. Decide which of the two is the real driver of value for this business model.
  4. If the excluded part is doing the work, state the condition under which the market's metric would start to matter.
Here: DNP.WA's like-for-like growth slowed and the market sold it. Slegers defines the term for readers, then supplies the aggregate: "In the first nine months of 2025, total revenue grew by 14.9%The limited sales growth of existing stores is more than offset by the sales growth from new stores." The 25 January update names the metric that actually matters for a rollout — new stores opened, a record 345 in 2025, almost one a day.
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5. Answer a disruption narrative with a mechanism, not with a denial

The repeatable method
  1. State the bear thesis in its strongest form and identify what would have to be true for it to hold.
  2. Locate the part of the business the technology actually touches, and the part it does not.
  3. Test the untouched part for the source of pricing power — switching cost, domain expertise, relationship, regulation.
  4. Note whether the company itself is treating the risk as live. A dedicated management call is evidence about the threat's seriousness, not about its outcome.
Here: on TOI.V — "Investors worry that VMS businesses are an easy target for AI disruption. The topic sparked so much interest that Constellation Software… held a conference call specifically to discuss the (potential) impact of AI. I personally don't believe AI will disrupt VMS companies." The mechanism is missing here and supplied two weeks later in the 22 January update: low churn, high pricing power, deep relationships — "AI can make it easier to code, and create software, but AI can't replace the industry-specific expertise and the human relationships that you need to sell and customize niche software."
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6. Read a reader survey as two different experiments, and report both

The repeatable method
  1. Rank the responses by popularity and by outcome, and publish both cuts.
  2. Compare what the crowd believes with what actually paid — they are usually different lists drawn from the same people.
  3. Check whether the most-held conviction did well. Conviction intensity is the variable most likely to be wrong.
  4. Keep the survey year on year, so the same names can be scored across a full cycle rather than one December.
Here: this issue ranks by popularity and produces a quality list (four of the ten are portfolio holdings) with three fallers; the 4 January issue ranks the same survey by return and produces a cyclical list the house disowns. The most-picked name of all, EVO.ST — with "nearly three times as many votes as the second pick" — fell 28.5%, while the list's best performer, TMDX at +82.9%, ranked seventh.
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Methods distilled from the archived Compounding Quality post for personal study. Not investment advice.