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.
1. Compute the value-versus-price gap explicitly, in percent
The repeatable method
- 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.
- Measure the change in the share price over the same period.
- Subtract: the difference is how much cheaper (or dearer) the business became, independent of what it is worth in absolute terms.
- 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").
Watch for
- Value measured on a metric the company chose. Owner's earnings and NPATA both add back real accounting charges — legitimate for a serial acquirer, and worth restating yourself once.
- A gap that keeps widening for years. At some point the market is pricing something the metric does not capture — which is the EVO.ST question.
2. Confirm a cheapness call with capital, not commentary
The repeatable method
- When you think a quality name is oversold, look for three independent parties putting money in at today's price.
- 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.
- Require the purchases to be open-market and recent — grants, options exercises and scheduled plans carry no signal.
- 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.
Watch for
- Buyback authorisations counted as purchases. An authorisation is permission, not execution — check the shares actually retired.
- Following a respected investor without knowing their position size or holding period.
3. When a cheap company is not buying back stock, find out why before concluding anything
The repeatable method
- Notice the absence — a founder-run business that calls its own shares undervalued and repurchases none is a contradiction worth resolving.
- Look for management's stated reason in a transcript or meeting, rather than inferring one.
- Distinguish the three possibilities: no capital, better uses for the capital, or no real conviction.
- "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."
Watch for
- A founder who is capital-constrained at the company level and also personally leveraged against his own shares — the combination that blows up in April 2026.
- "Overwhelmed with opportunities" as a permanent condition. It should show up as completed deals within a year or two.
4. Separate a slowing metric from a slowing business — then check the aggregate
The repeatable method
- Identify the single metric the market is reacting to, and define exactly what it excludes.
- Find the aggregate figure that includes what the metric leaves out, and see whether the total is still growing.
- Decide which of the two is the real driver of value for this business model.
- 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.
Watch for
- A store-opening programme that ends. Once the country is covered, like-for-like becomes the only metric there is.
- A company-level case that has quietly become a country-level one. The Dino defence rests on Polish convergence, which is a macro exposure.
5. Answer a disruption narrative with a mechanism, not with a denial
The repeatable method
- State the bear thesis in its strongest form and identify what would have to be true for it to hold.
- Locate the part of the business the technology actually touches, and the part it does not.
- Test the untouched part for the source of pricing power — switching cost, domain expertise, relationship, regulation.
- 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."
Watch for
- An assertion standing in for an argument. "I personally don't believe" is a position, not evidence — hold the author to the version with the mechanism attached.
- The same reasoning being applied selectively. It defends Topicus and Constellation, and is not applied to the software names outside the book.
6. Read a reader survey as two different experiments, and report both
The repeatable method
- Rank the responses by popularity and by outcome, and publish both cuts.
- Compare what the crowd believes with what actually paid — they are usually different lists drawn from the same people.
- Check whether the most-held conviction did well. Conviction intensity is the variable most likely to be wrong.
- 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.
Watch for
- Headline figures that do not reconcile between the two write-ups (+17.1% vs +16% for the index; +19.0% vs +17.3% for readers). Different cuts of the same year — neither number travels on its own.
- A survey whose respondents are subscribers of the person publishing it. The "wisdom of crowds" framing assumes independence the sample does not have.
Methods distilled from the archived Compounding Quality post for personal study. Not investment advice.