Measuring index concentration by shared driver rather than weight, testing a capex boom by the revenue it must produce, and replacing something by claiming its own selling points.
1. Measure index concentration by shared driver, not by weight
The repeatable method
- Get the top-ten weight and compare it to the long-run average — that is the starting fact, not the conclusion.
- Then classify the top names by what actually drives their earnings, not by their sector label.
- Count how many share a single driver. That number, not the weight, is the concentration that matters.
- Check the historical analogues on the same basis, so you are comparing like with like.
- Then ask what your own portfolio owns that is exposed to the same driver through a different door.
Here: Christopher Bloomstran's point from the AGM. The top ten are "nearly 40%" against a 140-year average of 24% — but "in the past, even when the index was concentrated, the companies were usually not all tied to the same trend or idea." The Nifty Fifty spanned IBM, Coca-Cola, Xerox and Polaroid; in 2000 the top ten still held Walmart, Exxon Mobil and Citigroup, which "had nothing to do with the internet boom." Today: "8 of the 10 are now related to Artificial Intelligence."
Watch for
- "Related to AI" being an elastic category — the classification is the whole argument, so it needs to be specific about which earnings depend on AI capex.
- Your own diversification failing the same test. The 7 May buy list is full of software companies exposed to the same question.
2. Test a capex boom by the revenue it must eventually produce
The repeatable method
- Take the industry's annual capital spending as a single number.
- Decide what return that capital must earn to have been worth deploying — 10% is a reasonable hurdle.
- Convert to required profit, then, at a plausible margin, to required revenue.
- Compare with actual revenue today, and with its growth rate.
- The ratio between the two is the burden of proof. It does not predict failure; it tells you what has to happen.
Here: "The hyperscalers are expected to spend $700 billion on AI infrastructure in 2026 alone. To earn a 10% return, they would need to generate $70 billion in profit… If AI businesses earn a 10% profit margin, they would need $700 billion in revenue… All AI-related revenue was estimated at only about $40 billion in 2025." Roughly a seventeen-fold gap, stated without claiming it cannot be closed: "AI revenue is growing quickly, but it still is very early in the journey."
Watch for
- The margin assumption. Software margins are far above 10%, which would shrink the required revenue considerably — the arithmetic is a frame, not a verdict.
- Spending that is defensive rather than return-seeking. Some hyperscaler capex protects an existing franchise and never appears as new revenue.
3. To displace a default choice, claim its own selling points one by one
The repeatable method
- Write down, honestly, why people choose the incumbent. Do not strawman it.
- Take each reason in turn and show whether the alternative matches or beats it.
- Only then add what the alternative offers that the incumbent cannot.
- Attach a number to the conclusion, sourced rather than asserted.
- State the residual reasons someone would still pick the incumbent.
Here: the four reasons for owning the S&P 500 are listed first — diversification, low cost, winners run and losers drop out, and a strong long record — then each is claimed for BRK.B: 26 listed holdings plus 60+ wholly-owned businesses; "there are no management fees to own Berkshire Hathaway stock"; Coca-Cola since 1988 and See's since 1972; and a better long-run record. Then the three extras: businesses "hard for AI to disrupt", $300bn+ of cash, and Greg Abel buying back stock. The number comes from a named third party: Bloomstran's $560-580 against a $475 price.
Watch for
- The residual case, which is not stated here: a single stock carries key-person, succession and governance risk that 500 do not.
- "Diversified inside one holding" still being one line item in your own portfolio, with one price and one tax position.
4. Explain a valuation metric before you use it, and cross-check it with a second
The repeatable method
- State what the metric measures and why its construction matters.
- Give the current reading and the historical comparison that makes it meaningful.
- Cross-check with an independent measure, so the conclusion does not rest on one construction.
- Then link starting valuation to subsequent returns, which is the only reason the level matters.
Here: "The Shiller P/E Ratio, also called the CAPE Ratio, compares a stock's current price to its average inflation-adjusted earnings over the past 10 years. Using 10 years of earnings helps smooth out short-term market ups and downs… Right now, it's over 40. It approaches the valuation we saw before the 2000s dot com crash." Cross-checked on forward PE, and connected to outcomes via the JP Morgan starting-valuation chart. Framed by Klarman: "the price you pay for an investment determines its risk."
Watch for
- Structural objections to CAPE — index composition has shifted toward higher-margin, asset-light businesses over the decade being averaged.
- A high CAPE saying much about ten-year returns and almost nothing about the next twelve months.
5. Never compare two portfolios reported in different base currencies without stripping the currency out
The repeatable method
- Check the transaction currency and the reporting currency for each portfolio.
- If they differ, the return contains an exchange-rate move that has nothing to do with security selection.
- Restate both in a single currency before comparing.
- Report the currency contribution separately, so the underlying performance is visible.
Here: the non-American portfolio shows a 18.4% CAGR ($12,429.24) against the American 13.5% ($11,075.21) — but the European transactions are priced in euros and the profit-and-loss columns are converted to dollars, so a weaker dollar shows up as European skill. The text half-concedes it without naming it: "over time, I expect both portfolios to generate similar returns."
Watch for
- Per-transaction tables that mix currencies within a single row, as these do (euro purchase price, dollar current value).
- Attributing a factor conclusion to what is actually an FX move.
6. Fix the transaction size and the cadence, so the only decision left is what to buy
The repeatable method
- Choose a fixed amount per purchase and never vary it for conviction or for the market level.
- Buy on a regular schedule, at the open, announced in advance.
- Let the choice of which holding to top up be the single judgement call.
- Keep a per-transaction record with the date, price and running return, so every decision is auditable years later.
Here: every one of the 37 transactions across both portfolios, going back to October 2023, is $500 or €500. This month's is MOAT for $500 at $100.80 and GOAT.AS for €500 at €31.61. The published tables carry date, quantity, price paid, current price, current value and profit for each — the best purchase being the January 2024 multifactor buy at +69.35%, the worst the March 2026 minimum-volatility buy at -2.95%, the only loss in either portfolio.
Watch for
- Fixed amounts meaning smaller positions in whatever has risen — the opposite of adding to winners, and a real drag if the winners keep winning.
- The narrative and the transaction diverging: the issue argues at length that quality and small caps are mispriced, and then buys the same fixed €500 of wide moat it always buys.
7. Read your own worst-performing sleeve as a factor reading, not a mistake
The repeatable method
- Group holdings by the factor they express — quality, size, minimum volatility, multifactor.
- Look at returns by group rather than by holding.
- A whole factor lagging is a market condition; a single holding lagging within a working factor is a selection problem.
- Decide in advance whether a factor drawdown is a reason to add or to stop, and write the rule down.
Here: USMV is the American portfolio's weakest sleeve — two purchases up only
+3.85% and +2.63% — and
MVOL.L holds the only losing transaction in either book. Both are minimum volatility. Read against the
7 May Bloomberg chart showing quality down ~25% since Liberation Day while momentum is up ~9%, this is the same factor drawdown appearing in the ETF book — not a fund-selection error.
Watch for
- Adding to a lagging factor for years. "The factor is cheap" and "the factor is broken" look identical for a long time.
- The equal-weight and small-cap sleeves being the structural answer to this issue's own concentration complaint — worth checking whether they are sized as if you believe it.
Methods distilled from the archived Compounding Quality post for personal study. Not investment advice.