The finding, stated fairly#
Three investors: one who refuses to buy at all-time highs and waits for a 10% pullback, one with the worst possible luck who invests annually at the exact peak, and one too nervous to start.
The Archbridge Family Office analysed S&P 500 returns back to 1990 across three strategies — waiting for a 10% pullback, buying immediately at every new all-time high, and investing on random days. Buying at all-time highs produced the highest average returns, beating both the dip-waiting and the random-day approaches.
The mechanism is structural. All-time highs cluster during earnings growth and economic expansion. Healthy markets set a run of records rather than one followed by collapse.
Which reframes the “disciplined” approach: waiting for a dip is market timing. It’s a bet that prices fall, and it loses whenever a bull market simply runs longer than expected.
The cost of waiting is opportunity cost — missed gains, missed compounding, missed reinvested dividends, missed time. And missing a handful of the best days is devastating: Hartford Funds’ work puts missing the 10 best days over 20 years at more than half your total return, and the 20 best days at over 70%. Those days cluster right after crashes, when fear peaks.
The behavioural driver is loss aversion and its cousin regret aversion. We register losses far more intensely than equivalent gains, so a record high triggers a danger reflex even though highs are usually followed by more highs.
All of that holds. Now the caveat.
Every number above is a US number#
The Archbridge study is S&P 500. The Hartford study is S&P 500. The historical pattern of highs begetting highs is the S&P 500’s pattern.
That matters because the index those studies describe has changed shape. Three data points define the problem:
Breadth. T. Rowe Price notes the MSCI All Country World Index holds roughly five times the companies of the S&P 500 — 2,500+ against about 500.
Sector concentration. Rockefeller Capital Management puts technology at around 41% of the S&P 500, a concentration not seen since the dot-com bubble. Independent measures agree the index is near 38% Information Technology by weight in mid-2026, with the top 10 stocks at roughly 37–40%.
Where the returns came from. Guinness Global Investors highlights the gap between the market-cap-weighted S&P 500 and its equal-weighted version — a direct measure of how much recent performance rests on a handful of mega-caps. The cap-weighted index puts close to 41% of your money in about 11 stocks.
That gap has recently run the other way, incidentally. The equal-weight version outpaced the cap-weighted index by nearly five percentage points through 2026 so far, driven by lower exposure to the largest technology names. One year proves nothing. It does illustrate that the concentration cuts in both directions.
What the caveat does and doesn’t change#
It doesn’t change the timing conclusion. The dip-waiter’s problem is identical regardless of which index they’re refusing to buy. Cash loses to inflation with certainty; that’s not an American phenomenon.
It does change what you buy at that record high. “Invest consistently at all-time highs” and “put everything into a single S&P 500 fund” are two separate recommendations, and only the first is supported by the research above.
Being fair to the US index: it has outperformed the MSCI World over the long run, and that wasn’t luck — American companies genuinely dominated. But past dominance is what produced the concentration, and buying the index today means buying that concentration at its current level, not at the level that generated the historical record.
If a handful of mega-caps stumble, the index falls because of them specifically. That’s a different risk from “the market might fall”, and it’s not one the all-time-high research addresses.
The analogy that holds up#
Does it matter whether you planted a tree on the sunniest day of the year or the cloudiest? Not meaningfully. What matters is that you planted it and gave it decades.
That’s the honest version of the timing argument, and it survives the caveat intact. Even the worst-luck investor who buys every annual peak — including right before the 2000 and 2008 crashes — comes out ahead of the one sitting in cash, because every year’s contribution has decades to recover and compound. A market peak today routinely looks like a valley from ten or twenty years out.
Just plant in more than one field.
Summary — and what to do about it#
Act on the timing research. Adjust the allocation.
- Invest a fixed amount on a schedule, regardless of the level. Dollar-cost averaging removes the decision you’re most likely to get wrong.
- Recognise dip-waiting as an active market call. It’s the same bet as picking a top, wearing the costume of caution.
- Don’t put everything in one heavily concentrated index. ~41% technology and ~40% in the top ten is a specific bet, whether or not you chose it.
- Add global breadth. The MSCI ACWI holds about five times as many companies. That’s the natural counterweight.
- Consider equal-weight exposure if top-10 concentration bothers you — different risks, different tracking, and worth understanding before buying.
- Expect the record high to feel dangerous anyway. Loss aversion doesn’t respond to evidence, so make the decision mechanical instead.
The timing advice is right and the default portfolio it’s usually attached to is more concentrated than the research it cites.
Sources & further reading#
- Hartford Funds, “Timing the Market Is Impossible” — the cost of missing the 10 and 20 best days.
- IndexBox on J.P. Morgan’s all-time-high research — returns from record-high entry points versus any-day entries.
- AlphaEx Capital, S&P 500 sector breakdown 2026 — technology’s share of the index.
- Pensions & Investments, S&P 500 index concentration — top 10 holdings approaching 40%.
- Benzinga on the equal-weight versus cap-weight debate — ~41% of the index in about 11 stocks.
- ETF Database, “Navigating New Dynamics: Equal Weight vs. Cap Weight” — the 2026 performance gap between the two versions.
- BehavioralEconomics.com on loss aversion — why record highs trigger a danger response.
