The star manager myth: how a phantom stock-picker beat the market
- Robin Powell
- May 2
- 8 min read
Imagine a fund manager whose portfolio returned 38.9 per cent a year for nearly four years — almost double the NASDAQ — without ever making a single investment decision. That manager doesn’t exist. But the track record is real, and it’s the cleanest disproof of the star manager myth that any investor is likely to see.
Picture the factsheet landing in your inbox. The annualised return reads 38.9 per cent. The benchmark, the NASDAQ Composite, has done about half as well. The portfolio has beaten its benchmark in all but one rolling 52-week period since launch. There’s a chart with the kind of curve fund marketers fight over.
What would you do? Almost everyone would invest. Some would shift their pension into it. Others would tell their friends. A few would hand over their entire ISA allowance and call it a year well spent.
Now the twist. There is no manager. The chair is empty. No one assembled this portfolio with research notes, screening models or contrarian insight. The ‘star’ is a phantom — a hypothetical equal-weighted basket of 179 stocks, each one added on the day a leveraged single-stock ETF was launched against it, by nine separate sponsors making product-marketing decisions. No skill was applied. No view was taken. No risk was managed. And yet the track record outperforms anything most active managers have ever produced.
That phantom is the work of Jeffrey Ptak, the Morningstar analyst whose newsletter Basis Pointing has quietly become one of the better corners of fund-industry writing. He calls his creation ‘the accidental star manager’. The label is precise. The returns are real. The skill is fictional.
The star manager myth is the belief that exceptional track records are produced by exceptional people. Ptak’s phantom shows that they don’t have to be. If marketing decisions can accidentally produce a star, what does that tell us about the real ones?
The stock-picker who wasn’t trying
The phantom’s portfolio was built by a rule, not a manager. Every time a leveraged single-stock ETF launched, the underlying stock entered the basket on inception day, equal-weighted with the rest. By March 2026, the basket held 179 names, drawn from products launched by nine different firms.
This was not stock-picking. It was reverse-engineered momentum.
Two-thirds of the stocks added had already risen sharply before their ETF launched. The average pre-launch six-month return was 78 per cent. The median was 19 per cent. ETF sponsors weren’t anticipating future winners. They were chasing the same names every retail trader had already noticed, waiting for a stock to go vertical, packaging it as a 2x daily product, and selling the trade to people who wished they’d bought sooner.
What the basket did next was the surprise. Nearly half the stocks earned a positive return after the ETF launched, with the average winner up 40.6 per cent and the average loser down 26 per cent. The blended result was a 38.9 per cent annualised return between August 2022 and 10 April 2026. The top contributors were the names you’d guess: Nvidia (cumulative return of 895.6 per cent), Meta (402.6 per cent), Palantir (378.2 per cent), Coinbase (78.1 per cent). Strategy, the company formerly known as MicroStrategy, made the list too — at minus 4.3 per cent.
The Strategy story is where the mechanics get interesting. The basket bought the stock at an 8.3 per cent weight in August 2024, just as it began a 250 per cent surge. By the time the price collapsed in mid-2025, the position had been diluted to 1.4 per cent — not because anyone trimmed it, but because new ETFs kept launching and the equal-weighting structure mechanically reduced every old position to make room. The phantom ‘managed risk’ by accident.
That is what the basket actually did: ride momentum into stocks that had already moved, then dilute its exposure to whichever ones blew up next, purely because new products kept arriving. No human sat at the desk. No research note was written. The chair stayed empty. And still the star manager myth was earning compound interest.
Momentum in a lab coat
Strip the basket’s returns through a four-factor regression and the magic dissolves.
The market beta loading is 1.29. The momentum loading is 0.54. The adjusted r-squared is 0.82. Alpha is positive but statistically insignificant — meaning that, after accounting for the basket’s exposure to the broad market and to the momentum factor, the remaining ‘skill’ is indistinguishable from chance. The factor data come from the Kenneth R. French Data Library, the standard reference in academic finance.
Translated: the phantom’s returns came from buying volatile, recently surging stocks. That is the textbook definition of high-beta, high-momentum exposure. There is no edge to identify because there is no decision being made. The basket bought what was hot. Hot stocks kept being hot for a while. The basket was rewarded for owning them.
What happens when the easy momentum runs out is the part that matters. Of the stocks added to the basket via ETFs launched in 2025 and 2026, 60 per cent have lost money since inception. The average return for that newer cohort is 1.9 per cent. The phantom’s flair for picking winners has, in real time, started to evaporate.
Ptak’s own line on this is dry: ‘Was that prescience? No. It was mechanics.’
He goes further at the end, and this is the line that matters for the rest of the article: ‘Which in a way makes it not all too different from actual star managers.’
The star manager myth exposed — again
The phantom’s anatomy — momentum plus beta equals apparent genius — is not a curiosity. It’s the same shape that emerges when researchers take real star managers apart.
Take the most famous case. Frazzini, Kabiller and Pedersen, in their 2018 paper Buffett’s Alpha, showed that Berkshire Hathaway’s celebrated outperformance becomes statistically insignificant once you control for two factors: betting against beta (a tilt toward safe, low-beta stocks) and quality minus junk (a tilt toward profitable, growing companies). Add cheap leverage from Berkshire’s insurance float, and the alpha ‘genius’ shrinks to a rounding error. Subsequent factor analysis attributes roughly 87 per cent of Buffett’s excess return to identifiable factor exposures. Since 2003, his alpha has been statistically indistinguishable from zero.
That isn’t a takedown of Warren Buffett. It is a statement about how skill should be measured. If most of an outperformer’s record can be replicated by exposure to systematic factors, then most of what looked like skill was actually the factor doing the work. The same logic explains Terry Smith’s Fundsmith, whose recent stretch of underperformance maps neatly onto a period when the quality factor stopped paying. The manager didn’t lose his touch. The factor went on holiday.
The luck side of the equation is just as unflattering. A 2025 working paper by Jiali Gao and Juan Yao at the University of Sydney, Luck and Skill in the World of Diseconomies of Scale, finds that the bulk of variation in mutual fund alphas is driven by luck rather than ability.
Lucky funds pull in inflows. Their AUM grows past the point where the manager can deploy it well. They then underperform over the following year or two — exactly when the new investors are watching. The mechanism is reliable enough to predict from the data.
If skill were real and persistent, you would expect the winners to keep winning. They don’t. The S&P Dow Jones Indices US Persistence Scorecard for year-end 2024 found that, of the actively managed equity funds that ranked in the top quartile in December 2020, not a single one was still in the top quartile four years later. Zero. Among the broader active universe, the proportion that even managed to stay above the median for those four years was a rounding error.
The objection at this point writes itself: ‘But the ETF sponsors did pick stocks that went up.’ Quite. And two-thirds of those stocks had already gone up before the ETFs launched.
Chasing recent winners is the textbook definition of momentum, and momentum works until it doesn’t, which is precisely what the 2025-2026 deterioration shows. The earlier sponsors caught the wave. The later ones are catching what’s left.
Apparent skill is what the data look like before you pull them apart. Skill is what’s left over when you do. In aggregate, almost nothing is left over. The star manager myth survives mostly because that pulling-apart rarely happens in public.
'Apparent skill is what the data look like before you decompose them. Skill is what's left over when you do.'
What track records actually tell you
A track record tells you what happened. It does not tell you why it happened, and it certainly does not tell you whether it will happen again. The gap between what the numbers show and what they mean is where the star manager myth lives.
Treating past performance as evidence of future skill is the single most expensive mistake retail investors make, and it persists because the alternative — accepting that skill cannot be reliably identified in advance — feels like surrender.
It isn’t surrender. It’s the start of a more useful test.
Before committing money to any fund, ask what the track record is actually made of. Run it through the obvious factor lenses — market beta, value, size, quality, momentum. If the returns line up with those exposures, you are paying active fees for what is, at root, a factor tilt. The same exposure is available far more cheaply through index funds or rules-based factor ETFs.
The cost dimension matters more than most investors realise. The gap between an active fee of, say, 0.85 per cent and a tracker fee of 0.07 per cent is not a small operating expense. Over 25 years, on a £200,000 portfolio, it compounds into the difference between a comfortable retirement and a comfortable retirement plus a second car. Investors paying premium fees for what amounts to packaged factor exposure are paying a performance theatre premium — money handed over for the appearance of skill, not the fact of it.
Three practical moves follow. First, look at every fund you currently hold and ask which factors explain its returns. Second, where the answer is mostly market beta, momentum or quality, swap it for a cheap fund that targets the same exposure deliberately. Third, if you genuinely want a factor tilt, use systematic, rules-based vehicles that don’t pretend to be doing something else.
You don’t need to find the next star. You need to stop paying for the last one.
The fund manager who should give you confidence
The chair is empty, and that’s the point. The best track record of the past four years belonged to nobody. It was momentum, mechanics and luck — dressed up well enough to fool anyone who only read the factsheet. The real star managers, when you take them apart, tend to look much the same.
This is not a counsel of despair. It is the opposite. If apparent skill cannot be reliably identified in advance, you don’t have to try. Own the market at low cost. Let compound growth do the work. Spend your time on the parts of life that don’t have a Bloomberg ticker.
The next time someone shows you a dazzling track record, remember the phantom manager and the star manager myth he so quietly demolished. The chair was empty. The returns were real. And the lesson is the one it’s always been: you don’t need a star. You need a plan.
Resources
Barras, L., Scaillet, O. & Wermers, R. (2010). False discoveries in mutual fund performance: Measuring luck in estimated alphas. Journal of Finance, 65(1), 179-216.
Frazzini, A., Kabiller, D. & Pedersen, L. H. (2018). Buffett’s alpha. Financial Analysts Journal, 74(4), 35-55.
Gao, J. & Yao, J. (2025). Luck and skill in the world of diseconomies of scale. University of Sydney Business School working paper. Available at SSRN.
Ptak, J. (2026, April 14). The accidental star manager. Basis Pointing (Substack).
S&P Dow Jones Indices. (2025). U.S. Persistence Scorecard Year-End 2024. S&P Dow Jones Indices LLC.
Where to go from here
Everything the phantom stock-picker teaches — that apparent skill is usually factor exposure, that track records deceive, that costs compound against you — is explored in practical detail in How to Fund the Life You Want by Robin Powell and Jonathan Hollow. The second edition is published by Bloomsbury and written for UK investors who want a plan that doesn't depend on finding a star. Buy it on Amazon.
If you'd rather talk to someone than read about it, our Find an adviser directory lists professionals who've committed to evidence-based investing — no performance theatre, no star manager sales pitch.
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