Performance Chasing: Why Buying Last Year's Top Fund Usually Backfires
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6 min read
You are scrolling through a list of funds, sorted by last year's return. One name sits at the top with a return that makes every other row look boring. It is tempting to buy it right now, before you miss the next great year. This habit has a name: performance chasing, buying whatever just had the best trailing return instead of judging it on anything else. The data on how often it works is not close.
Performance chasing is simple to describe and easy to fall into: you rank funds, ETFs, or reksa dana by their return over the last year (or three, or five), and you buy whichever one sits at the top. It feels like following the evidence. A fund that just returned 30% looks like proof that its manager, strategy, or sector pick is working right now.
The problem is that a single winning year mixes two things together: real skill and plain luck, including which sector happened to be in favor. Sorting by trailing return cannot tell those two apart, and the sector or style that carried a fund to the top of the list rarely stays in favor forever.
This is not a theory. S&P Dow Jones Indices tracks it directly in its U.S. Persistence Scorecard, which follows funds that ranked in the top quartile (the best-performing 25%) of their category and checks whether they stayed there.
The Year-End 2024 edition found that among U.S. domestic equity funds that ranked in the top quartile of their category as of December 2020, not one of them remained in the top quartile four years later, at the end of 2024. Zero out of however many started there. This finding has been reported consistently across multiple outlets covering the same S&P Dow Jones Indices data, including ETF Stream and the TKer newsletter, both summarizing the same underlying S&P Dow Jones Indices Persistence Scorecard report.
This is not a one-off, either. Older editions of the same scorecard found a nearly identical pattern in earlier periods: of the domestic equity funds that ranked in the top quartile as of March 2016, only a small handful, around 2%, were still in the top quartile two years later. Different starting years, same conclusion: a fund's rank against its peers does not carry forward in any reliable way.
There are two separate reasons a top fund tends to fall back toward the middle of the pack, and both matter.
The first is mean reversion in whatever sector or style was driving the return. A fund that led its category was very likely overweight the one corner of the market that happened to be hot that year, a specific sector, a small-cap tilt, a growth style. Sectors and styles rotate in and out of favor. The same overweight that produced an outsized gain when its sector was hot produces an outsized loss, or at least a below-average year, once that sector cools off or another one takes the lead.
The second is regression toward the average for anything with a large luck component. Fund returns are not pure skill. Even a genuinely skilled manager has years where market conditions do the heavy lifting and years where they work against the same process. A single top year is far more likely to be an unusually lucky year layered on top of ordinary skill than proof of a repeatable edge.
Persistence data explains why the strategy does not work on paper. Separate research shows what it actually costs real investors in practice.
Morningstar's Mind the Gap study compares two numbers every year: the total return a fund itself generated, and the return the average investor in that fund actually captured, based on the real timing and size of money flowing in and out. The 2025 edition, covering the ten years ended December 2024, found that the average dollar invested in U.S. mutual funds and ETFs earned an annualized 7.0%, while the funds themselves returned an annualized 8.2% over the same decade, a gap of 1.2 percentage points a year. Compounded over ten years, that gap meant the average investor captured roughly 15% less total return than simply holding the same funds the whole time would have delivered (Morningstar; also reported by CNBC).
Morningstar's own explanation for the gap is direct: investors tend to add more money to a fund right after it has posted strong returns, and pull money out right after it has posted weak ones. That is performance chasing in action, and the data shows it happening at scale, not as a rare mistake.
Here is a hypothetical example that shows the mechanics, not a real fund. Say "Fund A" ranks 1st out of 50 funds in its category in Year 1, with a standout return driven by a heavy bet on one hot sector. An investor sees that ranking and buys in at the start of Year 2, expecting more of the same.
| Year | Fund A's rank out of 50 |
|---|---|
| Year 1 | 1st |
| Year 2 | 34th |
| Year 3 | 22nd |
| Year 4 | 38th |
Nothing about Fund A changed overnight. What changed is that the sector bet driving Year 1's result stopped paying off, which is exactly the pattern the real persistence data above describes at scale, across hundreds of funds rather than one illustrative case.
None of this means active funds are never worth owning, or that last year's return is meaningless. It means a single year at the top of a ranking is weak evidence on its own. A few checks matter more:
Before buying whatever tops this year's return table, use NetWort's Explore screener to compare an asset's longer-run risk metrics, volatility, drawdown, and multi-year performance side by side, rather than relying on a single trailing-return column. A plain index-tracking fund like the SPDR S&P 500 ETF Trust will rarely top a one-year leaderboard, and that is close to the point: it also rarely falls off one the way last year's chart-topper often does.