Trick or Treat? Index Funds Beat Their Benchmarks More Often Than Active Funds Over the Past Decade

Morningstar recently launched some new data points that slice active and passive funds into finer cuts. Formerly, any active fund—whether it was run by a swashbuckling portfolio manager or driven by quantitative factors—got the “active” tag, while the different flavors of passive funds were all labeled “index.”

Now there are some new data points that bring out the shades of gray. They are:

(For a more complete and insightful breakdown, see my colleague Dan Sotiroff’s recent article here.)

Though these data points aren’t available to Morningstar.com users just yet (they’re being launched initially in our institutional research product, Morningstar Direct), I’ve been playing around with them and think they impart some useful lessons.

Lesson 1: Cost Is a Spectrum, Not a Bright Line

It was probably already obvious to some of you, but there’s always been a range of fees among active and passive funds. Indeed, there are some cut-rate actives as well as some nosebleed-expensive index funds, and vice versa.

The new data points bring that into sharper relief. Here are the average fees of active US stock funds based on their Morningstar Category as well as their active/passive classification. (For purposes of this analysis, I’m going to exclude the two “Other” active/passive strategy types, as they’re home to relatively few funds.)

Here is the same thing, but this time it’s the asset-weighted average expense ratio by peer group and active/passive classification.

In general, the average active-systematic fund was cheaper than the average active-discretionary fund, and that difference was even more pronounced when weighted by net assets.

How about passive funds? To really bring out the cost differences among those funds, I’ve used the new “Passive-Index Tracking” data point together with our existing “Strategic Beta” tag (our term for what some in the industry self-servingly calls “smart beta”).

Now for the same thing, but this time, weighted by net assets:

These charts are a little harder to suss out, but long story short, the average non-strategic-beta index-tracking fund tended to be cheaper than the average strategic-beta index-tracking fund in the blend categories but not the others. Why? Most of the index-tracking funds in the value and growth categories are classified as “strategic beta,” including the biggest and cheapest value and growth funds like Vanguard Value Index and iShares Russell 1000 Growth.

Since the category-by-category analysis can be a little noisy, here’s the breakdown for all US equity funds, irrespective of peer group. I’ve derived the average fee for each cohort on both an equal-weighted and asset-weighted basis.

What you find, not too surprisingly, is that discretionary active funds cost the most, on average, but it steps down from there, with the average active systematic fund charging markedly less and an even steeper drop-off as you get to the average strategic beta and traditional cap-weighted index-tracking funds.

Weighted by assets, the average index-tracking, non-strategic-beta fund recently cost (0.05%) about one-twelfth as much as the average active-discretionary fund (0.59%).

Lesson 2: Lower Costs, Better Odds of Success

The average cost differences aren’t necessarily a revelation, but the impact becomes even more apparent when you tally up average success rates and excess returns by active/strategy fund type.

For this one, I’m going to skip the Morningstar Categories and present the aggregate average results for each grouping: Here are the trailing one-, three-, five-, and 10-year success rates (which I defined as the percentage of active funds that had beaten their style-specific index) for each group as of Sept. 30, 2026.

The cheaper groupings—starting with active-systematic—were likelier to outperform over most of these periods than the priciest, active—discretionary. That was even true of the staidest of the staid: index-tracking nonstrategic beta, where funds succeeded more often against the benchmarks than the funds paid to do so, active-discretionary.

How could that be? It boils down to index differences: The index-tracking funds might follow a bogy that topped the benchmarks I used to derive each fund’s excess return. For instance, over the 10 years ended Sept. 30, 2026, the S&P 500 gained 15.33% per year, while the Russell 1000 rose 15.05% annually. As such, an S&P 500-tracking fund likely outperformed the Russell index, provided its fees were low and it tracked well. And so forth.

I also calculated the groupings’ average excess returns versus their indexes. Here’s how that looked over the decade ended Sept. 30, 2026.

Keep in mind that these figures (and the success rates above them) do not reflect the results of dead funds and, thus, are survivorship-biased higher. But because there are far more active funds than passive funds, and given active funds are less likely to survive longer periods than index-trackers, that bias inflates the active-discretionary grouping’s results the most.

Notwithstanding that, the same basic trend holds: The cheaper groupings fared better, or at least lagged to a lesser degree, on average than active-discretionary funds. On the bright side for active, note that cheaper active-systematic funds did best of all, underscoring the degree to which lower fees can bend the odds in one’s favor, albeit not enough to generate a positive average annual excess return over this time frame on an equal-weighted basis.

Lesson 3: Active Doesn’t Win on Risk, Either

One argument active-fund proponents make is that active managers can do a better job of avoiding downside and managing risk than a fund tethered to a cap-weighted index can.

Formerly, we’d have evaluated this as a binary: all the actives versus all the passives. But with the new data points, we can measure differences across the range of strategies. To that end, I started with the most obvious difference between funds in these groups: the degree to which they deviated from an index, that is, tracking error.

As one would expect, active-discretionary funds exhibited the highest tracking error over the 10 years ended Sept. 30, 2026, followed by active-systematic and strategic beta funds (which were similar), and finally by cap-weighted index trackers.

Next, I turned to volatility, measuring differences in the average standard deviation of these funds’ monthly returns over the 10 years ended Sept. 30, 2026.

All told, active funds—both discretionary and systematic—were slightly more volatile, on average, than index-tracking funds over the decade.

Moreover, when I compared the groups based on their maximum drawdown over this period, I found that the average maximum peak-to-trough downturn was slightly shallower for the index-tracking funds than it was for the two flavors of active funds.

To bring things together, I calculated the average difference between each grouping’s aggregate Sortino ratio and the ratio of their style-appropriate benchmarks. (A Sortino ratio is similar to a Sharpe ratio; it divides a security’s excess return versus the risk-free rate by the volatility of its returns, but the Sortino ratio plugs in downside standard deviation only.)

In summary, the cheaper the grouping, the better its risk-adjusted returns when compared with a relevant benchmark, with nonstrategic index-tracking funds coming out ahead.

Takeaways

This is an admittedly basic analysis: It’s limited to trailing periods as of Sept. 30, 2026, excludes dead funds, and doesn’t consider every possible dimension of risk and return. Nonetheless, there are a few things we can take away:

• The fund universe is kaleidoscopic, and it’s therefore worth distinguishing between otherwise ostensibly similar funds. For instance, we saw that the average cost and outcomes of active funds varied depending on whether they were fully discretionary or quantitatively driven. The new data points help to highlight those differences.
• Those distinctions could make the difference between a good fit and a mismatch. Suppose you wanted the wide diversification of an index fund but weren’t sold on a purely cap-weighted indexing approach. In such a scenario, it could make sense to look at cheap active-systematic or strategic beta funds, which tend to strike a balance between pure indexing and full-blown active investing. A binary—active and passive—won’t convey that.
• Differences aside, there’s no escaping the primacy of cost. The cheaper a grouping was, the better its odds of success were. If you’re a devotee of one type of investing or another, that’s fine. But it’s still worth keeping this relationship front of mind. Start with cost, work your way from there.
• The juice sometimes isn’t going to be worth the squeeze. The active/strategy groupings arguably double as a complexity map. The least simple approach, by its nature, is active-discretionary. Why so? Agency risk, that is, human beings steer those strategies and so their imperatives, incentives, and tics become yours in a sense. Sometimes that will work in your favor, but it’s almost unquestionable they’ll take more work to research and monitor. It’s at least worth considering, especially if there are other comparable strategies that might be less complicated, if still entailing their own trade-offs.

Switched On

Here are other things I’m reading and listening to:

• Trying to figure out how much you’re paying for that semiliquid fund that invests in other funds? Good luck with that
• “What if this is the worst time to retire?” with Christine Benz and Ben Carlson
• Dave Nadig with a good explainer on “perpetual futures” and ETFs
• ETFs are kicking the bucket sooner (via Eric Balchunas at Bloomberg, via Andre Yapp)
• “Exponential Wealth: Centuries of Stock and Bond Returns” by Roger Ibbotson
• Dwarkesh Patel on why the frontier labs need to release all their models, including the dangerous ones (h/t Kunal Kapoor)
• Phoebe Bridgers “Lost Weekend” (which has grown on me)

Don’t Be a Stranger

I love hearing from you. Have some feedback? An angle for an article? Email me at jeffrey.ptak@morningstar.com. If you’re so inclined, you can also follow me on Twitter/X at @syouth1, and I do some odds-and-ends writing on a Substack called Basis Pointing.