Why Active Funds Underperform Their Benchmarks

Most active equity funds fall short of their benchmark over long periods, and the reasons are largely structural rather than a verdict on any individual manager. Before costs, active investors as a group hold something close to the market, so the average active rupee earns close to the market return. After fees, trading costs, cash holdings and constraints, the average active fund ends up below the index it is measured against.

That is the whole argument in two sentences. The rest of this piece unpacks each link in the chain, because the details decide how much of the gap is unavoidable arithmetic, how much is cost, and how much is an artifact of how the comparison is set up.

The arithmetic that starts the whole thing

Begin with a simple accounting identity. Every share of every listed company is held by someone. Split those holders into two buckets: those who hold the market in index proportions, and everyone else. The index holders, by construction, earn the index return before costs. Since the two buckets together own the whole market, the second bucket must also earn the index return before costs, in aggregate and weighted by money.

This is not a claim about skill. It is subtraction. Active management is a zero-sum contest before costs, because one manager’s overweight is another’s underweight. For every fund that owns more of a winning stock than the index does, some other active holder owns less.

Now introduce costs. Index tracking is cheap. Active management costs more: research teams, higher fees, more trading. Once you subtract the higher cost from the same pre-cost average, the average active fund sits below the index. The gap is roughly the cost differential, and it shows up reliably rather than occasionally.

Two things follow that people often miss. First, the argument says nothing about whether markets are efficient. It holds even in a wildly inefficient market. Second, it is about the money-weighted average, so a small manager running a concentrated book is not bound by it individually. The average is.

Where the costs actually sit

The expense ratio is the visible layer, but it is not the only one. A fair accounting of what stands between a fund’s gross performance and its reported net return includes several items.

• The total expense ratio. Management fee, administration, distribution commission where applicable, and taxes on those fees. This accrues daily and is already deducted from the net asset value you see. It is covered in detail in expense ratio impact on returns.
• Transaction costs. Brokerage, securities transaction tax, stamp duty, exchange charges and, importantly, impact cost. These sit outside the expense ratio and scale with how much the fund trades. Higher portfolio turnover means more of this drag.
• Cash drag. An open-ended fund holds some cash for redemptions and for opportunities. In a rising market, that cash earns less than equities, so it pulls the fund below a fully invested benchmark. In a falling market it helps. Over long periods with rising equity markets, the net effect has usually been a drag.
• Flow timing. Money arrives after good runs and leaves after bad ones. A manager receiving large inflows near a market peak has to deploy at those prices, and a manager facing redemptions in a fall may have to sell into weakness. Neither is a research failure.

None of these are scandals. They are the price of running a pooled, daily-liquid vehicle. But they are real, they compound, and the benchmark pays none of them.

The benchmark got harder to beat

For years, Indian funds were commonly compared to price indices, which count only price movement and ignore dividends. A fund receives the dividends its holdings pay, so measuring it against a price index handed it a quiet head start. Since 2018, Indian mutual funds have been benchmarked against total return indices, which reinvest dividends and are therefore a higher bar. The mechanics of that difference are set out in total return index vs price index.

Benchmark choice matters in the other direction too. A fund whose real style is mid-cap heavy, measured against a large-cap index, will look brilliant in mid-cap rallies and terrible in mid-cap drawdowns, and neither result says much about the manager. Choosing a benchmark that actually matches the mandate is the first honest step in any comparison, which is why benchmark selection for portfolios is a research question rather than an administrative one.

Capacity, mandate and the shrinking opportunity set

A fund’s flexibility narrows as it grows. A strategy that worked on a small asset base can stop working at scale, because the positions that generated the edge become too small to matter or too large to build without moving the price. Buying a meaningful weight in a less liquid company takes days of volume, and selling it in a stressed market takes longer.

Category rules add another constraint. Indian scheme categorisation defines what a large-cap, mid-cap or small-cap fund may hold, and imposes minimum allocations to the defined universe. Those rules exist to make categories comparable and to prevent style drift, which is genuinely useful for investors. The side effect is that a manager who thinks the whole category is expensive cannot simply exit it. The mandate keeps them invested in the thing they are being measured on.

Add the practical limits: single-issuer exposure caps, liquidity rules, and the reality that a large fund’s biggest positions increasingly resemble the index’s biggest positions. A fund can drift toward the benchmark without anyone deciding to let it. Active share is the standard way to measure how far that drift has gone.

How the comparison itself shapes the answer

Even after all the real effects above, some of the observed gap depends on how the study is built.

Survivorship. Funds that perform badly get merged or closed. If a study only measures funds that exist today, the weak ones have quietly disappeared and active performance looks better than it was. Serious comparisons include funds that died during the period, which is one of the design choices behind the SPIVA scorecard. The same failure mode in strategy testing is covered in survivorship bias in backtests.

Equal weight versus asset weight. Counting each fund once answers “what did a randomly chosen fund do”. Weighting by assets answers “what did the average rupee experience”. These are different questions and often give different answers.

Period sensitivity. A comparison ending at a market top and one ending after a sharp fall can tell different stories about the same funds. Rolling returns exist precisely because a single start and end date is a fragile way to judge anything.

Plan and share class. Direct plans exclude distribution commission, so they carry a lower expense ratio than regular plans of the same scheme. Comparing the wrong plan quietly shifts the result.

What this argument does not tell you

This is the part that most summaries skip, and it is the part that keeps the conclusion honest.

• It is not a claim that every active fund underperforms. The arithmetic constrains the money-weighted average. Individual funds sit across a wide distribution, and some sit well above the index for long stretches.
• It does not tell you which funds will outperform next. Observing that a minority beat the index after the fact is easy. Selecting that minority in advance is a separate and much harder problem, and past outperformance is weak evidence about future outperformance.
• It does not say indexing is right for you. That depends on your objective, horizon, category, tax position and constraints, none of which this argument addresses. Passive vehicles have their own trade-offs, described in index funds vs ETFs in India.
• It does not apply cleanly to every asset class or category. The strength of the cost drag, the depth of the market, and the availability of a genuinely investable benchmark all vary. A conclusion drawn from large-cap equity does not automatically transfer elsewhere.
• It says nothing about the investor’s own returns. Fund returns and investor returns differ, because investors buy and sell at different times than the fund’s start and end dates. A fund can beat its benchmark while many of its investors do worse than the fund.

The useful takeaway is not “active is bad”. It is that the bar is set by structure, so any claim of outperformance deserves the same scrutiny you would apply to a backtest: a matching benchmark, net-of-cost returns, a survivorship-corrected universe, and a window long enough that luck has had time to average out.

Related reading

• Portfolio metrics explained: the hub for how portfolio and fund performance is measured.
• SPIVA India explained: what the scorecard measures and the methodology choices behind it.
• Expense ratio impact on returns: how the fee layer compounds over long horizons.
• Active share explained: measuring how different a fund really is from its index.
• Comparing fund manager portfolios at scale: a workflow for looking across many funds at once.

If you research funds for a living, the practical work is assembling a comparison that is fair on all four axes at once, which is the problem mutual fund research tools are built to make tractable.

This article is educational. Altys Labs is not a registered research analyst or investment adviser, and nothing here is investment advice or a recommendation to buy, sell, or hold any security.