Tracking error measures how much an investment’s returns fluctuate away from a benchmark’s returns. It is most often used to evaluate index funds, ETFs, and benchmark-aware strategies. A low tracking error may suggest the investment has closely followed its benchmark; a high tracking error may suggest its path has differed more. The key is that tracking error measures consistency relative to a benchmark, not whether an investment is “good” or “bad.” Fidelity describes tracking error as the annualized standard deviation of return differences between a fund and its underlying index, emphasizing that it is about variability rather than performance alone (Fidelity).
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Tracking Error: Formula, Example & Interpretation
investing11 min read
Tracking error measures how much an investment’s returns fluctuate away from a benchmark’s returns.
11 min read
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What Tracking Error Measures
Tracking error compares the return pattern of an investment with the return pattern of a benchmark. The investment might be an ETF, mutual fund, separately managed account, or broader portfolio. The benchmark might be a broad stock index, bond index, sector index, or another reference point used to represent the exposure the strategy is trying to match or outperform.
The basic idea is:
Active return = Investment return − Benchmark return
Tracking error = Standard deviation of active returns
If an ETF is designed to track an index and its return difference versus that index barely changes from period to period, tracking error should be low. If that return difference swings around—sometimes far ahead of the benchmark and sometimes far behind—tracking error should be higher.
This distinction matters because tracking error is not the same as total return. It does not directly answer, “Did the fund make money?” or “Did it beat the benchmark?” It answers, “How variable was the fund’s return gap versus the benchmark?”
A fund can have:
- Low tracking error and underperform if it trails the benchmark by a similar amount each period.
- High tracking error and outperform if it beats the benchmark over time but does so unevenly.
- Low tracking error and high absolute volatility if the benchmark itself is volatile and the fund closely follows it.
Before using tracking error in any investing decision, the benchmark, strategy, time period, costs, and risks should all be considered.
This article is for educational purposes only and does not constitute financial or investment advice. Finelo does not recommend any security, strategy, or transaction. Investing involves risk, including possible loss of principal.
Tracking Error vs. Tracking Difference
Tracking error and tracking difference are closely related, but they answer different questions.
Tracking difference usually refers to the average performance gap between a fund and its benchmark over a period. If a fund returned 7.8% in a year while its benchmark returned 8.0%, the tracking difference would be −0.2 percentage points for that year.
Tracking error focuses on how much that gap varies over time. A fund could have a small average gap but still have a large tracking error if it frequently moves away from the benchmark in both directions.
| Pattern | Tracking difference | Tracking error | What it may suggest |
|---|---|---|---|
| Fund trails benchmark by about 0.10 percentage points every month | Negative | Low | The gap is consistent, possibly due to expenses or predictable implementation effects |
| Fund alternates between strong outperformance and underperformance | Could average near zero | High | The fund is not moving consistently with the benchmark |
| Fund intentionally owns different securities from the benchmark | Could be positive or negative | Potentially high | Active positioning may be driving benchmark-relative swings |
| Fund closely replicates the index with small deviations | Small | Low | The fund may be tracking the benchmark closely |
For an index ETF, both measures can be useful. Tracking difference shows whether the fund has tended to lag or beat the benchmark after costs and implementation effects. Tracking error shows whether the fund’s benchmark-relative path has been stable.
For an active fund, higher tracking error is not automatically a problem. It may indicate that the manager is making larger active choices relative to the benchmark. The educational question becomes whether those differences are understandable, intentional, and consistent with the fund’s stated strategy.
How to Calculate Tracking Error: Worked Example
Professional tracking error calculations often use daily returns and annualize the result. To make the arithmetic easier to follow, this example uses six months of hypothetical monthly returns.
Assumptions:
- Benchmark: hypothetical stock index
- Fund: hypothetical ETF intended to track that index
- Return frequency: monthly
- Units: percentage points
- Active return = Fund return − Benchmark return
- Annualization method: monthly tracking error × √12
- Standard deviation method: sample standard deviation
Step 1: Calculate active returns
| Month | Benchmark return | Fund return | Active return |
|---|---|---|---|
| 1 | 2.0% | 1.9% | −0.10 percentage points |
| 2 | −1.0% | −0.8% | +0.20 percentage points |
| 3 | 3.0% | 2.7% | −0.30 percentage points |
| 4 | 1.5% | 1.6% | +0.10 percentage points |
| 5 | −2.0% | −2.2% | −0.20 percentage points |
| 6 | 2.5% | 2.8% | +0.30 percentage points |
The active returns are:
−0.10, +0.20, −0.30, +0.10, −0.20, +0.30
Step 2: Find the average active return
Average active return
= (−0.10 + 0.20 − 0.30 + 0.10 − 0.20 + 0.30) ÷ 6
= 0.00 ÷ 6
= 0.00 percentage points per month
The average active return is zero, so the tracking difference over these six observations is approximately zero. However, the fund still moved above and below the benchmark in individual months.
Step 3: Square each deviation from the average
Because the average active return is 0.00, each deviation equals the active return.
| Month | Active return | Deviation from average | Squared deviation |
|---|---|---|---|
| 1 | −0.10 | −0.10 | 0.0100 |
| 2 | +0.20 | +0.20 | 0.0400 |
| 3 | −0.30 | −0.30 | 0.0900 |
| 4 | +0.10 | +0.10 | 0.0100 |
| 5 | −0.20 | −0.20 | 0.0400 |
| 6 | +0.30 | +0.30 | 0.0900 |
Sum of squared deviations
= 0.0100 + 0.0400 + 0.0900 + 0.0100 + 0.0400 + 0.0900
= 0.2800
Step 4: Calculate monthly tracking error
Using sample standard deviation, divide by n − 1. Here, n = 6, so n − 1 = 5.
Sample variance = 0.2800 ÷ 5 = 0.0560
Monthly tracking error = √0.0560 = 0.237 percentage points
Monthly tracking error is approximately 0.237 percentage points.
Step 5: Annualize the figure
Annualized tracking error = 0.237 × √12
= 0.237 × 3.464
= 0.821 percentage points per year
The annualized tracking error is approximately 0.82 percentage points.
This means the fund’s return gap versus the benchmark varied at an annualized rate of about 0.82 percentage points over this sample. It does not mean the fund underperformed by 0.82 percentage points. In fact, the average active return in this example was zero.
Now compare a second hypothetical fund:
| Month | Benchmark return | Fund return | Active return |
|---|---|---|---|
| 1 | 2.0% | 1.9% | −0.10 |
| 2 | −1.0% | −1.1% | −0.10 |
| 3 | 3.0% | 2.9% | −0.10 |
| 4 | 1.5% | 1.4% | −0.10 |
| 5 | −2.0% | −2.1% | −0.10 |
| 6 | 2.5% | 2.4% | −0.10 |
This second fund trails by 0.10 percentage points every month. Its tracking difference is negative, but its tracking error over these observations is essentially zero because the gap is stable. That is why tracking error should not be read as a simple performance score.
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Why Tracking Error Matters and What Can Cause It
Tracking error is most useful when a benchmark is central to the investment’s purpose. That includes many index ETFs, index mutual funds, enhanced index strategies, and active funds that are evaluated against a stated benchmark.
For a benchmark-tracking fund, lower tracking error may indicate that the fund has historically behaved more like its index. For an active fund, tracking error can help show how much the manager’s choices have caused the fund to move differently from the benchmark.
Tracking error is also used in other performance metrics. Charles Schwab explains that the information ratio divides active return by tracking error: (Rp − Rb) ÷ TE, where Rp is the investment return, Rb is the benchmark return, and TE is tracking error (Charles Schwab).
For example:
Fund return = 12%
Benchmark return = 10%
Active return = 12% − 10% = 2 percentage points
Tracking error = 4 percentage points
Information ratio = 2 ÷ 4 = 0.50
The information ratio can help frame whether benchmark-relative return was high or low compared with the amount of benchmark-relative variability. It still depends heavily on benchmark choice, time period, and the risks behind the return pattern.
Tracking error can come from several sources:
Fees and expenses. Expense ratios can create a recurring drag versus an index. A fund with a 0.20% annual expense ratio has a visible hurdle that does not apply to the benchmark calculation in the same way.
Sampling. Some index funds do not hold every security in the benchmark. They may use a representative sample to reduce trading costs or manage hard-to-trade securities. Sampling can create return differences.
Cash holdings. Funds may hold cash for redemptions, dividend processing, collateral, or operational needs. Cash can cause differences when the benchmark is assumed to be fully invested.
Rebalancing timing. Indexes rebalance according to published schedules, while funds must trade in real markets. Differences in timing and execution prices can affect tracking. Readers who want related background can explore Finelo’s educational guide on how to rebalance a portfolio.
Dividend and tax treatment. Total return indexes often assume dividends are reinvested according to index rules. Funds may face different dividend timing, withholding taxes, or reinvestment mechanics.
Trading costs and bid-ask spreads. Benchmarks are calculations; funds incur real transaction costs. These costs can become more visible in less liquid markets.
Securities lending. Some funds lend securities and earn revenue, which may offset some expenses. It can also introduce operational considerations.
Market stress and liquidity. During volatile periods, an ETF’s market price may temporarily diverge from the value of its underlying holdings. Illiquid securities can also make close tracking more difficult.
Intentional active decisions. Some strategies deliberately overweight or underweight sectors, countries, factors, maturities, or individual securities. In those cases, tracking error may reflect the strategy rather than a tracking failure.
How to Read Tracking Error in Context
Tracking error becomes more useful when it is read alongside the fund’s objective, benchmark, costs, holdings, and time period.
Start with the benchmark. A U.S. large-cap fund should not be casually evaluated against an international index or a bond index. If the benchmark does not match the exposure being analyzed, the tracking error may be mathematically correct but not meaningful.
Next, identify the strategy type. A plain index fund may be expected to have relatively low tracking error versus its stated index. A concentrated active fund may naturally have higher tracking error because it is not trying to match the benchmark closely.
Then compare time periods. A one-year tracking error figure can be distorted by unusual markets, index reconstitutions, liquidity events, or one-time strategy changes. A longer period can add perspective, although older data may not reflect current managers, holdings, fees, or market structure.
A practical reading workflow could look like this:
- Find the fund’s stated benchmark.
- Confirm that the benchmark matches the exposure being evaluated.
- Read whether the fund is passive, enhanced index, or active.
- Compare tracking error with tracking difference.
- Review fees, holdings, turnover, cash levels, and rebalancing practices.
- Look at more than one time period when available.
- Treat the number as one input rather than a final verdict.
Tracking error should also be interpreted within the broader investment mix. A single fund’s tracking error does not describe total household risk, asset allocation, concentration, or downside exposure. For related education, readers may pair this topic with Finelo’s guide to building a diversified portfolio or its explanation of portfolio beta.
Limitations and Common Misinterpretations
Tracking error is useful, but it has important limitations.
It is backward-looking. Historical tracking error describes what happened during a past period. It does not guarantee future tracking behavior.
It depends on the benchmark. A fund can appear well-controlled against one benchmark and poorly matched against another. Benchmark mismatch is one of the biggest reasons the statistic can mislead.
It can hide steady underperformance. A fund that trails its benchmark by the same small amount every month may have low tracking error. That does not mean the performance gap is irrelevant; it means the gap was consistent.
It can make active risk look like a defect. A higher tracking error may be expected for a fund that intentionally differs from its benchmark. The key question is what choices created the difference and whether those choices match the stated strategy.
It can be unstable in short samples. A few unusual market days or months can push the figure higher or lower. Short periods may not represent normal fund behavior.
It does not capture all investor costs. Expense ratios, bid-ask spreads, taxes, and the timing of purchases or sales can affect an investor’s realized experience. Tracking error alone does not measure those personal outcomes.
It may vary by calculation method. Daily, weekly, and monthly data can produce different tracking error estimates. Annualized figures may look comparable even when the underlying data frequency differs.
Common misreadings include:
| Misinterpretation | Better interpretation |
|---|---|
| “Low tracking error means the fund performed well.” | It means the return gap versus the benchmark was less variable. |
| “High tracking error means the fund is bad.” | It may reflect active decisions, benchmark mismatch, or implementation challenges. |
| “Tracking error tells me how much I gained or lost.” | It measures benchmark-relative variability, not total return. |
| “The lowest number is always best.” | Lower may matter for index replication, but not every strategy aims to hug a benchmark. |
| “Funds with different benchmarks can be ranked by tracking error alone.” | Different benchmarks can make the comparison unreliable. |
Frequently Asked Questions About Tracking Error
Is tracking error the same as volatility?
No. Volatility usually describes how much an investment’s own returns fluctuate. Tracking error describes how much the investment’s returns fluctuate relative to a benchmark. A fund can be volatile in absolute terms but still have low tracking error if its benchmark is similarly volatile and the fund follows it closely.
Is tracking error the same as underperformance?
No. Underperformance asks whether the investment did worse than the benchmark. Tracking error asks how variable the return difference was. A fund can underperform consistently and still have low tracking error.
What is a “good” tracking error?
There is no universal number that is good in every case. For a plain index fund, a lower figure may be more consistent with close benchmark replication. For an active fund, some tracking error may be expected. The figure should be interpreted in light of the benchmark, strategy, costs, and time period.
Why might an ETF not match its index exactly?
An ETF may differ from its index because of fees, sampling, cash balances, trading costs, dividend timing, taxes, securities lending, liquidity, or rebalancing differences. In stressed markets, ETF market prices may also temporarily diverge from the value of underlying holdings.
How is tracking error used with the information ratio?
The information ratio divides active return by tracking error. It is one way to evaluate whether benchmark-relative return was large compared with the variability of that active return. It should still be read carefully because benchmark choice and time period can change the interpretation.
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