Active Risk Basics
Active risk describes how much a portfolio’s returns deviate from a chosen benchmark. In practice, it answers a simple question: if the benchmark return is the reference point, how variable is the portfolio’s “extra” return over time?
Two common metrics translate that idea into numbers. Tracking error measures the variability of active returns (portfolio return minus benchmark return). Information ratio measures the size of average active return relative to that variability, so it combines “how much outperformance” with “how consistently it happens.”
For example, two funds can both show the same average outperformance versus a benchmark. One may achieve it with small month-to-month swings, while the other may swing widely and still average out. Tracking error captures the swing; information ratio captures the trade-off between swing and average outperformance.
How To Read Tracking Error
Know What It Measures
Tracking error is typically computed as the standard deviation of active returns over a chosen period. In plain terms, it quantifies how much the portfolio’s return minus the benchmark return tends to fluctuate.
What to do: identify the exact method used by the data source. Common choices include monthly active returns over 36 months, or daily active returns over a shorter window. The same strategy can show different tracking error values depending on the sampling frequency and window length.
What it looks like in practice: if a fund’s tracking error is 2%, active returns tend to vary around zero with a typical spread near that scale. If tracking error is 8%, active returns tend to swing more widely, meaning the portfolio may look “right” in some periods and “wrong” in others.
Relevant tools: compare tracking error with the distribution of active returns, not only the single number. A simple way is to review a time series of monthly active returns and note how often they change sign.
Connect It To Active Return
Tracking error alone does not tell you whether active returns are positive on average. To interpret it, pair it with the average active return (or the benchmark-relative return) over the same period.
What to do: compute or request the average active return over the same window used for tracking error. Then judge whether the average is meaningfully above zero relative to the variability.
What it looks like in practice: a strategy with tracking error of 6% and average active return of 0.5% per year may produce a low information ratio, even if it sometimes has strong months. A strategy with tracking error of 3% and average active return of 0.6% per year may show a higher information ratio because the average is larger relative to the swings.
Relevant tools: use consistent time windows across strategies and avoid mixing annualized and non-annualized figures.
Using Information Ratio Correctly
Interpret The Ratio As Efficiency
Information ratio compares average active return to tracking error. Conceptually, it answers: how much extra return is achieved per unit of active return variability?
What to do: treat information ratio as a measure of efficiency, not a guarantee of future results. A higher ratio indicates that positive active returns have outweighed negative ones more consistently over the measurement window.
What it looks like in practice: if two strategies have similar average active returns but one has lower tracking error, the lower-volatility strategy will typically show a higher information ratio. If both have similar tracking error but one has a higher average active return, the higher-average strategy will show a higher information ratio.
Relevant tools: check whether the ratio is computed using arithmetic or geometric return conventions, and whether it uses the same active return series as the tracking error.
Watch For Benchmark And Window Effects
Information ratio is sensitive to benchmark choice and the return horizon. A strategy can look more skilled relative to one benchmark and less skilled relative to another because the “active” component changes.
What to do: verify that the benchmark is an appropriate reference for the strategy’s investable universe and constraints. Also check the measurement window length; short windows can inflate ratios due to random clustering of returns.
What it looks like in practice: a strategy that had strong active performance for 12 months may show a high information ratio over that short window, even if the longer-term pattern is weaker. A 36- or 60-month window usually reduces the influence of a single favorable period, though it can also mask recent changes in process.
Relevant tools: compare information ratio across at least two windows (for example, 3 years and 5 years) to see whether the signal is stable.
Educational Case Examples
Example 1: Same Average, Different Variability
A portfolio manager targets a benchmark-relative return of about 1% per year. Fund A shows tracking error of 2% and an average active return of 1% per year, producing a relatively high information ratio. Fund B shows tracking error of 7% with the same average active return, producing a lower information ratio because the extra returns come with larger swings.
How to interpret: Fund B’s average outperformance may still be real, but the path is less consistent. A reader focused on predictability would weigh the higher tracking error more heavily than the average active return.
Example 2: Different Benchmarks, Different “Skill”
Two reports evaluate the same strategy. One report uses a broad equity benchmark; the other uses a sector-specific benchmark closer to the strategy’s holdings. The strategy’s active returns differ because the benchmark return is different, so tracking error and information ratio change.
How to interpret: a higher information ratio in one report does not automatically mean better decision-making. It can reflect a benchmark that makes the strategy’s exposures more “active” or less “active.”
Comparison Checklist For Readers
| Item To Check | Tracking Error | Information Ratio | What It Means For Interpretation |
|---|---|---|---|
| Benchmark choice | Active return variability depends on the benchmark | Efficiency depends on the same active return definition | Compare only when benchmarks match |
| Time window | Short windows can exaggerate variability | Short windows can exaggerate ratios | Check multiple windows for stability |
| Average active return | Not captured by tracking error alone | Captured in the numerator | Pair tracking error with average active return |
| Direction of active returns | Can be high even when average is negative | Can be low or negative if average is negative | Do not equate variability with skill |
| Return frequency | Daily vs monthly changes the estimate | Ratio inherits the same frequency choice | Compare like with like |
Step-by-step checklist for decision support:
- Confirm the benchmark used for active returns.
- Confirm the return frequency and window length used for tracking error and information ratio.
- Check average active return (or benchmark-relative return) over the same window.
- Compare strategies only when benchmark, window, and frequency match.
- Review the time series of active returns to see whether outperformance is clustered or consistent.
Common Mistakes To Avoid
One mistake is ranking strategies by tracking error alone. Higher tracking error can reflect larger deviations from the benchmark, not better outcomes.
Another mistake is treating information ratio as a stable trait. Information ratio can change when the strategy’s exposures change, when market regimes shift, or when the benchmark becomes a poorer reference for the holdings.
Readers also over-interpret small differences. When two information ratios are close, the difference may reflect sampling noise rather than a meaningful change in process.
Finally, people sometimes ignore the denominator. A strategy can show a high information ratio because tracking error is low, even if the average active return is modest. Without checking the numerator, readers may misread what is driving the ratio.
FAQ
Is Tracking Error The Same As Volatility?
No. Tracking error measures variability of active returns relative to a benchmark, while volatility measures variability of absolute returns. A portfolio can have low tracking error and still be volatile in absolute terms.
Can A Strategy Have High Tracking Error And Still Underperform?
Yes. Tracking error describes variability, not direction. If active returns are negative on average, the strategy can underperform while still showing large swings versus the benchmark.
What Makes Information Ratio Hard To Compare?
Benchmark choice, return frequency, and the time window can change active returns and tracking error. Comparing ratios without matching these inputs can lead to misleading conclusions.
Does A Higher Information Ratio Mean Future Outperformance?
It suggests better historical efficiency of active returns relative to variability over the measured window. It does not guarantee future results, especially when market conditions or the strategy’s exposures change.
Why Do Ratios Change When The Window Changes?
Short windows can be dominated by a few months of active performance, which can inflate or deflate the ratio. Longer windows reduce that influence but may lag changes in the strategy.
Author's Insight
Tracking error and information ratio are statistical summaries of active performance relative to a benchmark. Tracking error isolates variability of active returns, while information ratio combines average active return with that variability. Both metrics depend on how active returns are defined, which includes benchmark selection, return frequency, and the measurement window. Readers get the most reliable interpretation by checking those inputs and pairing the metrics with the time series of active returns rather than relying on a single number.
Key Takeaways
- Tracking error measures how much active returns fluctuate versus a benchmark; it does not indicate direction or skill.
- Information ratio measures average active return relative to tracking error, combining “how much” with “how consistently.”
- Benchmark choice and time window strongly affect both metrics, so comparisons require matching inputs.
- Use a checklist: confirm benchmark, confirm window and frequency, review average active return, and inspect active return patterns over time.
