Tracking Error

Also known as the standard deviation of excess returns, tracking error measures how consistently a manager outperforms or underperforms the benchmark.

How Is it Useful?

Tracking error measures the consistency of excess returns. It is created by taking the difference between the manager return and the benchmark return every month or quarter and then calculating how volatile that difference is. Tracking error is also useful in determining just how “active” a manager’s strategy is. The lower the tracking error, the closer the manager follows the benchmark. The higher the tracking error, the more the manager deviates from the benchmark.

What Is a Good Number?

A good tracking error depends upon investor preference. If the investor believes markets are efficient and that it is difficult for active managers to consistently add value, then that investor would prefer a lower tracking error. Alternatively, if the investor believes that smart active managers can add significant value and should not be “tied down” to a benchmark, the investor would tolerate higher levels of tracking error.

What Are the Limitations?

Tracking error cuts both ways, measuring both periods of outperformance and underperformance versus the benchmark. An investor would prefer high tracking error if there was a high degree of outperformance but a low tracking error if there was consistent underperformance. Tracking error does not distinguish between the two.

What Do the Graphs Show Me?

Below are two very different active managers. The green bars represent months of outperformance. The red bars are months of underperformance versus the benchmark. Tracking error is created by taking the standard deviation of the red and green bars.

We can infer just how active a manager’s strategy is from the below information. The small performance deviations seen in the upper graph likely indicate the manager is only making small bets away from the benchmark. However, in order to generate the large monthly performance differentials (for better or worse) in the lower graph, the manager is likely taking big, active bets away from the benchmark.

What Are Typical Values?

There isn’t a typical value for tracking error. Instead, there is a wide spectrum of products available in every asset class ranging from purely passive to very active. Theoretically an index fund should have a tracking error of zero relative to its benchmark. Enhanced index funds typically have tracking errors in the 1%-2% range. Most traditional active managers have tracking errors around 4%-7%. Those active managers who are willing to take bigger bets away from an index might exhibit tracking errors in the 10%-15% range. Absolute return, benchmark-agnostic strategies could have even higher tracking errors.

Math Corner: 

Calculating tracking error is a three-step process. First, an excess return series is created by calculating the periodic differences between the manager and the benchmark. Next, the mean of that excess return series is calculated. Finally, the dispersion of individual observations from the mean excess return is calculated.

 
 

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