Episode 31 Adjusted For Risk Podcast Show Notes: Zephyr StatFacts – Tracking Error

During my recent “Zephyr StatFacts” version of the “Adjusted for Risk” podcast I focused on tracking error. Below are the show notes and additional supporting information on why I believe tracking error should be one of the risk metrics you should leverage when conducting a manager search or due diligence.

Tracking Error

There are a lot ways to measure investment risk, whether measuring volatility risk using standard deviation, or downside risk using the Zephyr Pain index. Tracking error is a benchmark relative risk measure the measures the volatility of excess returns. Tracking error is 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.

Tracking error is one of my go to risk metrics. Locating managers who consistently add value are highly sought after and bring a lot of value to an investment portfolio especially during times of volatility and uncertainty. Lastly, if you prefer actively managed mutual funds, you want to make sure the manager is indeed active and not a “closet index fund” since you are paying higher management fees. 

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