Tracking error, like anything that contains the word error, sounds terrible. But in reality it’s simply a way to quantify investment risk—not place a value judgment on it. And it occurs in just about every type of equity portfolio, whether active or passive. To help address any misconceptions, let’s define tracking error and why it’s important.
What is tracking error?
Managed portfolios behave slightly differently from their benchmark indexes on a day-to-day, month-to-month, and year-to-year basis—even portfolios designed to perfectly track their benchmark. In other words, there’s a “wobble” in the portfolio’s performance in relation to its benchmark. Tracking error measures the degree of this wobble.
How do you calculate tracking error?
Tracking error is formally defined as the standard deviation of the difference between the returns of the portfolio and the returns of the benchmark—or the dispersion of the excess portfolio returns compared with its benchmark. It’s typically expressed both as an annualized number and as a percentage. So, for example, we could say a portfolio has a tracking error relative to its benchmark of 1% per year. For a portfolio with a normal distribution of excess returns and an annualized tracking error of 1%, we would expect its return to be within 1% of its benchmark return approximately two out of every three years.
Why is tracking error important?
Tracking error distills all the differences between a portfolio and its benchmark into a single number. It indicates to portfolio managers how close they are to the benchmark, which is important to know since the benchmark value contains the consensus view of a large number of intelligent market participants. It’s the “neutral” point from which the portfolio manager makes decisions. It also plays an important client communication role in that it sets appropriate expectations for how large the difference between the benchmark and the portfolio return will likely be.
What is a good tracking error?
A “good” tracking error depends on the type of portfolio. Active portfolio managers typically show a large tracking error because they seek excess return (alpha) through their active positioning versus the benchmark. With active managers, it’s common to see return differences of more than 2% in a month, which leads to an annualized tracking error of 5%, as seen below.
Passive managers, on the other hand, usually seek to demonstrate low tracking error—like the 0.5% shown below—with return differences coming from the frictions of implementation, trading and liquidity costs, imprecise cash flows, tax costs, and so on.
Source: Parametric. The above graphs show examples of tracking error visually, plotting monthly return differences, each corresponding to a different level of tracking error. In these graphs we assume no alpha and simulate the return differences from a normal distribution. Tracking error = 0.5%. This is an index fund. Return differences each month are very small.
The bottom line
Tracking error can be an important consideration when choosing an investment manager. The smaller the number, the more tightly bound the portfolio return should be to the benchmark return. However, the degree of tracking error an investor is willing to accept is neither “good” nor “bad.” It’s a personal choice that depends on overall investment objectives.
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A Custom Core® SMA allows investors to take charge of their passive mandates. Portfolios are held as separate accounts, giving investors the ability to customize them to their needs. Investors can select from a wide range of benchmarks and then tailor their exposure to incorporate their unique objectives.
Rey Santodomingo, CFA, Managing Director of Investment Strategy
Rey is responsible for all aspects of Parametric’s tax-managed equity strategies. As one of the primary strategists for Custom Core®, he works closely with taxable clients and advisors to design, develop, and implement custom portfolio solutions. Prior to joining Parametric in 2008, Rey was a vice president in product management at MSCI Barra. He earned an MA in financial engineering from the University of California, Berkeley, and a BS in chemical engineering from the University of California, Santa Barbara. A CFA charterholder, Rey is a member of the CFA Society of Seattle and a prior board member of the CFA Society of Seattle. He has also served as an adjunct instructor at Seattle University's Albers School of Business and Economics.
The views expressed in these posts are those of the authors and are current only through the date stated. These views are subject to change at any time based upon market or other conditions, and Parametric and its affiliates disclaim any responsibility to update such views. These views may not be relied upon as investment advice and, because investment decisions for Parametric are based on many factors, may not be relied upon as an indication of trading intent on behalf of any Parametric strategy. The discussion herein is general in nature and is provided for informational purposes only. There is no guarantee as to its accuracy or completeness. Past performance is no guarantee of future results. All investments are subject to the risk of loss.