Investors and traders use metrics to measure risks associated with their investments. These metrics can be based on either simple calculations or more advanced statistical models. As a result, a huge number of metrics have emerged. The authors discuss the drawdown metric, a measure of downside returns over time, because it is intuitive and easy to understand.

The authors show that drawdown is calculated by comparing the value of a cumulative return with a previous peak that is the maximum cumulative return. They also show that the advantages of drawdown include ease of calculation—only historical data are required—and no assumptions about the underlying distribution of returns. Furthermore, the historical drawdown period is variable because it depends on the timing of the peak. As a result, drawdown reflects only factual returns from a past period and has no direct predictive value, which other metrics, such as volatility, imply.

The traditional drawdown approach compares the returns of only a single asset or portfolio. The alternative metric that the authors recommend compares a portfolio drawdown with another benchmark. Comparison with a benchmark is sensible because asset returns are commonly compared with a benchmark return—for example, the information ratio metric. Moreover, the rise in passively managed strategies (e.g., index tracking) has increased the importance of comparing returns with a benchmark. In addition, when there is a limit to risk taking, such a common and widely understood metric as drawdown ensures a common understanding on risk limits, resulting in an industry consensus on interpretation of the metric. Finally, the wide use of the Sharpe ratio (absolute) and the information ratio (relative) in the industry shows the need for both absolute and relative performance metrics.

Because performance metrics are a central piece of information for many investment professionals, this research will appeal to many people working in finance, especially those involved in passive trading strategies.

The authors assess three methods for determining a benchmark-relative drawdown. The first method compares the investments and benchmark drawdown. This method has its shortcomings owing to the choice of time period. Because there is no direct link between portfolio and benchmark drawdown, this approach can result in false conclusions. The second method uses the portfolio’s cumulative return over the benchmark. Although this approach is easy to calculate, it depends entirely on the choice of start date—and every new start date results in a different outcome—whereas the traditional drawdown metric depends on the high-water mark. The third method uses the ratio of cumulative excess return to maximum cumulative excess return (alpha) to define a high-water mark. This approach also suffers from the choice of start date, and when the time horizon is increased, the sensitivity of the measure decreases.

The authors suggest their own drawdown methodology, which is not sensitive to scale and is easy to understand. Their method uses cumulative returns in series, starting when the portfolio underperforms the benchmark and ending once the portfolio has captured the benchmark.

One of the advantages of this approach is that each new period starts with a drawdown of zero percent, so no scale issues arise. Furthermore, there is a matching of portfolio and benchmark drawdown dates. In addition, this measure reflects the behavior of investors, who tend to buy at peaks. Finally, their method provides simplicity of both presentation and interpretation.

The authors show that using the traditional drawdown measure on their sample results in different conclusions than under their approach. They also show that for their sample, traditional drawdown and active drawdown have a low correlation and thus provide different information.

Performance metrics play an important role in performance analysis and reporting. They provide useful shortcuts for analysts to avoid lengthy analyses and in-depth research. The drawback of these shortcuts is that analysts may not be fully aware of the shortcomings of some metrics. This research provides a good introduction for creating awareness of the strengths and limitations of performance metrics. It also shows that although some metrics may look the same (traditional drawdown and active drawdown), they may convey completely different information. When to use certain metrics—and when not to use them—is a topic that investment professionals should be more aware of, because the general sector approach tends to be a cookie-cutter method.

One of the main drawbacks of working with samples remains the issue of choice. In this article, the examples focus on a global, actively managed portfolio and benchmark, which exhibit a certain amount of volatility, as one would expect. In addition, the choice of time period affects how appropriate various metrics are. Some metrics perform better than others in high-volatility environments with nonsymmetrical returns. In this research, it would have been interesting to assess under what circumstances the traditional and active drawdown measures are most suitable for use. In addition, it would have been interesting to state the strengths and weaknesses of several types of performance measures so that analysts are better equipped to use the right metric given the particular circumstances. Nevertheless, this research makes the investment professional more aware of the limitations of performance measures, emphasizing that as a profession, we need to be cognizant of differences in both the interpretation and the use of metrics.