- Depending on the performance data you use, an investment can appear as both an underperformer and outperformer.
- Relying solely on point-in-time performance data can lead to potentially bad decisions and unrealistic expectations for returns.
- Rolling time-period data, much like a video vs. a snapshot, provides you with a more holistic view of an investment’s performance.
Imagine a recommendation to invest in a mutual fund that has lagged the returns for most of its category peers over a three-year period. Would you buy it?
What about a recommendation for a mutual fund that has topped the returns for most of its category peers over a three-year time period? Now would you buy it?
As it turns out, the underperformer and outperformer are the same fund, evaluated with different performance periods. This example is helpful for two reasons because it:
- Illustrates a trap that investors commonly fall into when using point-in-time data to make investment decisions.
- Highlights the significant amount of end-point sensitivity associated with point-in-time data.
The power of rolling time-period returns
Point-in-time data tells you what happened at one moment, frozen in time. Alternately, rolling time-period data tells you what happened throughout a span of time. As a result, the latter provides more robust way to assess performance because it is subject to less end-point sensitivity.
A good way to illustrate the difference between point-in-time data and rolling time-period data is to think about how a still-shot camera differs from a video camera. While the still-shot camera reflects what happened at a single moment, the video camera captures elapsed time and offers a more holistic way to record an event.
An additional shortcoming of point-in-time data is that it can give investors a false sense of stability. This happens from assuming the data is permanent and will remain constant in the future. As the following chart shows, the relative performance of a fund can be quite variable over time. The ability to observe this volatility in a data set throughout time — and essentially zoom out from a single point-in-time assessment — is a primary advantage of using rolling time-period analysis.
When using rolling time-period data, it’s important to acknowledge the impact of a leap-frog effect. That is, as time rolls forward, one observation period exits the data set and a new observation period enters. This is important because the data will appear to improve if a quarter with strong performance replaces a quarter with poor performance, or vice versa.
For example, in a five-year period there are 20 quarterly data-points (four quarters per year multiplied by five years). As time rolls on, 5% of the data in the series is replaced when one older quarter of data is removed and a new quarter is added.
In a shorter performance time period with fewer data points, the volatility of the performance metric increases. Given there are only four data points in a one-year assessment period, 25% of the performance data is replaced every quarter.
We encourage rolling time-period data
Relying solely on point-in-time performance data (especially short time periods) can lead to unrealistic performance expectations and potentially bad investment decisions. Therefore, as you and your Ameriprise financial advisor work together to maintain a diversified, long-term investment portfolio, we recommend factoring rolling time-period performance data into your decision-making process.