Rolling return charts have become prominent over the last few years, especially for mutual fund analysis. However, few investors understand how rolling return charts are computed and their benefits and limitations. A discussion.
Rolling lump sum, rolling SIP, and many other mutual fund and time series analysis and financial planning tools are available in the freefincal investor circle.
There are two popular ways to compute returns for a financial instrument that fluctuates:
Point-to-point returns: The effective annual compounded growth rate (CAGR) is calculated between two dates. You can calculate CAGR for your mutual fund and compare it with its benchmark from Jan. 1st to Dec 31st, or you can calculate CAGR for the year to date (last 365 days). So the start and end date can be anything convenient to us.
The trouble is that results depend on the start and end dates. Sometimes, the instrument will show excellent returns and sometimes poor. I can’t look at the last 3Y or 5Y return and assume that is how my experience will be after I invest.
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What we need to know before choosing a mutual fund, or more importantly before deciding to quit a mutual fund scheme, is how consistent the fund’s performance is when compared with its benchmark. To do this, we need to use Rolling returns.
Calculating Rolling returns: This helps me answer the question, “How has a fund or a stock or an index or a commodity performed over every possible 3Y (for example) duration in its history?”
For example, suppose data is available from 1st Jan 2000. Then the first possible 3Y window is from 1st Jan 2000 to 31st Dec 2002. The next possible period of the 3Y window is from 2-1-2000 to 1-1-2003 and so on, as shown below.
- 01-01-2000 to 31-12-2002
- 02-01-2000 to 01-01-2003
- 03-01-2000 to 02-01-2003
- …..
- 09-12-2020 to 10-12-2023
- 10-12-2020 to 11-12-2023
- 11-12-2020 to 12-12-2023
A rolling returns chart plots returns over all such time windows versus the start or end dates. Here is an example of 10-year rolling return data of several indices.
Each coloured line has 2262 data points (except the yellow line, which has more).
Suppose I am comparing the green line with the yellow line. The green could be a fund, and the yellow could be the benchmark. I can immediately, visually grasp how often or consistently the green has outperformed the yellow.
In our monthly equity mutual fund screeners, we quantify this outperformance consistently by computing the ratio of the number of periods the fund did better divided by the total number of periods.
Definition used in our fund reviews: Rolling return outperformance consistency: the fund returns are compared with category benchmark returns over every possible 3Y,4Y, and 5Y period from 1st Jan 2013. The higher the outperformance consistency, the better. Suppose 876 fund returns were compared with 876 benchmark returns, and the fund has beaten the benchmark 675 times. The consistency score will be 675/876 ~ 0.77 or 77%. A score of 1 means 100%.
This should not be interpreted as a probability of future outperformance!
The above is an example of rolling lump sum returns. You can also compute rolling SIP returns, rolling volatility (standard deviation), rolling alpha, upside capture, downside capture charts, etc.
This is an example of mutual fund analysis with rolling returns, rolling upside and rolling downside capture ratios: Mirae Asset Emerging Bluechip Fund: Performance Review.
Excel sheets for computing these charts (among other tools) for any mutual fund or index are available in the freefincal investor circle.
There is more to a rolling returns chart than return outperformance.
The first thing to notice is the time period studied or the x-axis window. The longer this window, the better the result. In the above graph, the window is only about 10 years. This means market history is short. So, we cannot take any of the inferences too seriously. This is a problem with almost all Indian market data.
When you have data for several decades, you can afford to plot and interpret better. For example, the stock market always moves up in the long term, but returns move up and down! The graph below spans over 90 years!
The most important aspect of a rolling returns chart is risk! We understand that the future is uncertain by looking at the spread of returns (max return to minimum return). We have no clue about future market returns! There are no guarantees. But there is a solution. See: Do not expect returns from mutual fund SIPs! Do this instead!
One should never take the average of a rolling return chart. This is because the data distribution is not a normal distribution (gaussian distribution or a bell curve), so the idea of an average is useless. See, for example, The distribution of stock market returns. Also, data in the middle of a rolling return sequence tends to have a higher weightage in the average than those near the start and end periods.
In summary, a rolling returns chart should primarily be used to appreciate risk via the spread in returns. They can used to ascertain outperformance consistency. However, the period studied is crucial. If this period is long enough, it reveals an investment truth that nothing outperforms or underperforms forever. Rolling returns should not be averaged, and its performance consistency should not be considered a probability of future performance.
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