Understanding Rolling Returns in Mutual Funds: A Practical Guide

Rolling returns measure how an investment performed across multiple, overlapping time periods within a longer investment horizon. Instead of calculating returns between just two fixed dates, rolling returns calculate returns for a chosen duration, such as three years, and then move that window forward at regular intervals, recalculating each time.

If you want to understand the typical 3-year return of a fund over the last decade, you don't rely on just one period like 2014 to 2017. You calculate 3-year returns for 2014-2017, then 2015-2018, then 2016-2019, and so on. The result shows the best, worst, and most common outcomes.

A single point-to-point return can look very different depending on whether the measurement begins near a market high or a market low. Rolling returns show performance across bull markets, bear markets, and sideways phases, giving you a complete picture of how the fund behaved under different conditions. This approach reduces the impact of choosing a favourable start or end date and gives a clearer picture of how returns behaved across different market conditions.

A single return number can change a lot depending on when you start measuring performance. Short-term CAGR figures after the 2020 market recovery can look very different based on the chosen start date. For example, returns measured from March 2020 may appear stronger because the period begins near a market low, while returns measured from January 2020 can

look different for the same fund. Rolling returns address this by showing outcomes across many starting points and market phases, which supports a more balanced view of consistency over time.

You select a return period, such as 5 years, and a frequency, such as monthly. Then you calculate the annualized return for every possible start date within the dataset.

Here's a rolling returns calculation using NAV growth for a hypothetical fund over five years.

Investment: ₹1,00,000

3-Year Rolling Returns

Rolling return frequency: Annual (each calculation shifts forward by one year)

First period (Year 0 to Year 3):
₹1,00,000 → ₹1,36,300

CAGR = [(1,36,300 / 1,00,000)^(1/3)] - 1 = 10.87%

Second period (Year 1 to Year 4):
₹1,10,000 → ₹1,52,660

CAGR = [(1,52,660 / 1,10,000)^(1/3)] - 1 = 11.54%

Third period (Year 2 to Year 5):
₹1,29,800 → ₹1,75,560

CAGR = [(1,75,560 / 1,29,800)^(1/3)] - 1 = 10.62%

The resulting 3-year rolling returns are 10.87%, 11.54%, and 10.62%. Even though individual years varied, outcomes stayed within a relatively narrow band.

Point-to-point returns measure performance between two fixed dates. If you invested ₹3,00,000 on January 1, 2019, and the value today is ₹5,50,000, the point-to-point return reflects only that single period. Rolling returns calculate this same return for every possible starting point within the dataset.

The strength of rolling returns performance evaluation lies in analyzing the distribution of results, not just the average.

Key aspects to observe:

• Average or median: The typical outcome across all rolling periods

• Range: The gap between the best and worst outcomes over the chosen time frame

For example, two funds may have the same average rolling return, but one may show a narrow range while the other shows a wide spread. The first reflects more consistent outcomes, while the second shows greater sensitivity to entry timing

Rolling returns are useful across asset classes, though interpretation differs.

For equity mutual funds:
Longer rolling periods, such as 5 or 7 years, are commonly analyzed. Equity markets undergo expansion and contraction phases, and shorter periods can amplify temporary fluctuations. Longer periods show how the fund behaved across full market cycles.

For debt mutual funds:
Outcomes are generally steadier. A narrow range aligns with stability expectations. Wider dispersion may indicate credit quality changes or interest rate movements during certain phases.

Both measures answer different questions.

CAGR is a single annualized number calculated between two specific dates. It answers the question of what annual return links the starting value to the ending value over that period.

Rolling returns generate many such annualized figures, one for each rolling window. Averaging these rolling CAGR values produces a rolling return average. This average reflects performance across varied market conditions rather than a single path, offering a more balanced view.

A high average rolling return means a good fund

Not necessarily. Two funds averaging 11%, one ranging 9 to 13%, another 3 to 18%, differ vastly in consistency. Outcomes can vary significantly based on entry timing, even when average returns appear similar.

Rolling returns predict future performance

They analyze past patterns. Past consistency doesn't guarantee future consistency. Fund manager changes, strategy shifts, and market dynamics alter performance.

Rolling returns replace point-to-point returns

Use both. Point-to-point for quick screening. Rolling returns for consistency assessment.