Mutual funds rolling returns - Category wise
Rolling returns are a more useful way to analyse mutual fund performance than point-to-point returns. This tool gives you different aspects of rolling returns to better your analysis. You will be able to compare a fund with others in its category and against the category’s average. Simply choose the return period and the category which you want to analyse. Understand more about this tool and rolling returns further down.
The rolling returns have been calculated as follows:
For 1-month, 6-months, 1-year and 3-year return periods, returns have been rolled daily for the past 3 years. For 5-year returns, returns have been rolled daily for the past 5 years.
You need to make your own interpretations from this tool. Please avoid making comparisons with our calls in the Review tool or in Prime Funds. For example, if you ask us why a fund has a high average rolling return and still a ‘hold’ in our review tool, we will be unable to take your query. The reason is that our proprietary methodology takes into account a wide variety of metrics, weights them based on their importance in each category, looks at trends, and other qualitative factors before arriving at a rating score and call. Therefore, kindly avoid queries on fund choices based on these tools.
- The normal 1-year, 3-year, 5-year returns that you see in factsheets and websites are point-to-point returns. That is, it shows the change in NAV from one point (date) to one point (date).
- Rolling returns are when you calculate the point-to-point returns at a specific frequency for over a period of time.
- Rolling returns have 3 aspects to it: the return period (such as 1 month, 1 year, 3 years etc), the frequency at which you are calculating this return (every day, every week, every month etc), and the length of time for which you want to see the return (such as for 3 years, 10 years etc)
- Take an example. Return period – 1 year. Frequency – daily. Length of time – 3 years. Here’s what this means: you are looking at the 1-year returns every day for a period of 3 years. In other words, you’re rolling the 1-year return every day for 3 years.
- Take the same example above with dates, to make it clearer. Say you’re carrying out this exercise on 25.4.2021. The current 1-year return would be that as on 25.4.2021 – so you are seeing the return from 25.4.2020 to 25.4.2021. Then you roll it by one day. So, you see the return from 24.4.2020 to 24.4.201. You roll again by one day – from 23.4.2020 to 23.4.2021. You do this until you cover 3 years of rolled returns. This makes the final date for which you have the 1-year return 25.4.2018. This way, you have the 1-year return every day from 25.4.2018 to 25.4.2021 (1 year return rolled every day for 3 years).
- The 1-year/3-year/5-year return (which you normally see) aren’t useful to understand a fund’s performance. These returns show the change in NAV on a single day in one year compared to a single day in another. It does not tell you what happened in between those two dates. Markets and/or the fund could be up or down in between those dates.
- Point-to-point returns are influenced by what happened on the start date or end date. If markets were down at the start date, returns would look strong on the end date and vice versa.
- When a fund’s 1-year/ 3-year/ 5-year returns look better (or worse) than others, it doesn’t tell you if the fund has always been better or whether it’s just the current point-to-point returns.
- Since rolling returns look at returns over a period of time, they are better able to capture trends in performance and average out the swings in returns.
- For this tool, we have rolled each period’s returns on a daily basis. We have balanced short-term and long-term periods in order to give the more realistic figures and avoid being overly influenced by past returns that may no longer be relevant.
- You can choose which return period you want to see the rolling return, but you will not be able to change the number of years for which these returns are rolled (the from-to dates). You can go to our Fund Rolling Return calculator to see different periods for funds you are interested in.
- We have gone by SEBI-defined categories only.
- We have given the data for direct plans alone. You can assume a similar performance trend for the regular plans as well; for instance, if the direct plan of Fund A does better than the direct plan of Fund B in the metrics given in the tool, the same will hold for the regular plans as well.
The tool slices and dices the rolling returns to give you different aspects of the fund’s performance. Combine the different metrics in the tool to arrive at a better understanding of how the fund has done.
- Average: The average of the rolled returns for a return period over the rolling timeframe. For example, say Fund A’s 1-year average rolling return over a 3-year period is 10%. It is calculated by averaging all the 1-year returns in the 3-year timeframe; so, the fund, on an average, delivered 10% in 1-year periods. Average returns even out return swings. When rolling returns are averaged, it covers different market phases and therefore presents a more realistic picture of what fund performance has been.
- Minimum: The lowest return that a fund generated for a return period over the rolling timeframe. For example, say Fund A’s 1-year minimum rolling return over a 3-year period is 2%. It means that the worst 1-year return the fund ever delivered in the past 3 years is 2%. A fund that’s got better minimum returns than its category or peers is able to contain downsides well.
- Maximum: The highest return that a fund generated for a return period over the rolling timeframe. For example, say Fund A’s 1-year maximum rolling return over a 3-year period is 30%. It means that the best 1-year return the fund ever delivered in the past 3 years was 30%. Use the minimum and maximum returns along with the other metrics to understand how returns can vary; high returns with poor minimums or high volatility can indicate a fund that’s able to participate well in rallies but unable to hold on to these gains.
- % Losses: Shows how often the fund slipped into losses for a return period over a rolling timeframe. For example, say Fund A’s % losses for 1-year returns over the past 3 years is 15%. It means that 15% of the time, Fund A delivered losses on a 1-year basis in the past 3 years. This measure helps understand how risky a fund can be and understand the nature of returns. For example, high proportion of losses means that a fund needs to do well on maximum returns and/or average returns in order to compensate for the losses. To give another example, high loss proportions combined with higher volatility or high maximum returns are indicators that a fund can fall and rise rapidly.
- Std. deviation: Shows the deviation in a fund’s returns for a return period over a rolling timeframe. Higher the standard deviation, the more the fund’s volatility. Combine this metric with others such as average returns or % over category to know if the higher volatility is hurting performance.
- % over category: Shows how often the fund beat its category for a return period over a rolling timeframe. For example, say Fund A’s % over category is 90%, for 1-year returns over the past 3 years. This means that the fund beat the category’s average 90% of the time. This metric is a measure of a fund’s consistency; high figures mean that the fund is pretty good at generating returns that are above peers. Consistency is important because it shows you that investing at any time in the fund will get you above-average returns. Low consistency indicates that a fund can easily falter and you may earn below-average returns.
- Category average: This is simply the average of the returns in a category, across metrics. Individual funds will have higher or lower figures than the category.
- The rolling returns data cannot be extrapolated as our recommendation or a call to buy/hold/sell funds. It cannot be estimates of future returns either.
- This tool is meant to help you gather useful data that is otherwise difficult to access and allow you to do your own analysis.
- The rolling returns data is the base to calculate a number of metrics including Sharpe, standard deviation, capture ratios and so on.