A simple way to measure how consistent a mutual fund has been in outperforming a benchmark over a given period, in the form a simple percentage is discussed in this post. Depending on your feedback I will include this in this months mutual fund return listings.
The simplest and most intuitive way to measure outperformance is to use rolling returns.As regular readers may be aware freefincal has rolling return calculators for both lump sum and SIP investments with a choice of 50 benchmark indices for comparison.
The most important advantage with rolling returns is that they are simple to understand. Way more simpler than the greek alphabets - alpha, beta, gamma, Sharpe, Sortino etc. and even simpler than upside and downside capture ratios.
What are rolling returns
Suppose I ask what is the return for the last 3 years for a fund. I am considering the NAV growth between two days. The current NAV and the NAV three years ago. This is known as a point to point calculation. The return value depends on when I choose to make a calculation.
Instead, if I have NAV data for the past 10 years and calculate the three return between
1st Jan 2006 to 31st Dec 2008
2nd Jan 2006 to 1st Jan 2009
3rd Jan 2006 to 2nd Jan 2009 and so on for every business day from 1st Jan 2006 to the current day.
So you can imagine that I will get 100s of 3Y return data points for the fund. Then I repeat the exercise by choosing an appropriate benchmark and compare the two. Here is an example.
The graph immediately tells you that the fund has been quite regular in beating the chosen benchmark. This gives a easy to understand visual measure of performance consistency.
This can be expressed as a percentage too:
Consistency score = (no of times fund has beat index)/(total no of rolling return entires). Expressed as a percentage.
Higher this score, the more consistent the fund has been in outpeforming the benchmark.
Three-year rolling return consistency score wrt Nifty 50 TRI
The funds which have a scopre above 70% are highlighted in green. The same data in bar chart form.
Three-year rolling return consistency score wrt Nifty 50 Value 20
The Nifty 50 Value 20 is a concentrated index of 20 value stocks from the Nifty 50. It is a much more stringent benchmark for evaluating large cap funds. However much of the index data is backtested. So we will have to check real time data for a while to evaluate the suitability of this index.
Amusing only ICICI Focussed Blue Chip has a reasonable track record against the Value 20 index.
The consistency score is a simple way to determine outperformance with respect to a chosen benchmark. Do let me know what you think about this.
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Fabulous article again.. Makes me wonder. Why botHer with mfs at all. Buy the stocks in the nifty 50 value 20 using a discount broker. Keep for long term. And we could beat most mfs without worrying about choosing the right fund.
Your thoughts please?
Thank you. That is a certainly a possibility. Only caveat is, much of the NV20 data available today is backtested. So one should keep an eye on how it fares against Nifty 50 in real time.
Thanks Pattu.
Nice post as always
Frequency of out-performance is important. However, can some weight be attributed to level of out-performance too?
Weight? You mean what is a good level of out performance?
This is definitely a simpler and more elegant way to assess consistency. You must adopt it in this month's MF returns listing.
A good work again..! Definitely a lot simpler but much relevant method to analyse past performance...!! Thanks a lot ...!!!
Thank you
Please share the excel used to calculate this and let us know from where you got the data regarding NAV and index
Please use the rolling return calculators links in this post or search for them and you can calculate it for any fund. The NAV is from AMFI and indices from moneycontrol, bse and nse
Good work Pattu and this should be added.
Thank you
Hello Pattu, This is a good concept. But this could also be biased because the data relies on past history of the fund.Past returns are no guarantee for future performance.
Also there would be very few data points for new funds say ppfas.This could actually make an investor biased.. Correct me if I am wrong
1) I have only shown funds with a 1000 3Y returns
2) Show me an analysis that does not depend on past performance. Happy to learn more about it.
Interesting analysis. Great work 🙂
Thank you
Buying the stocks in NV20 and keeping them for long term will not work as constituents of NV20 and their weights will keep changing every quarter. A better way would be to invest in R*Shares NV20 ETF and pay the Expenses which are very low (0.39% in 2015) where the AMC will keep the portfolio aligned with NV20. But will it be worth it as a fund like ICICI Focused Bluechip will still beat NV20 (and R*Shares NV20 ETF).
Such an etf has poor liquidity. It is not too difficult to track index changes and realign.
Maybe you add a threshold of beating to count consistency score. For example, whether fund beats benchmark by 1% or 0.01% count as same in current calculation. I am interested in knowing times fund beats benchmark by at least 0.5% (say) as premium for managing MF accounts and additional work.
I will interested in this tool if it can compare any fund and any (from select set of) benchmarks.
Also, long standing request, you should start versioning you tools. Difficult to keep track of latest versions.
Reg versions, I have stopped posting them in multiple places. So the change log is available. I will see how a threshold works. Thanks.
Dear Sir,
A great analytical tool. But how do we make use of it. Let us say there are two funds , say "Fund A" and Fund B". Fund " A" has beaten index 100% times whereas Fund "B" has beaten only 70% times. But every time fund "B" has outperformed it i by a very wide margin, whereas "A" more or less mimics index with a positive bias. At the end of day " B''s performance will be much better than that of "A". It shall be therefore better to invest in "B" , provided , of course that the score of out performance is not very low. Request comments as I may be wrong,
Regards.
V K Sood