Looking for the Best Mutual Funds to Invest? It’s a bit trickier than you think!

It is a capital mistake to theorise before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts. Arthur Conan Doyle, Sherlock Holmes

A lot of us have a very simple strategy for selecting the best mutual funds for our portfolio

Step 1: Open your web browser

Step 2: Go to a “famous” mutual fund rating website

Step 3: Pick the top rated funds

Step 4: Set up SIPs in them and done!

Well, it is not that simple and honestly, a bit dangerous! After all you are investing your hard-earned money.

Before we move on, lets get a few things out of the way.

  • There is no such thing as a one-size-fits-all list of “best” Mutual Funds. It’s pretty relative and depends on multiple factors such as your investment horizon, financial goals, style of investing, risk appetite and so on.
  • The rest of this post will focus specifically on equity mutual funds with a promise to come up with a separate dedicated post on debt funds shortly.
  • This post will not give you a laundry list of top-5 funds. Instead, we will focus on something much more powerful – building a comprehensive framework for selecting the best mutual funds.

So what’s the danger in picking the so-called “top rated” funds?

The rating methodology itself is, at best flawed and at worst disastrous.

There seems to be an excessive focus on single data points – especially last 1 year return and last 3-year return.

And so, you could see a 4-star rated fund go down to 2-star and then come back to 4-star, all in a matter of 9 months! We are talking about equity funds here. How can the rating fluctuate so much in 9 months?

This is because there is too much emphasis on ONE data point.

And there in lies the danger. Think about it…. 3-year return is just 1 data point (on 21st Dec 2017, it will be return generated by this fund from 21st Dec 2014 to 21st Dec 2017). This number is completely meaningless!

  • There is absolutely no information about consistency of performance over an extended time frame
  • And even though it may not seem so, there is no information about recent performance because, and I repeat, it is just ONE data point.

So what’s the correct way of assessing a fund’s performance?

Introducing the concept of annualized rolling returns

Instead of looking at one 3-year return, what if we looked at hundreds of such 3-year returns over an extended time frame?

Lets understand this with an example – suppose you want to assess the performance of a fund from 1st Jan 2010 onwards. And you want to assess 3-year returns of the fund during this period.

Then starting from 1st Jan 2010, assume that you invest in this fund daily and redeem exactly after 3 years. You keep on doing this till 1st Dec 2014. Here is how your investments go:

Investment 1: Invest on 1st Jan 2010 and redeem on 1st Jan 2013

Investment 2: Invest on 2nd Jan 2010 and redeem on 2nd Jan 2013

Investment 3: Invest on 3rd Jan 2010 and redeem on 3rd Jan 2013



Last Investment: Invest on 1st Dec 2014 and redeem on 1st Dec 2017

A quick pictorial representation of this process (don’t mind the scale):

Now each of your daily investment will have a 3-year return. These are called 3-year rolling returns – you are rolling the 3-year window by a day each time and looking at the return for that window. In our assessment period (1st Jan 2010 to 1st Dec 2014), you will have roughly 1200 such return numbers.

But how do we make sense of these 1200 numbers? Simple! You calculate the average and standard deviation and bingo….you have 2 very powerful numbers that not only convey the performance track record, but also the risk associated with the fund.

For further details on the implication of average rolling returns and standard deviation, refer my piece here – Can Mutual Fund risk be quantified and can it be eliminated?. For now, just keep in mind that higher the standard deviation, higher is the risk.

Therefore, when comparing mutual funds, you should always look at average rolling return in conjunction with the standard deviation.

What time frame should be used to assess the performance?

We just saw that average rolling return and standard deviation are 2 magical numbers that can help us compare mutual funds. But…

  • What should be the return period – should we look at 1-year return, 3-year return, 5-year return or something else?
  • What should be the period of assessment? Should we look at the performance since inception, last 10 years, last 5 years?

#1. What return period should we look at?

Commonly used return periods are 1-year, 3-year and 5-year. In my view, 1 year is too small a period and 5 years is too long.

Now don’t get me wrong! I am not saying that 5 years is long from an equity investment perspective. All I am trying to say is that 5 years is too long a period to stick with a mutual fund even if it is underperforming.

In general, 3 years is an optimal period to have a relook at your portfolio – weed out the underperformers and bring in the new top-performers. So our framework will stick with 3-year rolling returns.

#2. What should be the time period of assessment?

This is a slightly tricky and requires careful selection of the time period. When deciding on the time period, 2 factors should be kept in mind:

  • It should be long enough to showcase track record and consistency of performance
  • It should not give an unfair advantage (or an unfair disadvantage) to a certain set of funds over the other set

Let me explain. Suppose you decide to look at the performance of all funds since their respective inceptions. In this universe, there are funds that started before 2005 and there are Funds that started post 2010. Funds before 2005 would have faced the entire brunt of 2008-2009 market crash and their numbers will reflect that. Funds that started post 2010 never faced such a crash and hence have an unfair advantage.

Similarly, if your assessment period starts sometime in 2008 or 2009, the funds that were in existence then will have an unfair advantage over funds that started in 2010. Why? I will let you guys figure that one out!

Keeping the above 2 points in mind, an ideal assessment period would be from Jan 2010. This period gives you a decent 8 years of performance data. At the same time, it eliminates the impact of the crash and brings all the funds on a level playing field. See the chart below (NSE Nifty index closing prices) – by Jan 2010, markets were almost back to their pre-crash levels and hence the entire impact of crash is nearly eliminated.


Also, we should eliminate Funds that started post Jan 2013 simply because we do not have enough data points to showcase consistency of performance.

Another thing that we should do is to look at data from 2005 onwards but just for funds that were in existence at that point of time. Looking at this list will give us a good idea of how these funds performed during the crash. And if there are funds that exist in both the lists (2005 and 2010), you know that not only have these funds outperformed peers in bull market conditions, they were the best ones even during the crash.

What about recent performance? Does it count?

While our framework beautifully picks the “consistent” top performers over a long period of time, we should also look at recent performances and ideally ascribe some weightage to it.

If a fund has been consistently in the top-5 all along but has slipped recently, it should be a red flag and might require deeper investigation – is the recent underperformance a temporary blip or something fundamental has changed?

For the purpose of our framework, we will consider recent performance as 1-year rolling return for last 1 year.

…and finally, here are the results

We have defined 2 different performance blocks based on the period of assessment. Lets just quickly recap:

  • 3-year rolling returns of all funds from Jan 2010
  • 3-year rolling returns of all funds from Jan 2005

Tables below give you the top-3 funds in each of these blocks and across 3 categories (Large, Multi and Mid cap). The last column in each table is an indication of recent performance.

Table1: 3-year lumpsum rolling returns of all funds from Jan 2010

Table2: 3-year lumpsum rolling returns of all funds from Jan 2005

Please note that the tables above should not be construed as our official recommendation. At Finpeg, we follow a different approach. We build an investment strategy customized to your requirements and then select the funds best suited to run that particular strategy.

Notwithstanding that, here are some interesting takeaways from the data above:

  • Barring ICICI Value Discovery (in the multi cap category), none of the funds from the 2005 list figure in the 2010 list. This implies that although these were the best funds in the “Class of 2005 and before”, they could not keep pace with the “Class of 2010 and after” funds.
  • ICICI Value Discovery is on both the list and it says something about the fund. The only area of concern would be it’s recent (1 Year) under performance vis-à-vis its peers.

The story doesn’t end here. But it does get more interesting

While we have laid down a framework for filtering the top funds, there is an important to ponder upon:

It’s 2017 and we have our best funds. What about 2020?

Although you now have the framework to select the best mutual funds, do you have the time and bandwidth to create a portfolio of best mutual funds for yourself?

You may be able to do it once, but will you be able to actively keep your portfolio updated with the top performers. It’s 2017. What about 2020?

And even if you find the best funds, what would be the best strategy to shift your portfolio to the new funds? In other words, do you have the knowhow and/or the bandwidth to actively manage your portfolio?

This is where Finpeg’s algorithm driven investing comes in

For starters, we do not have a universal list of one-size-fits-all mutual funds. We follow a very different approach. We build custom investment strategies depending on your requirements and then select the funds best suited for that strategy.

At Finpeg, we follow an algorithm-driven investment approach, which comprehensively covers when to invest, redeem and rebalance.

By running millions of simulations, we arrive at an optimal combination of (a) the best mutual funds, (b) an entry strategy, (c) rebalancing rules and (d) an exit/reinvestment strategy that maximises returns and minimises risk.

Do visit our website and if you like what you see, please do get in touch!

This article has 2 comments

  1. Sunil Gavaskar Reply

    It’s right analysis, but what if fund manager changes and it’s impact !
    This point of consideration is missed out here

    • Shubham Satyarth Reply

      Important point. Fund managers are the heart and soul of the fund. One should pick fund manager and not a fund. We at Finpeg, work with something called Fund Manager NAV and pick managers. For the purpose of this blog, we have used fund NAV as the intention was to provide a framework.

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