Do active mutual funds offer downside protection? Or is it a myth?

We find out how frequently active mutual funds fall lower than the "market" also known as downside protection

Published: March 14, 2020 at 12:36 pm

Every time the market falls, investors tend to compare the fall in their active funds and indices. They expect their funds to fall less than the “market” every time or at least every time they bother to look. It is incorrect to take two random dates and claim either active mutual fund fall less, more or just as much as the indices. In this article, I explain how to define this so-called downside protection and if active mutual funds offer this often enough to justify their high fees.

What is downside protection? As far as I could tell this is more a colloquial term with no widely accepted technical definition. There are many ways to compare the NAV movement of a mutual fund (in this article this mean active funds only) and a relevant index.

  1. We could compare their absolute volatility using standard deviation. This is a measure of how monthly returns over a given duration fluctuate compared to the average monthly return. Higher the standard deviation, higher the fluctuation in the NAV. This takes into account both positive and negative movements.
  2. We could measure volatility in relative terms using beta. This tells you how much or less volatile a fund is with respect to the index. Or it tells you how the fund responds to a movement in the market. Beta > 1 = more volatile, Beta < 1 = less volatile,  Beta =1 = just as volatile. This also takes into account both positive and negative movements.
  3. We could measure the ulcer index. This is a measure of how much and how long the NAV falls from a peak. It measures volatility only when the NAV falls. Lower the ulcer index, lower the downside fluctuation and lower the stress (which was incorrectly thought to cause Ulcers)
  4. We could measure downside capture. This measures how much of a benchmark’s monthly losses (if monthly return < 0) a fund captures. A downside of 80% means a fund has captured only 80% of the index losses.

There are many other ways to measure downside risk. We shall use the downside capture as a measure of downside protection in this study.

How downside capture is computed: Study monthy returns over a given period (say 1 year or 3 years). Look at the fund returns for months when the index returns were negative.  Compute CAGR of the fund and CAGR of the index only using these months.

Downside capture = CAGR of fund/CAGR of the index.

How we shall define downside protection: Let us take the example of a five-year window. We find out downside capture ratios (DCR) for every possible five year period from April 3rd 2006 to Mar 9th 2020 (when this study was done). Suppose we have 2000 such DCRs.

Downside protection consistency = (no of DCRs < 100%)/(total no of DCRs)

This tells you the fraction of instances when the fund captured less than index losses over a given period (five years in this example). You may agree or disagree with this definition of downside protection.

If you disagree, please provide an alternative definition and test it rigorously. Do not pick any random dates and claim active mutual funds provide or do not provide downside protection. That is lazy work but then again most readers are not discerning enough to appreciate rigorous from lazy.  The trick is to set the bar well above that.

Do active mutual funds offer downside protection?

To answer this, we take 258 active mutual funds (regular plans for longer history) from these categories. The benchmarks used are also shown. As argued already, an aggressive hybrid fund is expected to fall lower than a broad market index, hence the use of Nifty 100. Nifty large and midcap 250 is a tougher benchmark than Nifty 200 or Nifty 500. Nifty Midcap 150 is a tougher benchmark than Nifty Smallcap 250. See: Why your small cap mutual fund must beat this benchmark!

Aggressive Hybrid FundNifty 100 TRI
Equity Linked Savings SchemeNifty 100 TRI
Focused FundNifty 100 TRI
Large Cap FundNifty 100 TRI
Value FundNifty 100 TRI
Large & Mid CapNifty Largemidcap 250 TRI
Multi Cap FundNifty Largemidcap 250 TRI
Mid Cap FundNiftyMidcap150TRI
Small cap FundNiftyMidcap150TRI

We shall compute rolling downside capture over 3,5,7,10 and 13 years and the compute the downside protection consistency as defined above. We shall define a downside protection consistency of 70% as “good”. That is 7 out of 10 windows an active fund is expected to fall less than the index. This is subjective but reasonable in my opinion.

Results for 13 years

  • min requirement 200 downside capture data points (this is necessary to ensure NAV history length of all the funds in the sample set is reasonably the same)
  • No of funds: 94
  • Funds with downside protection more than (or = to) 70% 78 (83%)

Results for 10 years

  • min requirement: 800 downside capture data points
  • No of funds: 97
  • Funds with downside protection more than (or = to) 70%  71 (73%)

Results for 7 years

  • min requirement: 1500 downside capture data points
  • No of funds: 101
  • Funds with downside protection more than (or = to) 70% 67(66%)

Results for 5 years

  • min requirement: 1000 downside capture data points
  • No of funds: 160
  • Funds with downside protection more than (or = to) 70%  108 (68%)

Results for 3 years

  • min requirement: 1000 downside capture data points
  • No of funds: 173
  • Funds with downside protection more than (or = to) 70%  103 (60%)

So it reasonable and fair to say, in the past, finding an active fund that tends to fall less than then the index was better than a coin toss (50%).

Now let us get to return outperformance. We shall define a,

return outperformance consistency = no of time fund beat index/tot no returns.

For example, say we have 100 13-year return data points and a fund got a return higher than the index in 65 of those instances. Then return outperformance consistency = 65/100 = 65%.

We shall exclude aggressive hybrid fund as they do not have a mandate to beat Nifty 100 (or NIfty) in terms of return.

Results for 13 years

Out of 80 funds, only 35 (44%) have a return outperformance consistency of >= 70%. Out of these, 34 funds have downside protection consistency >=70%

Results for 10 years

Out of 83 funds, only 37 (44%) have a return outperformance consistency of >= 70%. Out of these, 27 (73%) funds have downside protection consistency >=70%

Results for 7 years

Out of 87 funds, only 36 (41%) have a return outperformance consistency of >= 70%. Out of these, 22 (61%) funds have downside protection consistency >=70%

Results for 5 years

Out of  143 funds, only 62 (43 %) have a return outperformance consistency of >= 70%. Out of these, 47 (75%) funds have downside protection consistency >=70%

Results for 3 years

Out of 145 funds, only 34 (24%) have a return outperformance consistency of >= 70%*. Out of these, 24 (70%) funds have downside protection consistency >=70%

* Before we conclude anything about this low number, it would be better to wait for the market homogenize as mentioned here: Why this market crash could be healthy after all!

It is clear that the odds of active funds beating the index in terms of returns is just about a coin toss (50%).  For most of the funds that have beat the index, downside protection is a key ingredient.

If we consider downside protection independently, it is more common than a coin toss to see an active fund fall less than the index. Active funds to tend to provide downside protection. This, however, does not always help it beat the index.

In conclusion, 60-70% of active funds do provide downside protection. However, only 60-70% of such active funds go on to get a better return than the market. As an analyst, this is reasonable performance by active funds. As an investor, we must recognise that the problem is to “stay invested in active funds that beat the market in terms of risk and return”.

This is essentially a coin toss (~ 50% probability). That is why one should shift to index funds – because it is hard if not impossible to be invested in the right active funds at all times.  Not look at performance between random dates and jump to conclusions.

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About the Author Pattabiraman editor freefincalM. Pattabiraman(PhD) is the founder, managing editor and primary author of freefincal. He is an associate professor at the Indian Institute of Technology, Madras. since Aug 2006. Connect with him via Twitter or Linkedin Pattabiraman has co-authored two print-books, You can be rich too with goal-based investing (CNBC TV18) and Gamechanger and seven other free e-books on various topics of money management. He is a patron and co-founder of “Fee-only India” an organisation to promote unbiased, commission-free investment advice. He conducts free money management sessions for corporates and associations on the basis of money management. Previous engagements include World Bank, RBI, BHEL, Asian Paints, Cognizant, Madras Atomic Power Station, Honeywell, Tamil Nadu Investors Association. For speaking engagements write to pattu [at] freefincal [dot] com
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