A list of equity mutual funds with low risk and good returns

Last Updated on December 29, 2021 at 11:49 am

Here is a list of equity mutual funds with low risk and good returns. There are 53 such funds across ten fund categories. How and why I compiled this list: Parag Parikh Long-Term Equity Fund (PPLTE) turned five last May (I am an NFO investor). So I wanted to review this funds performance for a post and also consider adding it to my My Handpicked Mutual Funds (PlumbLine) (many have questioned why it is not part of the list as I have “skin in the game”!)

So I went to the Value Research multi-cap fund category list, went to the risk stats and look at this fund’s standard deviation That is, how much the fund’s individual monthly returns have deviated from its 3-year month return average. A “low” standard deviation is indicative of lower volatility and if we consider typical investor behaviour, also of low risk.  So for this post, we shall define low risk as a fund with a low standard deviation.

Anyone can tell by comparing the funds NAV growth vs that of its benchmark (NSE 500) that it does not “move up and down” as much as the benchmark. So I expected PPLTE to have low volatility. When I looked at the risk stats tab, not only did it have low volatility, it also has a high alpha. In fact, among the 257  equity funds listed at VR across categories, PPLTE has (as of today, this will change everyday!!) the lowest volatility and highest alpha.

So what is alpha? First of all, alpha is NOT excess return above benchmark. This is a lazy definition. It is important to understand what it means and what are its limitations.  To calculate alpha, a risk-free return is necessary. According to VR, “Risk-free return is defined as State Bank’s 45-180 days Term Deposit Rate”. So now, let us consider an example. The actual calculation is done with monthly returns and then annualized, but to keep it simple we will consider annual returns.

Let the fund return (over 1Y say) = 10%. Let the risk free return (over same period) = 6%. Let the benchmark return = 8%.

Now funds outperformance over risk free return = 10% – 6% = 4%.

Benchmarks outperformance over risk free return = 8% – 6% = 2%.

We can argue that the actual outperformance of the fund over the risk-free return is 4% – 2% = 2%. Before you say this is needless, I could have just done 10%- 8% = 2%, we need to consider how volatile the fund was wrt to the benchmark. This is calculated by a term known as beta. If beta = 0.8, this means the fund only displayed 80% of the benchmarks volatility. So we write,

The actual outperformance of the fund over the risk-free return =

(Fund return – risk-free return)  – (Benchmark return – risk-free return) x beta

That is we reduce the (Benchmark return – risk-free return) by a factor (beta) that represents how identical the funds volatility was wrt benchmark. Let me explain with numbers. So if beta = 0.8

Alpha = (10% – 6%) – (8%-6%) x 0.8

Since the fund was less volatile than the benchmark, we penalize the fund’s outperformance (wrt risk-free return) only by 80% of the benchmarks outperformance (wrt risk-free return).  This is known as alpha. It is a measure of excess returns on a risk-adjusted basis. Now suppose the beta was 1.2. This means the fund as 20% more volatile than the benchmark. So the penalty is higher.

Alpha = (10% – 6%) – (8%-6%)x1.2. So we immediately recognise that alpha can be negative if the fund is too volatile, even if its return is higher than that of the benchmark. Also, a positive large alpha does not automatically mean high returns. It can (usually does) mean good returns at lower relative risk.

While the standard deviation is a measure of absolute risk, beta is a measure of relative risk. It is important to recognise that these quantities assume the presence of a bell-curve like a fund return distribution. This is not true. So take this numbers with a good pinch of salt and treat them as merely indicative. Don’t get married to them.

So I downloaded the risk stats from VR and plotted the alpha vs standard deviation, to get this.

Clearly, not much can be done with this. So I calculated the excess standard deviation and excess alpha. These are defined as follows.

Excess standard deviation = standard deviation – median

Excess alpha = alpha – median.

Here median is the value that separates the standard deviation and alpha numbers into two. It represents the mid-point of the number distribution. The median is a better measure than the mean(average).

Excess standard deviation < 0 ==> Funds risk is in the bottom half. We will define this as low risk.

Excess alpha > 0 => Funds alpha is in the top half. We wil define this as good risk-adjusted returns. So now we plot the excess alpha vs excess standard deviation. In this graph, zero in the horizontal axis denotes the middle of the standard deviation number set and the zero in the vertical axis the middle of the alpha number set.

So we are interested in funds with a negative excess standard deviation (low risk) and positive excess alpha (good risk-adjusted return). So all the dots within the blue rectangle. That is a good 53 funds listed below.

Out of these 53, 48 of them had a return more than or equal to the median return. That is, they fell in the top half of the return distribution. Which is interesting, to say the least! Shown below is the absolute return and absolute alpha plotted against excess standard deviation (negative values only).

Notice that a high alpha does not mean high returns. You can see that clearly below. Fund returns between 12-18% have a similar alpha because it is risk adjusted.

Finally, let us look at the return distribution.

That is not half bad. Most of these 53 funds lie within the 12-15% return bracket and only one fund had a touch lower than 10% return.

Current star rating distribution of these 53: Unrated: 9 Two-star: 1 Three star: 17 Four-star: 21 and Five star: 5.

So what? What is the use of this list? Well, this is one way of shortlisting funds with low risk and good reward. Of course like blind men touching an elephant, each shortlisting method will result in different results. For example, low downside protection as measured by the downside capture ratio (used in my monthly fund screener) need not correlate well with low standard deviation. This only looks at one 3-year period. So what you see above is not indicative of consistent performance. If you are looking for a “message” from this post then: look beyond star ratings, look for funds with low volatility and reasonable performance. This is one way to shortlist. It will not help unless you have a clear goal, a defined return expectation and understanding of When to choose what mutual fund

The usual disclaimers apply: NO part of this post is to considered as invested advise.

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