What do Agrochemicals, Food Products and Household Appliances have in common?
What do Telecom Services, Oil Exploration & Production and Electric Utilities have in common?
The first set is three of the best performing sectors in India in terms of stock market returns over the last ten years. The second set, you guessed it, is three of the worst performing sectors for stock market investors.
Chart shows the top 10 best and worst performing sectors in India in terms of investor returns from 2008 to 2018
Note that cumulative returns here mean median of CAGR returns within each sector
Does this mean the key to great returns is to identify the “right” sectors and not worry about individual companies?
In other words, Buffett’s quote about picking businesses with good economics and not rely on management ability.
When a management with a reputation for brilliance tackles a business with a reputation for bad economics, it is the reputation of the business that remains intact.
Good or Bad economics typically apply to entire industries with exceptions being on account of differentiated business models. e.g. Asset light hotel models over traditional owned and operated properties.
Just median cumulative returns don’t answer that question. For that, we need to consider the amount of variability of returns within a given sector.
How do we measure variability of returns?
The most obvious metric for variability (or volatility) is standard deviation. But to make it comparable across sectors, we need a measure called coefficient of variation (CV) which is the [standard deviation of returns] / [average of returns]
Two illustrative sets of returns to show how Coefficient of Variation serves to compare variability within a sector
Returns of the three companies in the first set cluster around a value and tend to be positive or negative at the same time. For those in the second set, returns are widely dispersed and therefore show relatively higher Coefficient of Variation.
Think of the low returns variability sector as a commodity sector with low pricing power. High returns variability can be a sign that returns depend on management’s ability to pick a differentiated strategy and execute well.
We make one adjustment. We consider absolute values of CV, else the combined average value of CV for the three years would be artificially low because of the negative values.
The returns versus variability grid for Indian sectors
Each point represents a sector’s cumulative returns from 2008 to 2018 (higher returns to the right) and returns variability within that sector (higher variability towards to the top). The orange lines represent the median return and variability for the overall market.
Some of the extreme values have been labelled but it’s hard to make much of this chart except that most sectors cluster around 1.7x cumulative return and 1.95 CV as variability with a few exceptions.
Let’s look at sectors on the edges of both axes to put them into four boxes based on percentile values.
This chart is a zoomed in version of the previous one and only shows the sectors that fall outside the 25th to 75th percentile band on both axes.
How to interpret the four boxes:
- High Return, Low Variability – If you picked these sectors between 2008 and 2018, you almost certainly made money
- High Return, High Variability – You made money unless you made poor picks
- Low Return, High Variability – You lost money unless you made brilliant picks
- Low Return, Low Variability – You lost money almost certainly
Example: Agrochemicals companies delivered a cumulative return of almost 9x over 10 years. The variability in returns across companies was lower than the overall market. On the other hand, Realty showed negative returns over 10 years (return multiple < 1) and high variability in returns ~4.3 or 430%
Now go back to the first table showing overall best and worst performing sectors from 2008 to 2018.
Where the returns came from
Four of the 10 best performing sectors (Agrochemicals, Household Appliances, Fibres & Plastics and Auto Parts & Equipment) feature in the high return – low variability box. This means if you had identified these sectors as being of high interest to investors, your returns would exceed most of the market by a distance.
Specialty Chemicals has been another strong sector for investors. Except, there were some poor performers in there so some ability to screen poor companies was required.
Where value was destroyed
Three of the 10 worst performing sectors (Transport Related Services, Exploration & Production, Heavy Electrical Equipment) feature in the “Low Return – Low Variability” Box implying they have been out of favour for a while. No amount of skill at stock-picking would have helped deliver decent returns.
Three more poor performing sectors (Shipping, Education, Realty) are in the “Low Return – High Variability” Box. Think of these as minefields where finding the right stocks required a combination of extraordinary luck and skill.
The data suggests being in the right sectors certainly influences your chances of getting lucky as an investor.
So how do you find the “right” sectors for the future?
*Shrug* No idea.
My next step is to do a deep dive to find correlations between fundamentals and price moves for the best and worst-performing sectors. My starting hypothesis is that a combination of rising sales growth rates and ROCEs plus good press drives sector returns. But then again, it might also be mostly random.
More to come on this.