Notes on long term thoughtful investing

Month: December 2016

Part 2: Dreadful Business Combinations

This is in continuation with the previous post which can be read here. Basically, in the last post we tried learning what effects could operating leverage, business cyclicality and competition could play on business when they act harmoniously – as they often do. Today we would try to understand a bit deeper that which type of businesses has these as its recurring phenomenon and how should we think (or should not think) about these.

Knowing these businesses

Though I can explicitly spell out of the names of those industries which we know by and large faces these but that is not what we want to achieve. We rather want to understand the factors which make them so. But we can use these ‘infamous’ industries as our examples.

  • Fickleness in margins: This is easiest of all to tell. Just look at the trend of operating margins (operating margins is basically revenues which underlying business generates less expenses incurred to earn these). If as a % of sales these show high levels of variations over 5-8yr period, it may be an indicator that the business has relatively higher levels of operating leverage though not necessarily be so.

Remember, our previous example? India cements earned 9.7% margins in 2005 and 14% in 2007. Its long history shows much higher levels of variability in margins


  • Commodity-type business: Generally, these businesses would be the producers / suppliers of products which are not differentiated products sold on the basis of weight and where brand name has less to do with. These products are widely available.


Think of how we go about buying potatoes. Do we even care to think which farms are these coming from or who their producer is? We just buy them if they are looking okay. Same goes with cement, steel, milk, etc. We just ensure that underlying quality is not bad. Nothing beyond that. For slightly better-than-average quality of output we may pay a bit of a premium but that is related to the price of the ‘averages’ i.e. price of average quality product act as a reference point for our willingness to pay the premium.


On the other hand, do we ever think on similar lines while buying a Cadbury chocolate or Maggi noodles? Do we compare the MRP printed on those packets with the MRP of other chocolates or noodles available in the market? Probably no. To us, these are distinguished products and something which is not meant to be compared with other ‘generic’ chocolates or noodles available in the market. In other words, these products command pricing power which milk, cement, steel does not command.


  • Volatile demand scenario: In point (i) we talked about margins being volatile. One of the reason of them to be so is that their selling prices are quite variable. In India, over last five years we have seen, for example, prices for a bag of cement has been fluctuating between Rs 200 per 50kg bag to Rs 350. Such wide variations in prices leads to volatility in margins. And the reason why prices are so volatile is that demand of the end product is itself quite volatile. So if construction activities are on the rise, supply of cement will fall short, temporarily, of the demand for it. So either till the time new manufacturing capacities become operational or the demand stays at above average levels, one is bound to see cement prices staying at elevated levels.

Generally, in these businesses demand is bound to stay volatile – peaking at some point in time and then falling back to average kind of levels which causes mis-match in demand-supply equation from time to time.


  • Competition: Though as consumers, competition is good to an extent. But too much of it plays spoilsports for the prospects of the industry as a whole (and in some cases even lead to adverse consequences the consumers.. remember adulteration? Too much of anything is harmful – even competition. Regulators need to be vigilant for such practices becoming main stream).

Given the commodity nature of these businesses, one is bound to see hundreds of companies operating in the industry leading to intense rivalry being developed amongst them and prices being gravitated to lower levels from time to time taking profits for a hit. Had there being just one or two companies making and selling cement in the country, cement would have been one of the most profitable business to be in – just like oil has been (and would remain to be in the foreseeable future) for OPEC nations. Till the time they have the ability to adjust supply to demand, price is bound to stay in artificially established ‘equilibrium’. It may not work all the time, but it certainly works most of the time.


These are just some general pointers about such businesses which could act as clue for us. A business displaying all of these characteristics would probably fall in the category we are trying to study and which Buffett would probably term as ‘Gruesome’.


The challenge

Now since we have an approximate idea of how these businesses look like, we can move to the next section of how to go about valuing these and this is what is challenging. Due to the presence of operating leverage, volatile demand of end products leading to wide swing in realisations and cut throat competition, earnings are bound to be volatile.

And if the earnings are so volatile, then how should we go about valuing these? And if its earnings cannot be measured with accuracy, should we leave them aside and not consider owning them?

These are some interesting questions which would need some own space of their own which means there is yet another post to go for us to go before we wind-up this discussion.

Thanks for reading & happy new year!

Part 1: Dreadful Business Combinations!

Let’s start today’s discussion with a short quiz. Which of the following two companies, in absence of any other information, would you like to own?

You might feel as if things are rigged in favour of Company B! But that is what it is. B happens to be better in every aspect as against A. They both operate in the very same industry, at the very same geography, with similar asset & consumer base. Trust me, similarities are abound between these two.. because they are one and the same company! Yes, company’s name is India Cements. Column A has its reported numbers for the fiscal year 2005 and B represents what it did in the year 2007.

Before you think of hitting me, scroll back to the title of the post. We are here to discuss about some ‘dreadful’ business combinations or business characteristics which can (and does) take underlying business for a ride from time to time and how should we think about these as long term owners.


Understanding operating leverage

So what is this thing strange stuff called ‘operating leverage’ and what it has to do with the above example?

Without falling back upon text book definition, let me give you an example about it. Imagine there is swanky new residential complex planned in outskirts of the town. You happen to be the lucky one eligible to receive a permit to build a descent sized general store just opposite to this particular project. So you have your store up and running while the project is just weeks away from final completion after which several families are expected to move here. And after 12-18 months’ time there would be enough guys living in this locality that you start cribbing about traffic jams 🙂

Now, at the very first month of opening the store, your income statement would be quite under red since whether or not customers start flowing in, store rent has to be paid out – just like some other maintenance expenses. And things would continue to be more or less the same over next couple of months. But once the store has celebrated its 2nd birthday, you would be at peace to see the $$ coming. Why so, what has changed? So while your rent, staff cost, electricity and other overheads being more or less the same, increase in footfalls has led to higher inventory off-take which means higher commission earning and your bulk buying from distributors would mean that you are even eligible for some volume discount. And since your inventory are now selling quicker, you are able to convert stock into cash faster than other retailers which means you are paying early enough versus other retailers to your distributors thereby making you eligible for sweet little cash / early payment discount.

As investor, if you were to look at the initial financial numbers of this particular store say of first three months, the losses staring back at you from the income statement would suggest that it was a big time mistake in the first place. And the financials of the last three months before the end of second year would surprise you such that you may find it difficult to tell if they belong to that very store.

There has been no turnaround in the store owner’s fortunes. It is just that his business is such that most of costs are fixed and there is particular threshold of sales over and above which it starts earning money. If footfalls are expected to sustain at its current levels, it would mean that store would continue to earn those returns in the foreseeable future and those initial losses are a history.


Operating leverage, cyclicality in business earnings & intense competition

It is one of the most dreadful combinations in the business world – you can face them individually but together they can take the bottom line for a spin. Remember the example of the ‘two’ companies above?

To explain the point, let’s take our store example further. Your store looks good since its footfalls are expected to sustain at the current levels, margin spread is more or less stable and your location is pretty close to the residential complex. Now, what if there are ten more stores opened up adjacent to your store selling the very same merchandise as your store offer? Competitive rivalry would be intense which means footfalls would turn to quite volatile – fluctuating between 250 walk-ins a day to 25 depending upon the offers you dole out. This would invariably reduce your margins are well. And remember your major cost items, apart from inventory, like rent, salaries, electricity are fixed. And this means that your bottom-line is meant to show severe volatility from bursts of lucrative profits (say due to some differentiated merchandise which takes off and which other retailers don’t currently have) to prolonged period to stress.

This is exactly why this combination is very daunting (and also why you frequently do not see ten store selling same merchandise side by side in a tight locality). But there are some businesses of which these three are more or less the regular factors. Analysing these is challenging for us who are looking to own under-priced securities.

Which are these businesses? What all factors should we consider while evaluating them? How to be sure that we are not over paying for these? These are some of the questions I would try to answer in my next post which I hope would add to our understanding of such businesses.

They aren’t necessarily always bad as ‘investments’ (unlike their underlying tough business conditions), it is just that we need to clear our lenses with which we look at these and set our expectations straight while thinking about them.


Law of Small Numbers and Investing

A random event, by definition, does not lend itself to explanation, but collection of random events do behave in highly regular fashion – Daniel Kahneman

Currently, I’m reading one of the gem of a book ‘Thinking Fast & Slow’ by noble prize winner Daniel Kahneman which is basically, as one of my friend say, an encyclopaedia in the field of psychology & behavioural finance. A lot of the contents in the CFA level 3 subject on behavioural finance has been taken up from the experiments about which Kahneman talks at length in this book. Do yourself a favour and read (and absorb) what he has to say on the subject.

(F)law of small numbers

In one of the chapters he goes at length to discuss what he calls ‘law of small numbers’

He cites a study of kidney cancer carried on in United States –

A study of the incidence of kidney cancer in the 3,141 counties of the United States reveals a remarkable pattern. The counties in which the incidence of kidney cancer is lowest are mostly rural, sparsely populated, and located in traditionally Republican states in the Midwest, the South, and the West. What do you make of this?

Your mind has been very active in the last few seconds.. You deliberately searched memory and formulated hypotheses. Some effort was involved.. You probably rejected the idea that Republican politics provide protection against kidney cancer. Very likely, you ended up focusing on the fact that the counties with low incidence of cancer are mostly rural. The witty statisticians Howard Wainer and Harris Zwerling, from whom I learned this example, commented, “It is both easy and tempting to infer that their low cancer rates are directly due to the clean living of the rural lifestyle—no air pollution, no water pollution, access to fresh food without additives.” This makes perfect sense.

Now consider the counties in which the incidence of kidney cancer is highest. These ailing counties tend to be mostly rural, sparsely populated, and located in traditionally Republican states in the Midwest, the South, and the West. Tongue-in-cheek, Wainer and Zwerling comment: “It is easy to infer that their high cancer rates might be directly due to the poverty of the rural lifestyle—no access to good medical care, a high-fat diet, and too much alcohol, too much tobacco.” Something is wrong, of course. The rural lifestyle cannot explain both very high and very low incidence of kidney cancer.

Interesting right? Before we answer this, let’s dive down to another, easier to understand case study –

Imagine a large urn filled with marbles. Half the marbles are red, half are white. Next, imagine a very patient person (or a robot) who blindly draws 4 marbles from the urn, records the number of red balls in the sample, throws the balls back into the urn, and then does it all again, many times. If you summarize the results, you will find that the outcome “2 red, 2 white” occurs (almost exactly) 6 times as often as the outcome “4 red” or “4 white.” This relationship is a mathematical fact. You can predict the outcome of repeated sampling from an urn just as confidently as you can predict what will happen if you hit an egg with a hammer. You cannot predict every detail of how the shell will shatter, but you can be sure of the general idea. There is a difference: the satisfying sense of causation that you experience when thinking of a hammer hitting an egg is altogether absent when you think about sampling.

A related statistical fact is relevant to the cancer example. From the same urn, two very patient marble counters take turns. Jack draws 4 marbles on each trial, Jill draws 7. They both record each time they observe a homogeneous sample—all white or all red. If they go on long enough, Jack will observe such extreme outcomes more often than Jill—by a factor of 8 (the expected percentages are 12.5% and 1.56%). Again, no hammer, no causation, but a mathematical fact: samples of 4 marbles yield extreme results more often than samples of 7 marbles do. Now imagine the population of the United States as marbles in a giant urn. Some marbles are marked KC, for kidney cancer. You draw samples of marbles and populate each county in turn. Rural samples are smaller than other samples. Just as in the game of Jack and Jill, extreme outcomes (very high and/or very low cancer rates) are most likely to be found in sparsely populated counties. This is all there is to the story.

We all have read about ‘law of large numbers’ somewhere or the other. It basically says that as the number of experiments (samples) increases, the actual ratio of outcomes will converge on the theoretical or expected ratio of outcomes. But it is the flip side i.e. the law of small numbers which gets lesser attention intuitively and unless that is understood, we have not truly grasp the former concept.

There are few things to internalise here:

  1. Large samples are more precise than small samples. (What constitutes large enough sample size is another discussion entirely. )
  2. Small samples yield extreme results more often than large sample does.
  3. We, as humans, are bad intuitive statisticians as the reasoning behind the finding of kidney cancer survey highlights. This has been the recurring theme across the topics covered in this book.

So what this particular concept has to do with investing?

 A lot I would say when it comes to evaluating businesses and making decisions.

Studying limited or recent history of a business:

In my experience, most of the participants in the market look at last 2-3 year operating history of a business. Then based on those numbers and adjusting for what company has to say they extrapolate and make their own estimates for the next year or two. This seems to be highly inadequate. Ideally, for analysing a business, we need to assess how it has performed over an entire business cycle which includes peaks and troughs. Depending upon the business, these cycles could take anywhere between 3-8 years orbiting across multitude of business conditions.

Even a five-year analysis of past numbers could be inadequate. Remember 2003-08 period? Everything was hunky-dory during this period and someone who thought those margins and growth rates could sustain made lot of bad bets in 2007-2008 period. This could have been avoided if one rather looked at numbers from say the year 2000. FY2000-03 was a painful time for the economy as a whole. So essentially, for most of the businesses out there, 2000-2008 would have covered substantial part of their entire business cycle.

Clearly, two years does not seem to be reasonable sample size.

Implications while evaluating smaller businesses:

Businesses which rely on one narrow / niche type of an activity accounting for bulk of their revenues and operating over smaller geographical area are bound to see higher level of extreme fluctuations in their business operations versus a bigger, more operationally & geographically diversified company. This partly explains why we see higher volatility in stock prices of small caps & midcaps over large caps leading to higher beta – something which ‘modern portfolio theory’ shuns.

This does not mean smaller businesses are bad. In fact, they happen to be an ideal hunting ground for spotting upon mis-priced securities from time to time (but definitely not all the time). Only thing is that we acknowledge the occurrence of such fluctuations (in business as well as market quotations) and prepare to take advantage of the same as when time favours and not run-like-hell when things turn bad temporarily.

As Buffett once said ‘look at market fluctuations as your friend rather than your enemy; profit from folly rather than participate in it.’

Adopting an incorrect time horizon in your investment decision making:

This is one of the worst things investor can do for himself. Thinking in terms of days, weeks or months is hazardous for your investing life. This, in essence, runs polar opposite to what compounding aims to achieve.

One is bound to see higher fluctuations in market price over shorter periods and hence frequent conversion from black to red and then, if he sticks along, black. As Michael Mauboussin notes in one of the chapters of his book ‘More than you know’, probability of making positive spread over one day is 50% while over one year it rises impressively to 72%. Over 10 years, it is about 100%. By trading frequently and focusing on daily price movement, we trade in a 100% probability event for a 50% one. How rational is this?

There is lot to learn from Kahneman and this piece focused on some of the commonly overlooked follies relating to smaller numbers and how it applies to investing. It is recommended to read this book and absorb what it aims to deliver. I aim to post couple of the articles, as when I get time, on some of these concepts and how they are relevant for us as investors.

Thanks for reading. Cheers!