It is a normal practice of market analysts (especially buy side analysts) to extrapolate historical data for validating their pre-drawn conclusions. For example, there are numerous research reports and messages which rely on “low per capita consumption” and “moat” in India to make a strong case for buying a particular stock or sector. Based on this, many “new businesses” (and some established businesses) are given astronomical valuations. In this context, I find it pertinent to note the following:
· In the early 1990s, the number of Indian citizens using air transport for travelling purposes was extremely low. There was only one civil airline, viz., Indian Airlines. Then the civil aviation business was opened to private competition. Within a span of 2yr several private airlines started business, e.g., Sahara, NEPC, Damania, East West, Modiluft etc. All these ended as bankrupt in less than a decade. In the second tranche, some more private airlines started business, e.g., Jet Airways, Kingfisher, Deccan Airlines etc. Soon they became very popular with Jet Airways acquiring more than 50% market share (“moat”). These also ended bankrupt, along with Indian Airlines (later Air India).
· India was one of the worst countries in terms of financial inclusion in the early 1990s. Many first-generation private banks (Global Trust Bank, Time Bank, Bank of Punjab, Centurion Bank etc.)that were given license to start banking business in the 1990s, could not survive even for a decade.
· Numerous telecom operators and ISPs that commenced operation in the 1990s and early 2000s, ended up shutting their shop in less than a decade. This all happened in spite of very low telecom density.
· Steel and power sectors have been another anti-thesis for this “moat” and “low per capita consumption” argument. India still ranks amongst the lowest per capita consumers of steel and power. If we carefully analyze the banking sector crises during the 1990s and 2010s, these two sectors have been largely responsible for huge credit costs to the banks. There have been numerous bankruptcies and debt restructuring in these sectors in the past four decades. Even the sector leaders like SAIL and Tata Steel have been responsible for massive investors’ wealth destruction multiple times in these four decades. Several power producers like Lanco and GVK Power, ended bankrupt, while the leaders, like NTPC and Tata Power have underperformed the benchmark returns over the past two decades.
No one should be surprised if many of the businesses currently in investors’ favor, also wind up in the next one decade, despite huge scope for growth in businesses like AI, semiconductors, renewable energy, ecommerce, fintech, etc.
The point is that arguments like “per capita consumption” and “moat” may not necessarily work in a country where about 60% of the population is dependent on government support for necessities like food, cooking fuel, primary healthcare, education, and transport; and government is constitutionally mandated to keep policy framework largely socialist.
Investors accordingly need to adjust the denominator (total population) appropriately to calculate a realistic per capita number. The “moat” premium should be assigned to a business only after applying appropriate policy risk discount (Remember online gaming companies).
On the positive side, per capita income for the total addressable market will also be much higher than the official number. In particular, the investors must consider the following while evaluating a company for investment:
· It is not sufficient to only evaluate the debt servicing capabilities of the company. The ability to pay for the cost of other factors of production (e.g., wages, rent, plant and technology upgrade etc.) must also be evaluated.
· It is important to assess the dependence of the company on the global economy. It would be useful, for example, to incorporate the risk emerging from policy and geopolitical stressed developed economy consumers, high risk African and Latin American government owned (or regulated) businesses, highly regulated Chinese businesses, and volatile policy environment in the US, in the valuation matrix.
· Domestic policy risk, especially related to “sin consumption”; competition risk; perils of business with the government; and probability of economic offences, etc.
· Risk of obsolescence of products, technology and IPRs.
