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Saturday, August 12, 2017

Indian corporations have weak earnings growth

by Ajay Shah.

The broad set of Indian listed companies have a high trailing P/E ratio. This suggests that the market believes there will be high earnings growth in the future.

Some finance practitioners back out an earnings time series as Nifty market capitalisation divided by Nifty P/E. This `Implied Nifty Earnings' series shows strong growth over long time horizons.

In this article, we show that this quick-and-dirty method has an upward bias in the estimation of aggregate earnings growth. In truth, earnings growth by Indian firms has been stalled for a decade.

The trillion dollar question

Figure 1: The long time-series of the CMIE Cospi P/E ratio

The graph above shows the long time-series of the trailing P/E ratio of the CMIE Cospi index, which measures the broad market valuation. This shows that we are near some of the highest valuations in history.

These high P/E ratios would generally suggest that the stock market expects that a period of great earning growth is around the corner. It's important to look back at the recent history of earnings growth in order to evaluate this optimism.

Estimating aggregate earnings: a quick and dirty method

The P/E ratio is market capitalisation divided by earnings. Hence earnings is market capitalisation divided by the P/E ratio. It's easy to obtain a time-series of the Nifty P/E (from NSE), and the Nifty market capitalisation (obtained by summing up the market capitalisation of all Nifty member firms as seen in the CMIE database). This gives the time-series:

Figure 2: Time series of Nifty earnings (nominal rupees), quick and dirty method

As Nifty market capitalisation is measured in rupees, and the P/E ratio is dimensionless, the division yields an earnings value in rupees.

This shows pretty good growth in the earnings of the Nifty companies. In the latest few years, the growth is slow, but when compared with a decade ago, the earnings expansion is remarkable. Overall, it's a gain of 18$\times$ in 18 years, which is quite a performance. It is consistent with the common view that India is a high earnings-growth economy.

The quick and dirty method over-estimates earnings growth

The set of firms that make up Nifty changes through time. From 1996 to 2017, there were 118 firms which have been a member of Nifty atleast once.

The Nifty components at time $t_1$ are often different from those prevalent at time $t_2$. Some firms are added and some are removed. We tend to think that these are a few random fluctuations which would tend to cancel out. However, the changes in the set are non-random, and they do not cancel out.

The management of Nifty uses a rule set that roughly summarises to this: (a) A pool of eligible firms is formed where the firms have adequate stock market liquidity based on the Impact Cost measure, and (b) If an eligible firm is over 2$\times$ larger (by market capitalisation) than the smallest incumbent, then a set change is effected where the smallest incumbent is removed and the large new liquid firm is brought in. The earnings of the new entrant will generally be higher than the earnings of the smallest incumbent who is removed, as the market value of the new entrant is over 2$\times$ higher.

Here is one example, from the April-May-June 2016 quarter. In this quarter, three firms were removed (Vedanta, Cairn India, Punjab National Bank) and three firms were added (Aurobindo Pharma, Bharti Infratel and Eicher Motors) to Nifty. The remaining 47 firms were unchanged. Let's pull together the information about earnings across these changes.

Q1 2016 (Rs. million)
Q2 2016 (Rs. million)
Change (Per cent)
The 47 common firms 735,021 635,358 -14
Cairn India-2,459
Punjab National Bank-53,671
Aurobindo Pharma3,910
Bharti Infratel14,769
Eicher Motors3,371
The full 50 at a point in time 717,713 657,408 -8

Table 1: Example of how the quick and dirty method over-estimates earnings growth

The best estimator of earnings growth is that which is made using the identical set of firms observed at two points in time. In the above example, there are 47 firms in Nifty who were present at both points in time. Their aggregate earnings declined from Rs.735B to Rs.635B, a decline of 14%.

Three firms were present in Q1 2016 -- Vedanta, Cairn, PNB -- and when their earnings data is used, the aggregate earnings of the 50 firms in Nifty at that point in time works out to Rs.717B. These were replaced by Aurobindo Pharma, Bharti Infratel, Eicher Motors in Q2 2016, and when their earnings data is used, the aggregate earnings of the 50 firms in Nifty at that point in time works out to Rs.657B. The earnings growth obtained by comparing these two inconsistent sets was -8%, which is a more optimistic picture when compared with the decline of 14% for the consistent set.

There is a big discrepancy, of 6 percentage points across one quarter, and the direction of the bias in in favour of greater optimism.

The wrong method (merely comparing the profits across inconsistent sets across time) does not just introduce random noise, it is biased. It systematically overstates earnings growth of the Nifty set.

What actually happened to earnings growth of Indian firms?

How should we do this right? We exercise care with the following steps:

  1. Oil companies have extreme earnings fluctuations based on fluctuations of global crude oil prices. Their profits do not describe what is going on in India. Finance companies have problems in earnings data, such as the concealment of bad assets by banks. Hence, we look at non-oil non-finance companies only. Aggregation of accounting data for this set of firms is an excellent source of insight into India's business cycle fluctuations.
  2. At every two consecutive quarters, we construct a set of listed firms which are observed in both quarters. We sum up the earnings of this set at each of the two quarters. These two summed earnings are comparable across time, as they pertain to the identical set of firms.
  3. This yields a nominal percentage growth of aggregate earnings from one quarter to the next.
  4. We start an index at 100 and cumulate it up through time using each of these carefully constructed estimates of earnings growth.

This yields the following picture of the index of nominal aggregate earnings of the Indian corporate sector:

Figure 3: Index of earnings of all listed non-finance non-oil companies

This tells a story where the average earnings index grew from 126 in 2000 to 996 in 2010, but declined to 783.98 in the Oct-Dec 2016 quarter. Nominal earnings has stagnated in the last decade.

Ruling out an alternative explanation

There is one problem of non-comparability in the above analysis. The time-series of the P/E that was shown in Figure 1 pertains to the 2500 odd firms in the CMIE Cospi index. The time-series of earnings that's shown in Figure 3 pertains to the performance of all listed non-finance non-oil firms. These two groups are slightly different. Could this difference be an important issue? In order to examine this, we apply the careful method (that was used for Figure 3) to the full universe of all listed firms. The two series are superposed here:

Figure 4: Index of listed non-finance non-oil firms earnings and lll listed firm earnings (nominal)

This shows that there are some differences between the two groups, but this difference is small. From Oct-Dec 2006 to Oct-Dec 2016, i.e. in the latest decade, we have three estimates of the compound growth rate of earnings:

The quick and dirty method8.19%
All listed firms, the careful method-1.11%
Non-finance non-oil listed firms, the careful method      -0.41%

The compound average growth rate of earnings of all listed firms is similar to that of non-finance non-oil firms. Both estimates are roughly 8 percentage points per year below the quick and dirty method.


Fine points in handling firm data matter! It appears easy and expedient to use the Nifty market capitalisation time series, and the Nifty P/E ratio time series, to back out an estimate of Nifty earnings time series. The use of the phrase `Implied Nifty Earnings' sets off an analogy with the genius of implied volatility. However, this procedure is highly misleading. Let's superpose the wrong and the correct index time-series on one graph:

Figure 5: Superposing the quick and dirty earnings index and the careful index

The quick and dirty method suggests 18$\times$ earnings growth in 18 years. The correct method shows 8$\times$ earnings growth in the same period, and stagnation in the last decade.

The stock market believes that a great wave of earnings growth is around the corner, and India is generally considered a market with high earnings growth. However, earnings growth has been elusive for the last decade. More generally, in the past, the Indian stock market has done well on differentiating between firms -- in voting with a high P/E ratio for firms that will do well in the future -- but has fared poorly at macroeconomic thinking.

Reproducible research

This R program when run using data from CMIE Prowess DX (Mar-2017 vintage) replicates Figure 3 above.

I thank Nilesh Shah and Mahesh Vyas for valuable discussions. Pramod Sinha wrote the code and it was audited by Dhananjay Ghei and Shekhar Hari Kumar.

Monday, August 07, 2017

Interesting readings

Elements of the recovery by Ajay Shah in Business Standard, August 6, 2017.

A judgment for the ages by Chinmayi Arun in The Hindu, August 3, 2017.

Needed, a financial redressal agency editorial in The Economic Times, August 2, 2017. Also see.

India's complicated infrastructure story by Ashwini Mehra in Mint, August 2, 2017.

The Past Week Proves That Trump Is Destroying Our Democracy by Yascha Mounk in The New York Times, August 1, 2017.

Equity Derivatives versus Cash Equities in India by Jayanth Varma in Prof. Jayanth R. Varma’s Financial Markets Blog, July 31, 2017. Also see: Strategic thinking in financial markets policy, July 24, 2017.

Artificial Intelligence Is Stuck. Here's How to Move It Forward. by Gary Marcus in The New York Times, July 29, 2017. Also see: Project Tanzanite: Obtaining fundamental progress in the macroeconomics of developing countries, October 24, 2011.

Trump is something the nation did not know it needed by George F. Will in The Washington Post, July 28, 2017.

Why the Scariest Nuclear Threat May Be Coming from Inside the White House by Michael Lewis in Vanityfair, July 26, 2017.

Agrarian crisis: the challenge of a small farmer economy by Sudipto Mundle in Mint, July 21, 2017.

Emerging infectious diseases, One Health and India by Shahid Jameel in The Hindu, July 15, 2017.

Bitcoins are business as usual in Bengaluru by Aditi Phadnis in Business Standard, July 15, 2017.

History of Aadhaar: How Nandan's core team came together by Shankkar Aiyar in Yourstory, July 13, 2017.

How Do We Contend With Trump's Defiance of 'Norms'? by Emily Bazelon in The New York Times, July 11, 2017.

The Danger of Deconsolidation: The Democratic Disconnect by Roberto Stefan Foa and Yascha Mounk in Journal of democracy, July, 2016.

The empty brain by Robert Epstein in Aeon, May 18, 2016.

Wednesday, August 02, 2017

RBI's proposal for a Public Credit Registry

by Prasanth Regy.

In his recent speech at RBI's Annual Statistics Day Conference, RBI Deputy Governor Viral Acharya called for the creation of a Public Credit Registry (PCR). A PCR is a comprehensive database of all borrowings in the country. The Deputy Governor suggested that submission of information to this registry should be compulsory, and that it should be managed by the RBI. He added that the RBI intended to establish a task force for setting up the PCR.

In this article, we argue that there is no market failure that justifies the establishment of a PCR, and that there is no evidence that a PCR is required for an efficient credit market. Given that India has a surfeit of credit information entities, the creation of a new PCR in the RBI is unlikely to help.

The market failure test

The public economics approach is that markets work reasonably well in most situations. State intervention should be avoided if possible. Public choice theory suggests that a bureaucracy will try to expand its own budget and functions. A proposal by an agency that tries to enlarge itself should be treated with scepticism.

In this light, does India require a PCR run by the RBI? World Bank data shows that most countries around the world do not have PCRs. Countries such as the US, UK, Canada, Australia, New Zealand, Netherlands, Sweden, Norway, Japan, South Korea, all have highly developed credit markets without having PCRs. In these countries, private sector credit bureaus fulfil this function. The international examples the Deputy Governor cited in his speech (Thomson Reuters Dealstreet, and Dun & Bradstreet) are both private entities. The major Consumer Reporting Agencies in the US, as well as the Credit Reference Agencies in the UK, are all private entities functioning in competitive markets.

These examples suggest that the credit information industry need not suffer from market failures, as long as appropriate statutory frameworks are in place to deal with issues such as the privacy, safety, and sharing of information. The absence of PCRs in most well-functioning credit markets indicate that PCRs are not required for competitive credit markets.

Too much of a good thing

India already has a large number of entities involved in providing credit information. There are four Credit Information Companies (CICs), all regulated by the RBI. It is mandatory for institutional lenders to provide credit information to these companies. The RBI has extensive powers over CICs: even their membership fees and annual fees are decided by the RBI. Apart from this, the RBI has previously created the Central Repository of Information on Large Credits (CRILC). The Central Registry of Securitisation, Asset Reconstruction, and Security Interest (CERSAI) was created by the government to record the creation of security interests over property. The MCA21 database of the Ministry of Corporate Affairs is used to record charges on the assets of companies.

The Insolvency and Bankruptcy Code (IBC) has introduced yet another type of entity to this space: Information Utilities (IUs). The design of IUs has been thought through by the Bankruptcy Law Reforms Committee and by the Working Group on Information Utilities. The Insolvency and Bankruptcy Board of India (IBBI) has recently issued regulations that enable the registration and operation of IUs, though no IUs have started operations as of yet.

To justify a PCR, the RBI needs to explain not just what market failures it seeks to solve, but also why all these other entities were (or, in the case of IUs, will be) ineffective in solving those market failures, and why PCRs will succeed.


To make a case for having a PCR in India, the RBI needs to articulate what market failures the PCR will solve. We have seen above that market failures are not necessarily a feature of the credit information industry, and that PCRs are not necessary to achieve competitive credit markets.

In India, a number of entities exist that are related to providing credit information. They include four CICs, CRILC, CERSAI, other databases in the RBI and the Ministry of Corporate Affairs, and the upcoming IUs. Given the existence of all these entities, the RBI also needs to argue why the existing entities are not sufficient, and why yet another government agency needs to be set up. In the absence of such articulation, it is not clear that further state intervention in the form of PCRs is warranted.


World Bank, Credit Registry.

Shah, Ajay, Solving market failures through information interventions, Ajay Shah's blog, April 2015.

Government of India, The Report of the Bankruptcy Law Reforms Committee, chaired by Dr T K Vishwanathan, 4 November 2015.

Government of India, The Insolvency and Bankruptcy Code, 2016.

Ministry of Corporate Affairs, The Report Working Group on Information Utilities, chaired by K V R Murty, 11 January 2017.

Insolvency and Bankruptcy Board of India, Insolvency and Bankruptcy Board of India (Information Utilities) Regulations, 2017.


Prasanth Regy is a researcher at the National Institute of Public Finance and Policy, New Delhi.

The author would like to thank Anirudh Burman, Pratik Datta, and an anonymous referee, for helpful comments.