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Charts & Analysis

Making Sense of Sector Valuation Multiples

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A chart first to set context. Range of enterprise value (EV/EBIT) multiples prevailing by sector in India.

It shows two things:

  1. Investor willingness-to-pay. Higher the multiple, the more optimistic investors are about a sector. Ok. not particularly blinding insight
  2. Range between multiples. The narrower the range, the more similar (undifferentiated) firms in a sector are considered. Note the multiples are the 25th and 75th percentiles of all the firms, this eliminates one-off outliers which might otherwise have skewed the ranges.

For example, Personal Products (FMCG) commands willingness-to-pay (for the stock) but there is significant variation in how investors perceive firms in the sector. Large companies, the usual suspects like HUL, Marico, Dabur trade at eye-watering multiples while smaller companies like Cupid Ltd, JHS Svengaard Laboratories, J.L. Morison are valued modestly in comparison.

On the other hand, Consumer Electronics has a (relatively) narrower range of multiples implying overall optimism about the sector.

Now, take a look at Telecom Services.

Who decides these multiples? Fundamentalists (the Graham-Buffett kind) will say, the market values them based on future prospects. After all share prices are a function of future earning potential.

But let’s face it, future prospects tend to be driven largely by extrapolation of recent performance. And that makes sense. Only Venture Capital firms can afford to justify investing in firms with shambolic financial metrics. The rest of us have to be more conservative.

So what DO most investors consider?

We looked at a range of financial metrics including growth rates of top and bottomline, cash generation, profitability, even book value to see which of these seem to drive valuation multiples. We ran a multiple linear regression with the current median sector multiple as the output and the various historical financial metrics as inputs.

A regression is meant to provide the strength of the relationship between the input and outputs. For example, a regression of ice cream sales (output) against peak daily temperatures (input) tells us whether a relationship exists, and if yes, the strength of that relationship.

The outcome in our case looks something like this.

Factor Investing

Factors and their impact on sector multiples

If you’re not a statistics person, don’t be unduly impressed. This is quick and dirty, has a bunch of approximations, and is not exhaustive i.e. a blunt instrument.

If you are a statistics person, take a few deep breaths before pointing out all the flaws. This is quick and dirty, has a bunch of approximations, and is not exhaustive.

It does point us in the direction of which financial metrics seem to have some relationship with the outcome of sector multiple. It does this with t stats and p-values. p-values lower than 0.05 imply a relationship not explained away by randomness.

Hence, the three metrics of interest (highlighted in yellow) are

  • Average YoY Sales Growth for last 3 years
  • Average ROCE over the last 3 years, and
  • Cash generated by business operations as percentage of Net Profit over the last 3 years.

EBIT growth also shows some promise but lower than the other three.

The next three charts plot these individual factors against enterprise multiples.

X-axis shows performance of the sector on that factor. Y-axis shows the multiple the market gives that sector. You can rollover the chart to find sectors of your interest.

The charts don’t convey a lot except suggest a certain loose relationship. So we combine the three factors to look at a composite relationship.

So what?

What can / should we make of this?

  • The hate for some sectors like Telecom, Realty, Broadcasting & Cable TV is well-earned given how poorly they have delivered on all three factors we identified
  • There is disparity in the multiples of sectors delivering similar performance. Paper products versus Department Stores. Exploration & Production versus Household Appliances
  • Some sectors like Personal Products are way up there in terms of perception while others like Refineries are in the dumps

Again, these factors are not exhaustive. Valuations are the result of many interconnected factors and can stay seemingly irrational for long periods. For instance, sectors dominated by PSUs are likely to rightly be valued lower because of possible government actions. In other cases, like airlines, oil prices play a significant role.

Going back to the multiple ranges chart at the start of the post, you could look for sectors with high dispersion in valuation multiples, understand the differences between the firms at both ends of the spectrum. And then look for those that have characteristics similar to the high-multiple companies.

Another interpretation could be to think of the sectors to the right and bottom third of the chart as undervalued and fertile grounds for value picks. To exit or short the ones on the left. But then we also know from our various experiments that momentum is a thing, and it works, most of the time.

No one said investing is easy.

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