Stock Selection in India: A Price Multiple Perspective

Oscar Wilde once said, “A cynic is one who knows the price of everything but the value of none.” On similar lines, every asset, whether financial or real, has a value. However, the complexity with which one can derive this value differs with different valuation techniques. The role of valuation is different in diverse situations. When one talks about equity valuation, the two predominant techniques being used are discounted cash flow techniques (DCF) and relative valuation. The discounted cash flow technique, also known as fundamental valuation, is a valuation technique wherein the intrinsic value is calculated by discounting future cash flow projections at an appropriate discount rate to arrive at the present value.

Relative valuation, on the other hand, is a technique wherein the value of an asset is based on the price of the comparable assets in the same asset class. It is calculated by standardizing the price of the asset with common variables such as earnings, cash flows, book value, or sales. The first step in relative valuation is to standardize prices by converting them into multiples of corporate fundamentals and the second step is to find similar firms in terms of cash flows, risk, and growth potential. Over time it has been used by all the financial analysts, whether equity analysts, hedge fund analysts, venture capitalists, trading firms, investment bankers or merger and acquisition experts.

There are three major reasons for the popularity of relative valuation. The first is the ease with which one can compute it with fewer assumptions and secondly, it is simple to understand and easier to present to clients. Finally, for privately held companies it is the most acceptable technique to be used. Some of the predominant price multiples used by the industry are earnings multiples called as price to earnings (P/E) multiple in which case the earning could be of the past four quarters called as trailing P/E or expected earnings per share in the next year called as forward P/E . Similarly, analysts use book value as a value driver to calculate price-to-book value (P/B) ratio.

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Some analysts believe that earnings could be manipulated and hence a true measure of valuation could be the historical book value of the company. They therefore use the P/B ratio. Those who believe that book value is not the right measure of the true value of the asset use the replacement cost of the asset measure called as Tobin’s Q. Another important ratio being used by analysts is to standardize price with the sales revenue to get price-to-sales (P/S) ratio. In many cases, earnings or book values of a company may become negative and hence P/E or P/B ratios cannot be measured. In such cases, analysts rely on P/S as a measure of relative valuation.

The other reasons for using revenue multiples are that unlike P/E and P/B, revenue multiples are not influenced by accounting decisions as well as they are less volatile. Many analysts believe that a variant of P/E ratio i.e. price to cash flow (P/CF) is a better measure than the P/E ratio as the latter is impacted by the accounting treatment of certain non-cash items like depreciation and amortization, to name a few.

The three major price multiples viz. P/E, P/B and P/S are being used in equity valuation because of different informational content being given by each of them. It has been shown in literature that the P/E ratio is determined by three important determinants, namely dividend payout ratio, risk and growth. A firm with higher dividend payout and growth should have higher P/E ratio whereas a firm with higher risk is expected to provide a lower P/E. Theoretically, P/B ratio has an incremental content as it has an additional determinant in the form of firm profitability (return on equity) besides other drivers as in the case of P/E ratios. The P/S ratio, as comparable to P/E, is additionally driven by net profit margin (NPM). Since NPM forms part of ROE estimation in the Du Pont analysis, the P/B ratio seems to be rich in information as compared to other price multiples.

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There have been various dimensions of relative valuation on which prior research has been conducted both internationally as well as in India. One dimension has been to test how a price multiple behaves vis-a-vis its fundamental determinants. In the Indian context it has been found that the P/B is the only multiple with which fundamental determinants have shown some sort of relationship. Similarly, internationally it has been found that out of the the four price multiples viz. P/E, P/B, P/S and P/CF, P/E outperforms all other price multiples in terms of valuing equity. It has also been observed that forward multiples have performed better than historical price multiples.

However, in the Indian context the P/B ratio outperforms all other price multiples for valuing equity. International literature has also tried to combine the two price multiples viz. P/E and P/B because of their different informational content and shown that combined price multiples performed better than standalone price multiples. However, in the Indian environment it has been found that the standalone price multiples perform better than any combination of price multiples and P/B emerges a clear winner among standalone price multiples. One dimension that remained relatively untouched in prior literature was to find out the role of price multiples in forming trading strategies. We empirically examined this dimension for India.

Three prominent price multiples viz. P/E, P/B and P/S were used to form portfolios. A strategy may be formed by forming portfolios on standalone price multiples like price-to-earnings (P/E), price-to-book value (P/B), and price-to-sales (P/S) ratios. Another approach can be to combine price multiples and their key value drivers for portfolio formation, as for example combining  P/E with growth (g), P/B with return-on-equity (ROE) and P/S with net profit margin (NPM). In empirical work, researchers use growth as an important driver (over payout and risk) of P/E because it has led to generation of an important ratio called as PE/g ratio which is popularly used by the investment management industry.

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P/E ratios less than their expected growth are viewed as undervalued. Generally speaking, analysts who are tracking companies in the growth sector tend to use this ratio as it allows them to control differences in growth across various firms. Researchers prefer PE/g to P/E ratio owing to the difference in growth potential and the role it plays in equity valuation, especially for new economy stocks. ROE and NPM are used as the key value drivers for P/B and P/S respectively as they are the additional determinants (apart from payout, risk and growth) for these financial ratios respectively.

Portfolios may also be constructed following a bivariate criterion wherein securities are selected on the basis of price multiples and their value drivers. For example, low P/E and high-growth stocks are generally expected to be undervalued while high P/E and low-growth stocks may be overvalued. Similar conclusions can be drawn for low P/B (P/S) and high ROE (NPM) stocks as well as high P/B (P/S) and low ROE (NPM) stocks. One can also adopt multivariate sorting criteria wherein the information contained in the entire three price multiples and their key value drivers are employed for security selection.

We examined all the different approaches to determine an optimal strategy which provides the highest extra-normal profit. If the additional information in value drivers is incrementally useful for portfolio formation, the procedures that combine price multiples and value drivers should result in better performing portfolios vis-a-vis those based on standalone price multiples. Further, if each price multiple (combined with its value driver) provides information which is at least partly different compared to other price multiples (combined with their value drivers), a strategy based on all the price multiples as well as their value drivers should outperform all other trading strategies based on such price multiples.

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Fig 1: Different strategies of portfolio formation

We tried to empirically test the role of price multiples in forming trading strategies using the above mentioned four portfolio formations’ criteria. BSE 500 companies were used to form portfolios from a period of July 2001 to April 2013. All the financial information of the BSE 500 companies, viz. share prices, price multiples, profitability ratios and market capitalisation has been taken from CMIE Prowess Database. The BSE 500 index has been used as a market proxy. The BSE 500 index was constructed in the year 1999 and it represents around 93 per cent of the total market capitalisation of around 20 sectors. Up to 91 day Treasury bill yields have been used as risk-free proxy, information for which has been obtained from the Reserve Bank of India (RBI) monthly handbook of statistics published on the RBI website.

We sorted the sample securities in four possible ways. First, we ranked the stocks on the basis of standalone price multiples i.e. P/E, P/B and P/S. The stocks were classified into eight groups based on each price multiple criterion and hence eight equally weighted portfolios were formed. While P1 represents the stocks with the lowest price multiple (bottom 12.5 per cent), P8 comprises stocks with highest price multiple (top 12.5 per cent). In the second stage we wished to verify if value drivers combined with the respective price multiple generate better performing portfolios owing to use of additional information. We combined the prime value drivers with their respective price multiples in two different ways. We used PE/g, PB/ROE and PS/NPM as single sorting criteria for generating three different sets of eight portfolios based on the estimated procedure mentioned above. The only difference is that PE/g, PB/ROE and PS/NPM now replace P/E, P/B and P/S.

In a third approach, we used a variant of price multiple – value driver combination. We adopted a double sorting procedure and ranked the sample stocks on the basis of price multiple information into four groups. While P1 comprises the bottom 25 per cent stocks, P4 contains the top 25 per cent stocks based on price multiple. We then independently ranked the securities on the basis of value driver i.e. growth in case of P/E, ROE in case of P/B and NPM in case of P/S and formed two groups: g1 bottom 50 per cent and g2 top 50 per cent. The intersection of these two rankings helps us in generating eight portfolios. P1, i.e.P1g2, a portfolio with the lowest price multiple but high value driver, is expected to be the loser.

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The fourth and final portfolio formation process adopted by us involved combining the information contained in all the three price multiples as well as their value drivers. We performed independent sorts based on P/E, P/B, P/S, g, ROE and NPM, and form two groups i.e. low and high for each of the sorts. This generates 26 or 64 portfolios. Theoretically, for a portfolio which exhibits low P/E, P/B and P/S and high g, ROE and NPM should be the most undervalued and hence promise the highest returns. In contrast, the portfolio comprising stocks with high P/E, P/B and P/S and low g, ROE and NPM should be overvalued and therefore be the worst performing.

If the additional information in value drivers is incrementally useful for portfolio formation, the procedures that combine price multiples and value drivers should result in better performing portfolios vis-a-vis those based on standalone price multiples. Further, if each price multiple (combined with its value driver) provides information which is at least partly different compared to other price multiples (combined with their value drivers), a strategy based on all the price multiples as well as their value drivers should outperform all the other trading strategies based on price multiples.

We find that for all cases portfolios formed on low price multiples (P1) tend to beat portfolios formed on high price multiples. Portfolios formed on low P/E, P/B and P/S provide an unadjusted annualised return of approximately 42, 50 and45 per cent respectively. Out of the three price multiples, P/B tends to outperform the other two and hence is a better price multiple for forming portfolios.

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In order to adjust for risk we further employed risk models using regression analysis. In portfolio management the entire risk of a security can be divided into two parts viz. systematic risk or market risk and unsystematic risk. As we form portfolios of securities, the unsystematic risk can be eliminated whereas systematic risk can be measured by market beta. In the capital asset pricing model (CAPM) framework, systematic risk is measured as the sensitivity of a portfolio’s returns to the market index returns. This sensitivity coefficient is popularly known as market beta. So we first included market beta into our regressions using a variant of the capital asset pricing model and tested if statistically significant returns are generated after employing beta.

There are two other risk factors which have been widely used in literature viz. size risk and value or distress risk. Investors demand premium for investing in any small-sized company as they are more risky compared to large companies because of various factors like lack of product diversification, greater financial risk or because of high cost of borrowings, to name a few. Similarly, investors also demand value premium for investing in low P/B stocks as these stocks are fundamentally weak.

It means that their sales and profit growth rates are historically low as compared to high P/B stocks. Both these measures of risk are used in a three-factor framework popularly known as Fama-French Three Factor Model. We filtered our portfolio returns for the three risk factors in case of all the strategies and generated a measure of extra-normal return popularly known as alpha. We find that even after accounting for the risk factors, low P/B still remained the winner in terms of generating extra-normal profits. On a risk-adjusted basis we find that P/B provides return of around 28 per cent which is greater than return provided by any other strategy.

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We also tried to verify if long-short trading strategies could be developed based on price multiples (and their value drivers). We estimated alpha differentials for winner and loser portfolios and tested them for statistical significance at the 5 per cent level. None of the alpha differentials appear to be statistically significant; thus ruling out application of long-short strategies in the Indian environment. The results confirm that adding complexity in a portfolio formation process does not improve profitability of the trading strategies. Our findings are also consistent with the theoretical argument provided earlier that P/B is the most information-rich ratio in the price multiple family and hence its empirical success does not come as a surprise.

From the perspective of portfolio managers and other investment traders the following key observations can be drawn:

• P/B is the best multiple for portfolio formation as it provides annual return of 28 per cent on risk-adjusted basis over approximately 12 years for India.

• Complex strategies based on combination of value drivers underperform simple P/B-based value strategy. Hence, in case of price multiples, more complex security selection criteria are not recommended. 

• Investment managers should focus on the long side of a trading strategy which involves buying on the basis of P/B multiple. Long–short strategies are not desirable owing to a) high implicit cost of financing implied by short selling, b) regulatory restrictions on short selling, and c) lack of depth in stock futures market for many stocks that form part of the BSE 500 index.

About the authors

• Prof. Sanjay Sehgal is Professor of Finance, Department of Financial Studies, South Campus, University of Delhi.

E-mail: sanjayfin15@gmail.com.

• Dr. Asheesh Pandey is Associate Professor Finance, Fortune Institute of International Business,

• New Delhi. E-mail: asheeshpandey@rediffmail.com.