chhaya, g., & nigam, p. (2015). value investing with price-earnings ratio in india. iup journal...

The IUP Journal of Applied Finance, Vol. 21, No. 2, 201534© 2015 IUP. All Rights Reserved.

Value Investing withPrice-Earnings Ratio in India

Researchers and investment professionals have argued that ‘value strategies’ based on low price relative to earnings,dividends, book value and other fundamental measures, have outperformed the corresponding ‘growth strategies’ andthe market. In this study, we endeavor to explore this premise in the Indian context by forming equity portfolios basedon price-earnings ratios and evaluate their ex post returns on both absolute and risk-adjusted measures. During thestudy period, i.e., October 2000 to September 2013, we sample the market each quarter, a total of 48 iterations, andexamine the portfolio returns for holding periods up to five years. We find evidence of statistically significant valuepremium in the Indian stock market.

Gunjan Chhaya* and Prashant Nigam**

* Senior Consultant, Ethical Energy Petrochem Strategies Pvt. Ltd., Ahmedabad, Gujarat, India.E-mail: [email protected]

* * Business Consultant, ITC Infotech India Ltd., Bengaluru, Karnataka, India.E-mail: [email protected]

IntroductionAt its core, ‘value investing’ is about investing in securities where the per-share ‘intrinsicvalue1’ of the firm is greater than the ‘price’ paid for acquiring it. Even as the price of a securitycan be precisely quantified at any instant from the exchange ticker, determining the ‘intrinsicvalue’ of a firm at best delivers an imprecise valuation. Among many other dimensions, thefirm’s current asset valuation, broadly represented by ‘book value’, and potential to earnprofits in future, estimated with past ‘earnings’, are the two most fundamental dimensions onwhich its ‘intrinsic value’ is estimated. Therefore the ‘Price-Book’ (P/B) ratio and the ‘Price-Earnings’ (P/E) ratio, which roughly represent these dimensions, are the two most popularmeasures for a firm’s current valuation.

For more than half a century now, researchers have investigated the effectiveness of‘value strategies2’ of varying complexity and sophistication, and found evidence of existenceof ‘value premium3’ based on backtesting of listed securities data. While earlier studies focusedon securities listed in US and other developed countries, there are fewer studies investigatingthe existence of ‘value premium’ in India.

1 Intrinsic value is defined as “that value which is justified by the facts, e.g., the assets, earnings, dividends,definite prospects, as distinct, let us say, from market quotations established by artificial manipulation ordistorted by psychological excesses” (Graham and Dodd, 1934).

2 Value strategies are investment strategies which choose to invest in securities based on one or more valuedimensions.

3 Value premium is the excess returns earned by value stocks relative to growth stocks.

mailto:[email protected]

mailto:[email protected]

35Value Investing with Price-Earnings Ratio in India

This study endeavors to supplement the body of research in the Indian context, by focusingon the ‘earnings’ dimension and investigating investment returns, (a) over a broad liquidlytraded base-universe of 1,111 securities; (b) across 13 years of study timespan; (c) in 48independently formed market samples; and (d) over periods ranging from one to five years ofinvestment holding. We do this by (i) categorizing the sampled securities on the basis of theirP/E ratios as either ‘value stocks4’ or ‘growth stocks5’; (ii) forming portfolios of value orgrowth securities; (iii) measuring their ex post portfolio returns; and (iv) empiricallydetermining return-differentials with respect to a ‘market benchmark’. By taking a largenumber of study samples, across a long study timespan, covering different economic phasesand stock market cycles, and diligent design of study methodology, we strive to avoid ormitigate the errors in sampling such as ‘survivorship bias6’ or ‘window dressing7’ of end-of-year corporate announcements.

We also seek to answer the fundamental questions – Are these return-differentialssignificant on both absolute and risk-adjusted basis? And, whether the observed ‘valuepremium’ is statistically significant? For this, we compare portfolio returns in absolute andwith respect to risk-adjusted measures such as Treynor’s ratio, Sharpe’s ratio, Jensen’s alpha,Modigliani’s M2 and Fama’s net-selectivity. We use ANOVA statistical procedures andassociated post-hoc tests to ascertain the statistical significance of our results. To the best ofour knowledge, no published research has examined the ‘earnings’ value dimension withsimilar statistical rigor and market representation. We find evidence of ‘value premium’ inthe broad base of Indian securities.

Literature ReviewInterest of value-based investing can be traced back to the early 1930s, when in their seminalbook Security Analysis, Graham and Dodd (1934) advocated that value strategies on a long-term basis outperform the market. These strategies focus on exploiting the ‘margin of safety’,that is the differential between the security’s ‘intrinsic value’ and its ‘market price’,advantageously.

The academic body of research on value strategies is extensive. Nicholson (1960) was anearly proponent of superior performance of low P/E ratio strategy. In his seminal paper, Basu(1977) studied the existence of value premium in US market and found that on an average,both on absolute and risk-adjusted basis, the securities with low P/E ratios deliveredsignificantly higher returns than high P/E securities. Challenging the Efficient Market

4 Value stocks are securities with low ratio of price to earnings, book value, cash flow or other fundamentalmeasures of value.

5 Growth stocks are securities with high ratio of price to earnings, book value, cash flow or other fundamentalmeasures of value.

6 Survivorship bias is the tendency for failed or delisted companies to be excluded from performance studiesbecause they no longer exist.

7 Window dressing is the tendency to take or not take actions prior to issuing announcements in order to improveappearance of the financial statements.

The IUP Journal of Applied Finance, Vol. 21, No. 2, 201536

Hypothesis (EMH), he explored the possibility of this ‘P/E effect’ explaining the violationsof Capital Asset Pricing Model (CAPM). Subsequently, Chan et al. (1991), Fama and French(1996 and 1998), Troung (2009), Athanassakos (2009 and 2011), Gharghori et al. (2013) andothers found evidence of value premiums on different dimensions like market capitalization(size), book value, earnings, cash flow, sales growth, debt, dividends, etc. in the internationalmarkets.

However, the source of value premiums was keenly debated. DeBondt and Thaler (1985and 1987) based their explanation on behavioral psychology in the form of ‘investoroverreaction’. They argued that investors tend to overreact to recent information, and pricethe securities at either pessimistic or optimistic extremities. Subsequently, when these extremeexpectations prove erroneous, prices revert towards their base-rates, resulting in the observedreturn anomalies. While Fama and French (1992) also found evidence of value premium,they postulated its existence because value strategies are fundamentally risky. That is, higherreturn from value strategies is essentially a compensation for assuming higher fundamentalrisk. Contradicting this hypothesis, Lakonishok et al. (1994) proposed that value strategiesare not fundamentally riskier, but exploit the suboptimal investment behavior. They suggestthat factors such as judgmental errors, disregard of price for ‘good’ companies, screeningconstraints, investment ‘justifiability’, perceived ‘safety’, career concerns and short-timehorizons may be instrumental in causing suboptimal behavior of a typical investor.

Meanwhile, it was observed that incorporating certain risk-mitigation filters, duringformation of value portfolios, significantly improved the value premiums. Kelly et al. (2008)found that the use of business failure prediction tests such as Altman Z-score and Castagnaand Matolcsy model yielded superior portfolio returns for low P/E ratio securities. Similarly,Anderson and Brooks (2006) found that by smoothening the yearly variances of businessearnings, by constructing a P/E ratio based on a multi-year average instead of previous year‘earnings’, the value premium yield almost doubled for a potential investor. In time, as evidenceof existence of value premium was observed over greater number of markets, investors gainedconfidence and started employing value strategies in their investments. Bhattacharya andGalpin (2011) investigated 46 countries across the globe and found that value-weightedportfolios as an investment vehicle have gained popularity over the last quarter century.Specifically for India, however, even with a trend of increasing popularity, it is ranked amongthe bottom three countries where value-weighted portfolios are the least popular.

In the Indian context, equity prices and returns have been studied from various perspectives.Within the value investing framework, Sehgal and Tripathi (2007) evidenced the existenceof value effect in the Indian market with respect to four different value measures. Kumar et al.(2010) applied a neural network algorithm to a collection of accounting and value ratios toobserve superior portfolio returns, while Deb (2012) studied the book value dimension on abroad base dataset of listed companies and found prevalence of value premium on bothabsolute and risk-adjusted basis. Trivedi and Behera (2012) examined and observed linkagesbetween macroeconomic factors and equity prices.


Data and MethodologyThis study examines the relationship between P/E ratios and the investment performance ofequity securities in India. Even as it replicates much of Basu (1977) methodology, it seeks tostrengthen the validity of the findings with the use of improved statistical procedures. Foreach of the sampling periods under consideration, we ranked securities as per their P/E ratiosand formed five8 mutually exclusive portfolios based on this rank. Two additional portfolios,representing (i) high P/E securities excluding those of loss-making firms; and (ii) ‘marketbenchmark’, were also formed. Returns for these portfolios were compared for different holdingperiods.

The data was drawn from the ‘Prowess’ database of Centre for Monitoring Indian Economy(CMIE). We use daily closing ‘price’, trailing 12 months’ ‘earnings’ and ‘dividends’ data of thefirms listed on Bombay Stock Exchange (BSE). The study spans a period of 13 years, with adatabase of 1,111 liquid-investment-eligible securities which were traded on BSE betweenOctober 2000 and October 2013.

Liquidity, the degree to which a security can be transacted in the market without affectingits price, is an important criterion for investors. Many large and institutional investorsprefer, or are constrained, to avoid illiquid securities. During the study period, securities ofmore than 6,000 firms were listed on BSE. However, not all these securities were liquid andtherefore were not equally investment-worthy. We therefore use S&P BSE 500 index9 (BSE500), a group of 500 securities selected as per liquidity ranking, as a ‘liquidity qualifier’ inorder to exclude illiquid securities with either low transaction volume or value. Tables 1 and2 indicate the average daily trading volume and market capitalization for the major indiceson BSE, respectively, for the month of August 2012. It is evident that BSE 500 is representativeof the broad market, as on an aggregate basis its constituents contribute to about 85% ofaverage daily trading volume and 95% of market capitalization. A total of 1,111 securitiessatisfied the ‘liquidity qualifier’ requirements, at least at one point-of-time during the

8 Although the formation of five portfolios is arbitrary, it ensures a large number of stocks for each portfolio.9 S&P BSE 500 index inclusion criteria are, for preceding three months, (a) minimum 75% trading days;

(b) average market capitalization (75% weight); and (c) average turnover (25% weight).

BSE Indices No. of Average Daily Turnover

RemarksSecurities (in mn) (in %)

BSE SENSEX 30 4,986 25 Not Considered

BSE 100 100 9,371 47 Not Considered


BSE 500 500 16,847 85 Considered

All BSE Securities 5,058 19,902 100 Not Considered

Table 1: Average Daily Transaction Volumes of Constituents of Various Indicesof BSE for the Month of August 2012


BSE Indices No. of Market Capitalization

RemarksSecurities (in bn) (in %)

BSE SENSEX 30 29,343 48 Not Considered



BSE 500 500 58,022 95 Considered

All BSE Securities 5,058 61,288 100 Not Considered

Table 2: Average Daily Market Capitalization of Constituentsof Various Indices of BSE for the Month of August 2012

10 website: www.rbi.org11 Borel’s Law of Large Numbers (LLN) states that if an experiment is repeated a large number of times, independently

under identical conditions, then the proportion of times that any specified event occurs approximately equals theprobability of the event’s occurrence on any particular trial; the larger the number of repetitions, the better theapproximations tend to be.

12 In practice, the reciprocal of P/E ratio, earning yield, is used in ranking the stocks. Therefore, securities withnegative earnings (loss-making) are included in the highest P/E portfolio.

13 Since including loss-making securities in highest P/E portfolio is questionable, an additional highest P/E portfoliois formed after the loss-making securities are removed from the sample universe.

13-year timespan of the study. We use the annualized yields of 91-day Government of India(GoI) T-bills, available on the website of Reserve Bank of India (RBI)10, as a proxy for ‘risk-freeasset’ data.

The study samples the market on the last date of each quarter. Thus, from December 31,2000 and September 30, 2012, we form 48 sets of overlapping but independent portfoliosamples. Though the choice of sampling frequency is arbitrary, it is consistent with thefrequency of earnings updates which are announced on quarterly basis. This choice offrequency differs from Basu (1977) who samples and forms portfolios every year. However, thehigher number of samples not only leads to greater confidence in our results as per Law ofLarge Numbers (LLN)11, but also allows for greater cyclical changes in ‘price’ and ‘earnings’data.

For each sample, the sample universe consists of securities which are: (a) listed on BSE;(b) whose past 15 months’ accounting data is available; and (c) are constituents of BSE 500on the date of portfolio formation.

We computed the P/E ratio for all the securities. The numerator was defined as reportedclosing price of a single common stock as on that date and the denominator as the reportedannual per-share earnings available for common stockholders. We ranked12 and sorted thesecurities as per this ratio and formed five equally weighted portfolios (lowest P/E = A, B, C,D, E = highest P/E), 100 securities each, as per this ranking. An additional portfolio, sixth13,of 100 high P/E securities but with non-negative earnings (E*) was formed which eliminatedloss-making firms from its holdings. As aggregates, we categorize securities of low P/E portfolios

http://www.rbi.org


A and B as ‘value stocks’ and those of high P/E portfolios D, E and E* as ‘growth stocks’. Werequired ‘market benchmark’ returns for our analysis, for which a seventh portfolio (M) withequally weighted constituents of S&P BSE 100 index14 (BSE 100) was formed as a marketproxy. These portfolios could be considered representative of the different investmentpreferences which the investors may have, with respect to the ‘earnings’ dimension, in theirequity investments.

Portfolio securities are assumed to be purchased on the first trading day of the subsequentquarter, at their respective closing prices. We tracked and computed annual returns for eachof these portfolios for five subsequent holding periods (one, two, three, four and five years)with a buy-and-hold policy, assuming equal investments across all the portfolios.

Data for both ‘earnings’ and ‘price’ are available at different frequencies and with varyingtime lags. While the closing prices of the securities are available at the end of each trading daywithout any lag, the financial data such as earnings are announced each quarter with a lag ofmaximum 6015 days. We therefore consider trailing 12-month earnings ending previous quarterfor computing the P/E ratios. For example, the 27th sample period is computed on June 30,2007, which is a non-trading day. The P/E ratios for all securities were computed with theirrespective closing price as on June 29, 2007, the previous trading day, as the numerator and12-month per-share earnings as on March 31, 2007 as the denominator. The portfolios thusformed from ranked P/E ratios were assumed to be purchased on July 2, 2007, and sold on July1, 2008 for one-year holding period.

The portfolio returns were computed with dividends considered to be reinvested in therespective security on the date of their disbursal. We also adjusted individual security returnsto reflect any changes in the equity structure of the firm during the holding period. In case asecurity ceased to qualify on liquidity requirements, at any point of time, it was immediatelydivested and the proceeds were invested in risk-free assets for the remainder of the holdingperiod. Portfolio returns thus computed were annualized16 for 365-day year in order to makethem comparable with other sampling results.

For each holding period, the study evaluates portfolio performances across differentsamples. We compared aggregate performances of different portfolios on both absolute andrisk-adjusted basis. Absolute returns are compared, based on summary statistics such as averagereturns, standard deviation and portfolio’s Beta with respect to the market proxy. While weused measures such as Treynor’s ‘reward to volatility’ ratio; Sharpe’s ‘reward to variability’ratio; and its more intuitive representation Modigliani’s risk-adjusted performance (M2)measure; Jensen’s alpha, a risk-adjusted absolute performance measure; and Fama’s net-

14 S&P BSE 100 index inclusion criteria are: for preceding three months, (a) minimum 95% trading days;(b) average market capitalization (75% weight); (c) average turnover (18.75% weight); and (d) impact cost(6.25% weight).

15 BSE listing agreement clause 41 requires all listed firms to submit their quarterly financial results within 45 daysfor intermediate quarters and within 60 days for the last quarter of the financial year.

16 All returns are shown on continuously compounding basis as per the industry convention.


selectivity, a measure of excess return (can be negative) realized above that required tocompensate for the total risk undertaken, for risk-adjusted comparative.

The study uses statistical procedures to test the portfolio returns and infer about theexistence of statistically significant value premiums. Since we have more than two portfoliosto compare, conducting a series of t-tests could lead to higher probability of making Type I17

errors. We therefore used ANOVA18 procedure to compare multiple portfolio return-premiumsand test for statistically significant difference within them. Prior to initiating ANOVAcomputations, we tested the validity of homogeneity of variance assumption with Levene’sF-test. In instances where this assumption was met, we used one-way ANOVA; while inthose where this assumption was found to be violated, Welch’s ANOVA was used instead.Finally, we undertake either TukeyHSD or Games-Howell post hoc test, as is appropriate, forpairwise comparisons and to ascertain if the observed return-differentials are statisticallysignificant.

Results and DiscussionThe portfolio returns were computed for all the 48 samples between January 2001 andSeptember 2013. We therefore have 48 samples of one-year holding returns for each of theportfolios. Similarly, we have 44, 40, 36, and 32 samples for two, three, four, and five-yearholding returns, respectively. Table 3 shows the summary statistics of pooled portfolioperformances for all the holding periods. For all the portfolios we find that as the holdingperiod increases, (a) average returns improve from one to four years, after which they deterioratein the fifth-year; (b) volatility in returns consistently decreases, indicating increasingconfidence in the expected return values. All the portfolios, excepting portfolio D, havetheir portfolio beta greater than unity, which indicates high sensitivity to market volatilityand are therefore considered to have assumed greater risk as per CAPM. On an aggregatebasis, it is observed that ‘value stocks’ (portfolios A and B) consistently show superior absolutereturns than ‘growth stocks’ (portfolios D, E and E*) for all the holding periods.

17 Comparing group means: concluding that they are different when in reality they are not, would be a ‘falsepositive’ or Type I error; while concluding that they are not different when in reality they are, would be a ‘falsenegative’ or Type II error.

18 Analysis of Variance (ANOVA) is a statistical hypothesis testing technique which is used to determine whetherthere are any significant differences between means of three or more independent groups.

Holding Low P/E High P/E MarketPeriod A

B C DE

E*M

1 Year

Mean 22.4% 22.3% 20.4% 19.6% 13.6% 14.2% 20.1%

SD 41.4% 39.2% 37.5% 35.7% 40.9% 39.1% 38.0%

Beta 1.176 1.134 1.100 1.020 1.162 1.127 1.000

Table 3: Portfolio Summary Statistics


Holding Low P/E High P/E MarketPeriod A

B C DE

E*M

Table 3 (Cont.)

2 Years

Mean 24.0% 22.3% 22.8% 19.7% 15.8% 15.2% 21.5%

SD 25.3% 24.4% 25.8% 22.7% 25.8% 24.5% 24.0%

Beta 1.168 1.129 1.165 1.022 1.198 1.107 1.000

3 Years

Mean 25.1% 24.0% 24.0% 20.5% 17.6% 16.7% 22.9%

SD 19.4% 19.9% 20.2% 16.6% 21.3% 19.7% 19.0%

Beta 1.168 1.199 1.189 0.954 1.270 1.135 1.000

4 Years

Mean 26.0% 24.1% 24.0% 21.1% 18.4% 17.8% 23.2%

SD 17.1% 16.3% 16.1% 14.1% 17.9% 16.2% 16.0%

Beta 1.246 1.173 1.156 1.005 1.285 1.145 1.000

5 Years

Mean 24.2% 22.7% 22.8% 19.7% 17.2% 16.5% 21.7%

SD 15.4% 14.9% 14.6% 11.4% 16.0% 13.7% 14.3%

Beta 1.250 1.197 1.193 0.917 1.303 1.074 1.000

1 Year

Treynor’s Ratio 0.137 0.141 0.128 0.130 0.063 0.070 0.123

Sharpe’s Ratio 0.389 0.408 0.375 0.371 0.178 0.200 0.375

Modigliani’s M2 19.1% 19.7% 18.6% 18.5% 12.2% 12.9% 18.6%

Jensen’s Alpha 1.6% 2.0% 0.5% 0.7% –7.0% –6.0% –

t-statistic (0.69) (1.05) (0.31) (0.35) (–3.08) (–3.01)

Fama’s Net 0.6% 1.3% –0.0% –0.2% –8.1% –6.9% –Selectivity

2 Years

Treynor’s Ratio 0.153 0.143 0.143 0.132 0.081 0.081 0.135

Sharpe’s Ratio 0.707 0.662 0.645 0.594 0.374 0.369 0.654

Modigliani’s M2 20.7% 19.8% 19.4% 18.4% 13.9% 13.8% 19.6%

Jensen’s Alpha 2.2% 0.9% 1.0% –0.2% –6.3% –5.7% –

t-statistic (1.53) (0.70) (0.58) (–0.11) (–4.48) (–3.52)

Holding Period

Low P/E High P/E MarketA

B C DE

E*M

Table 4: Portfolio Risk-Adjusted Performance Measures


Table 4 shows the portfolio performances on different risk-adjusted evaluation measures.Treynor’s ratio is the beta adjusted excess portfolio returns with respect to risk-free assets.When compared with market benchmark, it measures the portfolio’s risk-premium per unitof market volatility. Sharpe’s ratio is similar to Treynor’s ratio but uses, instead, standarddeviation as a measure of risk. It therefore measures the portfolio’s risk-premium per unit ofvariability. Modigliani’s M2 is a more intuitive representation of Sharpe’s ratio. It adjusts theportfolio’s risk-level to match the benchmark’s level and measures the returns for this risk-matched portfolio. All the three measures discussed above are relative risk-adjusted measures,that is, they require comparison with market benchmark to infer on superior or inferiorperformances. Jensen’s alpha, on the other hand, is an absolute risk-adjusted measure. It

Holding Period

Low P/E High P/E MarketA

B C DE

E*M

Table 4 (Cont.)

Fama’s Net 1.3% 0.2% –0.3% –1.4% –7.2% –7.0% –Selectivity

3 Years

Treynor’s Ratio 0.168 0.150 0.151 0.152 0.091 0.094 0.150

Sharpe’s Ratio 1.014 0.901 0.890 0.869 0.543 0.543 0.932

Modigliani’s M2 22.4% 20.6% 20.4% 20.1% 14.8% 14.8% 21.1%

Jensen’s Alpha 2.2% 0.0% 0.3% 0.2% –7.3% –6.2% –

t-statistic (2.15) (0.02) (0.18) (0.13) (–5.66) (–3.91)

Fama’s Net 1.6% –0.6% –0.8% –1.1% –8.3% –7.6% –Selectivity

4 Years

Treynor’s Ratio 0.160 0.154 0.156 0.150 0.096 0.103 0.156

Sharpe’s Ratio 1.168 1.112 1.121 1.068 0.692 0.726 1.182

Modigliani’s M2 21.5% 20.7% 20.8% 20.1% 15.2% 15.6% 21.6%

Jensen’s Alpha 0.6% –0.2% 0.1% –0.5% –7.5% –6.0% –

t-statistic (0.48) (–0.14) (0.06) (–0.44) (–5.13) (–3.98)

Fama’s Net –0.2% –1.1% –1.0% –1.6% –8.8% –7.4% –Selectivity

5 Years

Treynor’s Ratio 0.145 0.138 0.140 0.149 0.085 0.097 0.147

Sharpe’s Ratio 1.181 1.115 1.142 1.193 0.693 0.760 1.241

Modigliani’s M2 20.1% 19.3% 19.6% 20.2% 14.3% 15.1% 20.8%

Jensen’s Alpha –0.2% –1.0% –0.8% 0.1% –8.0% –5.4% –

t-statistic (–0.16) (–0.77) (–0.74) (0.11) (–6.76) (–3.78)

Fama’s Net –0.9% –1.9% –1.4% –0.5% –8.8% –6.6% –Selectivity


measures the portfolio’s excess return with respect to that expected from CAPM, given thesystematic19 riskiness of the portfolio. Fama’s net-selectivity is an evolved absolute risk-adjusted measure. It measures the portfolio’s excess return, after accounting for systematicriskiness of the portfolio and unsystematic20 ‘concentration’ risk assumed by the portfolioinvestment strategy.

On both the relative and absolute risk-adjusted performance measures, we observe similarevaluation inferences. We find that the value stocks show superior risk-adjusted returns forone, two and three-year holding periods, and inferior risk-adjusted returns thereafter. Whilegrowth stocks on the other hand show consistently inferior risk-adjusted returns for all theholding periods. We also observe that the highest P/E portfolios E and E* show substantiallyinferior performance on all the three risk-adjusted measures. This observation is strengthenedas we observe the t-statistic of Jensen’s alpha, where their negative alpha values for portfoliosE and E* show very high significance.

Table 5 shows the results of Levene’s F-test, which tests for homogeneity of variancewithin the portfolios. This test assumes that there is no difference between the group variancesas its null hypothesis; thus the rejection of its null hypothesis indicates that the assumptionof homogeneity of variance is violated. We find that for one-year holding period the F-valueis significant and the null hypothesis is rejected, i.e., at least one of the portfolios hassignificantly different variance. For all other holding periods, the null hypothesis is retainedand homogeneity of variance assumption is met.

19 Systematic risk or ‘market risk’ is the uncertainty inherent in the entire market (or segment). Investors cannotreduce systematic risk by ‘diversifying’ within the same asset class (or segment).

20 Unsystematic risk or ‘specific risk’ is the uncertainty assumed by concentrating investments. Investor canreduce unsystematic risk by ‘diversifying’ within the same asset class (or segment).

Holding Period (Between) (Within) F-Value Significance RemarksDF DF p-Value

1 Year 5 282 2.299 0.045* Null Rejected

2 Years 5 258 0.342 0.887 Null Retained




Table 5: Equality of Variance

Note: (a) Level of significance set a priori at = 0.05 to test the assumption of homogeneity of variance.(b) * indicates significance at 0.05% level.

Homogeneity of variance is the key underlying assumption for one-way ANOVA.Therefore, for one-year holding period, where this assumption is found to be violated, weapply Welch’s ANOVA, which does not assume equal variance between groups, and adjustthe F-statistic accordingly. For all the other holding periods, as the underlying assumption ismet, we undertake one-way ANOVA. Table 6 shows ANOVA results for different holding


Holding Period (Between) (Within) F-Value Significance RemarksDF DF p-Value

1 Year $ 5 131.16 3.58 0.005** Null Rejected

2 Years 5 258.00 7.76 0.000*** Null Rejected




Table 6: Analysis of Variance

Note: $ For 1-year holding period, we adjust F-statistic with Welch’s procedure as the equal variance assumptionwas violated.

** and *** indicate significance at 0.01% and 0.001% levels, respectively.

periods. The null hypothesis in ANOVA is that the population means from which thesamples are selected are equal, and the alternative hypothesis is that at least one of groupmeans significantly differs from other portfolio means. Since we see that for all the holdingperiods the F-statistic is significant, we reject the null hypothesis and conclude that there isat least one portfolio whose returns are significantly different from other portfolio returns.

Table 7 shows the results of the post hoc tests. These tests undertake multiple pairwisecomparisons to find the statistical significance of the differences between portfolio means.As is appropriate, we use Games-Howell test for the one-year holding comparisons, andTukeyHSD test for all the other holding periods. We find that value stocks consistently showsuperior performance as compared to growth stocks, indicating statistically significant valuepremium for all the holding periods.

Table 7: Post Hoc Multiple Comparison Tests

Holding Period Multiple Pairwise Comparison

B C D E E*

1 Year$ A 0.16 2.07 2.89 8.86^ 8.29^

Games-Howell Test B 1.90 2.73 8.70* 8.12*

C 0.83 6.79 6.22

D 5.97 5.40

E –0.57

2 Years A 1.73 1.20 4.36 8.21*** 8.84***

TukeyHSD Test B –0.53 2.63 6.48** 7.11**

C 3.16 7.01** 7.64**

D 3.85 4.47

E 0.63

3 Years A 1.70 1.69 5.17* 8.07*** 8.96***

TukeyHSD Test B –0.02 3.47 6.37*** 7.25***


ConclusionThis study examines the performance of low P/E stocks over high P/E stocks in the Indianstock market from the year 2000 to 2013. We find that, for all buy-and-hold horizons, the‘value stocks’ outperform and the ‘growth stocks’ underperform the ‘market benchmark’ fora major part of the study period. This performance differential is observed on both absolute aswell as risk-adjusted performance measures such as Treynor’s ratio, Sharpe’s ratio, Modigliani’sM2, Jensen’s alpha and Fama’s net-selectivity. On an aggregate basis, evidence of consistentvalue premium was observed with respect to ‘earnings’ dimension. This was statisticallyvalidated by ANOVA. Finally, post hoc multiple pairwise comparison tests indicate that theperformance differential is more pronounced between the extreme value and growth portfoliopairs. Thus, the study findings suggest that risk does not adequately explain the existence of‘value premium’ in India. The study findings are relevant not only for individual investorsinterested in investing in India, but also for institutional investors who manage thematicinvestments and are interested in confirming the pervasiveness of value premiums in India.

Limitations: We used BSE 500 as a liquidity qualifier which limited our investment universeto 500 stocks only at any point of time. Though liquidity represents a practical constraint formany large investors, it does exclude from the study a majority of the listed but illiquid stockswhich may be of interest to an individual investor. The stock returns computation does not

Note: $ For 1-year holding period we use Games-Howell Test, which is an appropriate post hoc test for Welch’sANOVA.

*** , ** and * indicate significance at 0.001%, 0.01% and 0.05% levels, respectively; and ^ indicatessignificance at 0.1%.

Table 7 (Cont.)

Holding Period Multiple Pairwise Comparison

B C D E E*

C 3.48 6.38*** 7.27***

D 2.90 3.79

E 0.89

4 Years A 1.84 1.92 4.87** 7.55*** 8.19***

TukeyHSD Test B 0.08 3.03 5.71*** 6.35***

C 2.95 5.63*** 6.27***

D 2.68 3.33

E 0.64

5 Years A 1.56 1.44 4.51** 7.04*** 7.74***

TukeyHSD Test B –0.12 2.96 5.48*** 6.18***

C 3.08 5.60*** 6.30***

D 2.52 3.22^

E 0.70


consider transaction and holding costs like brokerage charges, transaction taxes, accountmaintenance charges, etc.

Also, at a conceptual level, under certain circumstances, value stocks may indeed carrygreater investment risk than growth stocks. Special situations in the firm’s business likesevere financial distress or bankruptcy, industrial supply overcapacity or demand destruction,sudden policy changes like import or export restrictions, geopolitical threats, regulatory orlegislative impositions, etc. may lead to extreme valuations and low P/E ratio. Some of thesespecial situations have quantitative proxies, but others remain a matter of subjectivequalitative assessment. Value investors typically do incorporate such business dimensions intheir investment framework. Even as we do not factor them in this study, we consider it atopic for interesting future research.

The study evaluates performance differential with respect to only one accountingdimension: ‘earnings’. Fundamental investing may include scrutiny of stocks on other valuedimensions like book value, size, cash flow, dividend, etc.; business dimensions like equity-debt structure, growth, capital efficiency, sales, profitability, etc.; and qualitative dimensionslike industry characteristics, competition, product life cycle, regulations, demand-supply,etc. In addition, macroeconomic factors may also be influential in determining the extent ofsuccess of an investment strategy. We would like to augment our study by evaluating equityreturns with respect to a combination of value and business dimensions.

Acknowledgment: The authors gratefully acknowledge the assistance extended by Entrepreneur Development

Institute of India (EDII) for providing access to its library resources. The authors also thank Dr. Anoop Singh

of IME Department, IIT Kanpur, for providing guidance for the study.

References1. Anderson Keith and Brooks Chris (2006), “The Long-Term Price-Earnings Ratio”, Journal

of Business Finance and Accounting, Vol. 33, Nos. 7-8, pp. 1063-1086.

2. Athanassakos George (2009), “Value versus Growth Stock Returns and the Value Premium:The Canadian Experience 1985–2005”, Canadian Journal of Administrative Sciences,Vol. 26, No. 2, pp. 109-121.

3. Athanassakos George (2011), “The Performance, Pervasiveness and Determinants of ValuePremium in Different US Exchanges: 1985-2006”, Journal of Investment Management,Vol. 9, No. 3, pp. 33-73.

4. Basu Sanjoy (1977), “Investment Performance of Common Stocks in Relation to TheirPrice-Earnings Ratios: A Test of Efficient Market Hypothesis”, Journal of Finance, Vol. 32,No. 3, pp. 663-682.

5. Bhattacharya Utpal and Galpin Neal (2011), “The Global Rise of the Value-WeightedPortfolio”, Journal of Financial and Quantitative Analysis, Vol. 46, No. 3. pp. 737-756.


6. Chan Louis, Hamao Yasushi and Lakonishok Josef (1991), “Fundamentals and StockReturns in Japan”, Journal of Finance, Vol. 46, No. 5, pp. 1739-1764.

7. Deb Soumya Guha (2012), “Value Versus Growth: Evidence from India”, IUP Journal ofApplied Finance, Vol. 18, No. 2, pp. 48-62.

8. DeBondt Werner and Thaler Richard (1985), “Does the Stock Market Overreact”, Journalof Finance, Vol. 40, No. 3, pp. 793-805.

9. DeBondt Werner and Thaler Richard (1987), “Further Evidence on Investor Overreactionand Stock Market Seasonality”, Journal of Finance, Vol. 42, No. 3, pp. 557-581.

10. Fama Eugene (1972), “Components of Investment Performance”, Journal of Finance,Vol. 27, No. 3, pp. 551-567.

11. Fama Eugene and French Kenneth (1992), “The Cross-Section of Expected StockReturns”, Journal of Finance, Vol. 47, No. 2, pp. 427-465.

12. Fama Eugene and French Kenneth (1996), “Multifactor Explanation of Asset PricingAnomalies”, Journal of Finance, Vol. 51, No. 1, pp. 55-84.

13. Fama Eugene and French Kenneth (1998), “Value versus Growth: The InternationalEvidence”, Journal of Finance, Vol. 53, No. 6, pp. 1975-1999.

14. Gharghori Philip, Stryjkowski Sebastian and Veeraraghavan Madhu (2013), “Value versusGrowth: Australian Evidence”, Accounting and Finance, Vol. 53, No. 2, pp. 393-417.

15. Graham Benjamin and Dodd David (1934), Security Analysis, McGraw-Hill of New York,New York.

16. Jensen Michael (1968), “The Performance of Mutual Funds in the Period 1954-1964”,Journal of Finance, Vol. 23, No. 2, pp. 389-416.

17. Kelly Simone, McClean Jenna and McNamara Ray (2008), “The Low P/E Effect andAbnormal Returns for Australian Industrial Firms”, 21st Australasian Finance andBanking Conference, Sydney.

18. Kumar Krishna M S, Subramanian S and Rao U S (2010), “Enhancing Stock Selection inIndian Stock Market Using Value Investment Criteria: An Application of ArtificialNeural Networks”, IUP Journal of Accounting Research and Audit Practices, Vol. 9, No. 4,pp. 54-67.

19. Lakonishok Josef, Shleifer Andrei and Vishny Robert (1994), “Contrarian Investment,Extrapolation and Risk”, Journal of Finance, Vol. 49, No. 5, pp. 1541-1578.

20. Modigliani Franco and Modigliani Leah (1977), “Risk-Adjusted Performance”, Journalof Portfolio Management, Vol. 23, No. 2, pp. 45-54.

21. Nicholson Francis (1960), “Price-Earnings Ratios”, Financial Analysts Journal, Vol. 16,No. 4, pp. 43-45.


22. Sehgal Sanjay and Tripathi Vanita (2007), “Value Effect in Indian Stock Market”, IUPJournal of Applied Finance, Vol. 13, No. 1, pp. 23-36.

23. Sharpe William (1966), “Mutual Fund Performance”, Journal of Business, Vol. 39, No. 1(Supplement), pp. 119-138.

24. Treynor Jack (1965), “How to Rate Management Investment Funds”, Harvard BusinessReview, Vol. 43, Nos. 1-2, pp. 63-75.

25. Trivedi Pushpa and Behera Samir Ranjan (2012), “Macroeconomic Fundamentals asDeterminants of Equity Prices: An Empirical Analysis for India”, IUP Journal of AppliedFinance, Vol. 18, No. 3, pp. 5-30.

26. Truong Cameron (2009), “Value Investing Using Price Earnings Ratio in New Zealand”,University of Auckland Business Review, Vol. 11, No. 1, pp. 1-7.

Reference # 01J-2015-04-03-01

Copyright of IUP Journal of Applied Finance is the property of IUP Publications and itscontent may not be copied or emailed to multiple sites or posted to a listserv without thecopyright holder's express written permission. However, users may print, download, or emailarticles for individual use.

chhaya, g., & nigam, p. (2015). value investing with price-earnings ratio in india. iup journal...

Documents