Do High Book-To-Market Stocks Offer Returns to Fundamental Analysis in Indi

Decision, Vol. 36, No.2, August, 2009

Do High Book-to-Market Stocks Offer Returns toFundamental Analysis in India?

Navdeep Aggarwal

Mohit Gupta

The objective of this paper is to investigate if an accounting-based fundamental analysis strategy can helpinvestors earn excess returns on a portfolio of high book-to-market companies in India. The strategy weadopt is based on Piotroski (2000) who identified 9 fundamental signals to form a composite score(F_SCORE) capable of separating out ex-post winners from losers among high book-to-market companiesin the US stock market. However, it is not clear whether the results of such a strategy could be directlyapplied to Indian stock markets,since there is evidence that market efficiency in India is at the most weakform. Also, during the 1990s when trading and investments were mostly domestic, the markets experiencedscams like the Harshad Mehta scam. However, of late, India has been among the most favoured investmentdestinations in the world. Using the F_SCORE framework from Piotroski (2000) but a different approachto portfolio formation (for practical purposes) we find convincing evidence that a fundamental analysisbased investment strategy for high book-to-market companies can separate winners from eventual losers.We show that portfolios with high F_SCORE (7 to 9) provide excellent returns far superior to market returnsand risk-adjusted returns. Portfolios with low F_SCORE (0 to 3) offer very poor returns and oftenunderperform the markets or required risk-adjusted returns. A value investor could shift distribution ofreturns rightwards by investing only in high F_SCORE companies. Shorting low F_SCORE could furtherenhance returns.

Keywords: High book-to-market stocks, fundamental analysis, Indian stock market

Reserch Papers

Navdeep Aggarwal is Professor at the Department of Business Management, Punjab Agricultural University,Ludhiana. E-mail: [email protected]

Mohit Gupta is Assistant Professor at the Department of Business Management, Punjab AgriculturalUniversity, Ludhiana.

Investment strategies based on book-to-market ratio have been investigated quite often infinance and accounting literature. Since the work by Graham and Dodd (1934), investmentstrategies that focused on buying stocks with low price-to-book (called value stocks) haveproduced higher returns than strategies based on growth stocks. In fact, evidences show apositive and relatively strong correlation between the book-to-market ratio of a firm and itsfuture stock performance. Many studies such as Rosenberg et al. (1984), Fama and French(1992; 1993; 1996), Lakonishok et al. (1994), Piotroski (2000), and Lopes and Galdi (2007)document the success of the high book-to-market strategy though the explanation aboutvalue-based strategies outperforming growth-based strategies has still remained a controversy(Doukas et al., 2002). For example, Fama and French (1992) attribute the book-to-marketeffect to unobserved risk factors captured by this ratio, but Lakonishok et al. (1994) arguein favor of mispricing.

Introduction

Do High Book-to-Market Stocks Offer Returns to Fundamental Analysis in India? 156


It is well established in practice and literature that value stocks have outperformed growthstocks. However, the practical problem that can potentially be faced by many investors is:“Are all stocks, which are high in book-to-market ratio, value stocks1?” Piotroski (2000)emphasizes that less than 44% of all high book-to-market companies2 earn positive marketadjusted returns in two years following the formation of the portfolio. Clearly, there issomething more than just the book-to-market ratio for a stock to be classified as value stockand this very ‘something’ could help investors discriminate, ex ante, between eventual strongand weak companies.

Mohanram (2005) argues that financial statement analysis attempts to separate ex-post winnersfrom losers on the basis of information from financial statements that is not correctly reflectedin stock prices. Piotroski (2000) applies financial statement analysis (accounting-basedfundamental analysis) on a broad portfolio of high book-to-market (HBM) American firms andshows that investors can create a stronger value portfolio. He argues that such analysis isespecially effective in HBM firms for three reasons:

1. HBM firms are often ignored by market participants and are hardly followed byanalysts

2. HBM firms have limited access to most informal information dissemination channels;therefore, published financial statements represent the most reliable and most accessiblesource of information

3. HBM firms tend to be financially distressed, which causes their valuation to be primarilybased on published accounting information.

From a valuation perspective also value stocks are inherently more conducive to financialstatement analysis than growth stocks. Growth stock valuations are typically based on long-term forecasts of sales and the resultant cash flows, where non-financial information plays avital role. Moreover, most of the predictability in growth stock returns appears to be momentumdriven (Asness, 1997). In contrast, the valuation of value stocks focuses on recent changesin firm fundamentals (such as financial leverage, liquidity, profitability, and cash flows). Theassessment of these characteristics is most readily accomplished through a careful analysisof financial statements.

As all the arguments presented above are universal in nature, accounting based fundamentalanalysis, thus, should work well in any market, including emerging markets where marketefficiency tends to be lower and the amount of predictability of returns tends to be higher(Coorey and Wickremsinghey, 2007; Harvey, 1995).

India, a strong emerging market, offers a unique opportunity to apply and test the profitabilityof accounting based fundamental analysis. This is so because in the 1990s, when trading and

1 In this paper, the terms value stocks/ portfolio and high book-to-market stocks/ portfolio have been usedsynonymously.2 Though literature distinguishes the two, in this paper, the terms value stocks and value companies havebeen used synonymously



investments were largely domestic only, the markets weathered financial scams like the HarshadMehta scam pointing towards the extremely poor efficiency in the market. Literature too hasestablished weak market efficiency in India (Gupta and Basu, 2007; Pandey, 2003). On theother hand the country is a constituent of the BRIC nations and has attracted huge investmentsand trading activity from the world. Whether domestic or foreign, the size of investments hasbeen swelling at a large rate. For example, from January 1999 to August 2008 the net FIIpurchases stood at Rs. 217,282 crores in equities with total purchases of Rs. 2,555,981 croresand net sales of Rs. 2,337,850 crores. During the same period mutual funds had net purchasesof Rs. 36,269 crores in equities with total purchases of Rs. 637,203 crores and net sales of Rs.604,305 crores. Similarly, very hectic activity both by mutual funds and FIIs has been observedin debt markets making India the biggest capital market in the region. Additionally, the numberof companies accessing global markets for raising capital through ADRs or GDRs or commercialborrowings etc. has seen a rise at a frantic pace. This is not likely to happen if the markets werestill prone to scams. Amidst these conflicting scenarios of reportedly weak market efficiencyand strong investment inflows, one would wonder if an accounting based fundamental analysisstrategy would really help investors creating a portfolio of value stocks that could earn themexcess returns in the Indian capital market.

The results of our study suggest that accounting based fundamental analysis can help investorsdiscriminate between eventual strong performing and weak HBM stocks. A portfolio of suchstrong stocks could help investors outperform the market both on gross and risk adjustedreturns basis.

The structure of the rest of the paper is as follows - in the next section we review past researchon value strategy / book-to-market effect and fundamental analysis. We then present the dataand methodology detailing the fundamental signals, selection of companies and performanceanalysis. In the proceeding section we present the empirical results followed by conclusions.

Past Research

For more than two decades, the value strategy has attracted significant attention from bothacademics and practitioners. Many of them focus on the vary book-to-market effect. Forexample, Fama and French (1992; 1993; 2006) provide evidence that value stocks earn positiverisk-adjusted returns and also outperform growth stocks. Other researchers, such as Piotroski(2000) and Lopes and Galdi (2007) lay emphasis on the very process behind the value strategy,called as fundamental analysis. The past research in the area therefore, has been classifiedinto two subgroups, namely, the book-to-market effect and the fundamental analysis.

The Book-to-Market Effect

The value strategy involves buying stocks that have low prices relative to earnings, dividends,historical prices, book assets, or other measures of value. A number of researchers includingAggarwal and Wang (2006), Chan and Chen (1991), Fama and French (1992; 1993; 1996;2006), LaPorta (1996), LaPorta et al. (1997) and Rosenberg et al. (1984) provide convincingevidence that stocks with high book values of equity relative to their market values outperform



the market. In fact, Fama and French (2006) find that a value premium exists for 1926 to 2005and that it is not confined to small firms.

Though there seems to be a consensus on the superior performance by high book-to-marketstocks, different explanations for value premium have been proposed in the extant literature.The first one to appear among these was from Fama and French (1992; 1996) who related theHBM effect to compensation for greater risk faced by these firms. Vassalou and Xing (2004)also document that the book-to-market risk is a proxy for default risk. However, Ohlson (2005)explores the framework provided by the residual income valuation model and shows that thediscount factor relates negatively to the book-to-market ratio.

Another major explanation proposed for the HBM effect lies in market inefficiency leading tomispricing of these stocks (Griffin and Lemmon, 2002; Lakonishok et al., 1994; LaPorta et al.,1997). According to theses researchers HBM are neglected stocks where prior performancecreates pessimistic expectations about future performances and real potential may actually bemissed. Ali et al. (2003) show that the book-to-market effect is greater for stocks with higheridiosyncratic return volatility, higher transaction costs and lower investor sophistication.Other factors leading to such market inefficiency could be small-cap stocks (Kothari, Shanken,and Sloan, 1995; Loughran, 1997), stocks with greater short-sales constraints (Nagel, 2005),and stocks with lower institutional ownership (Phalippou, 2007). Data-snooping biases (Conrad,Cooper, and Kaul, 2003) and greater divergence in investors’ opinions (Doukas, Kim, andPantzalis, 2004) have also been demonstrated as plausible reasons behind the high book-to-market effect.

Fundamental Analysis

As it is widely recognized that every high book-to-market stock cannot be a good performer3,researchers try finding ways and means of identifying potentially good performers. One suchapproach to separate ultimate winners from losers focuses a firm’s intrinsic value and/orsystematic errors in market expectations (Frankel and Lee, 1998). This strategy requires investorsto go long on stocks whose prices appear to be lagging their fundamental values. Here,undervaluation is identified by using analysts’ earnings forecasts in conjunction with anaccounting-based valuation model (for example, residual income model), and the strategy issuccessful at generating significant positive returns over a three-year investment window.

However, in general, financial analysts are less willing to follow poor performing, low-volume,and small firms (Hayes, 1998; McNichols and O’Brien, 1997). At the same time, managers ofdistressed firms could face credibility issues when trying to voluntary communicate forward-looking information to the capital markets (Koch, 1999; Miller and Piotroski, 2000). Therefore,a forecast-based approach, as suggested by Frankel and Lee (1998), has limited applicationfor differentiating value stocks.

3 Traditionally, these stocks have been called as winners and losers



In this very direction, a more dynamic investment approach involving use of multiple piecesof information from the firm’s financial statements is suggested by Ou and Penman (1989).They show that an array of financial ratios created from historical financial statements canaccurately predict future changes in earnings. Similarly, Holthausen and Larcker (1992) showthat a similar statistical model could be used to successfully predict future excess returnsdirectly. However, extremely complex methodologies and the need for vast amount of historicalinformation make use of these approaches limited. To overcome these calculation costs andavoid over-fitting the data, Lev and Thiagarajan (1993) utilize only 12 financial signals andprovide evidence that these fundamental signals are correlated with contemporaneous returnsafter controlling for current earnings innovations, firm size, and macroeconomic conditions.However, Abarbanell and Bushee (1997) contend that markets may not completely captureimpound value-related information in a timely manner and therefore investigate the ability ofLev and Thiagarajan’s (1993) signals to predict future changes in earnings and future revisionsin analyst earnings forecasts. They find evidence that these factors can explain both futureearnings changes and future analyst revisions. Consistent with these findings, Abarbanelland Bushee (1998) document that an investment strategy based on these 12 fundamentalsignals yields significant abnormal returns.

Piotroski (2000) aggregates the HBM effect to financial statement analysis and shows that themean return earned by a HBM investor can be increased by at least 7.5% annually through theselection of financially strong HBM firms. Mohanram (2005) combines traditional fundamentalanalysis with measures tailored for low book-to-market firms and documents significant excessreturns.

Beneish, Lee and Tarpley (2001) use a two-stage approach towards financial statementanalysis. In the first stage, they use market based signals to identify likely extreme performers.In the second stage, they use fundamental signals to differentiate between winners andlosers among the firms identified as likely extreme performers in the first stage. Their resultsindicate the importance of carrying out fundamental analysis contextually. Beneish et al.(2001) and more recently Lopes and Galdi (2007) also use market based signals to identifylikely extreme performers and then they use financial statement analysis to differentiatebetween winners and losers among these firms.

Given the scenario that has prevailed in India, it is not obvious that fundamental analysis inIndia will posses the same relevance as documented in previous researches, though manyother researches document that investors can benefit from trading on various signals offinancial performance. In contrast to a portfolio investment strategy based on equilibrium riskand return characteristics, these strategies seek to earn “abnormal” returns by focusing onthe market’s inability to fully process the implications of these financial signals. Examples ofthese strategies include but are not limited to ‘Dogs of the Dow’ strategy (Sahu, 2001), monthand turn-of-mouth effect (Karmakar and Chakraborty, 2000), contrarian and momentumstrategies (Sehgal and Balakrishnan, 2002) and size of stock effect (Mohanty, 2002). However,evidence on the success or failure of fundamental analysis in India is rather scant4. A systematicresearch in this direction is therefore, warranted.



Data and Methodology

The average high book-to-market firm is financially distressed (Chen and Zhang, 1998; Famaand French, 1995). This distress is associated with declining and / or persistently low margins,profits, cash flows, and liquidity and rising and / or high levels of financial leverage. Intuitively,financial variables that reflect changes in these economic conditions should be useful inpredicting future firm performance. This logic is used to identify the financial statementsignals incorporated in this paper.

Specifically, we followed the methodology suggested by Piotroski (2000) who builds acomposite score (F_SCORE) comprising nine fundamental signals reflecting changes in threeareas of the firm’s financial performance: profitability, financial leverage / liquidity, andoperating efficiency. These signals are intended to be useful in predicting future firmperformance. Each firm’s signal realization is classified as either “good” or “bad” dependingon the signal’s theoretical impact on future prices and performance. If the signal realization is“good” an indicator variable for that signal is equal to one (1); zero (0) if the signal is “bad”.We call the indicator variable as F_variable. For example, the indicator variable of return onassets (ROA) is F_ROA and the indicator variable for leverage is F_LEVER. The aggregate ofthese signals is termed as the F_SCORE, which is nothing but the sum of the nine binarysignals. It is important to state that the decision to classify a signal as “good” or “bad” isdriven by the presumption that high book-to-market firms are financially distressed.

Financial Performance Signals for Profitability

The four variables used to measure profitability are ROA, CFO, ∆ROA and ACCRUAL. ROAis defined as net income before extraordinary items scaled by beginning-of-the-year totalassets, while CFO is defined as the cash flow from operations also scaled by beginning-of-the-year total assets. If the firm’s ROA and CFO are positive, the indicator variables F_ROAand F_CFO are equal to one respectively, zero otherwise. ∆ROA is the current year ROA lessthe previous year ROA. If “ROA is positive, the indicator variable F_∆ROA equals one, zerootherwise. Sloan (1996) shows that earnings driven by positive accrual adjustments (that is,profits are greater than cash flow from operations) is a bad signal about future profitabilityand returns. Accordingly, we also considered the relationship between earnings and cashflows. Here, the variable ACCRUAL is defined as current year’s net income before extraordinaryitems less cash flow from operations, scaled by beginning of the year total assets. Theindicator variable F_ ACCRUAL equals one (“good”) if CFO>ROA, zero (“bad”) otherwise.

Financial Performance Signals for Leverage and Liquidity

The three financial signals used to measure changes in capital structure and liquidity include∆LEVER, ∆LIQUID, and EQ_OFFER. As Piotroski (2000) states, most HBM firms are financiallydistressed. Therefore, increase in leverage, weakening of liquidity and additional publicofferings of equity are “bad” signals. The change in the firm’s long-term debt level is

4 We apologise for any omissions



represented by ∆LEVER. It has been measured as the change in the ratio of long-term debt tototal assets in relation to the previous year. An increase in leverage (∆LEVER > 0) is a “bad”signal (therefore, F_∆LEVER =0) while a decrease is “good” (F_∆LEVER=1). The variable∆LIQUID captures the changes in the firm’s current ratio in relation to the previous year. Thecurrent ratio is defined as the ratio of current assets to current liabilities at the end of thefinancial year. An improvement in liquidity, that is, ∆LIQUID > 0 is considered a “good” signal(therefore, F_∆LIQUID=1), “bad” (F_∆LIQUID =0) otherwise. The variable EQ_OFFERrepresents the use of equity financing. If the firm did not issue additional equity in theprevious year, it is a “good” signal (EQ_OFFER equals one), “bad” (EQ_OFFER equals zero)otherwise. This is so because financially distressed firms that raise external funds only signaltheir inability to generate sufficient internal funds to service future obligations.

Financial Performance Signals for Operating Efficiency

Operational efficiency of a company is as important as its profitability or liquidity. Two variableswhich reflect two key constructs underlying a decomposition of return on assets have beenused to measure change in the operational efficiency of the company. These include ∆MARGINand ∆TURN. ∆MARGIN is defined as the current year gross margin ratio (gross marginscaled by total sales) less previous year current gross margin ratio. A positive change in grossmargin ratio means a “good” signal, while a negative change is classified as “bad”. This is sobecause an improvement in margins reflects an improvement in factor costs, reduction ininventory costs, or a rise in the price of the firm’s product. Accordingly, the variableF_∆MARGIN is given a value of one or zero respectively. Finally, we define ∆TURN as thechange in the firm’s current year asset turnover ratio (total sales scaled by beginning of yeartotal assets) as compared to previous year. As earlier, an improvement in asset turnovershows greater productivity from the asset base. This can arise from more efficient operations(fewer assets generating the same levels of sales) or an increase in sales (which could alsosignify improved market conditions for the firm’s products). Therefore, an improvement inassets turnover is a “good” signal, thus indicator variable F_∆TURN equals one, or zerootherwise.

The Composite Score

As discussed earlier, the composite score is called F_SCORE and represents the sum of allindicator variables mentioned above. Therefore,

F_SCORE = F_ROA + F_CFO + F_∆ROA + F_ACCRUAL + F_LIQUID + F_ ∆LEVER

+ EQ_OFFER + F_∆MARGIN + F_ ∆TURN

Since there are nine fundamental signals, F_SCORE can range from 0 (all “bad” signals) to 9(all “good” signals). Low F_SCORE represent firms with poor expected future performance



and stock returns, while high F_SCORE is associated with firms expected to outperform themarket. The investment strategy analyzed in this paper is similar to Piotroski (2000) and isbased on selecting firms with high F_SCORE5.

Selection of Companies and Portfolio Formation

The research was carried out for the period of financial year ending 2003 to financial yearending 2007. As on 31st March, 2004, all the companies listed on the National Stock Exchangewere arranged in descending order of book-to-market ratio using the CMIE database Prowess.On the basis of book-to-market ratio, these companies were divided into five quintiles. As thestudy was based on high book-to-market companies, the focus remained on the first quintile(that is, highest book-to-market ratio). Out of these companies those were selected which metthe following criteria:

The company had a sufficient stock price.The company had a positive book-to-market ratio.The company did not delist during the next three years.There was no stock split, reverse split, stock bonus etc.All the data as required in the study was available.

This resulted in 104 companies being selected for the study.All the nine fundamental indicators were calculated for all these companies using financialstatements for the financial year 2003 and 2004 and the composite F__SCORE was arrived at.Table 1 shows the distribution of F_SCORE among these companies.

It can very well be seen that most of the companies are clustered around the middle. 88companies out of 104 (84%) are in the range of 3 to 7. That is, they have nearly 50-70 per centpositive fundamental signals (in other words conflicting signals). A relatively much smallernumber of companies has large or small F_SCORE, that is, very low or very high percentageof positive signals. Appendix 1 summarizes the percentage of companies with positive signalfor each of the fundamental indicators.

5 This approach could be criticized on the basis that it employs an ad hoc aggregate performance metric(F_SCORE) to categorize companies into good or bad performers; especially when two accepted measuresof firm health and performance namely, financial distress (as measured by Altman’s z-score) and historicalchange in profitability (as measured by the change in return on assets) are available. Evidence however, hasbeen established that after controlling for financial distress and historical changes in profitability, F_Scorestill has additional explanatory power in discriminating between stronger and weaker firms (Lopes andGaldi, 2007; Piotroski, 2000). Also, the ability to discriminate winners from losers is not driven by a single,specific metric. Instead, future returns are predictable by conditioning on the past performance of the firm.The combined use of appropriate performance metrics through measures such as F_SCORE or a DuPontanalysis, simply improves the ability of an investor to distinguish strong companies from weak companies.Above all, this approach is simple and very easy to impliment.

F_SCORE 0 1 2 3 4 5 6 7 8 9

No. of Companies 0 1 5 16 16 26 16 14 9 1

Table 1: Distribution of F_SCORE among the Selected High Book-to-Market Companies

Out of these 104 companies with different F_SCORES, we developed three portfolios. Theseportfolios, hereafter called as Portfolio1, Portfolio 2, and Portfolio 3 consisted of companieshaving F_SCORE in the range of 0-3, 4-6, and 7-9 respectively6. Each portfolio consisted ofequally weighted 20 companies7 randomly selected from the respective F_SCORE groups (aneffort however, was made to have maximum diversification8).

Return Calculations and Performance Analysis

To calculate returns from each of the portfolios, annualized stock-specific yields were calculatedon a buy-and-hold basis for a period of one year and two years following portfolio formation.As the portfolios were equally-weighted, stock-specific returns were added to arrive at portfolioreturns. As suggested by Piotroski (2000) portfolios were formed after three months of financialyear-end so that all the information required was available. Thus, the period under study forportfolio returns was from July 2004 to June 2006.

To study the performance of the portfolios both absolute and market adjusted returns werecalculated. Market adjusted returns were calculated in two ways, viz. by calculating absoluteexcess returns over the market returns (Lopes and Galdi, 2007; Piotroski, 2000) and bycalculating required returns as driven by risk9 of the portfolio. Further, for market adjustment,returns on three market indices namely, S&P CNX Nifty, CNX Mid Cap, and S&P CNX 500were utilized10.

Empirical FindingsDescriptive Statistics for High Book-to-Market CompaniesTable 2 provides descriptive statistics for all the high book-to-market companies with respectto the fundamental signals taken up in the study. The average company in this quintile ofhighest book-to-market companies had a mean (median) BM ratio of 4.96 (3.51). In line withthe findings of Fama and French (1995), these high book-to-market companies were poor in

6 Early researches have focused only on buying high F_SCORE portfolio, that is, portfolio of companieswith F_SCORE in the range of 7 to 9 (Piotroski, 2000) or buying high F_SCORE portfolio whilesimultaneously shorting low F_SCORE portfolio as low F_SCORE company stocks are expected to performnegatively (Lopes and Galdi, 2007). However, to see the relative impact of F_SCORE on stock performance,we created three portfolios and go long on all of them.7 Researchers have traditionally included all high book-to-market firms in their studies. However, havinghundreds of stocks in a portfolio is possible only in academic research. For this study to have practicalimplications, we limited the size of portfolios to 20 stocks each. This is also supported by the fact thatdiversification beyond 18 to 20 stocks only leads to additional transaction costs.8 The criterion for selecting companies into respective portfolios was that a company should belong to aparticular F_SCORE group. No such consideration as industry growth or others were utilized. In case twocompanies from the same industry got included in the first instance, one of them was replaced with anothercompany having the same F_SCORE but from a different industry.9 Risk-adjusted required returns were calculated by computing â-adjusted returns for each portfolio.10 â-coefficients with respect to three market indices were calculated using monthly returns for the periodunder study.



performance. The average ROA of these companies was a meager 0.01. At the same time,compared to the previous year both average and median company saw a decline in ROA (-0.02and -0.01 respectively). The change in margin and turnover over the previous year alsoshowcased similar behavior (-0.12 and -0.01 respectively) and (-0.14 and -0.04 respectively).Lastly, an increase in leverage and a drop in liquidity were observed for an average high boo-to-market company.

However, a vast range and non-normal distribution (see skewness and kurtosis) was observedacross all the measures. Seeing this, selected descriptive statistics along with the percentageof companies with positive signals were also computed for the three portfolios (Appendix 2).As expected, the number of positive signals was highest in portfolio 3 (F_SCORE in range 7to 9) and lowest in portfolio 1 (F_SCORE in range 0 to 3). Therefore, it indicates that investingin high book-to-market companies with high F_SCORE could be profitable and one needs toavoid companies with low F_SCORE, however high is the BM ratio.

To investigate this point further, correlations between individual fundamental signal indicatorvariables, overall F_SCORE and one year and two years market adjusted returns (both excessmarket returns and â-adjusted returns) were computed. The same are presented in Table 3.

As expected, a high correlation was observed between the F_SCORE and one year and twoyears market adjusted returns (0.14 and 0.29 respectively). Apart from this, the three strongestexplanatory variables that emerged were CFO, ∆ROA, and ∆MARGIN (correlation of 0.20,0.22, and 0.26 with one year return).11



11 Piotroski (2000) found ROA and CFO as the strongest explanatory variables. Lopes and Galdi (2007)found ∆ROA and ∆LEVER

Table 2: Descriptive Statistics Regarding Nine Fundamental Signals (All Companies)Signal BM ROA CFO ∆ROA ACCRUAL ∆LEVER ∆LIQUID ∆MARGIN ∆TURN

Mean 4.96 0.01 0.05 -0.02 -0.04 -0.01 -0.03 -0.12 -0.14

Median 3.51 0.02 0.04 -0.01 -0.04 -0.01 -0.03 -0.01 -0.04

Stdev 3.67 0.06 0.08 0.08 0.10 0.05 2.53 1.54 0.47

Skew 2.95 -1.00 -1.01 2.71 1.26 -1.57 0.73 8.10 -2.41

Kurt 10.99 2.58 7.32 15.71 7.85 13.08 11.86 75.22 12.14

Max 25.00 0.15 0.31 0.51 0.50 0.16 13.37 14.36 1.43

Min 2.50 -0.22 -0.40 -0.25 -0.34 -0.32 -9.24 -3.44 -2.60



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Portfolio Performance

As discussed earlier, the basic emphasis of the study was to test the applicability offundamental analysis to high book-to-market stocks in a practical way, and that is why threeportfolios with different F_SCORE ranges were created. The following text focuses performanceof these portfolios. Table 4 provides the mean values of different fundamental signals for thethree portfolios separately. To check for significance of difference among the three portfolios,Kruskal-Walis statistics and related significance levels have also been shown.12 It can beobserved that except for ACCRUAL and change in liquidity, the three portfolios differsignificantly on all other fundamental variables. Accordingly, it can be expected that thesefundamental signals and therefore, the F_SCORE should be useful in discriminating amongthe future performances of the portfolios.

To evaluate this expectation, the performance of the three portfolios over the next one yearand two years from the date of portfolio formation was observed.

Table 5 provides information on absolute returns from the portfolios over one year and twoyears. The most striking result is the fairly monotonic out-performance by portfolio 3 againreinforcing the capability of the F_SCORE.

.12 As observed in Table 2, the distribution of fundamental signal values is non-normal. Also, literature hasevidenced concerns regarding the use of parametric tests (Kothari et al., 1997). Therefore, non-parametrictest statistics were applied.

Portfolio 1(F-score 1-3) 31.30 47.20Portfolio 2(F-score 4-6) 8.58 65.22Portfolio 3(F-score 7-9) 98.66 120.22

Table 5: Returns from the three portfoliosAnnualised yield (%)

Portfolio 1 year holding period 2 years holding period

Table 4: Comparison of Portfolios on Different Fundamental Signals

Portfolio ROA CFO ∆ROA ACCRUAL ∆LEVER ∆LIQUID ∆MARGIN ∆TURN

Portfolio 1(F-score 1-3) -0.02 0.00 -0.05 -0.02 0.02 -0.06 -0.26 -0.18

Portfolio 2(F-score 4-6) 4.63 -0.04 0.01 -0.13 -0.25 0.05 -0.00 0.00

Portfolio 3(F-score 7-9) 0.03 0.08 0.04 -0.05 -0.02 0.53 0.87 -0.11

KW 5.696 7.085 18.408 1.578 16.816 1.456 18.658 14.132

statistics (p<0.058) (p<0.029) (p<0.001) (p<0.458) (p<0.001) (p<0.795) (p<0.001) (p<0.001)



Absolute returns are of no significance unless compared with a benchmark. Excess returnsover the market returns were therefore, computed. Table 6 shows the returns performance ofthe portfolios vis-à-vis selected market indices.

Portfolio 1 and Portfolio 2 have shown mixed response in terms of excess returns over themarket indices. For both one year and two years holding periods, Portfolio 1 has underperformedthe CNX Mid Cap but outperformed the S&P CNX NIFTY and S&P CNX 500. Portfolio 2 onthe other hand, underperformed all indices for one year holding period but outperformed allindices for two years holding. However, Portfolio 3 has been very consistent in its performance.All the market indices have been outperformed for both one and two year’s holdings. Theamount of excess returns over the market returns too is substantially larger when comparedwith portfolio 1 and 2. This again signifies the strength of F_SCORE in crafting out superiorhigh book-to-market portfolios.

S&P CNX

NIFTY 14.91 16.39 Out perform

Portfolio 1 31.3 CNX 35.17 (3.87) Under perform

1 year holding MIDCAP

S&P CNX 500 21.6 9.7 Out perform

S&P CNX


47.20 CNX MIDCAP 48.69 (1.49) Under perform

2 years holding S&P CNX 500 41.29 5.91 Out perform

S&P CNX

NIFTY 14.91 (6.33) Under perform

Portfolio 2 8.58 CNX MIDCAP 35.17 (26.59) Under perform

1 year holding

S&P CNX 500 21.6 (13.02) Under perform

S&P CNX


65.22 CNX MIDCAP 48.69 44.6 Out perform

2 years holding S&P CNX 500 41.29 52 Out perform

Table 6: Comparison of Portfolio Returns with Selected Market Indices

PortfolioHoldingPeriod

AnnualizedPortfolioYield (%)

MarketIndex

AnnualizedMarket Yield

(%)

ExcessPortfolioReturns

(%)Portfolio

Performance



As a second step, â-adjusted portfolio returns were computed. Table 7 shows the comparisonof actual returns vis-à-vis â-adjusted returns with respect to three market indices (portfolio âhave been provided in Appendix 3). Returns for a holding period of two years have beenpresented.

Table 6: Comparison of Portfolio Returns with Selected Market Indices

PortfolioHoldingPeriod

AnnualizedPortfolioYield (%)

MarketIndex

AnnualizedMarket Yield

(%)

ExcessPortfolioReturns

(%)Portfolio

Performance

S&P CNX


Portfolio 3 98.66 CNX MIDCAP 35.17 63.49 Out perform

1 year holding S&P CNX 500 21.6 77.06 Out perform

S&P CNX



2 years holding S&P CNX 500 41.29 78.93 Out perform

Table 7: Comparison of Actual Returns and â-Adjusted Returns

Portfolio 1 S&P CNX NIFTY 99.86 18.20 Out perform

118.06 CNX MIDCAP 176.52 (58.46) Under perform

S&P CNX 500 125.01 (6.95) Under perform







Portfolio

ActualAnnualised

Yield (%)MarketIndex

Required â-adjusted Yield

(%)

ExcessYield

Over â-adjusted

Yield(%)

PortfolioPerformance



Outstanding performance by Portfolio 3 only becomes more visible in this case. Portfolio 1 isnot able to meet its risk-adjusted required returns in two (CNX MIDCAP and S&P CNX 500)out of three cases when risk adjustment is done with respect to three market indices (loss of58.46% and 6.95% respectively). While Portfolio 2 is able to meet the risk-adjusted returnrequirements with respect to all the three indices, the degree of outperformance is insignificantwhen compared to Portfolio 3. Portfolio 3 outperforms its risk-adjusted returns requirementsby huge margins (294.91%, 224.91% and 272.48% with respect to S&P CNX Nifty, CNXMIDCAP, and S&P CNX 500 respectively). This proves beyond doubt that portfolio craftedwith companies having high F_SCORE should be eventual winners while those portfolioswith low F_SCORE should be eventual losers. Therefore, a value investor who focuses onhigh book-to-market firms could actually shift his/her returns distributions rightwards usinga portfolio of value companies having high F_SCORE. The returns could further be improvedif he/she simultaneously shorts a low F_SCORE portfolio.

Conclusions

This paper investigates if an accounting-based fundamental analysis strategy can helpinvestors earn excess returns on a portfolio of high book-to-market companies in India. Thestrategy we adopt is based on Piotroski (2000) who identified 9 fundamental signals to form acomposite score (F_SCORE) capable of separating out ex-post winners from losers amonghigh book-to-market companies in the US stock market. However, it is not clear whether theresults of such a strategy could be directly applied to Indian stock markets. This is so becausethere is evidence that market efficiency in India is at the most weak form. Also, during the1990s when the trading and investments were mostly domestic, the markets weathered scamslike the Harshad Mehta scam and Ketan Parekh scam. On the other hand, of late, India hasbeen among the most favoured investment destinations in the world. Amidst these conflictingscenarios and scant evidence on the usefulness of fundamental analysis in India, theenvironment represents an additional challenge to the usefulness of fundamental / financialstatement analysis; a test of investment strategy based on fundamental analysis is stronglywarranted.

Using the F_SCORE framework from Piotroski (2000) but a different approach to portfolioformation (to make it practicable) we find convincing evidence that a fundamental analysisbased investment strategy for high book-to-market companies can separate winners fromeventual losers. We show that portfolios with high F_SCORE (7 to 9) provide outstandingreturns way above the market returns and risk-adjusted returns. At the same time, portfolioswith low F_SCORE (0 to 3) offer very poor returns and often under-perform the markets orrequired risk-adjusted returns. Thus a value investor could shift his distribution of returnsrightwards by focusing on investing only in high F_SCORE companies. Shorting low F_SCOREcould further amplify his returns.

During the period under the study, Indian stock markets witnessed a strong bullish phase – aplausible reason behind the strong performance of the investment strategy. However, hadthat been the case, then all the portfolios should have performed well, which actually did nothappen. Apparently, the most convincing reason was proposed by Piotroski (2000) that these



companies, which did not command large market price, were ignored by market participantsand were hardly followed by analysts. Moreover, our strategy, in contrast to early researchers,focuses on portfolio performance and not the performance of individual stocks. Whethermarket inefficiency or a rational pricing strategy or any thing else is a subject matter of furtherinvestigation.

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Selected Descriptive Statistics for Fundamental Indicators along with Percentage ofCompanies with Positive Signal

Portfolio 1 (F=0-3)

BM 5.710 3.704 4.990 N/AROA -0.022 0.001 0.075 52∆∆∆∆∆ROA -0.052 -0.021 0.063 00∆∆∆∆∆MARGIN -0.262 -0.072 0.736 00CFO 0.001 -0.004 0.053 47∆∆∆∆∆LIQUID -0.059 -0.060 2.465 33∆∆∆∆∆LEVER 0.018 0.014 0.029 14∆∆∆∆∆TURN -0.185 -0.116 0.227 14ACCRUAL -0.024 0.005 0.098 47

Percentage ofcompanies

with positivesignalVariable Mean Median Stdev

Appendix 1Percentage of Companies with Positive Signals for each of the Fundamental Indicators

(All Companies)

BM 4.634 3.280 3.458 N/AROA 0.010 0.016 0.053 58∆∆∆∆∆ROA -0.001 -0.010 0.072 29∆∆∆∆∆MARGIN -0.042 -0.014 0.211 29CFO 0.052 0.053 0.092 76∆∆∆∆∆LIQUID -0.252 -0.030 2.351 45∆∆∆∆∆LEVER -0.008 -0.008 0.046 66∆∆∆∆∆TURN -0.131 -0.052 0.410 24ACCRUAL -0.042 -0.045 0.112 77



Portfolio 2 (F=4-6)

Appendix 2

Fundamental BM ROA CFO ∆ROA ACCRUAL ∆LEVER ∆LIQUID ∆MARGIN ∆TURN Signal Percentage of N/A 58 36 34 71 44 54 31 69 Companies

Portfolio 3 (F=7-9)

Definition:

Note: All variables are measured as of the financial year-end prior to portfolio formation(year t) BM equals the firm’s book value of equity at the end of financial year t, scaled bymarket value of equity. ROA equals net income before extraordinary items for the financialyear preceding portfolio formation scaled by total assets at the beginning of year t.

ÄROA equals the change in annual ROA for the year preceding portfolio formation. ÄROA iscalculated as ROA for year t less the firm’s ROA for year t-1.

ÄMARGIN equals the firms gross margin (net sales less cost of goods sold) for the yearpreceding portfolio formation, scaled by net sales for the year, less the firm’s gross margin(scaled by net sales) from year t-1.

CFO equals cash flow from operations scaled by the beginning of year t total assets.

ÄLIQUID equals the change in the firm’s current ratio between the end of year t and year t-1.Current ratio is defined as total current assets divided by total current liabilities.

ÄLEVERAGE equals the change in the firm’s debt-to-assets ratio between the end of year tand year t-1. The debt-to-asset ratio is defined as the firm’s total long-term debt (including theportion of long-term debt classified as current) scaled by average total assets.

ÄTURN equals the change in the firm’s asset turnover ratio between the end of year t and yeart-1. The asset turnover ratio is defined as net sales scaled by average total assets for the year.

ACCRUAL is defined as net income before extraordinary items less cash flow from operations,scaled by beginning of the year total assets.





BM 5.097 3.571 2.713 N/AROA 0.029 0.030 0.032 83∆∆∆∆∆ROA 0.036 0.014 0.105 92∆∆∆∆∆MARGIN 0.870 0.027 3.072 91CFO 0.082 0.086 0.067 96∆∆∆∆∆LIQUID 0.536 -0.010 2.995 48∆∆∆∆∆LEVER -0.025 -0.007 0.073 78∆∆∆∆∆TURN -0.110 0.055 0.718 78ACCRUAL -0.053 -0.025 0.072 83



Appendix 3

Portfolio â Values with Respect to Selected Market Indices

Portfolio 1(F-score 1-3) 1.09 1.48 1.27

Portfolio 2 (F-score 4-6) 1.40 1.42 1.47

Portfolio 3 (F-score 7-9) 0.98 1.34 1.14

S&P CNX NIFTY CNX MIDCAP S&P CNX 500

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