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155 CHAPTER 5 ADDING VALUE TO VALUE STOCKS JOSEPH PIOTROSKI’S F-SCORE MODEL 5.1 INTRODUCTION The value investing consists of buying securities which are trading at prices lesser than their intrinsic value. The intrinsic value of the securities is determined through their fundamentals. The company showing good performance in terms of earnings, dividends, book value of assets, profitability etc. is said to be an intrinsically strong company. Thus, value investment strategies are based on fundamental analysis of a company. Firm‟s fundamental or intrinsic value is determined by the information reflected in the financial statements. Stock prices deviate at times from these values but slowly converge to these fundamental values thereby enhancing the market value of such firms (Elleuch and Trabelsi, 2009). The basic premise behind value investing strategies is that the sophisticated investors can use historical financial information to select profitable investment opportunities. Specifically, investors can earn returns in excess of the returns required for risk compensation by identifying undervalued or overvalued securities through an analysis of historical financial data (Piotroski, 2005). Within the varied value investing strategies, the investors look for the strategy that consistently identifies winners and the losers in the market with minimum risk and earn returns superior to those averaged by the market index. In actual effect, the existence of such a strategy would challenge the efficient market hypothesis, one of the main pillars of financial market theory (Dahl et al., 2009). The efficient market hypothesis states that the current stock price fully reflect available information about the value of the firm, and there is no way to earn excess profits (more than the market overall) by using any publically available or private information (Clarke et al., 2001). Thus, no trading rule or security selection strategy which uses only publically available information would provide an investor with the ability to earn, on average, positive abnormal returns in the market that is efficient in the semi strong sense. The Indian stock market, a strong emerging market, offers a unique opportunity to apply

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155

CHAPTER 5

ADDING VALUE TO VALUE STOCKS

JOSEPH PIOTROSKI’S F-SCORE MODEL

5.1 INTRODUCTION

The value investing consists of buying securities which are trading at prices

lesser than their intrinsic value. The intrinsic value of the securities is determined

through their fundamentals. The company showing good performance in terms of

earnings, dividends, book value of assets, profitability etc. is said to be an

intrinsically strong company. Thus, value investment strategies are based on

fundamental analysis of a company. Firm‟s fundamental or intrinsic value is

determined by the information reflected in the financial statements. Stock prices

deviate at times from these values but slowly converge to these fundamental values

thereby enhancing the market value of such firms (Elleuch and Trabelsi, 2009). The

basic premise behind value investing strategies is that the sophisticated investors can

use historical financial information to select profitable investment opportunities.

Specifically, investors can earn returns in excess of the returns required for risk

compensation by identifying undervalued or overvalued securities through an analysis

of historical financial data (Piotroski, 2005).

Within the varied value investing strategies, the investors look for the strategy

that consistently identifies winners and the losers in the market with minimum risk

and earn returns superior to those averaged by the market index. In actual effect, the

existence of such a strategy would challenge the efficient market hypothesis, one of

the main pillars of financial market theory (Dahl et al., 2009). The efficient market

hypothesis states that the current stock price fully reflect available information about

the value of the firm, and there is no way to earn excess profits (more than the market

overall) by using any publically available or private information (Clarke et al., 2001).

Thus, no trading rule or security selection strategy which uses only publically

available information would provide an investor with the ability to earn, on average,

positive abnormal returns in the market that is efficient in the semi strong sense. The

Indian stock market, a strong emerging market, offers a unique opportunity to apply

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

156

and test the profitability of accounting based fundamental analysis (Aggarwal and

Gupta, 2009). In addition, the Indian stock market has become comparable to other

mature markets due to various financial sector reforms initiated since early 1990s by

the Government of India. The positive fundamentals combined with fast growing

markets have made India an attractive destination for foreign institutional investors.

Significant amounts of capital are flowing from developed world to emerging

economy like India (Prassana, 2008). In such a situation, the profitability of an

accounting based fundamental analysis strategy aimed at yielding excess returns in

Indian stock market becomes imperative to explore.

Out of different financial variables aimed at assessing the intrinsic value of

securities, the book to market ratio has been considered as the most important and the

keystone of value investing studies. According to this valuation metric, the securities

that have higher book value in comparison to the market price, are called as

intrinsically strong or value securities. The book to market ratio of the companies is

calculated as:

Book to market ratio= Book value of a share for last financial year end/

Current market price of a share.

Book value per share is an accounting concept that measures what

shareholders would receive if all the firm‟s liabilities were paid off and all its assets

could be sold at their balance sheet value (Strong, 2004). The different studies, such

as, Stattman (1980), Rosenberg et al. (1985), Chan et al. (1991), Fama and French

(1992, 1998), Capaul et. al. (1993), Brouwer et al. (1996), Vos and Pepper (1997),

Vaidyanathan and Chava (1997), Mukherji et al. (1997), Bauman et al. (1998),

Arshanapalli et al. (1998), Dhatt et al. (1999), Doukas et al. (2001), Dimson et al.

(2003), Bird and Gerlach (2003), Ding et al. (2005) and Azzopardi (2006) have

studied the role of this ratio in providing value premium to investors.

The book to market ratio is one of the most extensively studied variables in the

finance literature. The reason behind the existence of value premium in high book to

market stocks has attracted multiple explanations. The explanation on the existence of

value premium has been explained first of all by Fama and French (1992) stating that

market judges the prospects of a high ratio of book to market equity firms to be poor

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

157

in relation to the firms with low book to market equity. Further, Lakonishok et al.

(1994) offered the mispricing explanation behind existence of value effect and hence

provided evidence that value strategies yield higher returns because these strategies

exploit the suboptimal behavior of the typical investor and not because these

strategies are fundamentally riskier. Further, Vassalou and Xing (2004) conjectured

that the value stocks based on high book to market ratio have higher default risk than

the stocks having low book to market ratio. Also, Campbell et al. (2008) found that

the investors make valuation errors and overprice these stocks as they fail to

understand their poor prospects.

However, when examining the potential of book to market ratio in generating

excess returns to investors, Piotroski (2000) studied that no doubt the stocks selected

on the basis of high book to market ratio yield value premium to investors but about

44% of the high book to market firms did not show any increment in their value in 2

years of portfolio formation. Thus, a handsome group of stocks have not shown any

increment in value. He further held that value stocks earn returns because of being

abandoned in the market. As a result, they are lesser suggested by analysts and thus

less followed by investment community. Also, being financially distressed, one

should focus on the accounting fundamentals of such stocks, such as, leverage,

liquidity, profitability trends, cash flow adequacy etc. before taking investment

decision in such firms. Thus, in order to avoid the distress risk associated with the

high book to market stocks and to extract true value maximizing securities, Joseph

Piotroski conceptualized and empirically proved the viability of the financial

statement information in yielding true value securities.

5.2 JOSEPH PIOTROSKI’S F-SCORE

In order to ensure the financial soundness and profitability of financially

distressed high book to market firms, Joseph Piotroski developed a comprehensive

financial signal known as F-score that measures three constructs pertinent to a

company‟s financial position: profitability, financial leverage along with liquidity,

and operating effectiveness. The three constructs of Pitoroski‟s summary measure „F -

score’, is the sum of nine binary signals related to these three constructs (Wellman,

2011). The nine signals aim at measuring the strength and quality of historical

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

158

performance trends are derived from the traditional financial statement analysis

(Piotroski, 2000; Fama and French, 2004). The F-score measure intends to spot out

the firms with the strongest enhancement in overall financial condition during the last

fiscal year while meeting a minimum standard of financial performance (Piotroski,

2005). Thus, the model based on set of financial variables aims to identify companies

that would actually increase in value out of total group of value stocks. The model is

called as F-score, whereby the 9 financial signals corresponding to the 3 constructs

i.e. profitability; leverage along with liquidity and the operating efficiency are used to

measure the financial performance of high book to market firms. The set of 9 binary

signals are used, where an indicator variable for the signal is equal to one (1) if the

signal‟s realization is good and zero (0) if the signal‟s realization is bad (Piotroski,

2000). Every year, firms are rated and classified on the basis of these recent signals.

“Strong” firms exhibit diverse improvements along a range of financial dimensions,

while “weak” firms have weakening (and generally poor) fundamentals along these

same dimensions (Piotroski, 2005). The score on each of the nine items are summed

to give the F score for the stock, ranging between zero and nine. The items together

with their desired properties are: (i) positive profitability, (ii) increase in profitability,

(iii) positive cash flow, (iv) negative accruals, (v) increase in profit margin, (vi)

increase in asset turnover, (vii) decrease in leverage, (viii) increase in financial

liquidity, and (ix) no issuance of new equity (Hyde, 2013). F-score, therefore, is the

sum of the nine binary signals and measures the financial stability, profitability and

the efficiency of the business.

The logical phenomenon behind the performance of F-score is the semi-strong

inefficiency of the market due to which it slowly incorporates the information present

in the fundamental values. Steadily, the investors‟ expectations are revised and the

gradual incorporation of information in security prices implies that over time,

investors who recognize that the strong financial condition stocks are undervalued,

initiate purchases of these stocks and drive prices higher and, analogously, investors

who realize weak financial condition stocks are overvalued, initiate sales of these

stocks and drive prices lower (Choi and Sias, 2012).

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

159

Given below are the three constructs of F-score model

Figure 5.1: Three constructs of F-score

A. Signals Relating to Profitability

According to Piotroski (2000), “Current profitability and cash flow realization

provide information about the firm‟s capacity to generate funds internally”. When a

firm is generating positive earnings, it is a signal of its capability to generate funds

through its operating activities. Further, the positive earnings trend for a company

ensures its future survival and its fundamental capability to yield positive future cash

flows. This construct of profitability takes four measures of profitabi lity i.e. return

on assets, change in return on assets, cash flow from operations and accrual. Given

below is the discussion of the measures:

1.) Return on Assets (ROA):

This ratio establishes the relationship between the earnings generated by the

firms before providing for interest and taxes from the total assets that a firm has at

the beginning of the year. The idea behind considering earnings before interest and

taxes is that the companies operate with different levels of debt and differing tax

Three constructs of F-score

th

Profitability Leverage and

Liquidity

Operating

Efficiency

ROA ROA CFO Accrual

Liquidity Equity

Gross

Margin

Asset

Turnover

Leverage

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

160

rates and through EBIT an investor can evaluate the operating earnings of different

companies without the distortions arising from differences in tax rates and debt

levels (Greenblatt, 2006). It therefore puts every company on equal footing while

comparing the return on assets of different companies. It is calculated as:

ROA= Net income before interest, taxes and extraordinary items at the end of

financial year / Total assets at the beginning of the year.

The firms generating positive ROA, obtain positive signal (i.e. F-ROA=1) and the

firms that are yielding negative ROA, get zero signal (i.e. F-ROA=0).

Positive ROA, thus, determines the stock‟s ability to produce funds internally

and represents the earnings productivity of total assets. Value is created when the

organization earns a return on its investment in excess of the cost of capital (Palepu

et al., 2010). Thus fulfillment of this signal avoids the risk of distress associated with

book to market firms. The rule, therefore, ensures that the firm has the capability to

generate funds internally (Ross et al., 2003).

2.) Change in Return on Assets ( ROA):

This signal measures the change in ROA in current year in comparison with

previous year‟s ROA. It is calculated as:

ROA= ROA for the year t – ROA for the year t-1.

The firms whose current year ROA is greater the previous year‟s ROA that

firm is given positive score i.e. if ROA> zero, the indicator variable F- ROA=1.

However if a particular firm has lesser ROA as compared to previous year‟s ROA,

that firm gets zero score i.e. F- ROA=0. Piotroski (2000) through this metric made

sure that firm has not incurred a loss in prior 2 years. Thus the metric ensures the

sound profitable position of the enterprise.

3.) Cash Flow from Operations (CFO):

By cash we mean cash and cash equivalents i.e. cash in hand, bank demand

deposits, all the short term investments which can be readily converted into cash

without delay in their value. The comprehensive view of the cash position of an

enterprise during an accounting period can be seen from the statement of cash flows.

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

161

The statement is divided into three activities, namely, operating, investing and

financing activities (Ramachandran and Kakani, 2008). The operating activities are

the prime revenue producing activities of an enterprise. Sometimes, an enterprise can

face the situation of huge profits, sound working capital and inadequate cash/ bank

position. Such a situation hampers the short term financial planning of the business,

ability to meet obligations such as payment to creditors, repayment of bank loan,

payment of interest, taxes and dividends etc. (Bose, 2010). Thus, the company should

have a sufficient cash position so as to pay its debts as they come due. It is calculated

as:

CFO= Net cash flow from operating activities at the end of financial year /

total assets at the beginning of the year.

Positive ratio denotes the operating cash flow generation ability of total

assets. Thus, if the firm in particular year has positive CFO, then the indicator

variable for that firm is F-CFO=1 and the firm having negative CFO, is given zero

score i.e. F-CFO=0.

4.) Accrual:

Under the cash basis of accounting, revenue is not reported until cash is

received and the expenses are not reported until cash is disbursed. Income under this

system of accounting is the excess of cash receipts over cash payments during a

particular accounting period. However, under accrual system of accounting, the items

of income or expenses are recognized when they are actually earned or incurred in an

accounting period. Actual cash receipts and actual cash payments are immaterial

under this method (Label, 2006). Accountants have long argued that the “quality” of

earnings is high for firms with low (or even negative) accruals, while the quality of

earnings is low for firms with high accruals (Ross et al., 2003). Sloan (1996) found

that earnings performance attributable to the accrual component of earnings exhibits

lower persistence than earnings performance attributable to the cash flow component

of earnings. Consequently, firms with relatively high (low) levels of accruals

experience negative (positive) future abnormal stock returns that are concentrated

around future earnings announcements. In fact, a strategy of buying stocks following

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

162

a reduction in accruals and simultaneously selling stocks following a buildup in

accruals would have generated an average return of about 10 percent per year. Thus,

Piotroski (2000) accorded positive signal to firms whose cash flow from operations

is greater than their profits (ROA).

In order to calculate accrual, the difference between the existing year‟s net

profits before extraordinary items and cash flow from operations is estimated and

divided by the total assets a firm has in the beginning of the year.

Thus, the accrual ratio receives the negative signal if firm‟s profits (ROA) are

higher than the firm‟s cash flow from operations (CFO) i.e. F-Accrual =1, if

CFO>ROA and F-Accrual=0, if CFO<ROA.

B. Signals Related to Leverage and Liquidity

The firms with high book to market ratio are associated with high financial

distress (Fama and French, 1992; Vassalou and Xing, 2004) which can lead to the

risk of financial debt and illiquidity. Thus, Piotroski (2000) devised financial signals

to assess changes in capital structure and the company‟s capacity to meet future debt

service obligations. These signals confirm that the firm has not increased its financial

debt compared to last year; it holds enough working capital and has not resorted

towards external financing. These signals are as under:

5. Change in Leverage

The term leverage refers to the association between two interrelated

variables. According to James Horne, “leverage is the employment of an asset or

resources for which the firm pays the fixed cost or fixed return.” Thus, it denotes

the ability of a firm to use fixed financial charges to amplify the effect of changes in

earnings before interest and taxes (EBIT) on its earnings per share (EPS). The fixed

financial charges such as interest on debentures, dividend on preference shares etc.

do not vary with the firm‟s profits. They are to be paid irrespective of the amount of

profits available (Chakravarty, 2004). Higher leverage implies higher proportion of

debt in the capital structure of the company. Thus, the capital structure decisions are

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

163

very critical for an organization as a shift in the firm‟s attitude towards leverage

could increase or decrease the financial strains on the company (Adami et al., 2010).

The impact of leverage on the value of the firm is the controversial issue.

Some authors have proposed its positive impact in enhancing overall returns and

other have opined the inverse relation of leverage with firm‟s value. Modigliani and

Miller (1958) are first to form the basis for modern opinion on capital structure.

They have asserted that in the absence of taxes and the transaction cost, the capital

structure does not have any influence on the firm‟s overall value. Therefore, it is

irrelevant whether the capital is financed by debt or equity. However, many authors

(Hamada, 1972; Ross, 1977; Heinkel, 1982; Bhandari, 1988; Mukherji et al., 1997;

Fama and French, 2002; Lasher, 2003; Ding et al. 2005; Dhaliwal et al., 2006;

Ward and Price, 2006; Sharma 2006; Tripathi, 2009) have found the positive

impact of leverage in enhancing the overall profitability and the market returns of

the firm.

On the other hand, many studies have found opposite results. Myers (1977)

stated that firms with excess debt overhang are prevented from raising funds to

finance positive net present value projects as the returns generated from such

investment would be transferred to debt holders and not the shareholders. So firms

with high growth opportunity may not issue debt in the first place and an inverse

relationship between growth opportunities and leverage is expected to hold (Niu,

2008). According to Piotroski (2000), “By raising external capital, a financially

distressed firm is signaling its inability to generate sufficient internal funds and in

addition, an increase in long-term debt is likely to place additional constraints on the

firm‟s financial flexibility”. It is defined as:

Leverage= long term debt at fiscal yearend/ Average total assets at fiscal year

end

Thus, if a firm has increased its leverage compared to previous year‟s leverage, that

firm gets zero signal i.e. F- Leverage=0 and if a firm‟s leverage has been reduced

compared to previous year, that firm gets positive signal i.e. F- leverage=1.

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

164

6. Change in Liquidity:

Liquidity means the easiness and promptness with which assets can be

converted into cash. A firm can easily meet its short term liabilities if it has a sound

liquidity position. Thus, the probability that a firm will avoid financial distress can

be linked to the firm‟s liquidity (Ross et al., 2003). From it, insight can be obtained

into the present cash solvency of the firm and its ability to remain solvent in the

event of adversities. Essentially, it compares short term obligations with the short

term resources available to meet these obligations (Van Horne, 1994). The liquidity

position of an enterprise is assessed through its current ratio. Higher the ratio, greater

the ability the firm has for paying its bills. This indicator measures the change in

current ratio of a firm in current year as compared to previous year‟s current ratio.

The current ratio is calculated as:

Current ratio= Current assets at the fiscal year end/ Current liabilities at the

fiscal year end Higher current ratio as compared to previous year‟s ratio

acknowledges company to get positive signal. Thus, if the indicator variable F-

Liquidity is greater than zero, the firm gets positive signal and vice versa.

7. Change in Equity:

Whether a firm performs better or worse after having completed an equity

issuance compared to firms who have not issued equity has been the subject of

several studies. In order to maximize current shareholder value, a management

should issue equity when they consider their stock to be overvalued and vice versa

(Dahl et al., 2009). Ikenberry et al. (1995) observed that average abnormal return on

announcement of share repurchases of value stocks due to undervaluation is 45.3%

as compared to glamour stocks where no positive drift in abnormal returns could be

observed. Moreover, Loughran and Ritter (1995) found that the companies issuing

seasoned equity significantly underperform relative to non issuing firms for 5 years

after the offering date. Thus, Piotroski (2000) defined the indicator variable F-

Issuance to equal one if the firm did not issue common equity in the year preceding

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

165

portfolio formation and F- Issuance equal to zero if the firm‟s number of issued

equity shares have increased in the current year compared to the previous year.

C. Measures Relating to Operating Efficiency

Operational efficiency can be defined as the ratio between the input to run a

business operation and the output gained from the business. Further, two financial

signals are intended to measure the operational efficiency of the business.

8. Change in Gross Margin Ratio:

This measure reflects the efficiency with which management produces each

unit of product. This ratio indicates the average spread between the cost of goods

sold and the sales revenue (Pandey, 1995). The ratio represents the excess of sales

proceeds during the period under observation over their cost, before taking into

account administration, selling and distribution and financial charges (Kishore,

2002). It is calculated as:

Gross margin= Gross profit at fiscal year end/ Total sales at fiscal year end. A

high gross profit margin is a sign of good management. A low gross profit margin

may reflect higher cost of goods sold due to firm‟s inability to purchase raw material

at favorable terms, inefficient utilization of plant and machinery, or over investment

in plant and machinery, resulting in higher cost of production (Pandey, 1995). The

gross margin, therefore, represents the limit beyond which reduction in sales price

falls outside the tolerance limit. The firm should have a reasonable margin to ensure

adequate coverage for operating expenses of the firm and sufficient return to the

owners of the business, which is reflected in the gross profit margin (Khan and Jain,

1994). Thus, Piotroski (2000), in order to measure the efficiency of the company‟s

operations compared the current year‟s gross margin to previous year‟s margin and

assigned the indicator variable F- Margin equal to one, if the change in gross margin

is positive and F- Margin equal to zero, if the change has been negative.

9. Change in Asset Turnover Ratio:

The assets are used to generate sales. It measures how effectively the firm

employs its resources. A firm should manage its assets efficiently to maximize sales.

The relationship between sales and assets is called as assets turnover. A firm‟s ability

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

166

to produce a large volume of sales for a given amount of net assets is the most

important aspect of its operating performance (Pandey, 1995). The ability to produce

a large volume of sales on a small asset base is an important part of the firm‟s profit

picture. Idle or improperly used assets increase the firm‟s need for costly financing

and the expenses for maintenance and keep up. By achieving a high asset turnover, a

firm reduces cost and increases the eventual profit to its owners (Hampton, 1980).

The asset turnover ratio is calculated as:

Asset turnover= Total sales at fiscal year end/ Total assets at the beginning of

the year.

According to Piotroski (2000), “An enhancement in asset turnover implies

larger efficiency from the asset base”. Such an up gradation can occur in two cases.

First one is the more efficient operations i.e. smaller amount of assets yielding the

same levels of sales and the second situation can be of an increase in the level of

sales due to enhanced market setting for the company‟s products. Thus, the indicator

variable F- Turnover equals one, if change in assets turnover ratio in current year

compared to previous year is positive and the variable F- Turnover is zero, if change

in turnover ratio is negative.

The aggregate fundamental score, F-score, is defined as the addition of the individual

binary signals, or

ROA

F- rg

F Score F F ROA F CFO F Accrual

F Leverage F Liquidity Issuance F Ma in

F Turnover

Given the nine underlying signals, F-score can range from a low of zero to a high of

nine, where a low F-score represents a firm with very few good signals about the

firm‟s financial condition and a high F-score represents a firm with mostly good

signals about its financial position. All these components of F-score altogether help

in extracting a refined value portfolio out of stocks with high book to market ratio.

Overall, the F-score strategy aims at eliminating the negative return observations i.e.

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

167

the left tail of the return distribution. The present study examines the relevance of

Piotroski‟s F-score in Indian stock market.

Table 5.1: Description of the Variables Discussed in Piotroski’s F-score and the

Formulae Applied

S.No. Variable Name Notation Description

1. Book to market

ratio

B/M ratio Book value of a share at fiscal year end/ Current

market price of a share

2. Return on

assets

ROA Net income before interest, taxes and extraordinary

items at the end of financial year / Total assets at

the beginning of the year

3. Change in

return on assets ROA ROA for the year t – ROA for the year t-1.

4. Cash flow from

operations

CFO Net cash flow from operating activities at the end

of financial year/Total assets at the beginning of

the year

5. Accrual ROA for the year t – CFO for the year t

6. Leverage Long term debt at fiscal year end/ Average total

assets at fiscal year end

7. Change in

leverage Leverage Leverage for the year t – Leverage for the year t-1.

8. Current ratio/

Liquidity

Current assets at the fiscal year end/ Current

liabilities at the fiscal year end

9. Change in

liquidity Liquidity Liquidity for the year t – Liquidity for the year t-1

10. Change in

equity Issuance Number of equity shares issued at fiscal year end t

– Number of equity shares issued at fiscal year end

t-1.

11. Gross margin

ratio

Gross profit at fiscal year end/ Total sales at fiscal

year end.

12. Change in gross

margin Margin Gross profit for the year t – Gross profit for the

year t-1

13. Asset turnover

ratio

Total sales at fiscal year end/ Total assets at the

beginning of the year

14. Change in asset

turnover ratio Turnover Asset turnover for the year t – Asset turnover for

the year t-1

5.3 HIGH BOOK TO MARKET STOCKS

In order to examine the relevance of F-score in Indian stock market, at first all the

stocks listed at Bombay Stock Exchange (excluding the financial stocks) with relevant

book to market information are screened. Further, the book to market ratio of all the

selected stocks is calculated using following formula:

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

168

Book to market ratio= Book value of a share for last financial year end/ Current

market price of a share.

A company's book value explains how much money would be left for

shareholders if the company were to immediately liquidate, sell all of its assets and pay

off all its liabilities. As per CMIE's (Centre for Monitoring Indian Economy) definition,

the outstanding reserve plus the paid-up capital at the end of a year is considered for the

calculation of book value. The net profit (net of prior period and extra-ordinary items)

earned in the interim period and adjusted for dividend outgo is added to the total net

worth to reflect the latest book value of the company (prowess.cmie.com)

Further, the stocks are divided into quintiles on the basis of book to market ratio.

The stocks in the highest quintile of book to market ratio are designated as value stocks.

Table 5.2 shows the number of value stocks selected each year from 1996 to 2010.

Table 5.2: Number of Stocks Selected in Highest Book to Market Quintile Each

Year

S.No. Year Number of stocks in high book to market quintile

1 1996 298

2 1997 298

3 1998 240

4 1999 233

5 2000 271

6 2001 249

7 2002 254

8 2003 253

9 2004 272

10 2005 302

11 2006 317

12 2007 354

13 2008 405

14 2009 416

15 2010 435

16 Across the period 4597

Table 5.2 shows that in the year 1996, the number of stocks forming the part of

portfolio is 298. In 1997 also, 298 stocks form the part of portfolio, 240 in 1998, 233 in

1999, 271 in 2000, and 249 in 2001. Across the period of 15 years, 4597 stocks form the

part of the study. The performance of these stocks selected is analyzed as under:

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

169

5.3.1 Analyzing the Performance of Stocks with High Book to Market Ratio

In order to analyze the performance of stocks falling in the highest book to market

quintile, the market adjusted returns of all such stocks have been calculated for two holding

periods i.e. 12 months, 24 months. Further, the significance of the returns has been

examined through one sample t-test. Thus, we proceed to test the following hypothesis:

H01 : The market adjusted returns of high book to market stocks is equal to

zero

The results examining the above hypothesis in both the holding periods are listed

in Table 5.3

Table 5.3: Results of the Significance of Market Adjusted Returns of High Book

to Market Stocks

Year No. of

stocks

12 months holding period 24 months holding period

Mean

(Annual) Std. Dev. T-Value P-Value

Mean

(Annualized) Std. Dev. T-Value

P-

Value

1996 298 -21.0625

(13.8021) 238.0280 -1.52591 .128

-1.0152

(3.726853) 64.33546 -0.2724 .786

1997 298 78.20839

(10.14116) 175.0635 7.711979 .000***

40.47185

(3.450373) 59.56268 11.7297 .000***

1998 240 77.03241

(9.92424) 153.7458 7.762039 .000***

56.77972

(3.706807) 57.4256 15.31769 .000***

1999 233 134.1327

(12.49738) 190.7643 10.73286 .000***

47.34687

(3.44252) 52.54779 13.75355 .000***

2000 271 28.62323

(4.15069) 68.32903 6.896013 .000***

49.77847

(2.379566) 39.1726 20.91914 .000***

2001 249 111.9621

(5.17454) 81.65298 21.63708 .000***

53.63793

(2.179734) 34.39562 24.60756 .000***

2002 254 61.39512

(7.507327) 119.6471 8.178027 .000***

28.20791

(2.805267) 44.7086 10.05534 .000***

2003 253 67.06019

(8.185056) 130.1913 8.193003 .000***

65.46658

(2.607224) 41.47044 25.10969 .000***

2004 272 193.157

(6.856627) 113.0824 28.17085 .000***

48.53954

(2.229048) 36.7624 21.77591 .000***

2005 302 -20.1481

(4.244383) 73.7595 -4.747 .000***

-5.74701

(2.092294) 36.3602 -2.74675 .000***

2006 317 24.12948

(4.108321) 73.14657 5.873321 .000***

13.64269

(1.846413) 32.87446 7.388753 .000***

2007 354 26.69862

(3.83673) 72.18781 6.958674 .000***

13.39258

(1.694185) 31.87591 7.905027 .000***

2008 405 19.15687

(2.017917) 40.6098 9.493387 .000***

23.29422

(1.217385) 24.4994 19.13464 .000***

2009 416 44.53731

(2.787409) 56.85221 15.97803 .000***

9.756745

(1.51241) 30.84724 6.451122 .000***

2010 435 -11.8571

(2.52908) 52.74815 -4.68831 .000***

-8.97528

(1.690349) 35.25503 -5.30972 .000***

Across

the period 4597

47.78089

(1.90865)

129.4111

25.033 .000***

25.47815

(0.706398) 47.89462 36.064 .000***

Note:

1. *, **, *** denotes p-values significant at 10, 5 and 1 percent level respectively

2. Standard error of mean has been reported in parenthesis

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

170

Table 5.3 shows that high book to market portfolio has yielded higher return than

the market in all the years except 1996, 2005 and 2010. In year 1997, 1998, 1999, 2000,

2001, 2002, 2003, 2004, 2006, 2007, 2008 and 2009, the returns of high book to market

portfolio have been significantly higher than the market returns in 12 months as well as

24 months holding periods. Across the period of 15 years, the high book to market

portfolio has yielded an annual return of 47.78% (significant at 1% level of significance)

in case of 12 months holding and an annualized rate of return of 25.47% (significant at

1% level of significance) in case of 24 months holding period. However, out of three

years with negative market adjusted return, the returns have been significant only in year

2005 and 2010. Thus, the portfolio of high book to market stocks has the potential to

outperform the market in Indian stock market.

The market adjusted performance showed above was based on the total portfolio

of high book to market stocks. The further, section shows the number of stocks out of

total portfolio of stocks that actually showed an increment in their value.

5.3.2 Stocks not showing the Increment in their Value

In this section attempt is made to know the number of stocks in the portfolio of

high book to market stocks which are not showing the increment in their value. The

returns of all the stocks are observed and the number of stocks with no increment in their

value in 12 months as well as 24 months period is listed. Table 5.4 reports the results.

Table 5.4: Number of High Book to Market Stocks that did not show an

Increment in their Value

Year Total book

to market

stocks

Number of stocks with

no appreciation in 12

months holding period

% of stocks with no

appreciation in 12

months holding period

Number of stocks with

no appreciation in 24

months holding period

% of stocks with no

appreciation in 24

months holding period

1996 298 237 79.5302 173 58.05369

1997 298 87 29.19463 70 23.48993

1998 240 81 33.75 38 15.83333

1999 233 34 14.59227 35 15.02146

2000 271 104 38.37638 58 21.40221

2001 249 12 4.819277 10 4.016064

2002 254 76 29.92126 65 25.59055

2003 253 77 30.43478 13 5.13834

2004 272 2 0.735294 22 8.088235

2005 302 210 69.53642 187 61.92053

2006 317 142 44.79495 115 36.2776

2007 354 143 40.39548 124 35.02825

2008 405 137 33.82716 63 15.55556

2009 416 78 18.75 136 32.69231

2010 435 293 67.35632 262 60.22989

Across

the period

4597 1713 37.26343 1371 29.8238

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

171

Table 5.4 shows that out of 298 high book to market stocks selected in year 1996,

237 (i.e. 79.53%) stocks did not showed an increase in their value in12 months holding

period. In case of 24 months holding period, 173 (i.e. 58.05%) stocks did not showed any

improvement. Thus, majority of the high book to market stocks did not increase in their

value in 12 months, 24 months period. In year 1997, out of total 298 stocks, 87 stocks

(i.e. 29.19%) did not showed any improvement in 12 months period and 70 (23.48%)

stocks did not increase in 24 months holding period. Similarly in all the years, the

number of stocks not showing any improvement in their value can be elicited from Table

5.4. Across the period of 15 years, the value of 1713 stocks out of 4597 stocks (i.e.

37.26%) did not increase in 12 months holding period and about 29.82% stocks did not

rise in their value in 24 months holding period. Therefore, it can be inferred that, no

doubt, the overall portfolio of high book to market stocks yield excess market returns to

investors but handsome number of stocks in high book to market portfolio have not

shown the increment in their value in 12 as well as 24 months holding period.

Piotroski (2000) found that less than 44% of the stocks in high book to market

portfolio earn positive market-adjusted returns in the two years following portfolio

formation in US stock market. He thus made an attempt to distinguish the stocks with

positive market adjusted returns from the stocks with negative market adjusted returns

through financial statement based heuristic, called as F-score. The further study explores

whether the F-score applied on high book to market stocks in Indian stocks market can

enhance the performance of value portfolio

5.4 F-SCORE APPLICATION

5.4.1 Descriptive statistics

In order to apply F-score on the stocks in value portfolio i.e. high book to market

stocks, the financial signals embedded in F-score are calculated for all high book to

market stocks. Table 5.5 contains the information regarding the basic characteristics of

high book to market portfolio. The mean, median, standard deviation and the positive

proportion of each of the individual financial signal attributed to 4597 observations for

the period 1996 to 2010 is shown as under:

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

172

Table 5.5: Financial and Return Characteristics of High Book to Market Stocks (4597

Observations across the Period of Study)

Variable Mean Median Standard

deviation

Proportion with

positive signal (%)

Market capitalization

(in millions)

253.816762 44.430000 1714.6507815 n/a

Assets (in millions) 1824.458612 443.100000 8568.4496308 n/a

Book to market ratio .301946 .240000 .2234880 n/a

ROA .048376 .044196 .1266639 81.25

Change in ROA -0.073993 -0.006928 3.0398143 40.69

Change in margin -0.057371 -0.005241 8.2404905 42.84

CFO (in millions) 105.990605 14.900000 1723.4411711 73.48

Change in liquidity -0.143910 -0.051110 25.4715463 47.15

Change in leverage -0.003192 -0.001332 .0832847 46.76

Change in turnover -1.581886 -0.025164 99.7809612 42.93

Accrual .005170 -0.008572 .2058045 45.41

Table 5.5 provides the descriptive statistics regarding the financial characteristics

of high book to market firms. It can be seen that average (median) firm in the value

portfolio (highest book to market quintile) has a mean (median) book to market ratio of

0.302 (0.240) and the market capitalization of 253.816 (44.43) million rupees. Further,

the average return on assets (ROA) realization is 0.0483 (0.0441) and about 81.25% of

high book to market firms have shown the positive value of ROA. This finding cannot be

considered with Piotroski (2000) findings of negative average realization of ROA (-

0.0054). Furthermore, the average and median firms in high book to market stocks shows

decline in respect of change in ROA (-0.0739, -0.00692), gross margin (-0.0573, -

0.00524), liquidity (-0.1439, -0.0511) and turnover (-1.5818, -0.02516) in current year as

compared to last year. No doubt the decline in the average and median firms in the

portfolio of high book to market firms in terms of leverage (-0.00319, -0.00133) in

current year compared to last year shows the lesser reliance of these firms on outside

funds but such stocks did not show any improvement in current year in terms of return on

assets, gross margin, liquidity and turnover compared to last year.

Further, Table 5.6 shows the 12 months, 24 months buy and hold returns for

complete portfolio of high book to market firms, with the percentage of firms with

positive raw and market adjusted returns.

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

173

Table 5.6: Return Distribution of the Stocks in High Book to Market Portfolio

Mean 10th

percentile

25th

percentile

Median 75th

percentile

90th

percentile

Percent

positive

(%)

12 months holding period

Raw returns 63.66213 -37.543444 -5.805557 35.907332 97.605512 197.299210 71.321

Market adjusted 47.78089 -52.500941 -19.721234 20.796098 81.569808 177.373127 62.693

24 months holding period

Raw returns 38.57398 -16.471807 8.053941 33.288619 63.848194 100.734062 52.686

Market adjusted 25.47581 -27.257827 -4.345306 21.495459 50.216663 84.261128 70.872

Table 5.6 shows that the mean and median market adjusted return in case of

one year period is 47.78% and 20.796% respectively. The two year holding period

shows the mean and median return of 25.475%, 21.495% respectively. It implies that

the mean as well as median market adjusted return of high book to market stocks have

been positive in 12 as well as 24 months holding period. Thus, high book to market

firms earn positive market adjusted returns in one, two year after the portfolio is

formed. It is consistent with the finding of Fama and French (1992), Lakonishok et al.

(1994) and Piotroski (2000). However, it is important to note that only 62.693% of

firms have positive market adjusted returns in 12 months holding period. Thus,

37.307% of the firms had zero or negative market adjusted returns in that period.

Therefore, the implementation of a strategy which could eliminate the negative values

from the return distribution will greatly improve the portfolio‟s mean return

performance.

5.4.2. Return Distribution Statistics Pertaining to High Book to Market Stocks

Table 5.7 and Table 5.8 show the distribution of the returns earned by the

highest quintile of book to market firms. For all the firms, the fundamental measure

F-score has been calculated as the sum of the nine individual binary signals as under:

ROA

F- rg

F Score F F ROA F CFO F Accrual

F Leverage F Liquidity Issuance F Ma in

F Turnover

Here, each binary signal is equal to one if the underlying variable is a good signal

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

174

about the future performance of the firm and the signal is set equal to zero if the

underlying realization is a bad signal regarding the future performance of a firm. Overall,

the total F-score equal to nine represents the fulfillment of the firm on all favorable set of

financial signals and the F-score equals to zero represents lack of fulfillment of the firm

on any of the favorable financial signal. Also, the firms having F-score of 8 or 9 are

accredited as high score firms and the firms having F-score of 1 or 2 are recognized as

low score firms. The return distribution of all the firms as well as different categories of

F-score along with the number of firms are shown in Table 5.7 and 5.8.

Table 5.7: Return Distribution of 12 Months Holding Period Market Adjusted

Returns to a value Investment Strategy based on Fundamental Signals

Mean 10th

percentile

25th

percentile

Median-

50th

percentile

75th

percentile

90th

percentile

%

positive

No. of

stocks

All

firms

47.780 -52.50094 -19.721234 20.796098 81.569808 177.373127 62.693% 4597

F-score

1 42.3705583 -122.1810 -38.179497 39.214676 96.368109 278.607971 52.631% 19

2 35.470623 -62.9225 -28.068712 7.6039213 66.16829 180.622 55.221% 134

3 32.161 -65.14982 -27.106796 10.738751 62.857590 140.456726 56.294% 421

4 36.301724 -60.73468 -27.358952 12.643242 67.849315 159.695358 58.221% 821

5 52.446492 -52.30267 -17.094806 21.149794 85.884505 187.095681 62.983% 1086

6 50.594022 -52.55888 -19.613851 24.921367 87.059939 185.271541 63.645% 993

7 37.47869 -43.188 -14.1829 27.93234 80.86259 170.126 66.39% 723

8 69.67008 -35.96663 -9.811126 38.272218 107.954141 197.382240 71.084% 332

9 49.16197 -54.58711 -3.929840 24.545073 95.707068 168.191531 70.588% 68

Low

score

36.32748 -63.27908 -29.676331 7.732254 68.595673 184.842333 54.248% 153

High

score

66.1837 -38.362941 -7.346701 36.099792 103.911244 192.765805 71.25% 400

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

175

Table 5.8: Return Distribution of 24 Months Holding Period Market adjusted

returns to a Value Investment Strategy based on Fundamental Signals

Mean 10th

percentile

25th

percentile

Median-50th

percentile

75th

percentile

90th

percentile

%

positive

No. of

stocks

All

firms

25.4780

-27.257827 -4.345306 21.495459 50.216663 84.261128 70.827% 4597

F-score

1 6.15643483 -71.475642 -36.777933 13.821805 58.841285 85.546457 57.894% 19

2 18.369136 -58.513943 -15.396339 22.22965 50.78732 88.67977 61.194% 134

3 18.919 -30.388811 -9.058623 13.396351 41.159449 78.409220 63.895% 421

4 20.185281 -36.053428 -8.113825 16.096589 45.023150 78.095235 66.747% 821

5 27.284321 -25.644070 -2.914118 24.393756 52.878989 85.783365 72.375% 1086

6 26.714918 -28.967615 -3.932375 22.758816 51.832613 86.467542 71.5005% 993

7 16.13732 -20.3655 0.090245 22.78009 50.148 81.15417 74.965% 723

8 34.24404 -17.289779 3.063441 26.195031 60.563508 98.448643 76.204% 332

9 35.06779 -18.374019 1.208544 31.416098 58.547406 84.318525 76.470% 68

Low

score

16.85253 -58.763618 -16.828178 17.418834 50.880058 87.911156 60.130% 153

High

score

34.38408 -17.496362 2.925959 27.430862 59.437687 96.926354 76.5% 400

Table 5.7 shows the return distribution statistics pertaining to 12 months holding

period and Table 8 shows the return classification information regarding 24 months

holding period. As evident from these tables, maximum number of observations i.e. 1086

observation out of 4597 observations is clustered around F-score of 5, followed by 993

observations around F-score of 6 and further, 821 observations are clustered around F-

score of 4. The number of firms having high score i.e. 8 or 9 are 400 and the number of

observations having low score 1 or 2 are 153. Thus, high F-score portfolio represents

8.7% (400 out of 4597) of the entire portfolio and the low F-score represents 3.33% (153

out of 4597) of the entire portfolio across the period of 15 years.

Table 5.7 further shows that the 12 months mean and median market adjusted

return of highest quintile of book to market firms is 47.78% and 20.79% respectively.

Out of sample of all high book to market firms i.e. 4597 observations across the period of

15 years, 62.69% of the firms have positive market adjusted returns in 12 months holding

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

176

period. Further, 52.63% of the firms having F-score 1 have positive market adjusted

return in 12 months holding period. It is important to note that the percentage of firms

having positive market adjusted return continued to increase with successive increase in

F-score. Along with it, different return partitions i.e. 10th

percentile, 25th

percentile, 50th

percentile, 75th

percentile and 90th

percentile also showed an increase in 12 months

market adjusted return with the successive increase in composite measure F-score.

Table 5.8 reports that when the holding period has been extended from 12 months

to 24 months, the mean and median market adjusted annualized rate of return of highest

quintile of book to market firms is 25.47% and 21.49% respectively. Further, 70.827% of

the firms in entire sample show the positive market adjusted returns in 24 months holding

period as compared to 62.69% in case of 12 months holding period. Similarly, 57.89% of

the firms with F-score equals to 1 have positive market adjusted return in 24 months

holding period compared to 52.63% in case of 12 months period. Also, 61.19% of the

firms with F-score equals to 2 had positive market adjusted return in 24 months holding

period compared to 55.22% in case of 12 months period. Along with it, different return

partitions i.e. 10th

percentile, 25th

percentile, 50th

percentile, 75th

percentile and 90th

percentile also showed an increase in 24 months market adjusted return with the

successive increase in composite measure F-score. Thus, F-score strategy helps in

shifting the distribution of returns (towards right) earned by a value investor.

The mean market adjusted return of high score portfolio is 66.18% in case of 12

months holding period and 34.38% in case of 24 months holding period. This return of

high score portfolio is greater than that of low score portfolio in both the holding periods

(low score portfolio has 36.32% return in 12 months period and 16.85% in case of 24

months period). Also important to note that return of high score portfolio even exceeds

the return of all high book to market firms. Thus, high score portfolio outperforms both

low score as well as all firms in respect of mean market adjusted returns in both the

holding periods. Thus, the evidence regarding the success of strategy to discriminate

between future winners and future losers can be elicited from the above results.

5.4.3 Examining the Significance of Difference in Returns

In order to examine the significance of the return difference between the portfolio

of high score stocks and the portfolio of low score stocks, independent sample t-test has

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

177

been used. Along with it, the significance of return difference amongst the high score

stocks and all stocks in high book to market portfolio is also examined using t-test. The

following hypothesis is tested:

H02 : There is no significant difference between the mean returns of the

stocks having high F-score and all the stocks having high book to

market ratio

H03 : There is no significant difference between the mean returns of the

stocks having high F-score and the stocks having low F-score

In order to examine H02, two groups of stocks are formed; one containing all high

book to market stocks and another containing stocks that have the F-score of 8 or 9.

Further, independent sample t-test has been used on the 12 months as well as 24 months

market adjusted returns of the two groups. In order to examine the H03, two groups are

formed; one containing the high score stocks (i.e. the stocks having 8 or 9 as the F-score)

and another containing the low score stocks (i.e. the stocks having 1 or 2 as the F-score).

Further, independent sample t-test has been used on the market adjusted returns of these

stocks. The analysis will help to determine whether the high score firms significantly

outperform the low score and all the value stocks. Table 5.9 reports the results.

Table 5.9: Results of t-test Employed on Firms with High Score- Low Score

Group, Firms with High Score- All High Book to Market Firms

criteria No. of

stocks

12 months market adjusted returns 24 months Market Adjusted Returns

Mean

returns

(Annual)

Std.

Dev.

Mean

difference

F-value

of

Levene’s

test

T-value

Mean

returns

(Annualized)

Std.

Dev.

Mean

difference

F-value

of

Levene’s

test

T-value

High 400 66.183 124.11 18.402 0.627 2.737*** 34.384 47.150 8.908 0.165 3.572***

All 4597 47.780 129.41 25.475 47.894

High 400 66.183 124.11 29.856 0.265 2.618*** 34.384 47.150 17.531 4.985** 3.601***

low 153 36.327 108.38 16.852 52.681

Note: *, **, *** denotes p-values significant at 10, 5 and 1 percent level respectively

Table 5.9 shows the results of significance of return difference between high

score stocks and all the stocks in high book to market portfolio in case of 12 months as

well as 24 months holding period. The F-value of Levene‟s test that measures the

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

178

equality of variances of two groups is 0.627 in case of 12 months holding period and

0.165 in case of 24 months holding period. The insignificant F-value of Levene‟s test

leads to acceptance of null hypothesis of equal variance in two groups. Further, the return

difference between the portfolio of high score and all high book to market stocks being

18.402% in case of 12 months holding period and 8.908% in case of 24 months holding

period, is significant at 1% level of significance. It therefore leads to rejection of null

hypothesis (H02) of no significant difference between the mean returns of the stocks

having high F-score and all the stocks having high book to market ratio. Thus, the firms

with F-score 8 or 9 significantly outperform all the firms in value portfolio in both the

holding periods.

In further analysis, the significance of return difference between high F-score

firms and the low F-score firms is examined. The F-value of Levene‟s test of equality of

variances is .265 in case of 12 months holding period showing that variances of two

groups are equal. In case of 24 months holding period, the value of Levene‟s test is 4.985

(significant at 5% level of significance) showing that the variance between two groups is

not equal. Thus, the results of Welch t-test become applicable in such a situation. The

mean difference between two groups (29.856% in case of 12 months holding period and

17.53% in case of 24 months holding period) is significant at 1% level of significance. It

also leads to rejection of null hypothesis (H03) of no significant difference between the

mean returns of the stocks having high F-score and the stocks having low F-score. Thus,

high F-score portfolio significantly outperforms the low F-score firms in both the holding

periods.

The above results show that high F-score portfolio significantly outperform the

entire value portfolio based on book to market ratio as well as the low F-score portfolio

on the basis of mean market adjusted returns. The following sections explore whether the

outperformance of high score stocks over low score stocks is attributed to small firm

effect. It is due to the reason that Piotroski (2000) opined that if the return predictability

is concentrated in smaller firms, an investor would not be interested in investing in such

stocks due to riskiness and low level of liquidity associated with such stocks. Thus, the

performance of the F-score strategy across size, share price level and trading volume

partitions is examined as under.

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

179

5.4.4 Return Conditional on Firm Size

Piotroski (2000) attempted to explore whether the excess returns observed

using the F-score based strategy is strictly a small firm effect or can be useful across

all size categories. In order to determine whether the excess returns to high score

portfolio and the outperformance of high score portfolio over low score portfolio is

due to small size effect, we took the data available on book to market ratio, market

capitalization of all the stocks listed on BSE every year on 30th

June. On basis of

market capitalization of all the stocks, the 33.3 percentile and 66.67 percentile are

calculated every year. The stocks with market capitalization up to 33.33 percentile are

classified as small stocks, the stocks having market capitalization in the range of

33.33 to 66.67 percentile are categorized as medium size stocks and the stocks

exceeding the percentile of 66.67 in terms of market capitalization are classified as

large size stocks. Thereafter, these percentiles are used to classify the high book to

market stocks into small, medium and large size portfolios.

5.4.4.1 Market Adjusted Returns of F-Score Stocks in Different Size Categories

After classifying the stocks into different size categories, out of total of 4597

high book to market stocks across the period of 15 years, 2512 stocks have small size,

1699 stocks have medium size and 386 stocks have large size. Table 5.10 shows that

out of 2512 small stocks, only 12 stocks have F-score of 1, further, 73 stocks have F-

score of 2, 219 stocks have F-score of 3, followed by 39 stocks having F-score of 9.

Similarly, amongst the medium size stocks, out of 1699 stocks, only 4 stocks have F-

score of 1 and 21 stocks have F-score of 9. Out of stocks with large size group, only 3

stocks have F-score of 1, only 8 stocks have F-score of 9 and the maximum number of

stocks i.e. 83 stocks have F-score of 6. The mean as well as median returns of the

stocks falling in different categories of F-score, low and high group in three size

partitions have been shown in Table 5.10 and Table 5.11.

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

180

Table 5.10: 12 Months Market Adjusted Returns Earned Using Value Investment

Strategy Based on Fundamental Signals in Different Size Partitions

Small firms Medium firms Large firms

Mean Median No. of

stocks

Mean Median No. of

stocks

Mean Median No. of

stocks

All

firms

66.249 30.8220 2512 28.563 11.218 1699 12.180 3.703 386

F- score

1 84.004 46.344 12 -33.887 -27.874 4 -22.488 -52.801 3

2 42.394 18.558 73 34.934 3.070 49 -4.460 -2.303 12

3 55.477 18.912 219 9.741 -1.272 164 -5.461 -23.923 38

4 58.576 24.809 434 14.461 9.087 320 -3.670 -9.410 67

5 75.976 36.995 560 29.211 10.172 433 18.941 1.836 93

6 68.587 34.078 557 30.359 11.760 353 15.901 15.925 83

7 65.159 33.383 414 36.930 14.377 249 15.091 16.907 60

8 75.463 35.741 204 63.249 43.501 106 47.138 32.475 22

9 41.748 21.864 39 69.644 25.049 21 31.535 18.967 8

Low 48.268 20.262 85 29.740 -1.755 53 -8.065 -12.338 15

high 70.029 32.472 243 64.306 43.436 127 42.977 28.719 30

Table 5.10 shows that mean and median of 12 months market adjusted returns of

all stocks having high book to market ratio and small size is 66.249%, 30.822%

respectively. Medium size group has mean and median return of 28.563%, 11.218%

respectively. The larger size group has mean return of 12.18% and median return of

3.703%. The small size portfolio thus has larger mean and median return than medium,

large size portfolio. It shows the presence of small firm effect i.e. stocks of small size

firm outperform the stocks with large size. Further, in all F-score categories, the small

size stocks outperform the larger size stocks in terms of 12 months mean market adjusted

returns. In respect of low score and high score portfolio also, the mean, median market

adjusted return of the small size portfolio is larger than that of medium size and large size

portfolio in case of 12 months holding period of such stocks. Table 5.11 shows the

similar return classification when the holding period is extended from 12 months to 24

months holding period.

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Table 5.11: 24 Months Market-Adjusted Returns Earned Using Value Investment

Strategy Based on Fundamental Signals in Different Size Partitions

Small firms Medium firms Large firms

Mean Median No. of

stocks

Mean Median No. of

stocks

Mean Median No. of

stocks

All

firms

33.339 26.309 2512 17.263 16.635 1699 10.452 11.466 386

F- score

1 26.5140 20.929 12 -25.110 -12.795 4 -33.584 -58.819 3

2 25.525 28.365 73 15.126 17.418 49 -11.922 -2.621 12

3 29.949 21.840 219 5.942 4.425 164 11.361 7.169 38

4 28.709 21.652 434 13.130 12.587 320 -1.335 4.352 67

5 36.182 29.872 560 18.189 17.871 433 16.050 18.559 93

6 36.979 28.976 557 15.018 17.861 353 7.572 12.308 83

7 32.444 24.861 414 22.628 21.394 249 16.960 11.691 60

8 34.518 24.593 204 34.635 30.255 106 29.815 30.876 22

9 31.120 24.070 39 49.031 51.137 21 17.654 1.564 8

Low 25.664 20.262 85 12.089 3.2667 53 -16.254 -3.040 15

high 33.973 24.462 243 37.015 32.795 127 26.572 25.101 30

Table 5.11 shows that the 24 months annualized mean, median market adjusted

return of small size firms is 33.33%, 26.309% respectively. The medium size firms have

the mean, median market adjusted annualized rate of return of 17.26% and 16.635%

respectively. Further, the large size portfolio has the mean return of 10.452% and the

median return of 11.466%. It is again important to note that in respect of 24 months

holding period also, the small size portfolio outperforms both medium as well as large

size portfolio. Further, in all F-score categories, the small size stocks outperform the

larger size stocks in terms of 24 months mean market adjusted returns. In respect of low

score and high score portfolio also, the mean, median market adjusted return of the small

size portfolio is larger than that of medium size and large size portfolio in case of 24

months holding period of such stocks. However, in order to see whether small firm effect

is accountable for excess returns on high score portfolio and to mark the outperformance

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of high score portfolio over low score portfolio, the difference between high score and

low score has to be statistically significant. The following section will explore whether

the difference between the high score and low score portfolio in different size categories

is statistically significant or not.

5.4.4.2 Examining the Significance of Return Difference between High F-Score

and Low F-Score Stocks in Different Size Categories

Independent sample t-test has been used to determine the significance of

difference between high score and low score portfolio in different size categories. The

following hypothesis is tested and reported in Table 12 as under:

H04 : There is no significant difference between the mean returns of the

stocks having high F-score and the stocks having low F-score across

different size partitions

Table 5.12 shows that in case of large size portfolio, the F-value of Levene‟s test

intended to measure the equality of variances in two groups (high score portfolio and low

score portfolio) is insignificant (at 5% level of significance) in 12 months holding periods

leading to the acceptance of null hypothesis of equal variance in two groups. In case of

24 months holding period, the significant value of Levene‟s test implies that the variance

of two groups are not equal. Hence, the results of Welch‟s t-test become applicable.

Further, the mean difference of 51.043% between high F-score and low F-score portfolio

in case of 12 months holding period is significant at 1% level of significance and the

difference of 42.827% between the two is significant at 5% level of significance in case

of 24 months holding period showing that in case of stocks with larger size, the high F-

score portfolio significantly outperforms low F-score portfolio. Similar results have been

found in case of medium size portfolio wherein high F-score portfolio significantly

outperforms low F-score portfolio.

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Table 5.12: Results of t-test Employed on Firms with high F-score as well as low F-score Across Different Size

Partitions

Size

No. of

stocks

Criteria

12 months market adjusted Returns 24 months Market Adjusted Returns

Mean

returns

(Annual)

Std.

Dev.

Mean

difference

F-value

of

Levene’s

test

t-value

Mean

returns

(Annualized)

Std.

Dev.

Mean

difference

F-value

of

Levene’s

test

t-value

Large

30 High 42.974 64.356 51.043 0.543 2.312**

26.572 27.472 42.827 10.798*** 3.041***

15 Low -8.065 79.935 -16.254 50.969

Medium 127 High 64.306 94.924

34.566 0.014 2.145** 37.015 41.738

24.925 11.384*** 2.70*** 53 Low 29.740 106.762 12.089 61.570

small 243 High 70.029 141.866

21.760 0.371 1.280 33.973 51.542

8.308 0.448 1.325 85 Low 48.268 112.299 25.664 44.199

Note: *, **, *** denotes p-values significant at 10, 5 and 1 percent level respectively

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In case of small size portfolio, the value of F-value (0.371 in case of 12 months;

0.448 in case of 24 months) of Levene‟s test is insignificant in both the holding periods

leading to the acceptance of null hypothesis of equal variance in two groups. The mean

difference between high F-score and low F-score portfolio (21.76% in case of 12 months

holding period; 8.3% in case of 24 months holding period) is statistically insignificant

showing that the high F-score portfolio does not significantly outperforms low F-score

portfolio in respect of stocks with small market capitalization. Thus, excess returns on

high F-score portfolio as well as outperformance of high F-score over low F-score

portfolio could not be attributed to small firm effect.

Returns Conditional on Alternative Partitions

Along with the size effect, the outperformance of high score portfolio over the

low score portfolio is examined taking other partitions such as trading volume and share

price level.

5.4.5 Trading Volume Partitions

Trading volume means the share turnover which is total number of shares traded

during the prior fiscal year scaled by the average number of shares outstanding during the

year. Similar to firm size, all the high book to market companies are placed into trading

volume partitions. Each year, all the firms on BSE with trading volume data and book to

market data are ranked on the basis of June end trading volume. On basis of trading

volume of all the stocks, the 33.3 percentile and 66.67 percentile are calculated every

year. The stocks with trading volume up to 33.33 percentile are classified as low volume

stocks, the stocks having volume in the range of 33.33 to 66.67 percentile are categorized

as medium volume stocks and the stocks exceeding the percentile of 66.67 in terms of

trading volume are classified as large volume stocks. Thereafter, these percentiles are

used to classify the high book to market stocks into low, medium and large volume

portfolios (Piotroski, 2000). The mean and median return of high book to market stocks,

low F-score stocks and high F-score stocks across different trading volume partitions

have been reported in Table 5.13.

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Table 5.13: Market Adjusted Returns Earned Using Value Investment Strategy

Based on Fundamental Signals in Different Trading Volume

Partitions

Panel A showing market adjusted returns in case of 12 months holding period

Low volume Medium volume Large volume

Mean

(%)

Median

(%)

No. of

stocks

Mean

(%)

Median

(%)

No. of

stocks

Mean

(%)

Median

(%)

No. of

stocks

All firms 63.087 32.479 1611 42.912 17.946 1791 34.415 12.896 1195

Low 65.079 29.799 50 26.498 -3.502 50 18.475 -0.047 53

high 63.625 41.225 168 70.282 34.162 142 64.492 31.091 90

Panel B showing the market adjusted returns in case of 24 months holding period

All firms 32.092 27.067 1611 24.099 21.437 1791 18.647 14.250 1195

Low 33.815 33.776 50 20.451 24.463 50 -2.546 -7.082 53

high 35.861 31.879 168 33.148 24.131 142 33.575 22.985 90

Table 5.13 shows that out of total of 4597 high book to market firms, 1611 firms

are categorized into low volume firms, 1791 as firms having medium trading volume and

1195 firms as firms with large volume. It is further important to note that the 12 months

mean market adjusted returns of high F-score portfolio have been greater than all firms as

well as low F-score portfolio in all levels of trading volume except the low volume

portfolio. The low F-score portfolio in case of low trading volume has the mean market

adjusted rate of return of 65.079% and the high F-score portfolio in similar group has the

mean market adjusted rate of return of 63.625%. Further, in case of 24 months holding

period, the mean market adjusted return of high score portfolio exceeds the mean return

of low score as well as all the firms in all levels of trading volume that shows the

outperformance of high F-score firms over low F-score as well as all firms having high

book to market ratio, irrespective of the trading volume. However, in order to prove the

existence of excess returns on high score portfolio over low score portfolio irrespective of

trading volume partitions, the difference between high score and low score has to be

statistically significant. The following section will explore whether the difference

between the high F-score and low F-score portfolio in different trading volume categories

is statistically significant or not.

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5.4.5.1 Examining the Significance of Return Difference between High F-Score

and Low F-Score Stocks in Different Trading Volume Categories

In order to examine whether the difference between the high score and the low

score portfolio across different categories of trading volume is statistically significant or

not, the independent sample t-test has been used. Thus, we examine the following

hypothesis:

H05 : There is no significant difference between the mean returns of the

stocks having high F-score and the stocks having low F-score across

different trading volume categories.

Table 5.14 reports the results as under

Table 5.14 shows that the F-value of Levene‟s test, which means the equality of

variance between two groups (high and low F-score portfolio) in case of low share

turnover firms is 0.914 in case of 12 months holding period and 0.164 in case of 24

months holding period, which is statistically insignificant. It shows that the variances of

two groups are equal. Further, the insignificant t-values in case of both the holding

periods make it evident that there is no difference in the mean market adjusted return of

the stocks that have high F-score and the stocks that have low F-score, in the category of

low trading volume. Thus, high returns to high F-score firms are not present when the

firms have low trading volume.

In case of firms with medium trading volume, the difference between the high

score and low score portfolio is significant (at 5% level of significance) in case of 12

months holding period and insignificant in case of 24 months holding period. Further, in

case of firms with large trading volume, the F-value of Levene‟s test (1.953 in case of 12

months; 0.742 in case of 24 months holding period) is insignificant in both the holding

periods leading to the acceptance of null hypothesis of equal variance in two groups. In

addition, the difference of 46.016% in case of 12 months and 36.122% in case of 24

months period between high F-score and low F-score firms is statistically significant. It

shows that high F-score stocks significantly outperform low F-score firms, if they have

huge trading volume.

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Note: *, **, *** denotes p-values significant at 10, 5 and 1 percent level respectively

Table 5.14: Results of t-test Employed on Firms with High F-score as well as low F-Score Across Different Trading

Volume Partitions

Trading

volume

Criteria

12 months market adjusted Returns 24 months Market Adjusted Returns

Mean

returns

(Annual)

Std.

Dev.

Mean

difference

F-value of

Levene’s

test

T-

value

Mean

returns

(Annualized)

Std.

Dev.

Mean

difference

F-value

of

Levene’s

test

T-value

Low 168 High 63.625 98.137

-1.453 0.914 -0.090 35.861 42.239

2.045 0.164 0.291 50 Low 65.079 106.59 33.815 47.691

Medium 142 High 70.282 135.669

43.783 0.170 2.035** 33.148 51.868

12.697 0.357 1.489 50 Low 26.498 115.691 20.451 51.785

Large 90 High 64.492 147.595

46.016 1.953 2.016** 33.575 48.473

36.122 0.742 4.168*** 53 low 18.475 99.012 -2.546 52.626

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Thus, we draw an inference through the above results that for F-score strategy to

work, the firms ought to have huge trading volume. The statistically significant

outperformance of high F-score strategy over low F-score strategy will disappear, if the

firms are thinly traded or have small share turnover.

5.4.6 Share Price Partitions

To examine, whether the performance of F-score strategy exists, irrespective of

low share price effect, all high book to market firms are classified into share price

partitions based on prior year‟s cut offs for all BSE firms. Similar to firm size, all the

high book to market companies are placed into share price partitions. Each year, all the

firms on BSE with sufficient share price and book to market data are ranked on the basis

of June end closing share prices. On basis of closing share prices of all the stocks, the

33.3 percentile and 66.67 percentile are calculated every year. The stocks with share

prices up to 33.33 percentile are classified as small priced stocks, the stocks having

closing prices in the range of 33.33 to 66.67 percentile are categorized as medium priced

stocks and the stocks exceeding the percentile of 66.67 in terms of closing share prices

are classified as large priced stocks. Thereafter, these percentiles are used to classify the

high book to market stocks into small, medium and large price portfolios. The mean and

median return of high book to market stocks, low F-score stocks and high F-score stocks

across different share price partitions have been reported in Table 5.15

Table 5.15: Market-Adjusted Returns Earned Using Value Investment Strategy based

on Fundamental Signals in Different Share Price Partitions

Panel A showing market adjusted returns in case of 12 months holding period

Small price Medium price Large price

Mean Median No. of

stocks

Mean Median No. of

stocks

Mean Median No. of

stocks

All firms 63.043 28.605 2559 31.252 13.634 1667 16.768 6.120 371

Low 46.874 17.820 95 22.622 6.194 46 5.363 -18.575 12

high 71.860 34.717 241 59.553 36.615 134 47.00 43.436 25

Panel A showing market adjusted returns in case of 24 months holding period

All firms 32.815 26.964 2559 17.584 15.589 1667 10.307 11.290 371

Low 22.159 26.247 95 7.614 2.773 46 10.251 18.719 12

high 36.927 27.647 241 29.071 26.489 134 38.344 31.688 25

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189

Table 5.15 shows that, out of total of 4597 high book to market firms, 2559 firms

have small share price, 1667 firms have medium price and only 371 firms have large

price. The 12 months market adjusted mean return of high F-score firms in all the

categories of share price (71.860% in small price; 59.553% in case of medium price;

47.00% in case of large price) exceeds the mean return of all high book to market firms

(63.043% in case of small price; 31.252% in case of medium price; 16.768% in case of

large price) as well as low score firms (46.874% in case of small price; 22.622% in case

of medium price; 5.363% in case of large price). The same results hold in case of 24

months holding period. It, thus, implies that the high F-score firms outperform all the

firms in high book to market portfolio as well as low F-score firms across all categories

of share prices.

However, in order to examine the statistically significant outperformance of high F-

score portfolio over low F-score portfolio irrespective of share price partitions, the

difference between two has to be statistically significant. The following section will

explore whether the difference between the high F-score and low F-score portfolio in

different share price categories is statistically significant or not.

5.4.6.1 Examining the Significance of Return Difference between High F-Score

and Low F-Score Stocks in Different Share Price Categories

In order to examine whether the difference between the high score and the low

score portfolio across different categories of trading volume is statistically significant or

not, the independent sample t-test has been used. Thus, we examine the following

hypothesis:

H06 : There is no significant difference between the mean returns of the

stocks having high F-score and the stocks having low F-score across

different share price categories.

Table 5.16 reports the results of testing the above hypothesis.

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190

Table 5.16: Results of t-test Employed on Firms With High F-Score as well as low F-Score Across Different Share Price

Partitions

Note: *, **, *** denotes p-values significant at 10, 5 and 1 percent level respectively

Share

price

No. of

stocks

Criteria

12 months market adjusted Returns (Annual) 24 months Market Adjusted Returns (Annualized)

Mean

returns

(Annual)

Std.

Dev.

Mean

difference

F-value of

Levene’s test

T-

value

Mean

returns

(Annualized)

Std.

Dev.

Mean

difference

F-value

of

Levene’s

test

T-value

Small 241 High 71.860 133.885

24.985 0.112 1.597 36.927 40.153

14.767 1.796 1.616 95 Low 46.874 116.108 22.159 50.169

Medium 134 High 59.553 112.138

36.931 0.723 1.996** 29.071 46.207

21.456 4.474** 2.527** 46 Low 22.622 95.905 7.614 58.722

Large 25 High 47.000 78.730

41.637 0.150 1.487 38.344 48.246

28.092 0.459 2.505** 12 low 5.363 81.815 10.251 49.665

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191

Table 5.16 shows that in case of stocks with small share price, the F-value of

Levene‟s test is insignificant showing no difference in the variances of the two groups

(high score and low score group). The difference in the mean market adjusted returns

of two groups (24.98% in case of 12 months; 14.76% in case of 24 months holding

period) in case of stocks with small share price is statistically insignificant in both

the holding periods. Further, significant difference is observed between two groups

in case of medium price in both the holding periods. In case of large price portfolios,

the market adjusted mean difference of 41.63% between high F-score and low F-

score portfolio is statistically insignificant in case of 12 months holding period of

such stocks. Nevertheless, statistically significant mean market adjusted difference of

28.092% between high F-score and low F-score portfolio is observed in case of 24

months holding period.

In a nutshell, the statistically significant outperformance of high F-score

stocks over low F-score stocks is channelized to firms with large size, huge trading

volume and medium to large price in Indian stock market. Hence, the risk involved

in investing in small and illiquid firms is not implicated in this investment strategy.

5.4.7 Analyzing the Predictive Ability of F-score Strategy

The prior results of significant difference between the high F-score and low

F-score shows the capability of F-score in predicting the returns. The model

development and estimation of the relationship between F-score and the overall

returns is as under:

5.4.7.1 Model Development

5.4.7.1 (A) Dependent variable-

In order to study the predictive ability of the criteria in explaining the risk

adjusted returns, the market adjusted stock return (annual in case of 12 months,

annualized in case of 24 months) variable has been taken as dependent variable as

advocated in Piotroski (2000), Michou (2007), Dahl et al. (2009) and Dosamantes

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

192

(2013). The reason behind taking excess of portfolio‟s return over market is to know

whether the excess returns yielded by the stocks are explained by F-score or not.

5.4.7.1 (B) Explanatory variables and hypothesis development

According to Piotroski (2000), there are certain known effects also which

could have strong relationship with F-score. Thus, before determining the role of F-

score in predicting stock returns, the following effects need to be controlled for. A

brief discussion of the explanatory variables used in the model is given below.

F-score- A comprehensive financial signal known as „F-score‟ measures three

constructs pertinent to a company‟s financial position: profitability, f inancial

leverage along with liquidity, and operating effectiveness. The three constructs of F-

score of a stockis the sum of nine binary signals related to these three constructs

(Wellman, 2011). In order to determine the relation between F-score and market

adjusted returns, the following null hypothesis is tested.

H07 : F-score of the stock has no significant impact on its market

adjusted returns

Under reaction or momentum effect: Chan et al. (1996) found the delayed reaction

of stock prices to the information in past returns and in past earnings. It is due to the

fact that there is tendency of the market to anchor too heavily on the past trends. The

investors therefore cut down the new information which is at odds with their

mindsets and alter their perceptions steadily. This fact of under reaction over

intermediate horizons suggests that a stock with high past returns will on average

experience high subsequent returns. Moreover, a substantial portion of under reaction

or momentum effect is concentrated around subsequent earnings announcements.

Thus, if the market is surprised by good or bad earnings news, then on an average the

market continues to be surprised in same direction for at least six months. Asness

(1997) however found the inverse relation between the momentum effect and the

value premium. As F-score comprises of the company‟s information regarding

profitability, it is necessary to control this effect, to examine the robustness of F-

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

193

score. Thus, the six months prior return on a particular stock has been used as the

control variable before estimating the relation between F-score and stock returns.

The following null hypothesis is tested.

H08 : The momentum effect of the stock has no significant impact on its

market adjusted returns.

Recent equity offering: Ikenberry et al. (1995) observed that average abnormal

return on announcement of share repurchases of value stocks due to undervaluation

is 45.3% as compared to glamour stocks where no positive drift in abnormal returns

could be observed. Moreover, Loughran and Ritter (1995) found that the companies

issuing seasoned equity offering significantly underperform relative to non issuing

firms for 5 years after the offering date. Thus, the companies issuing equity in the

preceding financial year are said to have negative stock returns in the current period.

Since the equity issuance variable is incorporated in the F-score, it is thereby

correlated to aggregate return metric. Thus, equity issuance variable has been taken

as control variable in order to estimate the relationship between F-score and overall

returns. The following null hypothesis is tested.

H09 : The issue of equity shares by a company in preceding financial

year has no significant impact on its market adjusted returns in

current period.

Accrual: Sloan (1996) observed that the firms with relatively high (low) levels of

accruals experienced negative (positive) future abnormal stock returns which were

concentrated around future earnings announcements. He also observed that the

strategy of buying stocks following a reduction in accruals and simultaneously

selling stocks following a buildup in accruals would have generated an average

return of about 10 percent per year in US stock market. Also, Takamatsu and Favero

(2013) found that current accruals were incapable of explaining future abnormal

return behavior in the firms that were analyzed. In addition, no significant abnormal

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

194

returns were reached in an accruals-based investment strategy. Therefore, the

companies having increased level of accruals in current fiscal year compared to the

level of accruals in previous fiscal year are said to have negative stock returns.

Again, the accrual variable is built-in in the F-score, it is thereby correlated to

aggregate return metric. Thus, the accrual variable has been taken as control variable

in order to estimate the relationship between F-score and overall returns.

H010 : The level of accrual of a company has no significant impact on its

market adjusted returns.

Size- The term „size‟ is measured as the market value of the equity shares (a stock‟s

price times shares outstanding). The different studies have found significantly

negative impact of size in explaining stock returns, see for example, Banz (1981),

Fama and French (1992), Mukherji et al. (1997), Anderson et al. (2003), Dunis and

Reilly (2004), Kumar and Sehgal (2004), Kyriazis and Diacogiannis (2007) and

Tripathi (2009). Thus, the variable „size‟ acts as control variable in order to estimate

the relationship between market adjusted returns and the total score. The following

hypothesis is formulated and tested.

H011 : The size of the stock has no significant impact on its market

adjusted returns.

Book to market effect- It is measured as the ratio of the book value of the company

to number of shares outstanding. Along with size, different studies have found the

significant role of book to market equity in explaining stock returns, see for example,

Lakonishok et al. (1991), Mukherji et al. (1997), Vos and Pepper (1997), Arshanaplli

et al. (1998), Dhatt et al. (2001), Sehgal (2001), Anderson et al. (2003), Karan and

Gonenc (2003), Malin and Veeraraghavan (2004), Bahl (2006), Bundoo (2008),

Senthilkumar (2009), Tripathi (2009). Thus, book to market ratio acts as control

variable in order to estimate the relationship between market adjusted returns and the

total score. The following hypothesis is formulated and tested.

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

195

H0 : The book to market equity of the stock has no significant impact

on its market adjusted returns.

5.4.7.2 Model Estimation

As the present data set entails both a spatial (cross sectional units i.e.

companies) and temporal dimension (periodic observations of a set of variables

characterizing these cross-sectional units over a particular time span); thus to

examine these issues, panel data analysis has been used. The following model is

examined:

1 2 3

4 5 6

logit it it it

i it it it

Marketadjustedreturns size booktomarket momentum

accrual equityoffer F score u

Here, 2~ 0,it uu N , 1,...,i N (N= no. of cross-sectional units), and 1,...,t T (T=

no. of time-series units).

It is important to mention that size means the market capitalization of the

stocks at fiscal yearend; momentum means the six-month market adjusted return1

over the six months directly preceding the date of portfolio formation; accrual means

the excess of ROA over CFO (as described in section 5.2) and the equity offer acts as

indicator variable where it is set equal to one if the firm raised equity during the

prior fiscal year and zero otherwise. In order to estimate the said model, the value of

momentum and accrual variables for all the observations (4597) are ranked in

ascending order and divided into deciles so as to assign ranks to the firms.

Thereafter, the values of these variables are replaced with their portfolio decile

ranking i.e. 1 to 10 in order to estimate the regression model. Before proceeding to

further analysis, Pearson‟s correlation matrix has been formed. Table 5.17 provides

Pearson correlation coefficients between the dependent variable (market adjusted

returns) and the explanatory variables (size, book to market, accrual, equity offer,

1 The six months market adjusted return of the stocks have been calculated by summing up the

monthly market adjusted returns of the stocks for a period of 6 months directly preceding the date

of portfolio formation.

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

196

momentum and F-score) and amongst different explanatory variables for the period

1996-2010.

Table 5.17: Pearson’s Product Moment Correlation Matrix

Size B/M ratio Equity

offer Accrual Momentum F-score

12 months

market

adjusted

return

(annual)

24 months

market

adjusted

return

(annualized)

Size 1 -.422*** .175*** .078*** .014 -.051*** -.290*** -.382***

B/M ratio -.422*** 1 -.063*** -.093*** .099*** -.002 .220*** .293***

Equity offer .175*** -.063*** 1 .056*** .001 -.189*** -.045*** -.098***

Accrual .078*** -.093*** .056*** 1 -.015 -.203*** -.035** -.061***

momentum .014 .099*** .001 -.015 1 .130*** -.117*** -.157***

F-score -.051*** -.002 -.189*** -.203*** .130*** 1 .062*** .087***

12 months

market

adjusted return

(annual)

-.290*** .220*** -.045*** -.035** -.117*** .062*** 1

24 months

market

adjusted return

(annualized)

-.382*** .293*** -.098*** -.061*** -.157*** .087*** 1

Note: *, **, *** denotes p-values significant at 10, 5 and 1 percent level respectively

Table 5.17 makes it evident that the explanatory variable; book to market ratio

has significantly positive correlation with the market adjusted returns (-0.220 in case of

12 months; -0.293 in case of 24 months holding period) in both the holding periods.

Also, significantly negative correlation has been observed between the market adjusted

returns and the size factor (-0.290 in case of 12 months; -0.382 in case of 24 months

holding period). It implies that increase in size results in fall in market adjusted returns.

Along with size factor, the variables; accrual, equity offer and momentum have also been

negatively associated with the returns in both the holding periods. In addition, F-score

has significantly positive association with the market adjusted returns (-0.062 in case of

12 months; -0.087 in case of 24 months holding period) which implies that as the value

of F-score increases, there would be an increase in market adjusted returns. As far as

correlation amongst the explanatory variables is concerned, it is lesser than the

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197

prescribed rule of thumb i.e. 0.8 (Gujarati and Sangeetha, 2007). We further proceed to

apply regression analysis.

In order to determine the relationship between F-score and the stocks returns, the

data regarding returns, accruals, equity offering, size, book to market ratio and

momentum returns have been calculated for all high book to market firms across the

period of 15 years i.e. 1996 to 2010. The total number of high book to market

companies across the period of study is 4597 which results in formation of 1462

mutually exclusive groups of companies across the period of 15 years. Since the present

data set entails both a spatial (cross sectional units i.e. companies) and temporal

dimension (periodic observations of a set of variables characterizing these cross-

sectional units over a particular time span); therefore to examine these issues, the panel

data analysis has been used (Agrawal and Khan, 2011). Broadly there are three methods

of estimating such a data set- pooled regression analysis, fixed effects panel data

analysis and random effects panel data analysis. Further, the time invariant and the

individual invariant effects can be estimated by including (N-1) individual dummies and

(T-1) time dummies in the existing model. Since the number of individuals (groups)

across the period of study are 1462, the introduction of 1461 (n-1 dummies are

introduced to avoid the dummy trap) dummies in the regression model would lead to an

enormous loss in degrees of freedom (Baltagi, 2005). Thus, the time invariant effects are

estimated using T-1 time dummies i.e. d2 for 1997, d3 for 1998, d4 for 1999,…….,d15

for 2010 in the existing regression model.

The different diagnostic tests have been used to determine an appropriate model

for estimation. The Chow test helps in determining whether the fixed effects panel data

model or pooled regression model is more appropriate to use and Lagrange Multiplier

(LM) test helps to find out whether the random effects panel data model or pooled

regression model is more suitable to use. Thereafter, Hausman test helps to choose

between fixed effects and random effects panel data model. The Table 5.18 reports the

results as under:

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198

Table 5.18: Results of Chow test, LM Test and Hausman Test to Determine an

Appropriate Model

Chow test

H (0): Pooled

OLS

H (a): Fixed

effects model

Lagrange Multipler

test

H (0): Pooled OLS

H (a): Random effects

model

Hausman test

H (0): Random

effects

H (a): Fixed effects

Final

model

12 months holding

period

45.89

(0.000)***

78.67

(0.000)***

79.42

(0.000)***

Fixed

effects

Fixed effects Random effects Fixed effects

24 months holding

period

44.57

(0.000)***

84.73

(0.000)***

160.49

(0.000)***

Fixed

effects

Fixed effects Random effects Fixed effects

Note: Significance at: p-values * , 0.10, * * , 0.05 and * * * , 0.01

Table 5.18 shows that the value of the Chow test in both the holding periods has

been significant at 1% level of significance. It leads to the rejection of null hypothesis of

no differences in coefficients across time. Hence, fixed effects panel data model is more

appropriate to use in such a situation. In addition, the Chi-squared statistic of Lagrange

Multiplier (LM) test is also significant in both the holding periods leading to the rejection

of null hypothesis of lack of presence of random effects in residuals. Thus, random

effects panel data model is more suitable to use in this situation. Further, to decide

whether fixed effects or random effects model is more appropriate to use, Hausman test

has been applied. The significant test statistic of Hausman test in both the holding periods

leads to the rejection of null hypothesis of no correlation between the individual effects

and the explanatory variables of the model. Hence, fixed effects panel data model is

considered more appropriate to apply in this situation.

After determining the choice of the model, the error term has to be free from

disturbances. Table 5.19 shows the results of testing of different assumptions of panel

data regression model.

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199

Table 5.19: Results of Testing Different Assumptions of Panel Data Regression

Model

Assumptions 12 months holding

period

24 months holding

period

Stationarity

Fisher type unit root test based on ADF tests

(Inverse chi-sq)

H(0): panel contains unit root

H (a): panel is stationary

Variables

Returns 3515.3462

(0.000)***

1877.1634

(0.000)***

F-score 2446.7234

(0.000)***

Bp ratio 2260.9789

(0.000)***

Equityoffer 2271.6692

(0.000)***

Accrual 3810.3653

(0.000)***

Momentum 3690.7845

(0.000)***

Size 2078.5712

(0.000)***

Autocorrelation

Woolridge test for autocorrelation (F-Statistic)

H (0): no autocorrelation

H (a): autocorrelation

0.770

(0.3806)

365.602

(0.000)***

Heteroskedasticituy

Breusch Pagan/ Cook Weisbery test (chi-sq statistic)

H (0): constant variance

H (a): heteroskedasticity

361.74

(0.000)***

75.26

(0.000)***

Multicollinearity

Vif test (mean vif)

1.89

Note:

1. P-value of the statistics has been reported in parenthesis

2. *** shows significant at 1% level of significance

Table 5.19 shows the results of testing of various assumptions on the given panel

data. The Fisher type unit root test based on Augmented Dickey Fuller test shows that all

the variable in the regression analysis are stationary i.e. unit root are not present in the

dataset. Further, the Woolridge test for autocorrelation shows the lack of presence of

autocorrelation in case of 12 months holding period. However, the 24 months data shows

the presence of autocorrelation. Also, the data is not homoskedastic in both the holding

periods. The mean value of variance inflation factor (VIF) lesser than 10 shows the lack

of presence of multicollinearity amongst the independent variable of the regression. The

analysis is said to be efficient only if the error term is free from all kind of disturbances.

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200

Thus, to remove the problem of autocorrelation and heteroskedasticity in the data set,

cluster (i) command (see Appendix IV) has been used in Stata and the robust standard

errors have been reported for the purpose of analysis. Table 5.20 shows the results of

panel data regression analysis.

Table 5.20: Results of Role of F-Score In Predicting The Stock Returns

Dependent

variables

12 months holding period 24 months holding period

Independent

variables

coefficient Robust

std. error

T-

value

P-value coefficient Robust std.

error

T-

value

P-value

F-score 4.93198 1.09944 4.49 0.000*** 1.507012 0.3687852 4.09 0.000***

Accrual 0.99023 .6061497 1.63 0.102 -0.119797 0.1986197 -0.60 0.546

Momentum -6.3174 1.021306 -6.19 0.000*** -2.108179 0.3222178 -6.54 0.000***

Size -27.5837 3.010006 -9.16 0.000*** -11.56885 0.8677475 -13.33 0.000***

Book to

market ratio

1.043611 .5500355 1.90 0.058* 0.428626 0.2297475 1.87 0.062*

Equity offer 7.66700 6.424987 1.19 0.233 -0.436018 2.309823 -0.19 0.850

D2 76.0292 12.73776 5.97 0.000*** 29.79618 3.94224 7.56 0.000***

D3 89.8549 13.63969 6.59 0.000*** 47.63443 4.574263 10.41 0.000***

D4 148.942 15.53715 9.59 0.000*** 41.23242 4.521249 9.12 0.000***

D5 42.6054 11.19391 3.81 0.000*** 44.90643 4.104481 10.94 0.000***

D6 122.9632 11.9297 10.31 0.000*** 45.401 4.012941 11.31 0.000***

D7 81.35074 13.20463 6.16 0.000*** 24.00364 4.48029 5.36 0.000***

D8 78.97681 13.72568 5.75 0.000*** 57.32358 4.416907 12.98 0.000***

D9 198.5947 12.41263 16.00 0.000*** 37.86312 3.937776 9.62 0.000***

D10 48.54385 9.902109 4.90 0.000*** 8.772375 3.879429 2.26 0.024**

D11 78.93341 9.806639 8.05 0.000*** 23.76946 3.5606 6.68 0.000***

D12 98.19876 9.623107 10.20 0.000*** 30.38913 3.660238 8.30 0.000***

D13 70.8737 9.464793 7.49 0.000*** 35.44618 3.440297 10.30 0.000***

D14 111.7876 9.797515 11.41 0.000*** 26.79123 3.709172 7.22 0.000***

D15 68.318 9.022126 7.57 0.000*** 13.87954 3.597484 3.86 0.000***

constant 77.05165 19.79172 3.89 0.000*** 45.82804 5.772199 7.94 0.000***

R-square 0.2148 0.2900

Wald chi-

square

(P-value)

1546.87

(0.000)

1621.99

(0.000)

Note: *, **, *** denotes p-values significant at 10, 5 and 1 percent level respectively

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201

Table 5.20 shows that Wald chi- square of the model which is joint significance

test for all the variables is significant at 1% level of significance showing the goodness of

fit of the model. Table 5.20 further reports the results of the predictability of stocks

returns through F-score after holding size, value, momentum, equity offer and accrual

constant in 12 months as well as 24 months holding period. The coefficient of the

momentum effect has been significantly negative in both the holding periods leading to

the rejection of null hypothesis (H08) of no significant impact of momentum of stocks on

their market adjusted returns. It therefore shows that the return six months prior to the

date of portfolio formation of high book to market stocks, affects the overall returns

negatively. It could be due to the fact that pursuing a value strategy (the strategy of

buying high book to market stocks) entails to buying the firms with poor momentum. The

value and momentum measures are negatively correlated (Asness, 1997). Since the

sample consists of only the value stocks, the momentum effect in such case could not be

positively affecting the stocks returns. These findings are consistent with that of (Dahl et

al., 2009) and inconsistent with the findings of Piotroski (2000) who found the

statistically positive relation between the returns and the momentum.

The coefficient of size factor has been significantly negative in both the holding

periods leading to the rejection of null hypothesis (H011) of no significant impact of size

on stock returns. It therefore shows the presence of size effect in the data i.e. the stocks

with lower market capitalization outperform the stocks with higher market capitalization.

Along with the size effect, the value effect i.e. the tendency of high book to market stocks

to outperform low book to market stocks is also present in the sample. The book to

market coefficient is positive and statistically significant (significant at 10% level of

significance) in both the holding periods leading to the rejection of null hypothesis (H012)

of no significant impact of book to market equity on market adjusted returns. It therefore

confirms the presence of value effect in Indian stock market.

Further, the coefficient of the accrual effect has been positive in case of 12

months holding period and negative in case of 24 months holding period. However, the

coefficients have been statistically insignificant in both the holding periods leading to the

acceptance of null hypothesis (H010) of no significant impact of level of accruals on the

market adjusted returns. The findings are not in line with the findings of developed

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202

economies where the significantly negative relation has been observed between the

returns and the accruals (Piotroski, 2000). However, in contrast with the findings of the

developed markets, high accrual portfolios tend to provide higher returns as compared to

low accrual portfolios in Indian stock market (Sehgal et al., 2012). The study however,

finds the positive relation up till one year holding periods only. When the holding period

is extended, the relationship turns negative. Nevertheless, the relation remains

statistically insignificant.

Along with accrual effect, the coefficients of the equity offer variables are

positive in both the holding periods showing that the firms that issued equity in the prior

fiscal years positively affect the subsequent returns. These findings are also inconsistent

with the findings of Loughran and Ritter (1995), Piotroski (2000) who found that the

companies that had issued equity in the preceeding financial year significantly

underperform relative to the non-issuing firms in the current year. However, the current

findings have been statistically insignificant leading to the acceptance of null hypothesis

(H09) of no significant impact of equity issuance by a company on its market adjusted

returns.

In respect of F-score we notice that, after controlling for size effect, book to

market effect, accrual effect, momentum effect and equity issuance effect, the

coefficients of F-score is positive and significant at 1% level of significance in both the

holding periods leading the rejection of null hypothesis (H07) of no significant impact of

F-score on market adjusted returns. It therefore implies that one point improvement in the

aggregate score is associated with an approximate 4.93% increase in one year market

adjusted return and about 1.5% increase in two year annualized market adjusted rate of

return earned subsequent to portfolio formation. Moreover, the addition of variables

designed to capture size effect, value effect, momentum effect, accrual reversal, and a

prior equity issuance has no impact on the capability of F-score to predict future returns

in Indian stock market.

5.5 CONCLUSION

The study examines the relevance of an accounting based fundamental strategy in

enhancing the overall returns of value stocks. For this, the fundamentals based investment

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203

strategy „F-score‟ given by Piotroski (2000) has been used on stocks having high book to

market ratio in order to eliminate the firms with poor future prospects from the entire

portfolio of value stocks. The F-score model is the sum of nine financial signals that

measures three constructs pertinent to a company‟s financial position: profitability,

financial leverage along with liquidity, and operating effectiveness. The results revealed

the presence of significant difference in the mean market adjusted return of stocks,

meeting all constructs of F-score as compared to the mean market adjusted return of the

entire value portfolio (18.402% in case of 12 months, 8.908% in case of 24 months)

across the period of study. Further, the significant mean return difference (29.856% in

case of 12 months, 17.531% in case of 24 months) found between the high F-score firms

and the low F-score firms, suggests that an investor could constitute a hedge portfolio

that generate positive return by selling expected losers stocks and buying expected

winners. Thus, an F-score strategy can help in shifting of the returns earned by an

investor.

In further analysis, the attempt was made to know if the excess returns earned

using fundamental analysis strategy is strictly a small firm effect or could be applied

across all categories of size, trading volume and share price partitions. The results did not

find the evidence of the statistically significant outperformance of high F-score stocks

over low F-score stocks in respect of firms with smaller size. However, the statistically

significant outperformance of high F-score stocks over low F-score stocks is found to be

channelized to firms having large size, huge trading volume and medium to large price in

Indian stock market. Thus, the risk involved in investing in illiquid and small firms is not

implicated in this investment strategy

Further, the role of F-score in predicting the overall returns is examined. Before

examining this relationship, there are certain known effects which could have a strong

relation with F-score are controlled. These effects include size effect, book to market

effect, momentum effect, recent equity offering and accrual effect. The results revealed

that after controlling for these known effects, one point improvement in aggregate F-

score is associated with an about 4.93% increase in market adjusted return and about

Adding Value to Value Stocks -Joseph Piotroski’s F-score Model

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1.5% increase in two year annualized market adjusted return earned subsequent to

portfolio formation. Thus, the addition of variables designed to capture size effect, value

effect, momentum effect, accrual reversal, and a prior equity issuance has no impact on

the capability of F-score to predict future returns in Indian stock market. Thus, an

investor can apply this strategy for enhancing the returns on value portfolio in Indian

stock market.