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IMPACT OF CORPORATE GOVERNANCE ON

FINANCIAL PERFORMANCE

In this chapter, an attempt has been made to analyze the impact of corporate

governance disclosure practices as per clause 49 of the listing agreement on financial

performance of companies through regression analysis. A set of four firm performance

measures have been selected, categorized into accounting based measures and market

based measures to ascertain the relationship between corporate governance and firm

performance. These measures are (i) Net Profit Margin on Sales, (ii) Return on Assets

(ROA), (iii) Return on Equity (ROE) and (iv) Tobin’s Q. First three measures are termed

as accounting based measures and the fourth one is known as a measure of market

valuation. The variables used for measuring the financial performance have been

explained in section 7.1.The explanation of control variables used in the present study has

been given in section 7.2. Section 7.3 explains the regression model employed for

measuring the impact of corporate governance on financial performance of the companies

under study and sections 7.4 and 7.5 give the results and discussions.

7.1 Measures of Financial Performance

As explained above, four financial performance measures have been selected for

the present study namely net profit margin on sales, return on assets (ROA), return on

equity (ROE) and Tobin’s Q. These are explained as given below:

7.1 (a) Net Profit Margin on Sales

Net profit margin on sales ratio establishes the relationship between net profit and

sales and indicates management’s efficiency in manufacturing, administering and selling

the products (Pandey, 2000, p.132). Higher the net profit margin better will be the

profitability position of the company. Rechner et al. (1991), Dalton et al. (1999), Brown

and Caylor (2004) and Phani et al. (2005) have used net profit margin on sales as a

measure of firm performance in their studies. Net profit margin on sales has been

calculated by dividing the earnings before interest and taxes (net of non-recurring

Impact of Corporate Governance on Financial Performance

231

transactions)1 by the net sales. Net sales are the sales excluding indirect taxes and duties

such as excise duty and octroi duty. Sales are also net of internal transfers.

Net Profit Margin on Sales= EBIT

100 Net Sales

7.1 (b) Return on Assets (ROA)

ROA is used as an accounting based measure of firm performance. ROA is

commonly used and well understood measure of firm performance, particularly

appropriate for manufacturing firms (Kim, 2005). ROA measures the ability of the

management to earn a return on resources and the firms using their assets efficiently have

higher returns (Sharan, 2005, p.283). Various researchers namely Rechner et al. (1991),

Klein (1998), Core et al. (1999), Dalton et al. (1999), Jog and Dutta (2004) and Phani et

al. (2005) have used ROA as a firm performance measure in their studies. We have

calculated return on assets by dividing the earnings before interest and taxes (net of non-

recurring transactions) by the total assets. Taxes are not controllable by the management

and also one may not know the marginal corporate tax rate while analyzing the

publishing data (Pandey, 2000, p.136). So, in order to remove this anomaly, we have

considered EBIT instead of profit after tax (PAT).

ROA = EBIT

100 Total Assets

7.1 (c) Return on Equity (ROE)

Return on equity has been considered as another measure of firm performance. A

number of researchers have employed ROE as firm performance measure in their studies

(Rechner et al. 1991, Dalton et al, 1999, Rhoades et al., 2001, Brown and Caylor, 2004

and Jog and Dutta, 2004). ROE is an important indicator which tells us how the company

has used the resources of its owners. This ratio reflects the extent to which the objective

of wealth maximization of shareholders has been achieved. In the present study, we

1 As per Prowess Database of CMIE, net of non-recurring transactions includes profit or loss on sale

of fixed assets and investments, provision written back, prior period income or expenses,

insurance claims, etc. So, the above figure of EBIT is adjusted of NNRT.


232

calculated ROE by dividing the profit after tax (net of non-recurring transactions)

adjusted with preference dividend by net worth minus preference share capital.

ROE= PAT – Preference Dividend

100 Net Worth-Preference Share Capital

7.1 (d) Tobin’s Q

Tobin’s Q as a measure of market valuation has been extensively used by the

researchers in their studies (Farrer and Ramsay, 1998, Chen, 2001, Mohanty, 2002, Weir

et al., 2003, Brown and Caylor, 2004, Jog and Dutta, 2004, Dwivedi and Jain, 2005 and

Khiari et al., 2005). Tobin’s Q ratio has been devised by James Tobin. This ratio is based

on the notion that combined market value of all the companies on the stock market

should be equal to their replacement costs (www.investopedia.com/terms/q/qratio). This

is the ratio of market value of equity and debt divided by the replacement costs of total

assets. Firms displaying Tobin’s Q greater than unity are considered to be using scarce

resources effectively, while those with Tobin’s Q less than unity are using resources

poorly (Chen, 2001).

Tobin’s Q as a measure of firm performance represents the value that investors

put on in firm’s shares above the total value of assets of the firm and thus represents

investor’s confidence which in turn is an indicator of the effectiveness of corporate

governance mechanisms of the firm (Dwivedi and Jain, 2005).

We calculated Tobin’s Q ratio as a market value of equity plus book value of debt

divided by book value of total assets. Since debt is not traded in the Indian stock market

and replacement cost of assets is not available in case of Indian companies, so we used

book value of debt and total assets. Hence, we computed it as follows:

Tobin’s Q = 365 Days Average Market Capitalization + Book Value of Debt

100 Book Value of Total Assets

Debt here includes both short term and long term liabilities. So far as market

capitalization is concerned, we have considered annual average market capitalization and

this figure has been extracted out from the Prowess Database.


233

7.2 Control Variables

The study of existing literature reveals that there are certain other factors also

which may affect the performance of the firms. These factors are size of the firm, age,

risk, leverage, industry type, etc. Therefore, it is essential to control these variables while

analyzing the impact of corporate governance on financial performance. The details of

control variables are given as below:

7.2 (a) Age

Age is considered to be a significant factor affecting the firm performance. Due to

the effects of learning curve and survival bias, older firms are considered to be more

efficient than younger ones (Chen, 2001). Older firms have established themselves firmly

in the market and are able to reap the benefits of the economies of scale which the

younger ones or newcomers find it difficult to achieve. The researchers have controlled

the impact of age while analyzing the impact of corporate governance mechanisms on

firm performance (Chen, 2001, Mohanty, 2002, Jog and Dutta 2004, Kim 2005, Phani et

al. 2005, Sheu and Yang 2005 and Mayur and Saravanan, 2006).

7.2 (b) Size of the Firm

Numerous researchers have examined the relationship between size and

performance of the firms. In product market, size reflects entry barriers that might result

from economies of scale and in capital market, size reflects financial barrier of entry due

to the ability of large companies to finance investment projects from internal sources as

well from issue of new equity (Phani et al., 2005). There are mixed evidences available in

the existing literature on relationship between size and firm performance. Chen (2001),

Mohanty (2002), Weir et al. (2003), Mollah and Talukdar (2007) found out significant

negative relationship between size and firm performance. On the other hand, Jog and

Dutta (2004) and Kim (2005) found significant positive relationship between firm size

and performance. There are no. of ways available for measuring the size of the firm.

Chen (2001), Kim (2005), Wan and Ong (2005), Saravanan (2006) and Mollah and

Talukdar (2007) have measured the size in terms of log of assets. While on the other

hand, Fuerest and Kang (2000), Carson (2002), Mohanty (2002) and Jog and Dutta

(2004) have considered market capitalization as proxy of a firm size. A few researches


234

have measured size in terms of sales also (Weir et al. 2003, Phani et al. 2005 and

Subramanian, 2006).

In the present research, log of net sales has been used as a proxy of firm size. As

there is curvilinear relationship between the size and firm performance, we have

employed log of net sales instead of simply taking net sales figures. It is expected that

firms with larger size outperform the smaller ones due to the certain advantages of

economies of sales. So, it is essential to control the impact of size on firm performance.

7.2 (c) Risk

Risk denotes some degree of hazard which can result on account of various

factors such as short term fluctuations in profits, change in consumer tastes, change in

technology, change in government policy, strategic moves of competitors, etc. Risk is

associated with the future events. Since future is always uncertain and can’t be predicted

with accuracy. So, entrepreneurs have to take future decisions by keeping in mind the risk

factor. Based on the concept of ‘higher the risk, higher will be the return’, various

researchers have tried to find out the relationship between risk and profitability of the

firm. In line with Mohanty (2002), Jog and Dutta (2004) and Mollah and Talukdar (2007)

beta has been incorporated as a measure of risk.

7.2 (d) Leverage

Leverage has also been employed by researchers in their studies on firm

performance (Daily and Dalton, 1994; Chen, 2001; Carson, 2002; Dwievedi and Jain,

2005; Khiari, et al. 2005; Kim, 2005 and Mayur and Saravanan, 2006). When the firm’s

cost of debt is lower than the firm’s rate of return on its assets, then shareholders’ returns

in form of EPS and return on equity increase and hence, leverage will have favourable

impact on profitability. However, shareholders’ returns will fall, when the firm obtains

the debt at higher cost than the rate of return on its assets. There are mixed evidences

available in the literature on the relationship between leverage and profitability. Chen

(2001) found out the negative relationship between leverage and profitability. On the

other hand, Kim (2005), Khiari et al. (2005) examined the positive association between

leverage and profitability. There are various measures of financial leverage employed by

the researchers. But in line with Chen, 2001, Khiari et al., 2005 and Kim 2005 debt-


235

equity ratio has been employed as a measure of financial leverage. We have used the

following formula to compute debt-equity ratio of the firm.

Leverage = Long Term Debt

100 Shareholder’s Funds

7.2 (e) Industry Effects

Industry characteristics have vital concern in the analysis of firm performance.

Firms in new and expanding industries are expected to outperform those operating in old

and declining industries (Kumar, 1985, Singh, 1997 and Kaur, 2005). The firms in those

industries, where exist growth opportunities, concentrated competitors and stable markets

should have higher profits than industries that are in decline (Coles et al., 2001). It has been

persistently shown that firms in a particular industry earn comparatively above normal

profits by virtue of some favourable structural characteristics (Amato and Wilder, 1990).

Like Mohanty (2002), Dwivedi and Jain (2005) and Mollah and Talukdar (2007), we have

captured the industry effects by introducing 8 industries dummies in regression model.

Table: 7.1

Summary of Control Variables Used in Various Studies

S.No Variables

Studies

Size Age Risk Leverage Industry

Effects

1 Daily and Dalton (1994)

2 Klein (1998)

3 Fuerest and Kang (2000)

4 Chen (2001)

5 Carson (2002)

6 Mohanty (2002)

7 Weir et al. (2003)

8 Jog and Dutta (2004)

9 Dwivedi and Jain (2005)

10 Khiari et al. (2005)

11 Kim (2005)

12 Phani et al. (2005)

13 Sheu and Yang (2005)

14 Wan and Ong (2005)

15 Mayur and Saravanan (2006)

16 Subramanian (2006)

17 Mollah and Talukdar (2007)


236

7.3 Regression Model

In order to find out the impact of corporate governance on financial performance

of the companies, ordinary least square (OLS) regression model with enter method has

been employed. SPSS 13.00 version has been used to compute the results of multiple

regression models. The following models have been used to analyze the impact of

corporate governance on various measures of financial performance:

Model I

Net Profit Margin on Sales = 0 + 1 (Corporate Governance) + 2 (Age) + 3

(Size) + 4 (Risk) + 5 (Leverage) + 6

(D_Textiles) + 7 (D_Iron & Steel) + 8

(D_Automobile) + 9 (D_Cement) + 10

(D_Drugs & Pharmaceuticals) + 11 (D_Software)

+ 12 (D_Sugar) + 13 (D_Paper) + ε

Model II

Return on Assets = 0+ 1 (Corporate Governance) + 2 (Age) + 3

(Size) + 4 (Risk) + 5 (Leverage) + 6

(D_Textiles) + 7 (D_Iron & Steel) + 8

(D_Automobile) + 9 (D_Cement) + 10

(D_Drugs & Pharmaceuticals) + 11 (D_Software)

+ 12 (D_Sugar) + 13 (D_Paper) + ε

Model III

Return on Equity = 0+ 1 (Corporate Governance) + 2 (Age) + 3

(Size) + 4 (Risk) + 5 (Leverage) + 6 (D_Textiles)

+ 7 (D_Iron & Steel) + 8 (D_Automobile) + 9

(D_Cement) + 10 (D_Drugs & Pharmaceuticals) +

11 (D_Software) + 12 (D_Sugar) + 13 (D_Paper)

+ ε

Model IV

Tobin’s Q = 0+ 1 (Corporate Governance) + 2 (Age) + 3



237




+ ε

Whereas (D_Industry) represents industry dummies.

In the above four models, Net Profit Margin on Sales ratio, ROA ratio, ROE

ratio and Tobin’s Q ratio have been used as measures of firm’s performance and

considered as dependent variables. However, corporate governance, firm

characteristics and industry dummies have been used as explanatory variables. β0 is a

constant term and ε denotes error term here. 1, 2, 3…………….13 are regression

coefficients. One industry dummy has been omitted from the above four models.

Dummy variables are essentially a device to classify the data into mutually exclusive

categories (Gujarati, 2004, p.298). If the variable (here industry) has m categories

then we include m-1 dummy variables. In our analysis, we have the sample of 9

industries and 8 dummies have been introduced. The industry for which no dummy

variable has been assigned is known as base or reference industry. In the present

analysis, power industry has been omitted and hence, known as base industry. The

coefficients attached to the dummy variables are known as the differential intercept

coefficients as they tell how much value of the intercept that receives the value of 1

differs from the intercept coefficient of the benchmark category (Gujarati, 2004,

p.302). We assigned code 1 to all the firms who are members of a particular industry

category and 0 for otherwise.

The above stated models have been run with both unequal weights and equal

weights methods. Hence, 8 regression models have been obtained with same

dependent variable, control variables and industry dummies. All the listed companies

were required to adopt the provisions of clause 49 of the listing agreement up to the

year 2002-03. So, there exists difference in the number of companies for the first

three years of analysis. For the last two years, difference in the number of companies


238

is due to the missing data. So, in order to overcome this problem, regression models

have been employed year wise instead of taking average of the whole data.

While applying the regression models the assumptions of normality,

multicollinearity and autocorrelation have been tested. The assumption of normality

of data has been checked out through descriptive statistics i.e. skewness and kurtosis.

With the help of SPSS package, both standard error and statistics value have been

computed. Z value has been derived manually for all the variables by dividing the

standard error with statistic value. If calculated value of z exceeds ± 2.58 and ± 1.96

then we can reject the assumption of normality of data at .01 and .05 significance

levels respectively. Normality has been checked out for age, risk and net sales. From

the existing literature, no evidence has been found out for checking the normality of

leverage. So, in line with the existing research evidence, leverage has been excluded

from the test of normality. Age and risk found to be at normal distribution level. So

far as net sales were concerned, test statistics have rejected the assumption of

normality. After checking out the normality of the data, the next step is to transform

the variable which is not normal. There are various measures suggested by the

researchers for transformation of data such as taking square root of the variable,

logarithms and inverse of the variable. However, net sales have been transformed by

taking its logarithm.

The presence of multicollinearity among the independent variables may affect

the overall regression results and will lead to wrong estimations. In order to detect the

problem of multicollinearity, variance inflation factor (VIF) has been computed for

each of the individual independent variables by using SPSS package. As a rule of

thumb, if VIF of variable exceeds 10, then there is a problem of multicollinearity with

that variable. In line with the above rule of thumb, we did not find any variable in our

analysis, whose VIF value exceeds 10. Hence, our data is free from multicollinearity

problem.


239

The next assumption of autocorrelation of multivariate analysis has been

checked through Durbin Watson (d) statistic value. As per decision rule, if d is 2 or

close to 2, then there is no first order autocorrelation either positive or negative. In

our analysis, data is free from autocorrelation problem also.

7.4 Results and Discussions

This section explains the regression results of all the four models stated in Sec

7.3. Table 7.2(a) summarizes the regression results of impact of corporate governance

with equal weights on net profit margin on sales. F statistics are significant at 1%

level for all the five years which reveal that our model is appropriate. Corporate

governance is found to be significantly (5% level) positively associated with net

profit margin on sales for just one year out of the period of five years. However,

adjusted R² is also high for this year i.e. 2001-02. One of the possible reasons for this

phenomenon might be the adoption of more no. of items of clause 49 of listing

agreement by the companies in the second year of its implementation. For the years

2000-01 and 2004-05, negative but insignificant relationship has been found out

between corporate governance and net profit margin on sales. Similarly, for the years

2002-03 and 2003-04, though there is positive relationship between corporate

governance and firm performance yet the relationship is insignificant one.

Age is found to be negatively associated with net profit margin on sales for the

period of five years but it is found to be significant for the years 2001-02, 2002-03 and

2003-04 only. There are mixed evidences available in the literature regarding the

relationship between age and firm performance. However, our findings are in line with

Chen (2001), Singh (1997), Sheu and Yang (2005) and Kaur (2005). One of the possible

reasons for such a negative relationship could be the stickiness of older firms towards

older technology and old means of production. These firms resist for the adoption of new

technology available these days. Moreover, as per the product life cycle theory, firms

generally earn higher profits during the growth and maturity stage. But as soon as the

maturity phase gets over and declining stage starts, profits start declining. Thus, age

factor after a certain period of time has negative impact on profitability.


240

Size is found to have positive and significant relationship with net profit

margin on sales for four out of five years. Our findings in this context are in

consonance with Jog and Dutta, 2004 and Kim, 2005 and hold the view that firms

with larger size outperform the smaller ones in size because of certain advantages of

economies of scale.

Risk has negative but insignificant relationship with net profit margin on sales

for all the five years. In other words, no significant impact of risk has been found out

on net profit margin on sales. Our findings in this context do not support the notation

of ‘Higher the risk, higher will be the return’. One of the possible reasons of negative

relationship between risk and profitability can be attributable to risk management

strategies adopted by the firm where managers always seek to minimize the risk and

maximize the profits.

Leverage is found to be significantly negatively associated with net profit

margin on sales for the year 2003-04 only. However, the nature of relationship is not

found to be consistent for the whole period under study. For the first two years, a

positive but insignificant relationship has been observed between leverage and net

profit margin on sales. Afterwards, this relationship becomes negative for the rest of

the three years period. One of the reasons of this negative relationship can be

explained in terms of cost of capital. Due to increase in debt- equity ratio of the firm,

the cost of capital will increase which will ultimately lower the profitability.

So far as sectoral effects are concerned, it has been observed that textiles, iron

and steel, automobile, cement and sugar industries are showing relationship with

financial performance for a few years only. Textile industry has highly significant but

negative relationship with net profit margin on sales for four out of five years. Iron

and steel industry has also been showing consistently negative relationship with net

profit margin on sales from 2000-01 to 2003-04. However, this relationship is found

to have significant association for first four years only. Automobile industry has been

significantly negatively associated with net profit margin on sales for the entire period

of study. Similarly, cement industry is found to have negative association with net


241

profit margin on sales but this relationship is significant for the period of four years

i.e. from 2001-02 to 2004-05. Drugs & Pharmaceuticals and paper industries have

negative but insignificant association with net profit margin on sales. Software

industry has insignificant positive association with net profit margin on sales. Sugar

industry is found to have negative relationship with net profit margin on sales for the

entire period of study but the relationship is found to be significant for three years

only i.e. from 2001-02 to 2003-04.

So far as regression results with unequal weights of corporate governance are

concerned, no significant variations in R² and adjusted R² values have been observed.

With both the methods the results have been found to be almost same for all the

explanatory variables. However at some places, the level of significance is varying.

Table 7.2(b) reveals that corporate governance is significantly (10% level) associated

with net profit margin on sales for the year 2001-02. For the rest of the years, no

significant impact has been found for corporate governance on net profit margin on

sales. Similarly, age is found to be significantly negatively associated at 5% level

with net profit margin on sales for the years 2001-02, 2002-03 and 2003-04. Size is

found to be highly significant but positively associated with net profit margin on sales

for four out of five years. It has been observed from the results that there is no impact

of risk on net profit margin on sales. Mixed results have been found out for

association of leverage with financial performance of the companies. There is

negative but significant association of leverage with net profit margin on sales for the

years 2002-03 and 2003-04. So far as sectoral effects are concerned, it has been found

out that automobile industry has significant negative association with the net profit

margin on sales. Textiles, iron & steel and cement industries have been found to be

significantly negatively associated with net profit margin on sales for four out of five

years, whereas drugs & pharmaceuticals and paper industries have negative but

insignificant relationship with net profit margin on sales. On the other hand, no

significant relationship has been observed of software industry with net profit margin

on sales. Sugar industry is also found to have significant negative association with net

profit margin on sales for three years out of five years.


242

Table: 7.2 (a)

Regression Results of Impact of Corporate Governance (Equal Weights) on Net

Profit Margin on Sales

S.No Explanatory Variables 2000-01 2001-02 2002-03 2003-04 2004-05

1 Corporate Governance -.013

(-.10)

.183**

(2.02)

.142

(1.45)

.015

(.163)

-.058

(-.594)

2 Age -.086

(-.67)

-.27*

(-2.79)

-.26**

(-2.54)

-.188**

(-2.056)

-.050

(-.516)

3 Size .191

(1.49)

.232**

(2.59)

.264*

(2.67)

.381*

(4.126)

.287*

(2.781)

4 Risk -.047

(-.30)

-.062

(-.58)

-.004

(-.037)

-.093

(-.883)

-.160

(-1.472)

5 Leverage .103

(.87)

.071

(.820)

-.15

(-1.58)

-.213**

(-2.478)

-.144

(-1.590)

6 Textiles -.26

(-1.41)

-.45*

(-3.86)

-.32**

(-2.54)

-.386*

(-3.308)

-.420*

(-3.432)

7 Iron & Steel -.32***

(-1.89)

-.49*

(-4.3)

-.26**

(-2.13)

-.223**

(-2.026)

-.095

(-.826)

8 Automobile -.48**

(-2.6)

-.53*

(-4.37)

-.44*

(-3.31)

-.553*

(-4.557)

-.607*

(-4.789)

9 Cement -.32

(-1.58)

-.31**

(-2.59)

-.29**

(-2.34)

-.256**

(-2.257)

-.253**

(-2.136)

10 Drugs & Pharmaceuticals -.072

(-.35)

-.14

(-1.14)

-.073

(-.56)

-.055

(-.465)

-.156

(-1.249)

11 Software .37

(1.53)

.12

(.91)

.060

(.39)

.010

(.074)

.081

(.575)

12 Sugar -.19

(-1.09)

-.24**

(-1.96)

-.24***

(-1.83)

-.244**

(-2.025)

-.127

(-1.014)

13 Paper -.056

(.33)

-.16

(-1.29)

-.088

(-.66)

-.122

(-.999)

-.205

(-1.596)

R Square .488 .489 .413 .473 .404

Adj. R Square .361 .412 .323 .396 .318

F Statistics

( Significance)

3.82

.000

6.329

.000

4.594

.000

6.202

.000

4.700

.000

Note: Figures within the parentheses indicate the t-values, (*) indicates significance at

1% level, (**) indicates significance at 5% level and (***) indicates significance

at 10% level.


243

Table: 7.2 (b)

Regression Results of Impact of Corporate Governance (Unequal Weights) on Net

Profit Margin on Sales



(-.076)

.153***

(1.661)

.146

(1.481)

.027

(.309)

-.603

(-.649)

2 Age -.087

(-.686)

-.259**

(-2.650)

-.265**

(-2.578)

-.192**

(-2.102)

-.048

(-.500)

3 Size .189

(1.511)

.248*

(2.746)

.271*

(2.771)

.378*

(4.122)

.288*

(2.812)

4 Risk -.047

(-.302)

-.073

(-.692)

-.003

(-.026)

-.095

(-.897)

-.156

(-1.428)

5 Leverage .103

(.868)

.071

(.822)

-.156***

(-1.653)

-.215**

(-2.495)

-.141

(-1.565)

6 Textiles -.257

(-1.408)

-.447*

(-3.771)

-.331**

(-2.573)

-.388*

(-3.329)

-.419*

(-3.419)

7 Iron & Steel -.318***

(-1.877)

-.479*

(-4.200)

-.252**

(-2.095)

-.223**

(-2.035)

-.097

(-.846)

8 Automobile -.477**

(-2.600)

-.520*

(-4.242)

-.443*

(-3.326)

-.556*

(-4.604)

-.607*

(-4.797)

9 Cement -.324

(-1.580)

-.297**

(-2.498)

-.299**

(-2.372)

-.258**

(-2.279)

-.253**

(-2.141)


(-.346)

-.126

(-1.020)

-.080

(-.606)

-.058

(-.489)

-.155

(-1.244)

11 Software .367

(1.536)

.156

(1.157)

.069

(.466)

.008

(.059)

.076

(.544)

12 Sugar -.196

(-1.090)

-.234***

(-1.878)

-.241***

(-1.855)

-.246**

(-2.040)

-.129

(-1.032)

13 Paper -.055

(-.319)

-.152

(-1.207)

-.083

(-.624)

-.122

(-1.003)

-.209

(-1.628)

R Square .488 .482 .413 .473 .405

Adj. R Square .361 .404 .324 .397 .319

F Statistics

( Significance)

3.819

.000

6.153

.000

4.606

.000

6.212

.000

4.709

.000



at 10% level.


244

Table 7.3 (a) reveals the regression results of impact of corporate governance with

equal weights on return on assets. F-statistics are highly significant at 1% level for all the

five years which show that our model is fit to be used. It has been observed that corporate

governance is significantly positively associated with the return on assets for two out of

five years. For the years 2003-04 and 2004-05, there is positive but insignificant

relationship between corporate governance and return on assets. Age is found to be

significantly negatively associated with the return on assets for two out of five years. Size

is found to have highly significant but positive relationship with return on assets for the

entire period of five years. So far as risk is concerned, there exists negative but

significant relationship with return on assets from the year 2000-01 to 2004-05. Leverage

is found to have negative but significant relationship with return on assets for the year

2003-04 only. It has been observed from the industry effects that software industry has

significant positive impact on return on assets. Textiles industry is found to have

significant negative relationship with ROA for one out of five years only. On the other

hand, iron & steel industry is found to be significantly associated though negatively with

return on assets for the year 2001-02. This relationship turns positive and significant in

the year 2004-05. Drugs & Pharmaceutical industry has positive but significant

relationship with profitability for three out of five years. Sugar industry has significant

positive relationship with return on assets in the year 2004-05 only. For the rest of the

industries dummies, no significant relationship has been observed with ROA. Similarly,

table 7.3 (b) reveals almost the similar results as reported in table 7.3 (a). The impact of

corporate governance has been observed on ROA of the companies for two years i.e.

2001-02 and 2002-03. So far as other explanatory variables are concerned, we didn’t find

much variation in their β coefficients. The value of F statistics is also significant at 1%

level for the entire period of five years. Similarly, the values of adjusted R² are also

almost same as given in table 7.3 (a).


245

Table: 7.3 (a)

Regression Results of Impact of Corporate Governance (Equal Weights) on Return

on Assets



(-.77)

.186**

(2.13)

.20**

(2.04)

.041

(.428)

.005

(.048)

2 Age -.080

(-.68)

-.22**

(-2.37)

-.21**

(-2.12)

-.122

(-1.257)

.035

(.361)

3 Size .32*

(2.7)

.26*

(2.99)

.36*

(3.66)

.382*

(3.911)

.341*

(3.270)

4 Risk -.37**

(-2.53)

-.31*

(-2.99)

-.24**

(-2.19)

-.234**

(-2.102)

-.352*

(-3.192)

5 Leverage .039

(.36)

.044

(.53)

-.11

(-1.18)

-.221**

(-2.425)

-.098

(-1.075)

6 Textiles .027

(.16)

-.22**

(-1.95)

-.095

(-.76)

-.090

(-.727)

-.076

(-.617)

7 Iron & Steel -.096

(-.62)

-.25**

(-2.32)

-.053

(-.44)

.109

(.934)

.331*

(2.843)

8 Automobile -.082

(-.49)

-.12

(-1.05)

.006

(.044)

-.011

(-.082)

-.113

(-.881)

9 Cement -.13

(-.71)

-.14

(-1.22)

-.14

(-1.16)

-.024

(-.198)

.049

(.407)

10 Drugs & Pharmaceuticals .24

(1.27)

.21***

(1.78)

.29**

(2.26)

.324**

(2.567)

.142

(1.128)

11 Software .90*

(4.06)

.50*

(3.79)

.25***

(1.71)

.269***

(1.90)

.445*

(3.126)

12 Sugar .004

(.021)

-.015

(-.13)

-.008

(-.06)

.035

(.271)

.241***

(1.898)

13 Paper .089

(.56)

.018

(.15)

.095

(.72)

.106

(.934)

.076

(.583)

R Square .564 .523 .43 .410 .390

Adj. R Square .455 .451 .342 .325 .302

F Statistics

( Significance)

5.17

.000

7.26

.000

4.925

.000

4.809

.000

4.433

.000



at 10% level.


246

Table: 7.3 (b)

Regression Results of Impact of Corporate Governance (Unequal Weights) on

Return on Assets


1 Corporate Governance

-.129

(-1.198)

.159***

(1.787)

.195**

(2.016)

.031

(.336)

-.037

(-.375)

2 Age -.067

(-.579)

-.213**

(-2.260)

-.219**

(-2.164)

-.119

(-1.227)

.046

(.467)

3 Size .319*

(2.788)

.276*

(3.169)

.367*

(3.806)

.386*

(3.977)

.359*

(3.462)

4 Risk -.375**

(-2.603)

-.309*

(-3.047)

-.254**

(-2.268)

-.235**

(-2.105)

-.346*

(-3.122)

5 Leverage .037

(.344)

.045

(.531)

-.118

(-1.271)

-.223*

(-2.445)

-.098

(-1.073)

6 Textiles .023

(.138)

-.213**

(-1.863)

-.101

(-.797)

-.088

(-.714)

-.072

(-.585)

7 Iron & Steel -.085

(-.549)

-.246**

(-2.237)

-.043

(-.358)

.111

(.962)

.329*

(2.835)

8 Automobile -.069

(-.410)

-.111

(-.942)

.004

(.031)

-.007

(-.055)

-.107

(-.838)

9 Cement -.131

(-.695)

-.131

(-1.144)

-.149

(-1.195)

-.022

(-.181)

.051

(.428)


(1.297)

.228***

(1.919)

.285**

(2.188)

.327**

(2.600)

.149

(1.184)

11 Software .919*

(4.199)

.525*

(4.043)

.268***

(1.842)

.275**

(1.978)

.451*

(3.201)

12 Sugar .003

(.016)

-.004

(-.034)

-.012

(-.095)

.037

(.288)

.244***

(1.930)

13 Paper .089

(.563)

.029

(.242)

.103

(.790)

.110

(.858)

.079

(.610)

R Square .571 .517 .429 .409 .391

Adj. R Square .463 .444 .342 .324 .303

F Statistics

( Significance)

5.316

.000

7.081

.000

4.913

.000

4.799

.000

4.450

.000



at 10% level.


247

Table 7.4 (a) reveals the regression results of impact of corporate governance on

return on equity. No significant relationship has been observed between corporate

governance and return on equity for the entire period of study. Age is found to be

significantly negatively associated with the firm performance for one out of five years.

Size is significantly positively associated with return on equity for the year 2003-04 only.

So far as risk is concerned, a significant negative association has been observed with

return on equity for the first two years only. Similarly, leverage is highly significant but

negatively related to return on equity from the year 2001-02 to 2004-05. However, from

the industry effects, it has been observed that that iron & steel industry has significant

negative association with return on equity for the years 2000-01 and 2001-02 whereas

this relationship turns positive for the year 2004-05. Software and paper industries have

significant positive association with firm performance for one out of five years. For rest

of the industries dummies, no significant relationship has been observed with return on

equity. The value of adjusted R² has been observed to be at maximum level for the year

2003-04.

Similarly, table 7.4 (b) reveals the regression results of impact of corporate

governance on return on equity with unequal weights of corporate governance. No

significant relationship has been observed between corporate governance and return on

equity for the entire period of study. Age is found to be significantly negatively

associated with the firm performance for the year 2003-04 only. In the same way, size

has positive but significant impact on return on equity for the year 2003-04 only. Risk is

found to be significantly negatively associated with return on equity for first two years

only. So far as leverage is concerned, a highly significant but negative association has

been observed with return on equity for the period of four out of five years. Iron & steel

industry has significant negative association with return on equity for the years 2000-01

and 2001-02 but this relationship turns positive for the years 2002-03 and 2004-05. Paper

industry has significant positive relationship with the return on equity for the year 2003-

04 only. For rest of the industry dummies, no significant relationship has been observed

with return on equity.


248

Table: 7.4 (a)

Regression Results of Impact of Corporate Governance (Equal Weights) on Return

on Equity


1 Corporate Governance .172

(1.27)

.092

(.95)

.036

(.41)

-.012

(-.179)

-.007

(-.089)

2 Age .035

(.25)

-.029

(-.28)

-.13

(-1.44)

-.155**

(-2.182)

-.014

(-.176)

3 Size -.006

(-.041)

.10

(1.05)

.061

(.69)

.248*

(3.454)

-.027

(-.309)

4 Risk -.42**

(-2.47)

-.44*

(-3.89)

.035

(.35)

-.013

(-.160)

-.040

(-.431)

5 Leverage .056

(.43)

-.31*

(-3.36)

-.76*

(-8.99)

-.783*

(-11.727)

-.710*

(9.240)

6 Textiles .033

(.16)

-.122

(-.98)

-.021

(-.18)

.031

(.348)

.010

(.093)

7 Iron & Steel -.47**

(-2.54)

-.39*

(-2.99)

.17

(1.61)

-.008

(-.095)

.165***

(1.688)

8 Automobile -.15

(-.76)

-.15

(-1.16)

-.046

(-.39)

-.069

(-.728)

.032

(.298)

9 Cement -.19

(-.88)

-.13

(-1.02)

.075

(.67)

.069

(.782)

.052

(.514)


(.24)

.004

(.029)

-.033

(-.28)

.133

(1.436)

.031

(.290)

11 Software .39

(1.49)

.30**

(2.07)

-.12

(-.91)

.014

(.131)

.014

(.118)

12 Sugar -.088

(-.45)

-.055

(-.42)

.06

(.54)

.141

(1.502)

.161

(1.511)

13 Paper -.020

(-.11)

-.024

(-.18)

-.092

(-.77)

.313*

(3.306)

-.065

(-.593)

R Square .397 .426 .535 .682 .570

Adj. R Square .246 .339 .464 .636 .507

F Statistics

( Significance)

2.63

.007

4.91

.000

7.520

.000

14.865

.000

9.158

.000



at 10% level.


249

Table: 7.4 (b)


Return on Equity


1 Corporate Governance .113

(.875)

.026

(.271)

-.019

(-.213)

.014

(.200)

-.047

(-.574)

2 Age .054

(.386)

-.003

(-.026)

-.111

(-1.211)

-.163**

(-2.300)

-.004

(-.054)

3 Size .020

(.145)

.107

(1.118)

.074

(.848)

.240*

(3.367)

-.010

(-.116)

4 Risk -.421**

(-2.451)

-.431*

(-3.881)

.038

(.376)

-.015

(-.179)

-.033

(-.360)

5 Leverage .057

(.439)

-.310*

(-3.362)

-.757*

(-9.022)

-.784*

(-11.717)

-.710*

(-9.250)

6 Textiles .033

(.164)

-.108

(-.864)

-.006

(-.054)

.027

(.297)

.014

(.131)


(-2.547)

-.339*

(-2.804)

.185***

(1.726)

-.011

(-.126)

.163***

(1.675)

8 Automobile -.153

(-.762)

-.120

(-.924)

-.025

(-.209)

-.076

(-.807)

.037

(.348)

9 Cement -.197

(-.880)

-.106

(-.842)

.091

(.809)

.064

(.729)

.054

(.537)


(.214)

.029

(.224)

-.011

(-.092)

.127

(1.372)

.037

.353)

11 Software .423

(1.616)

.326

(2.287)

-.096

(-.729)

.005

(.049)

.019

(.159)

12 Sugar -.093

(-.473)

-.042

(-.320)

.074

(.640)

.137

(1.467)

.164

(1.542)

13 Paper -.038

(-.201)

-.006

(-.046)

-.080

(-.674)

.310*

(3.292)

-.063

(-.576)

R Square .387 .419 .534 .682 .571

Adj. R Square .234 .331 .463 .636 .509

F Statistics

( Significance)

2.530

.009

4.771

.000

7.500

.000

14.867

.000

9.216

.000



at 10% level.


250

Table 7.5 (a) reveals the regression results of impact of corporate governance with

equal weights on Tobin’s Q ratio. Adjusted R² is high for the year 2004-05 and explains

34% of variation in model. F- statistics are significant at 1% and 5% level which depicts

that our model is appropriate for the entire period of study. It has been observed from the

β coefficients of corporate governance score that there is no significant relationship

between corporate governance and Tobin’s Q. Our findings are in consonance with Jog

and Dutta (2004) who examined no significant relationship between corporate

governance variables and firm performance measured by Tobin’s Q. Age is found to be

significantly positively associated with firm performance for three out of five years.

Similarly, size is positively and significantly associated with firm performance for the

period of two out of five years. Risk is found to be negatively but significantly related to

Tobin’s Q for the years 2000-01 and 2003-04 whereas no significant association has been

observed between leverage and Tobin’s Q. Textiles industry has been found to be

significantly positively related to Tobin’s Q for the period of four out of five years.

Cement industry is found out to be significantly positively related to firm performance

for the year 2004-05 only. For drugs and pharmaceutical industry, a significant positive

association has been observed with Tobin’s Q for the period of two out of five years. So

far as software industry is concerned, a significant but positive association has been

observed with Tobin’s Q for the entire period of study. For rest of the industries

dummies, no significant association has been observed with Tobin’s Q.

Table 7.5 (b) reveals the regression results with unequal weights of corporate

governance. It has been observed from the analysis that there is no significant impact of

corporate governance on firm performance as measured by Tobin’s Q. For rest of the

variables, the regression results are almost same as depicted in table 7.5 (a) except for

drugs and pharmaceuticals and cement industries. For cement industry, results are found

to be significant at 11% level for the year 2004-05 as compared to 10% reported in table

7.5 (a). So far as drugs and pharmaceuticals industry is concerned, a significant but

positive association has been observed for three out of five years. F- statistics in this

model are significant at 1% and 5% level which reveals that our model is fit for the entire

period of study. Adjusted R² is the highest for the year 2004-05 and explains 34%

variation in model.


251

Table: 7.5 (a)

Regression Results of Impact of Corporate Governance (Equal Weights) on

Tobin’s Q



(-.61)

.076

(.701)

.077

(.70)

-.006

(-.062)

-.072

(-.754)

2 Age .43*

(3.09)

.264**

(2.31)

.28*

(2.48)

-.082

(-.854)

-.009

(-.099)

3 Size .068

(.49)

.068

(.63)

.063

(.57)

.319*

(3.266)

.383*

(3.779)

4 Risk -.37**

(-2.18)

-.12

(-.91)

-.14

(-1.15)

-.236**

(-2.116)

-.168

(-1.566)

5 Leverage -.032

(-.25)

.086

(.83)

.054

(.51)

.020

(.220)

.038

(.426)

6 Textiles .40**

(2.04)

.31**

(2.22)

.37**

(2.59)

.205***

(1.666)

.090

(.749)

7 Iron & Steel .14

(.79)

.013

(.099)

.038

(.28)

.005

(.041)

-.034

(-.300)

8 Automobile .148

(.752)

.032

(.22)

.057

(.38)

.088

(.687)

.089

(.716)

9 Cement .17

(.78)

.047

(.33)

.059

(.42)

.165

(1.379)

.192***

(1.649)


(1.13)

.22

(1.51)

.18

(1.24)

.437*

(3.471)

.468*

(3.822)

11 Software 1.05*

(4.07)

.49*

(2.97)

.47*

(2.81)

.612*

(4.336)

.607*

(4.392)

12 Sugar .074

(.38)

-.013

(-.085)

-.017

(-.12)

.009

(.068)

.060

(.485)

13 Paper .021

(.11)

-.023

(-.15)

-.020

(-.13)

.064

(.500)

.065

(.517)

R Square .419 .262 .267 .412 .426

Adj. R Square .271 .151 .155 .327 .343

F Statistics

( Significance)

2.83

.004

2.354

.010

2.380

.009

4.846

.000

5.130

.000



at 10% level.


252

Table: 7.5 (b)


Tobin’s Q



(-1.146)

-.001

(-.013)

.010

(.092)

.002

(.025)

-.034

(-.357)

2 Age .445*

(3.288)

.296**

(2.529)

.302**

(2.627)

-.083

(-.855)

-.019

(-.195)

3 Size .074

(.556)

.072

(.663)

.080

(.726)

.313*

(3.224)

.365*

(3.620)

4 Risk -.379**

(-2.261)

-.105

(-.834)

-.143

(-1.124)

-.237**

(-2.119)

-.169

(-1.570)

5 Leverage -.035

(-.273)

.083

(.797)

.050

(.474)

.016

(.176)

.040

(.451)

6 Textiles .394**

(2.029)

.330**

(2.323)

.387*

(2.684)

.204***

(1.651)

.087

(.725)

7 Iron & Steel .158

(.878)

.036

(.264)

.054

(.401)

.005

(.047)

-.035

(-.313)

8 Automobile .165

(.842)

.067

(.457)

.083

(.553)

.086

(.672)

.083

(.666)

9 Cement .175

(.800)

.074

(.517)

.077

(.547)

.163

(1.365)

.189

(1.620)


(1.154)

.252***

(1.710)

.207

(1.403)

.436*

(3.467)

.462*

(3.760)

11 Software 1.081*

(4.244)

.518*

(3.216)

.505*

(3.054)

.611*

(4.387)

.595*

(4.330)

12 Sugar .073

(.378)

.001

(.008)

-.003

(-.022)

.007

(.051)

.054

(.439)

13 Paper .017

(.092)

-.002

(-.013)

-.004

(-.024)

.072

(.561)

.058

(.462)

R Square .430 .257 .263 .410 .423

Adj. R Square .284 .144 .150 .324 .339

F Statistics

( Significance)

2.956

.003

2.284

.012

2.330

.011

4.801

.000

5.071

.000



at 10% level.


253

7.4.1 Regression Results with Pooled Data

In pooled data, the elements of both time series and cross section units are

present. In the previous section, an attempt has been made to study the impact of

corporate governance on financial performance of the companies on yearly basis. The

corporate governance code was not applicable to all the listed companies in the first year

of its implementation. Hence, we found inconsistency in the no. of observations for the

first three years of study. Results were also not consistent for the entire period of study.

In order to be more conclusive, an attempt has been made to separate those companies

which were following the corporate governance practices as per clause 49 of the listing

agreement for the period of five years under study. A total no. of 68 companies has been

selected from the sample of 112 which started following the corporate governance code

from the first year of its implementation. After pooling the data of 68 companies and

applying the industries dummies as well as year dummies, the following regression

models have been used:

Model 1

Net Profit Margin on Sales = 0 + 1 (Corporate Governance) + 2 (Age) + 3





+ 14 (D1) + 15 (D2) + 16 (D3) + 17 (D4) + ε

Model 2

Return on Assets = 0 + 1 (Corporate Governance) + 2 (Age) + 3




11 (D_Software) + 12 (D_Sugar) + 13 (D_Paper) +

14 (D1) + 15 (D2) + 16 (D3) + 17 (D4) + ε

Model 3

Return on Equity = 0 + 1 (Corporate Governance) + 2 (Age) + 3





254


14 (D1) + 15 (D2) + 16 (D3) + 17 (D4) + ε

Model 4

Tobin’s Q = 0 + 1 Corporate Governance) + 2 (Age) + 3





14 (D1) + 15 (D2) + 16 (D3) + 17 (D4) + ε

Here, D1, D2, D3 and D4 represent year dummies

Table 7.6 (a) represents the regression results of impact of corporate governance

with equal weights on financial performance of the companies. Results revealed no

significant association between corporate governance and financial performance. Age is

found to be significantly positively associated with one of the financial performance

measures i.e. Tobin’s Q. It has been observed that size has positive and significant impact

on financial performance. However, significant negative association has been observed

between risk and financial performance. Leverage is found to have negative and significant

impact on measures of profitability. But so far as measure of market valuation is concerned,

no association has been observed with leverage. Regarding the sectoral effects, textiles

industry is found to be significantly negatively associated to some extent with profitability

but positive impact has been observed on the measure of market valuation. Iron and steel

industry is found to be significantly negatively associated with net profit margin on sales

and ROE. However, the same is significantly positively associated with ROA. So, mixed

results have been observed for the measures of profitability. Automobile and cement

industries are highly significant but negatively related to one of the measures of

profitability i.e. net profit margin on sales. Hence, it is related to some extent with

profitability. A positive impact has been observed for drugs & pharmaceuticals industry on

one of the measures of profitability and market valuation i.e. ROA and Tobin’s Q

respectively. So far as software industry is concerned, results revealed highly significant

but positive relationship with financial performance. Regarding the year dummies, we

observed that year 2000-01 has significant positive impact on Tobin’s Q. On the other

hand, year 2001-02 is related negatively to financial performance in terms of ROE. For rest

of the year dummies, no significant impact has been observed on financial performance.


255

Table: 7. 6 (a)

Regression Results of Impact of Corporate Governance (Equal Weights) on

Financial Performance

S.No Explanatory Variables Model I Model II Model III Model IV


(-.128)

.001

(.023)

.082

(1.282)

.029

(.443)

2 Age -.001

(-.025)

.010

(.186)

.091

(1.614)

.155*

(2.740)

3 Size .194*

(3.492)

.325*

(5.879)

.111***

(1.867)

.192*

(3.208)

4 Risk -.141**

(-1.997)

-.396*

(-5.641)

-.339*

(-4.506)

-.173**

(-2.273)

5 Leverage -.112**

(-2.090)

-.138**

(-2.597)

-.144**

(-2.521)

.003

(.048)

6 Textiles -.354*

(-4.175)

-.009

(.106)

.046

(.510)

.185**

(2.039)


(-2.530)

.151***

(1.896)

-.195**

(-2.286)

.039

(.459)

8 Automobile -.471*

(-5.610)

.014

(.163)

-.023

(-.254)

.063

(.703)

9 Cement -.313*

(-3.370)

-.010

(-.107)

-.056

(-.567)

.114

(1.142)


(-1.229)

.292*

(3.092)

.121

(1.199)

.255**

(2.496)

11 Software .217**

(2.110)

.684*

(6.703)

.371*

(3..397)

.675*

(6.118)

12 Sugar -.237*

(-2.882)

.076

(.933)

.017

(.191)

.062

(.703)

13 Paper -.128

(-1.588)

.073

(.916)

.029

(.335)

.054

(.629)

14 D1 -.005

(-.081)

.049

(.765)

-.106

(-1.529)

.182*

(2.615)

15 D2 -.048

(-.811)

.020

(.330)

-.151**

(-2.376)

.053

(.824)

16 D3 -.055

(-.950)

-.013

(-.218)

-.092

(-1.476)

.038

(.606)

17 D4 -.055

(-.952)

.005

(.080)

-.042

(-.684)

-.011

(-.178)

R Square .353 .363 .268 .256

Adj. R Square .316 .327 .227 .214

F Statistics

( Significance)

9.618

.000

10.059

.000

6.475

.000

6.062

.000

Note: Figures within the parentheses indicate the t-values, (*) indicates significance at 1% level,

(**) indicates significance at 5% level and (***) indicates significance at 10% level.


256

Table: 7.6 (b)


Financial Performance

S.No Explanatory Variables Model I Model II Model III Model IV


(-.244)

-.041

(-.713)

.016

(.262)

-.012

(-.193)

2 Age -.001

(-.012)

.016

(.301)

.093

(1.65)

.162*

(2.875)

3 Size .196*

(3.576)

.336*

(6.189)

.131**

(2.248)

.203*

(3.462)

4 Risk -.141**

(-1.986)

-.395*

(-5.620)

-.341*

(-4.520)

-.173**

(-2.271)

5 Leverage -.113**

(-2.105)

-.143*

(-2.687)

-.151*

(-2.647)

-.002

(-.029)

6 Textiles -.354*

(-4.184)

.005

(.065)

.039

(.436)

.182**

(2.000)


(-2.529)

.151***

(1.902)

-.195**

(-2.281)

.040

(.460)

8 Automobile -.470*

(-5.592)

.017

(.201)

-.022

(-.245)

.065

(.720)

9 Cement -.313*

(-3.365)

-.009

(-.097)

-.059

(-.594)

.113

(1.136)


(-1.223)

.293*

(3.103)

.117

(1.155)

.254**

(2.486)

11 Software .217**

(2.119)

.688*

(6.777)

.384*

(3.519)

.681*

(6.197)

12 Sugar -.236*

(-2.872)

.078

(.962)

.017

(.192)

.063

(.714)

13 Paper -.128

(-1.595)

.069

(.870)

.021

(.246)

.050

(.581)

14 D1 -.009

(-.133)

.028

(.440)

-.136**

(-1.951)

.163**

(2.329)

15 D2 -.049

(-.826)

.014

(.234)

-.160**

(-2.515)

.047

(.738)

16 D3 -.056

(-.956)

-.015

(-.257)

-.095

(-1.522)

.036

(.573)

17 D4 -.055

(-.952)

.005

(.083)

-.042

(-.676)

-.011

(-.175)

R Square .353 .364 .265 .255

Adj. R Square .316 .328 .223 .213

F Statistics

( Significance)

9.622

.000

10.106

.000

6.350

.000

6.050

.000

Note: Figures within the parentheses indicate the t-values, (*) indicates significance at 1% level,

(**) indicates significance at 5% level and (***) indicates significance at 10% level.


257

Similarly, table 7.6 (b) reveals regression results of impact of corporate

governance (unequal weights) on financial performance of the companies. F statistics are

significant at 1% level for all the four models which show the fitness of our models.

Results revealed no significant association between corporate governance and financial

performance. However, age is related to some extent with the financial performance. A

positive impact has been observed for size on financial performance of the companies. So

far as risk is concerned, a significant negative impact has been observed on financial

performance of the companies. A significant negative impact has also been observed for

leverage on measures of profitability. Regarding the industry effects, the results of tables

7.6 (a) and 7.6 (b) are almost the same but little variation has been observed in case of

year dummies effects. D1 and D2 have significant negative impact on ROE whereas D1

also shows significant positive impact on Tobin’s Q. For rest of the year dummies, no

significant impact has been observed on financial performance of the companies.

7.5 Conclusion:-It has been observed from the analysis given in section 7.4 that the

corporate governance has positive impact on measures of profitability to some extent. But

so far as measure of market valuation is concerned, no significant relationship has been

observed with corporate governance. Similarly, in case of pooled data, no impact of

corporate governance has been observed on financial performance. Hence, on the basis of

measures of profitability, one can says that there exists a very weak relationship between

corporate governance and firm performance. On the whole, we found vexing results on

the relationship between corporate governance and firm performance. Similarly for age,

the results are different for measures of profitability and market valuation. Age is found

to be positively associated with measure of market valuation and negatively with

profitability. The reason for this negative relationship can be attributable to the product

life cycle hypothesis where firms earn supernormal profits during growth and maturity

stage but as soon as the declining stage begins, profits start declining. On the other hand,

a direct relationship of age has been observed with Tobin’s Q. Size is found to have

positive impact on financial performance. Our findings in this context hold the view that

the firms with larger size enjoy both internal as well as external economies of scale.

Hence, these firms outperform the smaller ones in size and earn above normal profits.


258

Similarly, risk is found to have negative impact on financial performance of the

companies. The reason for this negative relationship can be explained in context of risk

management concept where managers always try to minimize the risk and maximize the

profits. A significant negative impact has been observed for leverage on the measures of

profitability. Due to increase in the debt equity ratio of the firm, the interest expenses

paid to the lenders increase and ultimately it reduces the profits available for distribution

to the shareholders. Hence, shareholders return falls and cost of debt increases. Increase

in cost of debt will lower the profitability. Regarding the industry effects, it has been

observed that textile industry has negative impact on profitability to some extent and

positive on measure of market valuation. So, mixed results have been found out for

impact on financial performance in case of textile industry. The main reason for this

negative impact on financial performance can be the competition faced by the Indian

textile industry from China. Moreover, traditional textile industry like power loom and

hand loom is in critical situation now-a-days because of change in technology and

demand patterns. A positive impact has been observed on measure of market valuation.

The reason for this could be the elimination of quota restrictions by the Government and

encouraging the foreign institutional investors to invest in this industry. Market

capitalization of the companies has been affecting in a positive way due to the

involvement of FIIs and ultimately it affects the financial performance of the companies.

Moreover, in apparel textile industry, demand for products is high in foreign countries.

Similarly, for iron and steel industry, mixed relationship has been observed with

measures of profitability. This core industry operates under the control of Government. In

India, raw material required for steel industry is not available in abundance. Thus,

scarcity of resources leads to increase in the cost of production. Indian iron and steel

industry is now at developing stage. This is evident from the acquisition of Corus

Company by Tata Steel Ltd in the year 2008. Moreover, modern techniques employed in

this industry for production will improve the profitability position.

Similarly, a negative relationship has been observed for automobile industry with

measures of profitability to some extent. Lower profitability in this case may be due to

the excessive competition among the manufactures. Moreover, cartels agreements among

the manufacturers do not allow the sellers to charge the price beyond the certain limit.


259

Sometimes high cost firms have to operate at that lower price and this lead to the lower

profitability. Other reason could be the rapid increase in demand for two wheelers and

four wheelers. In order to meet the demand, the companies have to increase the

production. This results in the huge investment in production process and hiring

additional manpower. This ultimately affects the profitability position of the company.

Again for cement industry, a significant negative relationship has been observed with one

of the measures of profitability. One of the possible reasons for this might be the excess

supply situation and fall in prices during the financial year 2001-02. The manufacturers

have to cut down the production in order to match the demand and supply pattern. For

drugs & pharmaceuticals sector, a positive impact has been observed for one of the

measures of profitability and Tobin’s Q. Pharmaceuticals industry in India is recognized

as one of the emerging sectors with new technological developments, low cost of

production, low research & development cost, modernized and well equipped national

laboratories, etc. Moreover, de-licensing in Pharma sector by the Government has also

raised the future prospects of this industry. So far as sugar industry is concerned, a very

weak but negative relationship has been observed with the measures of profitability. The

reason for this negative relationship can be the reduction in stock of sugar during 2003-05

due to the crop failures. On the other hand, positive impact has been observed for

software industry on financial performance of the companies. Software industry is one of

the growing industries in India. Initiatives taken by the Government to permit 100% flow

of foreign direct investment and reduction in the major duties on import of basic

components and products have helped this sector to grow.

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