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Effect of Size, Age and Return on Government Securities Upon Cost of Capital
328
Chapter- VII
EFFECT OF SIZE, AGE AND RETURN
ON GOVERNMENT SECURITIES UPON
COST OF CAPITAL
Cost of capital is minimum expected rate of return expected by suppliers of
funds to a firm. The expected rate of return depends upon the risk characteristics of
the firm, risk perception of the investors and a host of other factors. It is assumed that
size and age play an important role in raising external finance either by way of debt or
equity. Size is also an indicator of borrowing capacity of firms. Large sized firms are
able to take advantage of economies of scale in issuing long-term debt and have
bargaining power over creditors. Large sized firms have higher borrowing capacity
with lower cost of borrowing and better access to capital markets. Similarly a
company which is smaller in size has to face problems while raising external finance
as compared to a bigger concern. Large sized firms enjoy easy access to capital
markets, receive higher credit ratings for their debt issues and pay lower interest rates
on their borrowed funds and have lower cost of debt capital (Kdat), lower cost of
equity capital (Ke) and lower overall cost of capital (Ko). Similarly a newly
established firm has to face problems while raising funds through debt or equity. Age
is considered as measure of risk and reputation. Barton and others (1989) stated that
it is expected that mature firms will experience lower earnings volatility and these
enterprises will have higher debt ratios and lower overall cost of capital (Ko).
Return on Government securities has direct impact upon cost of capital. The
return expected by an investor from a particular security depends upon risk associated
with particular security. The return expected by an investor is composition of risk free
rate of return plus risk premium. The risk free rate of return is based upon the rate
fixed by Government on its securities. The rate of interest on time deposits of post-
office is taken as proxy to represent return on Government securities (ROGS) for the
present study.
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
329
7.1 Methodology
For analyzing the relationship between size, age and return on government
securities (ROGS) with overall cost of capital (Ko1 and Ko2), technique of backward step-
wise panel data regression analysis has been used. The two expressions of overall cost of
capital (Ko1 and Ko2) are taken as dependent variables in regression equations for present
analysis. The independent variables here refer to size, age and return on Government
securities (ROGS). Hence two regression equations have been fitted to analyze the
impact of these selected variables upon overall cost of capital (Ko1 and Ko2) over the
entire period of study covering 27 years.
The regression models estimated were:
Ko1 = α + β1S1it+ β2 S2it+β3 AGEit+β4 ROGSit+ β5Dit+ β 6C1+…………β nCn-1+ µit
Ko2 = α +β1S1it+ β2 S2it+β3 AGEit+β4 ROGSit+ β5Dit+β6C1+………… β nCn-1+ µit
Here C represents company/ industry dummy and n-1 represents total company/industry
dummies introduced in regression equations. Hence numbers of company/industry
dummies introduced are 4, 7, 6, 28, 12, 20, 6, 9 and 7 respectively.
Where,
Ko1 = Overall cost of capital as computed by taking into account Kdat, Kp and
Ke1 multiplied by their respective weights in financing mix.
Ko2 = Overall cost of capital as computed by taking into account Kdat, Kp and Ke2
multiplied by their respective weights in financing mix.
S1 = Size measured in terms of logarithm of net sales.
S2 = Size measured in terms of logarithm of total assets.
AGE = Number of years since incorporation.
ROGS = Return on Government Securities.
The regression models are estimated by considering first overall cost of capital
(Ko1) as dependent variable and then overall cost of capital (Ko2) as dependent variable.
The regression estimates of the model are presented for the entire study period in Tables
7.1 to 7.18. A sample of 100 companies representing eight (power, metal, cement,
textiles, paper, general engineering, sugar and tea) industries as exhibited in chapter-III in
Table 3.2 has been taken for the purpose of the study.
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
330
7.2 Backward Step-wise Panel Data Regression Analysis of Selected Companies
in Selected Industries (Ko1 as Dependent Variable)
Tables 7.1 to 7.8 exhibit results of backward step-wise panel data regression
analysis of 100 selected companies representing 8 industries such as power, metal,
cement, textiles, paper, general engineering, sugar and tea over the entire study period.
Table 7.9 reveals the results of panel data regression analysis taking into account all
selected industries in one regression equation over the selected study period. The
overall cost of capital (Ko1) is taken as dependent variable and is regressed against
selected explanatory variables. The results have been derived over the entire period of
study covering 27 years.
7.2.1 Power Industry
Table 7.1 shows results of backward step-wise panel data regression analysis of
selected companies in power industry over the entire period of study covering 27 years.
In the first run equation, all selected independent variables have been observed as
significantly associated with overall cost of capital (Ko1). The significant variables
include size measured in terms of net sales (S1), size measured in terms of total assets
(S2), age and return on Government securities (ROGS) respectively. Three of the selected
companies namely Reliance Energy Ltd. (C2), Tata Power Co. Ltd. (C3) and Torrent
Power A E C Ltd. (C4) respectively have been observed as significant in the first run
equation. Three of the selected explanatory variables are significantly related to overall
cost of capital (Ko1) in the final run equation. These are size measured in terms of net
sales (S1), size measured in terms of total assets (S2) and return on Government securities
(ROGS) respectively. The dummy (Dt) variable has been observed as negative and
insignificant indicating no change in overall cost of capital (Ko1) of selected companies in
this sector after liberalization policies. The difference in R2 has been observed as only
0.038 from 0.365 in the first run equation to 0.337 in the final run equation. This shows
that the non-significant variables contribute only 3.80 percent variation in overall cost of
capital (Ko1). The coefficient of multiple determination is 0.337 in case of fixed effects
model and 0.327 in case of random effects model. The restricted F-ratio between the two
coefficients of multiple determination has been worked out at 0.015, which has been
observed as non-significant. This shows that both the fixed as well as random effects
models are equally important for the study.
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
331
Table 7.1
Results of Regression Analysis of Power Industry from 1979-80 to 2005-06
Variable FEM REM
Intercept Slope
First Model
(Constant) 48.509
S1 20.857
(1.942)**
S2 -21.235
(3.061)***
AGE -0.241
(-1.562)*
ROG -1.473
(-1.896)**
Dt -5.432
(-1.271) 48.509 -9.769
C2 15.162
(2.042)** 63.671 5.393
C3 13.316
(1.488)* 61.825 3.547
C4 14.154
(1.420)* 62.663 4.385
C5 6.214
(0.679) 54.723 -3.555
Constant 58.278
R
2 0.349
R2 0.365
Final Model
(Constant) 130.062
S1 -11.927
(-6.718)***
AGE -0.159
(-3.082)***
ROG -2.373
(-4.450)*** 130.062 -7. 671
C2 13.322
(4.718)*** 143.384 5.651
C3 9.958
(3.141)*** 140.020 2.287
C4 7.405
(2.405)** 137.467 -0.266
Constant 137.733
R
2 0.327
R2 0.337
Source: Compiled and Analyzed from the Basic Data Obtained from Bombay Stock Exchange Official
Directory, Prowess Database (CMIE) and Annual Reports of Companies.
Notes: 1.Figures in Parentheses represent t-values.
2. Significance at 10%, 5% and 1% is indicated by one, two and three asterisks respectively.
Dependent Variable = Ko1 N= 5
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
332
The coefficient of size measured in terms of net sales (S1) has been observed as -
11.927. This indicates that size measured in terms of net sales (S1) is inversely related to
overall cost of capital (Ko1). Similarly, age and ROGS cause decline in the overall cost of
capital (Ko1) over the study period.
The Fixed Effects Model (FEM) shows the common slope of 130.062 for C1, C2,
C3 and C4 respectively while the intercepts have been observed as 143.384 for C2,
140.02 for C3 and 137.467 for C4 respectively during the selected study period. In
Random Effects Model (REM), the intercept has been observed as 137.733 around which
the intercepts of FEM deviates. In this way, the slope has been worked out as -7.671 for
C1, 5.651 for C2, 2.287 for C3 and -0.266 for C4 respectively. This shows that there is
fixed as well as random increase in overall cost of capital (Ko1) in case of selected
companies in this sector over the selected study period except C1 and C4 in which
random decline has been observed over the selected study period.
7.2.2 Metal Industry
Table 7.2 shows results of backward step-wise panel data regression analysis of
selected companies in metal industry over the entire period of study covering 27 years. In
the first run equation, only one variable such as size measured in terms of net sales (S1)
and only one company namely Electrosteel Castings Ltd. (C2) is significantly related to
overall cost of capital (Ko1) whereas the remaining variables are not statistically
significant in having relationship with the overall cost of capital (Ko1). Three variables
are significantly related to overall cost of capital (Ko1) in the final run equation. These are
size measured in terms of net sales (S1), size measured in terms of total assets (S2) and
age respectively. It is important to note that three more companies namely Electrosteel
Castings Ltd. (C2), Goetze (India) Ltd. (C5) and Tinplate Co. Of India Ltd. (C8)
respectively turn out as significant in the final run equation. The regression coefficient of
dummy (Dt) variable appears with negative and significant impact upon overall cost of
capital (Ko1). The negative coefficient of dummy variable indicates decline in overall cost
of capital (Ko1) during post-liberalization period as compared to pre-liberalization period.
The difference in R2 has been observed as only 0.026 from 0.106 in the first run equation
to 0.080 in the final run equation. This shows that the non-significant variables contribute
only 2.60 percent variation in the overall cost of capital (Ko1). The coefficient of multiple
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
333
Table 7.2
Results of Regression Analysis of Metal Industry from 1979-80 to 2005-06
Variable FEM REM
Intercept Slope
First Model
(Constant) 44.591
S1 -14.548
(-2.455)**
S2 7.937
(1.327)
AGE 0.700
(1.019)
ROG 0.293
(0.211)
Dt -9.132
(-1.331) 44.591 7.054
C2 -11.681
(-2.069)** 32.910 32.812
C3 0.263
(0.037) 44.854 44.854
C4 0.321
(0.046) 44.912 44.912
C5 -21.262
(-0.972) 23.329 16.557
C6 -7.736
(-1.008) 36.855 8.613
C7 -3.459
(-0.427) 41.132 23.071
C8 -12.881
(-1.062) 31.710 6.417
Constant 37.537
R
2 0.098
R2 0.106
Contd….
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
334
Variable FEM REM
Intercept Slope
Final Model
(Constant) 35.391
S1 -4.695
(-1.745)**
AGE 0.645
(2.401)**
Dt -7.646
(-1.873)** 35.391 6.772
C2 -7.079
(-1.741)** 28.312 28.242
C5 -17.330
(-2.190)** 18.061 18.061
C8 -10.098
(-2.028)** 25.293 25.293
Constant 28.619
R
2 0.07
R2 0.080
Source: Compiled and Analyzed from the Basic Data Obtained from Bombay Stock Exchange Official
Directory, Prowess Database (CMIE) and Annual Reports of Companies.
Notes: 1. Figures in Parentheses represent t-values.
2. Significance at 10%, 5% and 1% is indicated by one, two and three asterisks respectively.
Dependent Variable = Ko1 N= 8
determination is 0.080 in case of fixed effects model and 0.071 in case of random effects
model. The restricted F-ratio between the two coefficients of multiple determination has
been worked out at 0.010, which has been observed as non-significant. This shows that
both the fixed as well as random effects models are equally important for the study.
However, the explanatory power of the model is very weak. Even then some significant
variables demand attention and need to be elaborated.
The coefficients of size (S1 and S2) have been worked out as -4.695 and -7.646
respectively. This indicates that size (S1 and S2) is inversely related to overall cost of
capital (Ko1). On the other hand, age of the company causes increase in overall cost of
capital (Ko1). The negative coefficient of dummy (Dt) variable points out decline in overall
cost of capital (Ko1) during post-liberalization period as compared to pre-liberalization
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
335
period. This shows that liberalization leads to decline in overall cost of capital (Ko1) of
selected companies in this industry.
The Fixed Effects Model (FEM) shows the common slope of 35.391 for C1, C2,
C5 and C8 respectively while the intercepts have been observed as 28.312, 18.061 and
25.293 for C2, C5 and C8 respectively. In Random effects model (REM), the intercept
has been observed as 28.619 around which the intercepts of FEM deviates. In this way,
the slope has been worked out as 6.772 for C1, 28.242 for C2, 18.061 for C5 and 25.293
for C8 respectively. This reveals that there is a fixed as well as random increase in overall
cost of capital (Ko1) in case of selected companies in metal industry.
7.2.3 Cement Industry
Table 7.3 shows results of backward step-wise panel data regression analysis of
selected companies in cement industry over the entire period of study covering 27 years.
In the first run equation, two of the selected explanatory variables such as size measured
in terms of net sales (S1) and size measured in terms of total assets (S2) have been
observed as significantly related to overall cost of capital (Ko1) whereas age and return on
Government securities (ROGS) are not statistically significant in having relationship with
overall cost of capital (Ko1). The Mangalam Cement Ltd. (C6) is the only company that
turns out as significant in the first run equation. The dummy (Dt) variable appears with
positive and insignificant impact upon overall cost of capital (Ko1) in the first run
equation. The same variables have been observed as significant in final run equation. The
regression coefficient of dummy (Dt) variable appears with negative and significant
impact upon overall cost of capital (Ko1) in the final run equation. The negative
coefficient of dummy variable indicates decline in overall cost of capital (Ko1) during
post-liberalization period as compared to pre-liberalization period. It is important to note
that four companies namely Chettinad Cement Corpn. Ltd. (C2), India Cements Ltd.
(C4), Madras Cements Ltd. (C5) and Mangalam Cement Ltd. (C6) have been observed as
significant in the final run equation. The difference in R2 has been observed as only 0.002
from 0.288 in the first run equation to 0.286 in the final run equation. This shows that the
non-significant variables contribute only 0.20 percent towards variation in overall cost of
capital (Ko1). The coefficient of multiple determination is 0.286 in case of fixed effects
model and 0.271 in case of random effects model. The restricted F-ratio between
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
336
Table 7.3
Results of Regression Analysis of Cement Industry from 1979-80 to 2005-06
Variable FEM REM
Intercept Slope
First Model
(Constant) 53.240
S1 24.585
(3.030)***
S2 -23.982
(-3.607)****
AGE -0.814
(-1.282)
ROG 0.331
(0.296)
Dt 7.015
(1.230) 53.240 7.281
C2 -12.040
(-0.819) 41.200 -4.759
C3 2.104
(0.229) 55.344 9.385
C4 -4.938
(-0.724) 48.302 2.343
C5 -5.904
(-0.481) 47.336 1.377
C6 -33.584
(-1.486)* 19.656 -26.303
C7 3.395
(0.517) 56.635 10.676
Constant 45.959
R
2 0.279
R2 0.288
Contd…
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
337
Variable FEM REM
Intercept Slope
Final Model
(Constant) 73.738
S1 23.520
(3.534)***
S2 -25.270
(-4.465)***
AGE -0.823
(-3.575)***
Dt 8.350
(2.721)*** 73.738 11.271
C2 -15.113
(-3.113)*** 58.625 -3.842
C4 -6.445
(-1.903)** 67.293 4.826
C5 -8.157
(-1.857)** 65.581 3.114
C6 -37.087
(-5.075)*** 36.651 -25.816
Constant 62.467
R
2 0.271
R2 0.286
Source: Compiled and Analyzed from the Basic Data Obtained from Bombay Stock Exchange Official
Directory, Prowess Database (CMIE) and Annual Reports of Companies.
Notes: 1. Figures in Parentheses represent t-values.
2. Significance at 10%, 5% and 1% is indicated by one, two and three asterisks respectively.
Dependent Variable = Ko1 N= 7
the two coefficients of multiple determination has been worked out at 0.021, which has
been observed as non-significant. This shows that both the fixed as well as random
effects models are equally important for the study.
The coefficients of size (S1 and S2) have been observed as 23.52 and -25.27
respectively. This indicates that size (S1) is positively related whereas size (S2) is
inversely related to overall cost of capital (Ko1). The regression coefficient of size (S1)
appears with positive sign. The coefficient of size (S2) appears with negative sign which
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
338
indicates that large sized companies have lower overall cost of capital (Ko). Age causes
decline in overall cost of capital (Ko1). The significantly negative coefficient of dummy
(Dt) variable points out decline in overall cost of capital (Ko1) of selected companies in
this industry after liberalization policies.
The Fixed Effects Model (FEM) shows the common slope of 73.738 for C1, C2,
C4, C5 and C6 while the intercepts have been observed as 58.625, 67.293, 65.581 and
36.651 for C2, C4, C5 and C6 respectively. In Random Effects Model (REM), the
intercept has been observed as 62.467 around which the intercepts of FEM deviates. In
this way, the slope has been worked out as 11.271 for C1, -3.842 for C2, 4.826 for C4,
3.114 for C5 and -25.816 for C6 respectively. This shows that C1, C2, C4, C5 and C6
respectively have positive fixed effect on overall cost of capital (Ko1) in case of selected
companies in this industry. Similarly, random effect of these companies is also positive
except C2 and C6, where the random effect is negative on overall cost of capital (Ko1),
neutralizing the positive random effect. This reveals that there is a fixed as well as
random increase in overall cost of capital (Ko1) of selected companies in this industry
except C2 and C6 where random decline has been observed over the study period.
7.2.4 Textiles Industry
Table 7.4 shows results of backward step-wise panel data regression analysis of
selected companies in textiles industry over the entire period of study covering 27 years.
In the first run equation, only one variable such as return on Government securities
(ROGS) has been observed as significantly related to overall cost of capital (Ko1) whereas
the remaining variables are not statistically significant in having relationship with overall
cost of capital (Ko1). The Ruby Mills Ltd. (C24) is the only company that turns out as
significant in the first run equation. The dummy (Dt) variable has been observed with
positive and insignificant impact upon overall cost of capital (Ko1) in the first run
equation. It indicates that no change has been observed in overall cost of capital (Ko1) of
selected companies in this industry after liberalization policies. There is no addition in the
number of significant variables in the final run equation. Two companies namely Century
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
339
Table 7.4
Results of Regression Analysis of Textiles Industry from 1979-80 to 2005-06
Variable FEM REM
Intercept Slope
First Model
(Constant) -15.101
S1 0.692
(0.114)
S2 -1.262
(-0.110)
AGE 0.048
(0.069)
ROG 2.160
(1.389)*
Dt 5.935
(0.706) -15.101 -8.756
C2 3.408
(0.146) -11.693 -5.348
C3 8.423
(0.271) -6.678 -0.333
C4 6.587
(0.205) -8.514 -2.169
C5 2.235
(0.115) -12.866 -6.521
C6 -2.288
(-0.058) -17.389 -11.044
C7 28.063
(1.024) 12.962 19.307
C8 8.190
(0.296) -6.911 -0.566
C9 15.931
(0.473) 0.830 7.175
C10 5.299
(0.218) -9.802 -3.457
C11 2.885
(0.146) -12.216 -5.871
C12 2.663
(0.066) -12.438 -6.093
C13 4.647
(0.232) -10.454 -4.109
C14 2.746
(0.157) -12.355 -6.010
C15 18.240
(0.646) 3.139 9.484
Contd….
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
340
Variable FEM REM
Intercept Slope
C16 4.312
(0.182) -10.789 -4.444
C17 10.127
(0.303) -4.974 1.371
C18 3.340
(0.123) -11.761 -5.416
C20 6.054
(0.334) -9.047 -2.702
C22 4.558
(0.293) -10.543 -4.198
C23 12.919
(0.394) -2.182 4.163
C24 62.668
(2.697)** 47.567 53.912
C25 7.874
(0.338) -7.227 -0.882
C26 8.292
(0.236) -6.809 -0.464
C27 1.062
(0.042) -14.039 -7.694
C28 2.068
(0.063) -13.033 -6.688
C29 9.710
(0.348) -5.391 0.954
Constant -6.345
R
2 0.058
R2 0.066
Final Model
(Constant) -2.914
ROG 1.933
(1.960)** -2.914 -25.993
C7 20.227
(1.922)** 17.313 -5.766
C24 57.752
(5.486)*** 54.838 31.759
Constant 23.079
R2 0.049
R2 0.055
Source: Compiled and Analyzed from the Basic Data Obtained from Bombay Stock Exchange Official
Directory, Prowess Database (CMIE) and Annual Reports of Companies.
Notes: 1. Figures in Parentheses represent t-values.
2. Significance at 10%, 5% and 1% is indicated by one, two and three asterisks respectively.
Dependent Variable = Ko1 N= 29
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
341
Enka Ltd. (C7) and Ruby Mills Ltd. (C24) turn out as significant in the final run
equation. The model has very week statistical power as the R2 has been observed as very
low in this industry, indicating no definite trend in overall cost of capital (Ko1) and
other explanatory variables among different companies in this industry. Thus the model
cannot explain significantly the variation in overall cost of capital (Ko1) in this industry.
However, the difference in R2 has been observed as only 0.011 from 0.066 in the first run
equation to 0.055 in the final run equation. This shows that the non-significant variables
contribute only 1.10 percent variation in overall cost of capital (Ko1). The coefficient of
multiple determination is 0.055 in case of fixed effects model and 0.049 in case of
random effects model. The restricted F-ratio between the two coefficients of multiple
determination has been worked out at 0.004, which has been observed as non-significant.
This shows that that both the fixed as well as random effects models are equally
important for the study.
The coefficient of return on Government securities (ROGS) has been observed as
1.933. This indicates that return on Government securities (ROGS) has been positively
related to overall cost of capital (Ko1). No other variable depicts significant relationship
with overall cost of capital (Ko1) in this industry. Even, the non significant coefficient of
dummy (Dt) variable points out no change in overall cost of capital (Ko1) during post-
liberalization period as compared to pre-liberalization period. This shows that
liberalization cannot exert any significant effect on overall cost of capital (Ko1) of
selected companies in this industry.
The Fixed Effects Model (FEM) shows the common slope of -2.914 for C1, C7,
and C24 while the intercepts have been observed as 17.313 for C7 and 54.838 for C24
respectively. In Random Effects Model (REM), the intercept has been observed as 23.079
around which the intercepts of FEM deviates. In this way, the slope has been worked out
as -25.993 for C1, -5.766 for C7 and 31.759 for C24 respectively. This shows that C1, C7
and C24 have the positive fixed effect on overall cost of capital (Ko1) in this industry,
while random effect of all these companies is negative, except C24 where the random
effect is positive on overall cost of capital (Ko1). This reveals that there is a fixed increase
in overall cost of capital (Ko1) of selected companies in this industry whereas the
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
342
randomly C1 and C24 are responsible for decline in overall cost of capital (Ko1), which
can be neutralized by positive random effect of C24.
7.2.5 Paper Industry
Table 7.5 shows results of backward step-wise panel data regression analysis of
selected companies in paper industry over the entire period of study covering 27 years. In
the first run equation, size (S1 and S2) has been observed as significantly related to overall
cost of capital (Ko1) whereas the remaining variables are not statistically significant in
having relationship with overall cost of capital (Ko1). Six companies turn out as
significant in the first run equation. These are Aurangabad Paper Mills Ltd. (C2),
Balkrishna Industries Ltd. (C3), Jayant Paper Mills Ltd. (C5), Rohit Pulp & Paper Mills
Ltd. (C7), Star Paper Mills Ltd. (C12) and West Coast Paper Mills Ltd. (C13)
respectively. Size (S1 and S2) and age turn out as significant determinants of overall cost
of capital (Ko1) in the final run equation. The regression coefficient of dummy (Dt)
variable appears with negative and significant impact upon overall cost of capital (Ko1) in
the final run equation. The negative coefficient of dummy variable indicates decline in
overall cost of capital (Ko1) of selected companies in this industry during post-
liberalization period as compared to pre-liberalization period. The Ballarpur Industries
Ltd. (C4) is the only company that turns out as significant in final run equation. The
difference in R2 has been observed as 0.049 from 0.148 in the first run equation to 0.099
in the final run equation. This shows that the non-significant variables contribute only 4.9
percent variation in overall cost of capital (Ko1). The coefficient of multiple
determination is 0.099 in case of fixed effects model and 0.086 in case of random effects
model. The restricted F-ratio between the two coefficients of multiple determination has
been worked out at 0.005, which has been observed as non-significant. This shows that
both the fixed as well as random effects models are equally important for the study.
The coefficients of size (S1 and S2) have been observed as 5.543 and -4.056
respectively. This indicates that size (S1) is positively related whereas size (S2) is
inversely related to overall cost of capital (Ko1). Age is positively associated with overall
cost of capital (Ko1) in this industry. The significantly negative coefficient of dummy (Dt)
variable points out decline in overall cost of capital (Ko1) due to it. This shows that after
liberalization policies, overall cost of capital (Ko1) has declined significantly for selected
companies in this industry.
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
343
Table 7.5
Results of Regression Analysis of Paper Industry from 1979-80 to 2005-06
Variable FEM REM
Intercept Slope
First Model
(Constant) 26.728
S1 4.035
(1.896)**
S2 -6.597
(-2.149)**
AGE 0.360
(1.128)
ROG 0.128
(0.208)
Dt -3.469
(-1.106) 26.728 7.162
C2 -9.044
(-2.012)** 17.684 -1.882
C3 -7.684
(-2.339)** 19.044 -0.522
C4 -9.699
(-1.551) 17.029 -2.537
C5 -10.434
(-2.376)** 16.294 -3.272
C6 -5.976
(-1.014) 20.752 1.186
C7 -6.443
(-1.595)* 20.285 0.719
C8 -3.639
(-1.150) 23.089 3.523
C9 -3.501
(-1.124) 23.227 3.661
C10 -5.436
(-1.280) 21.292 1.726
Contd…
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
344
Variable FEM REM
Intercept Slope
C11 -8.657
(-1.004) 18.071 -1.495
C12 -13.392
(-1.411)* 13.336 -6.230
C13 -9.203
(-2.167)** 17.525 -2.041
Constant 61.14
R
2 0.129
R2 0.148
Final Model
(Constant) 5.222
S1 5.543
(3.459)***
S2 -4.056
(-1.948)**
AGE 0.145
(2.722)****
Dt -3.250
(-2.121)** 5.222 2.319
C4 -4.638
(-1.754)** 0.584 -2.319
Constant 2.903
R2 0.086
R2 0.099
Source: Compiled and Analyzed from the Basic Data Obtained from Bombay Stock Exchange Official
Directory, Prowess Database (CMIE) and Annual Reports of Companies.
Notes: 1. Figures in Parentheses represent t-values.
2. Significance at 10%, 5% and 1% is indicated by one, two and three asterisks respectively.
Dependent Variable= Ko1 N=13
The Fixed Effects Model (FEM) shows the common slope of 5.222 for C1 and
C12 while the intercept for C4 has been observed as 0.584. In Random Effects Model
(REM), the intercept has been observed as 2.903 around which the intercepts of FEM
deviates. In this way, the slope has been worked out as 2.319 for C1, which is
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
345
neutralized by same negative coefficient of an equal magnitude of C4. This shows
that C1 and C4 have positive fixed effect on overall cost of capital (Ko1) in this
industry. This reveals that there is a fixed increase in overall cost of capital (Ko1) of
selected companies in this industry.
7.2.6 General Engineering Industry
Table 7.6 shows results of backward step-wise panel data regression analysis
of selected companies in general engineering industry over the entire period of study
covering 27 years. All selected explanatory variables have been observed as
significant in the first run equation. Nine companies termed C2, C3, C4, C7, C10,
C11, C13, C16 and C17 respectively have been observed as significant in the first run
equation. The same variables have been observed as significant in final run equation.
Eleven companies termed C2, C3, C5, C6, C7, C8, C9, C11, C16, C17 and C20
respectively turn out as significant in the final run equation. The regression
coefficient of dummy (Dt) variable appears with negative and significant impact upon
overall cost of capital (Ko1) in both first run and final run equations. The negative
coefficient of dummy (Dt) variable indicates decline in overall cost of capital (Ko1) of
selected companies in this industry during post-liberalization period as compared to
pre-liberalization period. The difference in R2 has been observed as only 0.027 from
0.160 in the first run equation to 0.133 in the final run equation. This shows that non-
significant variables contribute only 2.70 percent variation in the overall cost of
capital (Ko1). The coefficient of multiple determination is 0.133 in case of fixed
effects model and 0.127 in case of random effects model. The restricted F-ratio
between the two coefficients of multiple determination has been worked out at 0.007,
which has been observed as non-significant. This shows that both the fixed as well as
random effects models are equally important for the study.
The coefficients of size (S1 and S2) have been observed as 11.338 and -17.333
respectively. This indicates that size (S1) is positively related whereas size (S2) is
inversely related to overall cost of capital (Ko1). Age and return on Government securities
(ROGS) cause increase in overall cost of capital (Ko1) of selected companies in this
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
346
Table 7.6
Results of Regression Analysis of General Engineering Industry from 1979-80 to
2005-06
Variable FEM REM
Intercept Slope
First Model
(Constant) -30.894
S1 14.329
(3.031)***
S2 -14.788
(-2.508)**
AGE 0.868
(2.194)**
ROG 1.267
(1.731)**
Dt -8.316
(-2.258)** -30.894 -10.390
C2 23.696
(2.399)** -7.198 13.306
C3 19.237
(2.284)** -11.657 8.847
C4 13.306
(1.944)** -17.588 2.916
C5 3.105
(0.681) -27.789 -7.285
C6 5.181
(0.821) -25.713 -5.209
C7 21.078
(2.922)*** -9.816 10.688
C8 -1.926
(-0.372) -32.82 -12.316
C9 -16.795
(-1.289) -47.689 -27.185
C10 23.318
(2.221)** -7.576 12.928
Contd…
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
347
Variable FEM REM
Intercept Slope
C11 14.524
(2.003)** -16.37 4.134
C12 -3.28
(-0.686) -34.174 -13.670
C13 37.343
(3.318)*** 6.449 26.953
C14 2.136
(0.483) -28.758 -8.254
C15 7.741
(1.377) -23.153 -2.649
C17 35.273
(3.003)*** 4.379 24.883
C18 -4.644
(-1.012) -35.538 -15.034
C19 18.222
(1.999)** -12.672 7.832
C20 -6.656
(-0.96) -37.55 -17.046
C21 1.113
(0.208) -29.781 -9.277
Constant -20.504
R
2 0.116
R2 0.122
Final Model
(Constant) 5.001
S1 11.938
(2.907)***
S2 -17.333
(-3.715)***
AGE 0.879
(4.235)***
ROG 1.045
(2.179)**
Contd….
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
348
Variable FEM REM
Intercept Slope
Dt -6.082
(-2.591)** 5.001 -11.064
C2 17.222
(3.113)*** 22.223 6.158
C3 11.794
(2.369)** 16.795 0.730
C4 8.318
(2.034)** 13.319 -2.746
C7 15.295
(3.609)** 20.296 4.231
C9 -21.407
(-2.924)*** -16.406 -32.471
C10 20.251
(3.492)*** 25.252 9.187
C11 11.201
(2.645)** 16.202 0.137
C13 32.080
(5.003)*** 37.081 21.016
C16 22.538
(4.298)*** 27.539 11.474
C17 27.021
(4.204)*** 32.022 15.957
C19 10.954
(2.144)** 15.955 -0.110
C20 -11.432
(-2.703)*** -6.431 -22.496
Constant 16.065
R
2 0.103
R2 0.107
Source: Compiled and Analyzed from the Basic Data Obtained from Bombay Stock Exchange Official
Directory, Prowess Database (CMIE) and Annual Reports of Companies.
Notes: 1. Figures in Parentheses represent t-values.
2. Significance at 10%, 5% and 1% is indicated by one, two and three asterisks respectively.
Dependent Variable = Ko1 N= 21
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
349
industry over the study period. Return on Government securities (ROGS) has direct
impact upon cost of debt (Kdat), cost of equity capital (Ke) which in turn affects overall
cost of capital (Ko).
The Fixed Effects Model (FEM) shows the common slope of 5.001 for the 11
companies having positive intercepts for these companies except C9 and C20 where
the intercepts have been observed negative over the selected study period. In Random
Effects Model (REM), the intercept has been observed as 16.065 around which the
intercepts of FEM deviates. The slope in REM has been observed as negative for C1, C4,
C9, C19 and C20 while the slope has been observed positive for remaining 8 companies
for this sector. This shows that there is a fixed increase in overall cost of capital (Ko1) of
selected companies in this industry except C9 and C20. The random effect of 5
companies termed C1, C4, C9, C19 and C20 is also negative while it is positive in other
companies. This reveals that there is a fixed as well as random increase in overall cost of
capital (Ko1) of selected companies in this industry with exception of few of selected
companies in which fixed as well as random decline has been observed over the study
period.
7.2.7 Sugar Industry
Table 7.7 shows results of backward step-wise panel data regression analysis of
selected companies in sugar industry over the entire period of study covering 27 years. In
the first run equation, size measured in terms of net sales (S1) and size measured in terms
of total assets (S2) have been observed as significant determinants of overall cost of
capital (Ko1). The Kothari Sugars & Chemicals Ltd. (C4) is the only company that turns
out as significant in first run equation. The regression coefficient of dummy (Dt) variable
appears with negative and significant impact upon overall cost of capital (Ko1) in both
first run and final run equations. The negative coefficient of dummy variable indicates
decline in overall cost of capital (Ko1) during post-liberalization period as compared to
pre-liberalization period. The same variables have been observed as significant in the
final run equation. Two companies namely Balrampur Chini Mills Ltd. (C3) and Kothari
Sugars & Chemicals Ltd. (C4) have been observed as significant in the final run equation.
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
350
Table 7.7
Results of Regression Analysis of Sugar Industry from 1979-80 to 2005-06
Variable β FEM REM
Intercept Slope
First Model
(Constant) 22.093
S1 15.020
(2.555)**
S2 -19.288
(-3.565)***
AGE 0.465
(0.881)
ROG 0.386
(0.444)
Dt -8.430
(-1.950)** 22.093 -4.767
C2 -5.781
(-0.663) 16.312 -10.548
C3 17.038
(1.168) 39.131 12.271
C4 13.536
(2.109)** 35.629 8.769
C5 -11.488
(-1.106) 10.605 -16.255
C6 9.900
(1.236) 31.993 5.133
C7 10.162
(0.909) 32.255 5.395
Constant 26.860
R
2 0.301
R2 0.311
(Constant) 16.917
Contd…
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
351
Variable β FEM REM
Intercept Slope
S1 14.516
(2.779)***
S2 -13.586
(-3.202)***
Dt -8.420
(-3.449)*** 16.917 -4.118
C3 3.683
(1.635)* 20.600 -0.435
C4 8.672
(3.703)*** 25.589 4.554
Constant 21.035
R2 0.287
R2 0.293
Source: Compiled and Analyzed from the Basic Data Obtained from Bombay Stock Exchange Official
Directory, Prowess Database (CMIE) and Annual Reports of Companies.
Notes: 1. Figures in Parentheses represent t-values.
2. Significance at 10%, 5% and 1% is indicated by one, two and three asterisks respectively.
Dependent Variable = Ko1 N= 7
The difference in R2 has been observed as 0.118 from 0.251 in the first run equation to
0.133 in the final run equation. This shows that the non-significant variables contribute
only 11.80 percent variation in overall cost of capital (Ko1). The coefficient of multiple
determination is 0.251 in case of fixed effects model and 0.246 in case of random effects
model. The restricted F-ratio between the two coefficients of multiple determination has
been worked out at 0.007, which has been observed as non-significant. This shows that
both the fixed as well as random effects models are equally important for the study.
The coefficients of size (S1 and S2) have been observed as 14.516 and -13.586
respectively. This indicates that size (S1) is positively related whereas size (S2) is
inversely related to overall cost of capital (Ko1). The negative and significant
coefficient of dummy (Dt) variable points out decline in overall cost of capital (Ko1)
of selected companies in this industry after liberalization policies.
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
352
The Fixed Effects Model (FEM) shows the common slope of 16.917 for C1,
C3 and C4 while the intercepts have been observed as 20.600 for C3 and 25.589 for
C4 respectively. In Random Effects Model (REM), the intercept has been observed
as 21.035 around which the intercepts of FEM deviates. In this way, the slope has
been worked out as -4.118 for C1, -0.435 for C3 and 4.554 for C4 respectively. This
shows that C1, C3 and C4 have the cost increasing fixed effect while randomly C1
and C3 reduce overall cost of capital (Ko1) in this industry. This reveals that there is
a fixed increase in overall cost of capital (Ko1) of selected companies in this industry
while there is random decline in overall cost of capital (Ko1) in this industry except
C4 which exhibits increase over the selected study period.
7.2.8 Tea Industry
Table 7.8 shows results of backward step-wise panel data regression analysis
of selected companies in tea industry over the entire period of study covering 27
years. Only one variable such as age has been observed as significant and none of the
selected companies has been observed as significant in the first run equation. Size
measured in terms of net sales (S1) and age have been observed as significant in the
final run equation. 5 out of total selected 10 companies have been observed as
significant in the final run equation. These are Assambrook Ltd. (C2), Hasimara
Industries Ltd. (C4), Dhunseri Tea & Inds. Ltd. (C5), Jay Shree Tea & Inds. Ltd. (C6)
and Tata Tea Ltd. (C8) respectively. The difference in R2 has been observed as 0.016
from 0.145 in the first run equation to 0.129 in the final run equation. This shows that
the non-significant variables contribute only 1.60 percent variation in the overall cost
of capital (Ko1). The Fixed Effects Model (FEM) shows the common slope of 10.291
for C2, C4, C5, C6 and C8 while the intercepts have been observed as 27.792 for C2,
44.354 for C4, 54.027 for C5, 23.990 for C6 and -9.453 for C8 respectively. In
Random Effects Model (REM), the intercept has been observed as 30.200 around
which the intercepts of FEM deviates. In this way, the slope has been worked out as -
19.909 for C1, -2.408 for C2, 14.154 for C4, 23.827 for C5, -6.210 for C6 and -9.453
for C8 respectively. It has been observed that there is fixed increase but random
decline in overall cost of capital (Ko1) of selected companies in this industry except
C4 and C5 in which random increase has been observed over the study period.
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
353
Table 7.8
Results of Regression Analysis of Tea Industry from 1979-80 to 2005-06
Variable β FEM REM
Intercept Slope
First Model
(Constant) 40.544
S1 3.760
(1.086)
S2 -5.703
(-1.348)
AGE -0.573
(-1.407)*
ROG -0.121
(-0.145)
Dt 3.287
(0.786) 40.544 -9.819
C2 14.525
(1.095) 55.069 4.707
C3 -5.786
(-1.086) 34.758 -15.605
C4 30.076
(1.169) 70.620 20.258
C5 36.228
(1.151) 76.772 26.410
C6 13.379
(1.008) 53.923 3.561
C7 -5.114
(-1.299) 35.430 -14.933
C8 12.976
(1.923)** 53.520 3.158
C9 0.229
(0.067) 40.773 -9.590
C10 1.672
(0.465) 42.216 -8.147
Constant 50.363
R
2 0.128
Contd….
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
354
Variable β FEM REM
Intercept Slope
R2 0.145
Final Model
(Constant) 10.291
S1 3.145
(1.662)*
AGE -0.590
(-4.868)*** 10.291 -19.909
C2 17.501
(3.857)*** 27.792 -2.408
C4 34.063
(4.310)*** 44.354 14.154
C5 43.736
(4.618)*** 54.027 23.827
C6 13.699
(3.01)*** 23.990 -6.210
C8 10.456
(3.024)*** 20.747 -9.453
Constant 30.200
R2 0.119
R2 0.129
Source: Compiled and Analyzed from the Basic Data Obtained from Bombay Stock Exchange Official
Directory, Prowess Database (CMIE) and Annual Reports of Companies.
Notes: 1. Figures in Parentheses represent t-values.
2. Significance at 10%, 5% and 1% is indicated by one, two and three asterisks respectively.
Dependent Variable = Ko1 N= 10
7.2.9 Backward Step-wise Panel data Regression Analysis of Selected Industries
(Ko1 as Dependent Variable)
Table 7.9 shows the results of backward step-wise panel data regression
analysis of selected industries (power, metal, cement, textiles, paper, general
engineering, sugar and tea) over the study period. In the first run equation, only two
variables such as size measured in terms of net sales (S1) and size measured in terms
of total assets (S2) have been observed as significant. None of the industries have
been observed as significant in the first run equation. The same variables have been
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
355
observed as significant in the final run equation. Three industries namely metal, paper
and tea turn out as significant in the final run equation. The difference in R2 has been
observed as 0.002 from 0.013 in the first run equation to 0.011 in the final run
equation. This shows that the non-significant variables contribute only 0.20 percent
variation in the overall cost of capital (Ko1). The coefficient of multiple determination
is 0.011 in case of fixed effects model and 0.010 in case of random effects model.
This shows that either the explanatory variables included in the equation or the
overall cost of capital (Ko1) or both register unexpected ups and downs during the
period under study. The restricted F-ratio between the two coefficients of multiple
determination has been worked out as non-significant. This shows that both the fixed
as well as random effects models are equally important for the study.
The coefficient of size (S1 and S2) has been observed as 3.948 and -6.249
respectively. This indicates that size measured in terms of net sales (S1) is positively
related whereas size measured in terms of total assets (S2) is inversely related to
overall cost of capital (Ko1). The regression coefficient of dummy (Dt) variable has
been observed as positive and insignificant in the first run equation. It points out that
after liberalization, no change has been observed at overall level while in most of
industries as explained earlier it has been observed as significant over the study
period.
The Fixed Effects Model (FEM) shows the common slope of 34.420 for
power, metal, paper and tea industries while the intercepts have been observed as
39.012 for metal, 30.480 for paper and 30.826 for tea industries respectively. In
Random Effects Model (REM), the intercept has been observed as 33.685 around
which the intercepts of FEM deviates. In this way, the slope has been worked out as
0.736 for power, 5.328 for metal, -3.205 for paper and -2.859 for tea industries
respectively. This shows that power, metal, paper and tea industries have the pos itive
fixed while at random, power and metal industries have the positive effect which is
neutralized by paper and tea industries respectively. This reveals that there is fixed as
well as random increase in overall cost of capital (Ko1) of selected companies in selected
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
356
Table 7.9
Results of Regression Analysis of Selected Industries from 1979-80 to 2005-06
Contd…
Variable β FEM REM
Intercept Slope
First Model
(Constant) 28.787
S1 3.776
(1.930)**
S2 -6.130
(-2.937)***
AGE -0.004
(-0.151)
ROG 0.454
(1.348)
Dt 1.114
(0.713) 28.787 0.217
Metal 4.991
(1.365) 33.778 5.208
Cement 1.797
(0.487) 30.584 2.014
Textiles 1.663
(0.540) 30.450 1.880
Paper -3.570
(-1.026) 25.217 -3.353
General Engineering -0.443
(-0.139) 28.344 -0.226
Sugar -2.928
(-0.775) 25.859 -2.711
Tea -3.243
(-0.892) 25.544 -3.026
Constant 28.570
R
2 0.011
R2 0.013
Final Model
(Constant) 34.420
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
357
Source: Compiled and Analyzed from the Basic Data Obtained from Bombay Stock Exchange Official
Directory, Prowess Database (CMIE) and Annual Reports of Companies.
Notes: 1. Figures in Parentheses represent t-values.
2. Significance at 10%, 5% and 1% is indicated by one, two and three asterisks respectively.
Dependent Variable = Ko1 N= 8
industries except paper and tea where random decline has been observed over the selected
study period. This may be attributed to the non-significant interaction of dummy (Dt)
variable with size, age and ROGS in most of the industries. This finding creates the need to
identify those companies/ industries which cannot depict any definite trend in the overall
cost of capital (Ko1).
Overall it can be said that the behavior of size, age, ROGS, dummy (Dt) variable
and companies in different industries is unpredictable. Size (S1 and S2) and dummy (Dt)
variable exert significant effect on overall cost of capital (Ko1) but age and ROGS cannot
bring significant results. The coefficient of multiple determination is generally low in the
present equation of panel data. Moreover, the effect of companies is greater than that of
variables. The overall cost of capital (Ko1) has been observed as independent of age and
return on Government securities (ROGS) over the study period.
Variable β FEM REM
Intercept Slope
S1 3.948
(2.115)**
S2 -6.249
(-3.322)*** 34.420 0.736
Metal 4.592
(1.982)** 39.012 5.328
Paper -3.940
(-2.061)** 30.480 -3.205
Tea -3.594
(-1.652)* 30.826 -2.859
Constant 33.685
R2 0.010
R2 0.011
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
358
7.3 Backward Step-wise Panel Data Regression Analysis of Selected Companies
in Selected Industries (Ko2 as Dependent Variable)
Tables 7.10 to 7.17 represent results of backward step-wise panel data regression
analysis of selected companies in selected industries such as power, metal, cement,
textiles, paper, general engineering, sugar and tea over the entire study period covering
27 years. Table 7.18 shows the results of panel data regression analysis taking into
account all selected industries in one regression equation over the study period. The
overall cost of capital (Ko2) is taken as dependent variable and is regressed against
selected explanatory variables such as size (S1 and S2), age and return on Government
securities (ROGS) to derive meaningful results.
7.3.1 Power Industry
Table 7.10 shows results of backward step-wise panel data regression analysis of
selected companies in power industry over the entire study period covering 27 years.
None of the selected explanatory variables has been observed as significantly related to
overall cost of capital (Ko2) in the first run equation,. None of the selected companies
have been observed as significant in the first run equation. The regression coefficient of
dummy (Dt) variable appears with negative and significant impact upon overall cost of
capital (Ko2) in both first run and final run equations. The negative and significant
coefficient of dummy (Dt) variable indicates decline in overall cost of capital (Ko2) during
post-liberalization period as compared to pre-liberalization period. Only one variable
such as. age has been observed as significant in the final run equation. Three companies
namely Tata Power Co. Ltd. (C3), Torrent Power A E C Ltd. (C4) and Torrent Power S E
C Ltd. (C5) respectively have been observed as significant in the final run equation. The
difference in R2 has been observed as only 0.010 from 0.270 in the first run equation to
0.260 in the final run equation. This shows that the non-significant variables contribute
only 1 percent variation in the overall cost of capital (Ko2). The coefficient of multiple
determination is 0.260 in case of fixed effects model and 0.253 in case of random effects
model. The restricted F-ratio between the two coefficients of multiple determination has
been worked out at 0.009, which has been observed as non-significant. This shows that
both the fixed as well as random effects models are equally important for the study.
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
359
Table 7.10
Results of Regression Analysis of Power Industry from 1979-80 to 2005-06
Variable β FEM REM
Intercept Slope
First Model
(Constant) 22.904
S1 7.396
(0.566)
S2 -6.795
(-0.805)
AGE 0.197
(1.049)
ROG -0.523
(-0.554)
Dt -11.835
(-2.277)** 22.904 4.677
C2 1.209
(0.134) 24.113 5.886
C3 -8.718
(-0.801) 14.186 -4.041
C4 -7.513
(-0.620) 15.391 -2.836
C5 -8.364
(-0.751) 14.540 -3.687
Constant 18.227
R2 0.264
R2 0.270
Final Model
(Constant) 18.431
AGE 0.260
(4.127)***
Dt -12.937
(-5.313)*** 18.431 7.492
C3 -11.401
(-3.229)*** 7.030 -3.909
C4 -10.279
(-2.727)*** 8.152 -2.787
C5 -8.287
(-2.356)** 10.144 -0.795
Constant 10.939
R2 0.253
R2 0.260 Source: Compiled and Analyzed from the Basic Data Obtained from Bombay Stock Exchange Official Directory,
Prowess Database (CMIE) and Annual Reports of Companies.
Notes: 1. Figures in Parentheses represent t-values.
2. Significance at 10%, 5% and 1% is indicated by one, two and three asterisks respectively.
Dependent Variable = Ko2 N= 5
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
360
The coefficient of age has been observed as 0.260. The positive coefficient of age
indicates that as the age of selected companies in this industry increases, its overall cost
of capital (Ko2) also increases. The significantly negative coefficient of dummy (Dt)
variable indicates decline in overall cost of capital (Ko2) of selected companies in this
industry after liberalization policies.
The Fixed Effects Model (FEM) shows the common slope of 18.431 for C1, C3,
C4 and C5 while the intercepts have been observed as 7.03 for C3, 8.152 for C4 and
10.144 for C5 respectively. In Random Effects Model (REM), the intercept has been
observed as 10.939 around which the intercepts of FEM deviates. In this way, the slope
has been worked out as 7.492 for C1, which is neutralized by C3 (-3.909), C4 (-2.787)
and C5 (-0.795) respectively. This reveals that there is fixed increase but random decline
in overall cost of capital (Ko2) of selected companies in this industry over the study
period.
7.3.2 Metal Industry
Table 7.11 shows results of backward step-wise panel data regression analysis of
selected companies in metal industry over the entire period of study covering 27 years. In
the first run equation, only two variables have been observed as significantly related to
overall cost of capital (Ko2) whereas the remaining variables are not statistically
significant in having relationship with the overall cost of capital (Ko2). Size measured in
terms of net sales (S1) and size measured in terms of total assets (S2) have been observed
as significant determinants of overall cost of capital (Ko2) in the first run equation. The
Graham Firth Steel Products (India) Ltd. (C6) is the only company that turns out as
significant in both first run and final run equations. None of the selected variables has
been observed as significant in the final run equation. The R2 has declined from 0.108 in
the first run equation to 0.051 in the final run equation. The coefficient of multiple
determination is 0.051 in case of fixed effects model and 0.046 in case of random effects
model. This indicates that the explanatory power of the model is very weak. The Fixed
Effects Model (FEM) shows the common slope of 19.003 for C1and C6 while the
intercept has been observed as 37.240 for C6. In Random Effects Model (REM), the
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
361
Table 7.11
Results of Regression Analysis of Metal Industry from 1979-80 to 2005-06
Variable FEM REM
Intercept Slope
First Model
(Constant) -38.182
S1 19.871
(2.092)**
S2 -14.068
(-1.467)*
AGE 0.906
(0.822)
ROG -0.328
(-0.147)
Dt -11.693
(1.064) -38.182 1.105
C2 12.110
(1.338) -26.072 13.215
C3 -5.463
(-0.474) -43.645 -4.358
C4 -1.828
(-0.165) -40.010 -0.723
C5 -18.137
(-0.517) -56.319 -17.032
C6 19.109
(1.553)* -19.073 20.214
C7 3.055
(0.235) -35.127 4.160
C8 -17.684
(-0.910) -55.866 -16.579
Constant -39.287
R2 0.099
R2 0.108
Final Model
(Constant) 19.003 19.003 -9.119
C6 18.237
(3.062)*** 37.240 9.119
Constant 28.122
R2 0.046
R2 0.051 Source: Compiled and Analyzed from the Basic Data Obtained from Bombay Stock Exchange Official
Directory, Prowess Database (CMIE) and Annual Reports of Companies.
Notes: 1. Figures in Parentheses represent t-values.
2. Significance at 10%, 5% and 1% is indicated by one, two and three asterisks respectively.
Dependent Variable = Ko2 N= 8
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
362
intercept has been observed as 28.122. The slope has been worked out 9.119 for C1,
which is neutralized by C6. This shows that in metal industry C1 and C6 has the positive
fixed effect on overall cost of capital (Ko2). Similarly, random effect of C1 is positive,
which is neutralized by C6. This reveals that there is fixed increase in overall cost of
capital (Ko2) of selected companies in this industry.
7.3.3 Cement Industry
Table 7.12 shows results of backward step-wise panel data regression analysis
of selected companies in cement industry over the entire period of study covering 27
years. In the first run equation, only one variable such as size measured in terms of
total assets (S2) is significantly related to overall cost of capital (Ko2) whereas the
remaining variables are not statistically significant in having relationship with the
overall cost of capital (Ko2). None of the selected companies has been observed as
significant in the first run equation. Size (S1 and S2) has been observed as significant
variables in the final run equation. The regression coefficient of dummy (D t) variable
appears with positive and significant impact upon overall cost of capital (Ko2) in both
first run and final run equations. The positive coefficient of dummy (Dt) variable
indicates increase in overall cost of capital (Ko2) during post-liberalization period as
compared to pre-liberalization period. It is important to note that three companies
namely Chettinad Cement Corpn. Ltd. (C2), Madras Cements Ltd. (C5) and
Mangalam Cement Ltd. (C6) respectively turn out as significant in the final run
equation. The difference in R2 has been observed as only 0.012 from 0.245 in the first
run equation to 0.233 in the final run equation. This shows that the non-significant
variables such as age, ROGS and remaining companies contribute only 1.2 percent
variation in overall cost of capital (Ko2). The coefficient of multiple determination is
0.233 in case of fixed effects model and 0.224 in case of random effects model. The
restricted F-ratio between the two coefficients of multiple determination has been
observed as non-significant. This shows that both the fixed as well as random effects
models are equally important for the study.
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
363
Table 7.12
Results of Regression Analysis of Cement Industry from 1979-80 to 2005-06
Variable β FEM REM
Intercept Slope
First Model
(Constant) 57.941
S1 10.817
(1.469)
S2 -17.504
(-2.901)***
AGE -0.018
(-0.031)
ROG 0.351
(0.346)
Dt 3.972
(0.767) 57.941 1.287
C2 -6.316
(-0.473) 51.625 -5.029
C3 0.944
(0.113) 58.885 2.231
C4 -3.740
(-0.605) 54.201 -2.453
C5 8.567
(0.769) 66.508 9.854
C6 -9.448
(-0.461) 48.493 -8.161
C7 0.982
(0.165) 58.923 2.269
Constant 56.654
R
2 0.237
R2 0.245
Final Model
(Constant) 71.893
S1 11.380
(2.073)**
S2 -19.865
(-3.962)***
Contd…
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
364
Variable β FEM REM
Intercept Slope
Dt 4.747
(1.846)** 71.893 1.678
C2 -6.303
(-2.066)** 65.590 -4.626
C5 9.104
(3.239)*** 80.997 10.782
C6 -9.511
(-3.025)*** 62.382 -7.834
Constant 70.216
R2 0.224
R2 0.233
Source: Compiled and Analyzed from the Basic Data Obtained from Bombay Stock Exchange Official Directory,
Prowess Database (CMIE) and Annual Reports of Companies.
Notes: 1. Figures in Parentheses represent t-values.
2. Significance at 10%, 5% and 1% is indicated by one, two and three asterisks respectively.
Dependent Variable = Ko1 N= 7
The coefficients of size (S1 and S2) have been observed as 11.380 and -19.865
respectively. This indicates that size (S1) is positively related whereas size measured (S2)
is negatively related to overall cost of capital (Ko2). The significantly positive coefficient
of dummy (Dt) variable points out increase in overall cost of capital (Ko2) of selected
companies in this industry due to liberalization policies.
The Fixed Effects Model (FEM) shows the common slope of 71.893 for C1, C2,
C5 and C6 respectively while the intercepts have been observed as 65.59 for C2, 80.997
for C5 and 62.382 for C6 respectively. In Random Effects Model (REM), the intercept
has been observed as 70.216 around which the intercepts of FEM deviates. In this way,
the slope has been worked out as 1.678 for C1, -4.626 for C2, 10.782 for C5 and -7.834
for C6 respectively. Similarly, random effect of these companies is also positive, except
C2 and C6, where the random effect is negative on overall cost of capital (Ko2),
neutralizing the positive random effect. This reveals that there is a fixed increase but
random decline in overall cost of capital (Ko2) of selected companies in this industry
except C1 and C5 in which random increase has been observed over the selected study
period.
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
365
7.3.4 Textiles Industry
Table 7.13 shows results of backward step-wise panel data regression analysis
of selected companies in textiles industry over the entire period of study covering 27
years. None of the selected variables is significantly related to overall cost of capital
(Ko2) whereas only one company named Ruby Mills Ltd. (C24) has been observed as
significant in the first run equation. In the final run equation, only one variable such
as return on Government securities (ROGS) is significantly related to overall cost of
capital (Ko2) whereas the remaining variables are not statistically significant in having
relationship with the overall cost of capital (Ko2). Two companies namely Century
Enka Ltd. (C7) and Ruby Mills Ltd. (C24) have been observed as significant in the
final run equation. The model has very week statistical power as the R2 has been
observed as very low in this industry, indicating no definite trend in overall cost of
capital (Ko2) and other explanatory variables between different companies of textile
industry. Thus the model cannot explain significantly the variation in overall cost of
capital (Ko2) in textile industry. However, the difference in R2 has been observed as
only 0.011 from 0.066 in the first run equation to 0.055 in the final run equation. This
shows that the significant variables as well as companies i.e. ROGS, C1 and C24
explain only 5.50 percent variation whereas the non-significant variables contribute as
low as 1.10 percent variation in the overall cost of capital (Ko2). The coefficient of
multiple determination is 0.055 in case of fixed effects model and 0.049 in case of
random effects model. The restricted F-ratio between the two coefficients of multiple
determination has been worked out as non-significant. This shows that both the fixed
as well as random effects models are equally important for the study.
The coefficient of return on Government securities (ROGS) has been observed
as 1.939. This indicates that ROGS is positively related to overall cost of capital
(Ko2). No other variable depicts significant relationship with overall cost of capital
(Ko2) in textile industry. Even, the non significant coefficient of dummy (D t) variable
points out no change in overall cost of capital (Ko2) due to it. This shows that
liberalization cannot exert any significant effect on overall cost of capital (Ko2) of
selected companies in this industry.
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
366
Table 7.13
Results of Regression Analysis of Textiles Industry from 1979-80 to 2005-06
Variable
FEM REM
Intercept Slope
First Model
(Constant) -15.002
S1
0.666
(0.110)
S2
-1.181
(-0.103)
AGE
0.042
(0.061)
ROG
2.155
(1.386)*
Dt
5.846
(0.696) -15.002 -8.819
C2
3.293
(0.141) -11.709 -5.526
C3
8.671
(0.279) -6.331 -0.148
C4
6.447
(0.2010 -8.555 -2.372
C5
2.199
(0.113) -12.803 -6.620
C6
-1.983
(-0.0500 -16.985 -10.802
C7
27.889
(1.017) 12.887 19.070
C8
8.375
(0.303) -6.627 -0.444
C9
16.205
(0.481) 1.203 7.386
C10
5.172
(0.213) -9.830 -3.647
C11
2.775
(0.140) -12.227 -6.044
C12
2.994
(0.074) -12.008 -5.825
C13
4.717
(0.236) -10.285 -4.102
C14
2.827
(0.162) -12.175 -5.992
C15
18.137
(0.642) 3.135 9.318 Contd…
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
367
Variable
FEM REM
Intercept Slope
C16
4.481 (0.189) -10.521 -4.338
C17
9.925 (0.297) -5.077 1.106
C18
4.072 (0.150) -10.930 -4.747
C19
0.723 (0.015) -14.279 -8.096
C20
6.008 (0.332) -8.994 -2.811
C22
4.598 (0.296) -10.404 -4.221
C23
12.664 (0.396) -2.338 3.845
C24
62.823 (2.704)*** 47.821 54.004
C25
7.740 (0.332) -7.262 -1.079
C26
8.095 (0.230) -6.907 -0.724
C27
1.246 90.049) -13.756 -7.573
C28
2.322 (0.071) -12.680 -6.497
C29
9.908 (0.355) -5.094 1.089
Constant -6.183
R
2 0.061
R2 0.066
Final Model
(Constant) -2.942
ROG
1.939 (1.966)** -2.942 -25.971
C7
20.194 (1.918)** 17.252 -5.777
C24
57.719 (5.483)*** 54.777 31.748
Constant 23.029
R2 0.049
R2 0.055
Source: Compiled and Analyzed from the Basic Data Obtained from Bombay Stock Exchange Official
Directory, Prowess Database (CMIE) and Annual Reports of Companies.
Notes: 1.Figures in Parentheses represent t-values.
2. Significance at 10%, 5% and 1% is indicated by one, two and three asterisks respectively.
Dependent Variable = Ko2 N= 29
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
368
The Fixed effects model (FEM) shows the common slope of -2.942 for C1, C7,
and C24 respectively while the intercepts have been observed as 17.252 for C7 and
54.777 for C24 respectively. In Random effects model (REM), the intercept has been
observed as 23.029 around which the intercepts of FEM deviates. In this way, the slope
has been worked out as -25.971 for C1 and -5.777 for C7, which is neutralized by
positive coefficient of C24. This shows that there is a fixed increase in overall cost of
capital (Ko2) in case of selected companies in this industry.
7.3.5 Paper Industry
Table 7.14 shows results of backward step-wise panel data regression analysis of
selected companies in paper industry over the entire period of study covering 27 years.
Only one variable such as size measured in terms of total assets (S2) has been observed as
significant in the first run equation. The Star Paper Mills Ltd. (C12) is the only company
that turns out as significant in both first run and final run equations. None of the variables
has been observed as significant in the final run equation. The regression coefficient of
dummy (Dt) variable appears with negative and significant impact upon overall cost of
capital (Ko2) in both first run and final run equations. The negative coefficient of dummy
(Dt) variable indicates decline in overall cost of capital (Ko2) of selected companies in
this industry during post-liberalization period as compared to pre-liberalization period.
The model has very week statistical power as the R2 is found as very low in this industry,
indicating no definite trend in overall cost of capital (Ko2) and other explanatory variables
between different companies of this industry. Thus the model cannot explain significantly
the variation in overall cost of capital (Ko2) of selected companies in this industry. The
difference in R2 has been observed as 0.017 from 0.063 in the first run equation to 0.046
in the final run equation. This shows that the non-significant variables contribute only
1.70 percent variation in overall cost of capital (Ko2). The coefficient of multiple
determination is 0.046 in case of fixed effects model and 0.041 in case of random effects
model. The restricted F-ratio between the two coefficients of multiple determination has
been worked out as non-significant. This shows that both the fixed as well as random
effects models are equally important for the study.
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
369
Table 7.14
Results of Regression Analysis of Paper Industry from 1979-80 to 2005-06
Variable FEM REM
Intercept Slope
First Model
(Constant) -3.922
S1 -22.171
(-1.361)
S2 40.332
(1.717)**
AGE -2.115
(-0.866)
ROG -1.268
(-0.269)
Dt -8.533
(-0.356)
-3.922 -14.587
C2 -31.188
(-0.907)
-35.110 -45.775
C3 0.458
(0.019)
-3.464 -14.129
C4 14.108
(0.295)
10.186 -0.479
C5 24.953
(0.743)
21.031 10.366
C6 21.906
(0.486)
17.984 7.319
C7 34.735
(1.124)
30.813 20.148
C8 -6.409
(-0.265)
-10.331 -20.996
C9 -6.514
(-0.273)
-10.436 -21.101
C10 -32.750
(-1.007)
-36.672 -47.337
C11 46.755
(0.708)
42.833 32.168
Contd…
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
370
Variable FEM REM
Intercept Slope
C12 106.473
(1.466)*
102.551 91.886
C13 17.102
(0.526)
13.180 2.515
R2 0.063
Constant 10.665
R2 0.058
Final Model
(Constant) 28.293
(3.431)***
Dt -15.587
(-1.630)*
28.293 -26.568
C12 53.136
(3.325)***
81.429 26.568
Constant 54.861
R2 0.041
R2 0.046
Source: Compiled and Analyzed from the Basic Data Obtained from Bombay Stock Exchange Official
Directory, Prowess Database (CMIE) and Annual Reports of Companies.
Notes: 1. Figures in Parentheses represent t-values.
2. Significance at 10%, 5% and 1% is indicated by one, two and three asterisks respectively.
Dependent Variable = Ko2 N= 13
The coefficient of size (S2) has been observed as 40.332 in the first run equation.
The positive coefficient of size (S2) indicates that there is direct relationship of overall
cost of capital (Ko2) and size of the selected companies in this industry. It means that as
the size of a company increases, its overall cost of capital (Ko2) also increases. These
results are more consistent with M-M view. On the other hand, the significantly negative
coefficient of dummy (Dt) variable points out decline in overall cost of capital (Ko2) due
to it in the final run equation. This shows that after liberalization policies, overall cost of
capital (Ko2) of selected companies has declined significantly in this industry.
The Fixed Effects Model (FEM) shows the common slope of 28.293 for C1 and
C12 while the intercept for C12 has been observed as 81.429. In Random Effects Model
(REM), the intercept has been observed as 54.861. The slope has been worked out as -
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
371
26.568 for C1, which is neutralized by positive coefficient of an equal magnitude of C12.
This shows that C1 and C12 have the positive fixed effect on overall cost of capital (Ko2)
in this industry. This reveals that there is a fixed increase in overall cost of capital (Ko2)
of selected companies in this industry.
7.3.6 General Engineering Industry
Table 7.15 shows results of backward step-wise panel data regression analysis of
selected companies in general engineering industry over the entire period of study
covering 27 years. Three out of total selected explanatory variables have been observed
as significant in both first run and final run equations. These variables are size measured
in terms of total assets (S2), return on Government securities (ROGS) and age
respectively. 17 out of 21 selected companies have been observed as significant in the
first run equation. The significant companies are termed as C2, C3, C4, C5, C6, C7, C8,
C9, C10, C11, C12, C13, C16, C17, C18, C19 and C21 respectively. But in the final run
equation, 1 more company termed as C14 in addition to above companies turns out as
significant. The regression coefficient of dummy (Dt) variable appears with negative and
significant impact upon overall cost of capital (Ko2) in both first run and final run
equations. The negative coefficient of dummy (Dt) variable indicates decline in overall
cost of capital (Ko2) of selected companies in this industry during post-liberalization
period as compared to pre-liberalization period. The difference in R2 has been observed
as only 0.003 from 0.136 in the first run equation to 0.133 in the final run equation. This
shows that the non significant variables contribute negligible variation in overall cost of
capital (Ko2). The coefficient of multiple determination is 0.133 in case of fixed effects
model and 0.127 in case of random effects model. The restricted F-ratio between the two
coefficients of multiple determination has been observed as non-significant. This shows
that both the fixed as well as random effects models are equally important for the study.
The coefficient of size (S2) has been observed as -3.171 whereas age and return
on Government securities (ROGS) have been observed as 0.627 and 0.918 respectively.
This indicates that size measured in terms of total assets (S2) is inversely related while
age and ROGS cause increase in overall cost of capital (Ko2) of selected companies in
this industry. The significantly negative coefficient of dummy (Dt) variable points out
decline in overall cost of capital (Ko2) of selected companies in this industry after
liberalization policies.
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
372
Table 7.15
Results of Regression Analysis of General Engineering Industry from 1979-80 to
2005-06
Variable FEM REM
Intercept Slope
First Model
(Constant) -4.357
S1 3.344
(1.267)
S2 -6.031
(-1.833)**
AGE 0.605
(2.739)***
ROG 0.841
(2.058)**
Dt -6.630
(-3.225)*** -4.357 -8.092
C2 15.445
(2.801)*** 11.088 7.353
C3 11.692
(2.487)** 7.335 3.600
C4 8.474
(2.217)** 4.117 0.382
C5 6.050
(2.375)** 1.693 -2.042
C6 9.890
(2.807)*** 5.533 1.798
C7 11.109
(2.758)*** 6.752 3.017
C8 -4.959
(-1.717)** -9.316 -13.051
C9 -11.018
(-1.51)* -15.375 -19.110
C10 18.419
(3.143)*** 14.062 10.327
Contd…
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
373
Variable FEM REM
Intercept Slope
C11 11.446
(2.828)*** 7.089 3.354
C12 9.196
(3.446)*** 4.839 1.104
C13 16.295
(2.594)** 11.938 8.203
C14 3.172
(1.286) -1.185 -4.920
C15 0.992
(0.316) -3.365 -7.100
C16 19.016
(3.527)*** 14.659 10.924
C19 7.622
(1.498)* 3.265 -0.470
C20 1.191
(0.307) -3.166 -6.901
Constant 3.735
R2 0.131
R2 0.136
Final Model
(Constant) -2.859
S2 -3.171
(-2.065)**
AGE 0.627
(3.656)***
ROG 0.918
(2.482)**
Dt -6.800
(-3.499)*** -2.859 -8.883
C2 15.366
(2.943)*** 12.507 6.483
C3 10.959
(2.514)** 8.100 2.076
C4 8.440
(2.368)** 5.581 -0.443
C5 5.852
(2.832)*** 2.993 -3.031
Contd…
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
374
Variable FEM REM
Intercept Slope
C6 10.548
(3.222)*** 7.689 1.665
C7 11.167
(2.987)*** 8.308 2.284
C8 -5.007
(-2.506)** -7.866 -13.890
C9 -11.694
(-2.604)** -14.553 -20.577
C10 18.754
(3.487)*** 15.895 9.871
C11 11.781
(3.071)*** 8.922 2.898
C12 8.867
(3.428)*** 6.008 -0.016
C13 15.781
(2.729)*** 12.922 6.898
C14 3.527
(1.614)* 0.668 -5.356
C16 19.957
(3.959)*** 17.098 11.074
C17 19.536
(3.140)*** 16.677 10.653
C18 6.209
(2.509)** 3.350 -2.674
C19 7.751
(1.609)* 4.892 -1.132
C21 10.985
(3.936)*** 8.126 2.102
Constant 6.024
R2 0.127
R2 0.133
Source: Compiled and Analyzed from the Basic Data Obtained from Bombay Stock Exchange Official
Directory, Prowess Database (CMIE) and Annual Reports of Companies.
Notes: 1. Figures in Parentheses represent t-values.
2. Significance at 10%, 5% and 1% is indicated by one, two and three asterisks respectively.
Dependent Variable = Ko2 N= 21
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
375
The Fixed Effects Model (FEM) shows the common slope of -2.859 for the 18
companies having significant coefficients with the positive intercepts for these
companies, except C8 and C9. In Random Effects Model (REM), the intercept has
been observed as 6.024 around which the intercepts of FEM deviates. The slope in
REM has been observed as negative for C1, C4, C5, C8, C9, C12, C14, C18 and C19
while the slope has been observed as positive for C2, C3, C6, C7, C10, C11, C13,
C16, C17 and C21 respectively. This shows that there is fixed increase in overall cost
of capital (Ko2) of selected companies in this industry with exception of C8 and C9
during the study period.
7.3.7 Sugar Industry
Table 7.16 shows results of backward step-wise panel data regression analysis
of selected companies in sugar industry over the entire period of study covering 27
years. In the first run equation, only one variable such as size measured in terms of
total assets (S2) has been observed as significantly related to overall cost of capital
(Ko2), whereas remaining variables are not statistically significant in having
relationship with overall cost of capital (Ko2). The Kothari Sugars & Chemicals Ltd.
(C4) is only company that turns out as significant in the first run equation. The same
variable has been observed as significant in the final run equation. Two companies
namely Kothari Sugars & Chemicals Ltd. (C4) and Sakthi Sugars Ltd. (C6) have been
observed as significant in the final run equation. The difference in R2 has been
observed as 0.047 from 0.180 in the first run equation to 0.133 in the final run
equation. This shows that the non-significant variables contribute only 4.70 percent
variation in the overall cost of capital (Ko2). The coefficient of multiple determination
is 0.133 in case of fixed effects model and 0.126 in case of random effects model. The
restricted F-ratio between the two coefficients of multiple determination has been
observed as non-significant. This shows that both the fixed as well as random effects
models are equally important for the study.
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
376
Table 7.16
Results of Regression Analysis of Sugar Industry from 1979-80 to 2005-06
Variable β FEM REM
Intercept Slope
First Model
(Constant) 20.018
S1 6.355
(1.390)
S2 -10.913
(-2.594)**
AGE 0.319
(0.779)
ROG 0.713
(1.053)
Dt -0.791
(-0.235) 20.018 -1.678
C2 -6.785
(-1.001) 13.233 -8.463
C3 9.182
(0.809) 29.200 7.504
C4 7.490
(1.501)* 27.508 5.812
C5 -10.000 (-1.238)
10.018 -11.678
C6 7.825
(1.256) 27.843 6.147
C7 4.037
(0.464) 24.055 2.359
Constant 21.696
R
2 0.172
R2 0.180
Final Model
(Constant) 32.927
S2 -3.387
(-3.189)*** 32.927 -3.219
C4 5.256
(2.97)*** 38.183 2.037
C6 4.400
(2.456)** 37.327 1.181
Constant 36.146
R2 0.126
R2 0.133
Source: Compiled and Analyzed from the Basic Data Obtained from Bombay Stock Exchange Official
Directory, Prowess Database (CMIE) and Annual Reports of Companies.
Notes: 1.Figures in Parentheses represent t-values.
2. Significance at 10%, 5% and 1% is indicated by one, two and three asterisks respectively.
Dependent Variable = Ko2 N= 7
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
377
The coefficient of size (S2) has been observed as -3.387, which indicates that
size measured in terms of total assets (S2) is inversely related to overall cost of
capital (Ko2). It indicates that large sized companies have lower overall cost of
capital (Ko). The non-significant coefficient of dummy (Dt) variable points out no
change in overall cost of capital (Ko2) of selected companies in this industry after
liberalization policies.
The Fixed Effects Model (FEM) shows the common slope of 32.927 for C1,
C4 and C6 while the intercept has been observed as 38.183 for C4 and 37.327 for C6
respectively. In Random Effects Model (REM), the intercept has been observed as
36.146, while the slope has been worked out as -3.219 for C1, 2.037 for C4 and
1.181 for C6 respectively. This shows that C1, C4 and C6 respectively have the cost
inducing fixed effect in this industry while at random C1 can reduce the overall cost
of capital (Ko2), which is neutralized by C4 and C6. This reveals that there is a fixed
increase in overall cost of capital (Ko2) of selected companies in this industry.
7.3.8 Tea Industry
Table 7.17 shows results of backward step-wise panel data regression analysis
of selected companies in tea industry over the entire period of study covering 27
years. In the first run equation, two variables such as age and return on Government
securities (ROGS) have been observed as significant over the study period. The
companies namely Dhunseri Tea & Inds. Ltd. (C4) and Hasimara Industries Ltd. (C5)
have been observed as significant in the first run equation. It is strange to observe
that no variable as well as company can retain its significance in the final run
equation. The R2 has been observed as 0.054 in the first run equation. This shows
that the explanatory power of the model is very weak. The coefficients of age and
return on Government securities (ROGS) have been observed as 2.17 and 5.52
respectively. It indicates that age and return on Government securities (ROGS) are
positively related to overall cost of capital (Ko2). There is a fixed decline in overall
cost of capital (Ko2) of selected companies in this industry over the study period.
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
378
Table 7.17
Results of Regression Analysis of Tea Industry from 1979-80 to 2005-06
Variable β FEM REM
Intercept Slope
First Model
(Constant) -79.986
S1 0.373
(0.031)
S2 1.494
(0.102)
AGE 2.170
(1.535)*
ROG 5.520
(1.906)**
Dt -26.393
(-1.819)** -79.986 32.469
C2 -61.767
(-1.342) -141.753 -29.298
C3 4.729
(0.256) -75.257 37.198
C4 -130.62
(-1.463)* -210.605 -98.150
C5 -152.99
(-1.402)* -232.978 -120.523
C6 -52.814
(-1.147) -132.800 -20.345
C7 18.964
(1.389) -61.022 51.433
C8 -21.433
(-0.916) -101.419 11.036
C9 0.334
(0.028) -79.652 32.803
C10 -1.715
(-0.137) -81.701 30.754
Constant -112.455
R
2 0.009
R2 0.054
Source: Compiled and Analyzed from the Basic Data Obtained from Bombay Stock Exchange Official
Directory, Prowess Database (CMIE) and Annual Reports of Companies.
Notes: 1. Figures in Parentheses represent t-values.
2. Significance at 10%, 5% and 1% is indicated by one, two and three asterisks respectively.
Dependent Variable = Ko2 N= 10
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
379
7.3.9 Backward Step-wise Panel data Regression Analysis of Selected Industries
(Ko2 as Dependent Variable)
Table 7.18 shows the results of backward step-wise panel data regression analysis
of selected industries (power, metal, cement, textiles, paper, general engineering, sugar
and tea) over the study period. In the first run equation, only one variable such as return
on Government securities (ROGS) is positively related to overall cost of capital (Ko2),
whereas remaining variables are not statistically significant in having relationship with
overall cost of capital (Ko2). None of the selected industries has been observed as
significant in the first run equation. The same variable has been observed as significant in
the final run equation. Two industries namely general engineering and sugar have been
observed as significant in final run equation. The difference in R2 has been observed as
0.002 from 0.007 in the first run equation to 0.005 in the final run equation. This shows
that the model has very thin explanatory power. However, the positive coefficient of
return on Government securities (ROGS) indicates an increase in overall cost of capital
(Ko2) of selected companies in selected industries over the study period. The non-
significant coefficient of dummy (Dt) variable points out no change in overall cost of
capital (Ko2) of selected companies in this industry after liberalization policies. The
coefficient of multiple determination is 0.005 in case of fixed effects model and 0.004 in
case of random effects model. This shows that either the explanatory variables included
in the equation or the cost of capital or both register unexpected ups and downs during
the study period. The restricted F-ratio between the two coefficients of multiple
determination has been worked out as non-significant. This shows that both the fixed as
well as random effects models are equally important for the study.
The Fixed Effects Model (FEM) shows the common slope of 11.164 for power,
general engineering and sugar industries while the intercepts have been observed as 5.670
for general engineering and 5.347 for sugar industries respectively. In Random effects
model (REM), the intercept has been observed as 7.394 while the slope has been worked
out as 3.770 for power, which is neutralized by the negative random coefficients of -
1.724 for general engineering and -2.046 for sugar industries respectively. This shows
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
380
Table 7.18
Results of Regression Analysis of Selected Industries from 1979-80 to 2005-06
Variable FEM REM
Intercept Slope
First Model
(Constant)
S1 1.597 (0.58)
S2 -2.695
(-0.918)
AGE 0.030
(0.754)
ROG 0.688
(1.451)*
Dt -1.413
(-0.643) 19.265 1.632
Metal 1.322
(0.257) 20.587 2.954
Cement -2.097
(-0.404) 17.168 -0.465
Textiles 0.229
(0.053) 19.494 1.861
Paper 0.94
(0.192) 20.205 2.572
General Engineering -5.143
(-1.148) 14.122 -3.511
Sugar -5.858
(-1.103) 13.407 -4.226
Tea -2.448
(-0.479) 16.817 -0.816
Constant 17.633
R
2 0.006
R2 0.007
Final Model
(Constant) 11.164
ROG 0.819
(1.835)** 11.164 3.770
General Engineering -5.494
(-2.551)** 5.67 -1.723
Sugar -5.817
(-1.691)* 5.347 -2.046
Constant 7.393
R
2 0.004
R2 0.005
Source: Compiled and Analyzed from the Basic Data Obtained from Bombay Stock Exchange Official
Directory, Prowess Database (CMIE) and Annual Reports of Companies.
Notes: 1. Figures in Parentheses represent t-values.
2. Significance at 10%, 5% and 1% is indicated by one, two and three asterisks respectively.
Dependent Variable = Ko2 N= 8
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
381
that power, general engineering and sugar industries have the positive fixed while at
random, power industry has positive effect on overall cost of capital (Ko2) which is
neutralized by general engineering and sugar industries. This reveals that there is a fixed
increase in overall cost of capital (Ko2) of selected Indian companies in selected
industries. This finding is attributed to the non-significant interaction of liberalization
with size, age and ROGS in most of the industries. This finding points out the need to
identify those companies/industries which cannot depict any definite trend in overall cost
of capital (Ko2).
Overall it can be said that the behavior of size (S1 and S2), age, ROGS and
liberalization in selected companies in selected industries is unpredictable. Size and
liberalization exert significant effect on overall cost of capital (Ko2) but age and ROGS
cannot bring significant results. The coefficient of multiple determination is generally
low in the present equation of panel data. Moreover, the effect of companies is greater
than that of variables. In general, age of the selected companies and return on
Government securities (ROGS) cannot affect the overall cost of capital (Ko2) in selected
industries. In general, the overall cost of capital (Ko2) has been observed as independent
of size, age, return on Government securities (ROGS) and liberalization in selected
companies in selected industries.
7.4 CONCLUSIONS
Following findings emerge from the regression analysis taking into account
overall cost of capital (Ko1) as dependent variable:
1. The regression results of power industry reveal that all selected independent
variables are significantly associated with overall cost of capital (Ko1) in the first
run equation. Three companies namely Reliance Energy Ltd. (C2), Tata Power
Co. Ltd. (C3) and Torrent Power A E C Ltd. (C4) respectively have been
observed as significant in both first run and final run equations. Size (S1 and S2)
and return on Government securities (ROGS) respectively have been observed as
significant determinants of overall cost of capital (Ko1) in the final run equation.
This shows that there is a fixed as well as random increase in overall cost of
capital (Ko1) of selected companies in this sector over the study period except C1
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
382
and C4 in which random decline has been observed over the selected study
period.
2. In metal industry, the regression results reveal that only one variable such as size
measured in terms of net sales (S1) and only one company i.e. Electrosteel
Castings Ltd. (C2) is significantly related to overall cost of capital (Ko1) in the
first run equation. Size (S1 and S2) and age respectively emerge as significant
determinants of overall cost of capital (Ko1) in the final run equation. Three
companies namely Electrosteel Castings Ltd. (C2), Goetze (India) Ltd. (C5) and
Tinplate Co. Of India Ltd. (C8) respectively turn out as significant in the final run
equation. There is a fixed as well as random increase in overall cost of capital
(Ko1) of selected companies in this industry over the study period.
3. In cement industry, size (S1 and S2) have been observed as significant
determinants of overall cost of capital (Ko1) in the first run equation. The
Mangalam Cement Ltd. (C6) is the only company that turns out as significant in
first run equation. The same variables have been observed as significant in the
final run equation. It is important to note that four companies namely Chettinad
Cement Corpn. Ltd. (C2), India Cements Ltd. (C4), Madras Cements Ltd. (C5)
and Mangalam Cement Ltd. (C6) have been observed as significant in the final
run equation. It has been observed that companies termed as C1, C2, C4, C5 and
C6 have positive fixed effect on overall cost of capital (Ko1) of selected
companies in this industry. Similarly, random effect of these companies is also
positive, except C2 and C6, where the random effect is negative on overall cost of
capital (Ko1), neutralizing the positive random effect, it indicates that there is a
fixed increase in overall cost of capital (Ko1) of selected companies in this
industry over the study period.
4. The regression results of textile industry reveal that only one variable such as
return on Government securities (ROGS) is significantly related to overall cost of
capital (Ko1) in the first run equation. The Ruby Mills Ltd. (C24) is the only
company that turns out as significant in the first run equation. There is no addition
in the number of significant variables in the final run equation. Two companies
namely Century Enka Ltd. (C7) and Ruby Mills Ltd. (C24) turn out as significant
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
383
in the final run equation. There is a fixed increase in overall cost of capital (Ko1)
of selected companies in this industry whereas randomly C1 and C24 are
responsible for decline in overall cost of capital (Ko1), which can be neutralized
by C24 over the study period.
5. In paper industry, size (S1 and S2) is significantly related to overall cost of capital
(Ko1). Six companies namely Aurangabad Paper Mills Ltd. (C2), Balkrishna
Industries Ltd. (C3), Jayant Paper Mills Ltd. (C5), Rohit Pulp & Paper Mills Ltd.
(C7), Star Paper Mills Ltd. (C12) and West Coast Paper Mills Ltd. (C13)
respectively turn out as significant in first run equation. Size (S1 and S2) and age
have been observed as significant determinants of overall cost of capital (Ko1) in
the final run equation. The Ballarpur Industries Ltd. is the only company that
turns out as significant in final run equation. There is a fixed increase in overall
cost of capital (Ko1) of selected companies in this industry over the study period.
6. The regression results of general engineering industry reveal that all selected
variables have been observed as significant in first run equation. Nine companies
termed as C2, C3, C4, C7, C10, C11, C13, C16 and C17 respectively have been
observed as significant in the first run equation. The same variables have been
observed as significant in the final run equation. Eleven companies termed as C2,
C3, C5, C6, C7, C8, C9, C11, C16, C17 and C20 respectively turn out as
significant in the final run equation. There is a fixed as well as random increase in
overall cost of capital (Ko1) of selected companies in this industry with exception
of few of selected companies for this sector over the study period.
7. In sugar industry, the regression results reveal that size (S1 and S2) has been
observed as significant determinants of overall cost of capital (Ko1) in the first run
equation. The Kothari Sugars & Chemicals Ltd. (C4) is the only company that
turns out as significant in first run equation. The same variables have been
observed as significant in the final run equation. Two companies namely
Balrampur Chini Mills Ltd. (C3) and Kothari Sugars & Chemicals Ltd. (C4) have
been observed as significant in the final run equation. There is a fixed increase in
overall cost of capital (Ko1) of selected companies in this industry while there is
random decline in overall cost of capital (Ko1) in this industry except C4 which
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
384
exhibits increase over the study period.
8. In tea industry, only one variable such as age has been observed as significant and
none of the selected companies has been observed as significant in the first run
equation. Size (S1) and age have been observed as significant determinants of
overall cost of capital (Ko1) in the final run equation. 5 companies namely
Assambrook Ltd. (C2), Hasimara Industries Ltd. (C4), Dhunseri Tea & Inds. Ltd.
(C5), Jay Shree Tea & Inds. Ltd. (C6) and Tata Tea Ltd. (C8) respectively turn
out as significant in the final run equation. There is a fixed increase but random
decline in overall cost of capital (Ko1) of selected companies in this industry
except C4 and C5 in which random increase has been observed over the study
period.
9. The panel data regression analysis has been applied taking into account all
selected industries in one regression equation. Size (S1 and S2) have been observed
as significant in the first run equation. None of the selected industries has been
observed as significant in the first run equation. The same variables have been
observed as significant in the final run equation. Two industries namely paper and
tea turn out as significant in the final run equation. There is a fixed as well as
random increase in overall cost of capital (Ko1) of selected companies in selected
industries except paper and tea industries in which random decline has been
observed over the study period.
10. The dummy (Dt) variable appears with negative and significant impact upon
overall cost of capital (Ko1) in case of general engineering and sugar industries.
This variable appears with negative and insignificant impact upon overall cost of
capital (Ko1) in case of power, metal and paper industries. The negative
coefficient of dummy (Dt) variable indicates decline in overall cost of capital
(Ko1) during post-liberalization period as compared to pre-liberalization period.
This variable appears with positive and insignificant impact upon overall cost of
capital (Ko1) in case of cement, textiles, tea and in panel data regression equation
applied for selected industries. The positive coefficient of dummy (Dt) variable
indicates increase in overall cost of capital (Ko1) during post-liberalization period
as compared to pre-liberalization period.
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
385
Following findings emerge from the regression analysis taking into account
overall cost of capital (Ko2) as dependent variable:
1. The regression results of power industry reveal that none of the variables and
selected companies has been significantly related to overall cost of capital (Ko2) in
the first run equation. Only one variable such as age has been observed as
significant in the final run equation. Three companies namely Tata Power Co.
Ltd. (C3), Torrent Power A E C Ltd. (C4) and Torrent Power S E C Ltd. (C5)
respectively have been observed as significant in final run equation. There is a
fixed increase but random decline in overall cost of capital (Ko2) of selected
companies in this industry over the study period.
2. In metal industry, size (S1 and S2) has been observed as significant determinants
of overall cost of capital (Ko2) the first run equation. The Graham Firth Steel
Products (India) Ltd. (C6) is the only company that turns out as significant in both
first run and final run equations. None of the selected variables has been observed
as significant in the final run equation. There is a fixed increase in overall cost of
capital (Ko2) of selected companies in this industry over the study period.
3. In cement industry, the regression results reveal that only one variable such as
size measured in terms of total assets (S2) is significantly related to overall cost of
capital (Ko2) in the first run equation. Size (S1 and S2) has been observed as
significant determinants of overall cost of capital (Ko2) in the final run equation.
Three companies namely Chettinad Cement Corpn. Ltd. (C2), Madras Cements
Ltd. (C5) and Mangalam Cement Ltd. (C6) respectively turn out as significant in
the final run equation. There is a fixed increase but random decline in overall cost
of capital (Ko2) of selected companies in this industry except C5 in which random
increase has been observed over the study period.
4. In textile industry, none of the selected variables is significantly related to overall
cost of capital (Ko2) whereas only one company named Ruby Mills Ltd. (C24) has
been observed as significant in the first run equation. Only one variable such as
return on Government securities (ROGS) emerges as significant determinant of
overall cost of capital (Ko2) in the final run equation. Two companies namely
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
386
Century Enka Ltd. (C7) and Ruby Mills Ltd. (C24) have been observed as
significant in the final run equation. There is a fixed increase in overall cost of
capital (Ko2) of selected companies in this industry over the study period.
5. The regression results of paper industry reveal that only one variable such as size
measured in terms of total assets (S2) has been observed as significant in the first
run equation. The Star Paper Mills Ltd. (C12) is the only company that turns out
as significant in both first run and final run equations. None of the variables has
been observed as significant in the final run equation. There is a fixed increase in
overall cost of capital (Ko2) of selected companies in this industry.
6. In general engineering industry, three variables such as size measured in terms of
total assets (S2), return on Government securities (ROGS) and age respectively
have been observed as significant in both first run and final run equations. 17 out
of 21 selected companies termed as C2, C3, C4, C5, C6, C7, C8, C9, C10, C11,
C12, C13, C16, C17, C18, C19 and C20 respectively turn out significant in the
first run equation. Two more companies termed as C14 and C20 in addition to
above significant companies turn out significant in the final run equation. There is
a fixed increase in overall cost of capital (Ko2) of selected companies in this
industry with exception of two companies i.e. C8 and C9 over the study period.
7. The regression results of sugar industry reveal that only one variable such as size
measured in terms of total assets (S2) has been significantly related to overall cost
of capital (Ko2) in the first run equation. The Kothari Sugars & Chemicals Ltd.
(C4) is only company that turns out as significant in first run equation. The same
variable has been observed as significant in the final run equation. Two
companies namely Kothari Sugars & Chemicals Ltd. (C4) and Sakthi Sugars Ltd.
(C6) have been observed as significant in the final run equation. There is a fixed
increase but random decline in overall cost of capital (Ko2) of selected companies
in this industry with exception of C4 and C5 in which random increase has been
observed over the study period.
8. In tea industry, two variables such as age and return on Government securities
(ROGS) have been observed as significant in the first run equation. The
companies namely Dhunseri Tea & Inds. Ltd. (C4) and Hasimara Industries Ltd.
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
387
(C5) have been observed as significant in the first run equation. None of the
selected variables has been observed as significant in the final run equation and
only one company named Apeejay Tea Ltd. (C1) has been observed as significant
in the final run equation. There is no fixed as well as random increase in overall
cost of capital (Ko2) of selected companies in this industry over the study period.
9. The panel data regression analysis has been applied taking into account all
selected industries in one regression equation. Only one variable i.e. return on
Government securities (ROGS) is significantly related to overall cost of capital
(Ko2) in the first run equation. None of the selected industries has been observed
as significant in first run equation. The same variable has been observed as
significant in the final run equation. Two industries namely general engineering
and sugar have been observed as significant in final run equation. There is a fixed
increase in overall cost of capital (Ko2) in selected companies in selected
industries during the study period.
10. The dummy (Dt) variable appears with negative and significant impact upon
overall cost of capital (Ko2) in case of power, cement and tea industries. This
variable appears with negative and insignificant impact upon overall cost of
capital (Ko2) in case of paper, general engineering, sugar and in panel data
regression equation applied for selected industries. The negative coefficient of
dummy (Dt) variable indicates decline in overall cost of capital (Ko1) during post-
liberalization period as compared to pre-liberalization period. This variable
appears with positive and insignificant impact upon overall cost of capital (Ko1) in
case of metal, textiles and tea industries. This variable appears with positive and
significant impact upon overall cost of capital (Ko1) in case of cement industry.
This variable appears with positive and insignificant impact upon overall cost of
capital (Ko2) in case of metal and textiles industries. The positive coefficient of
dummy (Dt) variable indicates increase in overall cost of capital (Ko2) during
post-liberalization period as compared to pre-liberalization period.
It has been observed from the above analysis that the regression coefficients of
size (S1 and S2) variables appear with both positive and negative signs. The negative
coefficients of size (S1 and S2) imply that large companies have lower overall cost of
Effect of Size, Age and Return on Government Securities Upon Cost of Capital
388
capital (Ko1 and Ko2) and vice-versa. It is easier to raise either debt or equity in case of
large companies. In other words, small companies have less goodwill resulting in low
market value due to which they face huge problems in raising the required funds in form
of debt or equity. The negative coefficient of age implies that as the age of a company
increases, its overall cost of capital (Ko1 and Ko2) declines over a period of time and vice-
versa. It is due to reason that a well established firm due to its creditworthiness can raise
external finance at reasonable cost leading to lower overall cost of capital (Ko1 and Ko2).
Return on Government Securities (ROGS) has direct impact upon overall cost of capital
(Ko1 and Ko2). All investors can lend and borrow at risk free rate of interest. The cost of
each specific source of finance is composition of risk free rate plus risk premium. The
investor includes the risk free security with their market portfolio in order to reduce their
risk. This has impact upon return expected by investors for holding a particular security
which in turn has impact upon overall cost of capital (Ko1 and Ko2).
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