Capital Structure And Shareholder's Wealth

RESULTS AND DISCUSSION

Now that we have applied all the tools necessary for hypothesis testing, the final results can be discussed in detail. All variables with respect to their relation to the capital structure will be discussed separately. Not only the figures have been interpreted as per the mathematical rules, but they have also been analyzed according to the prevalent conditions in the cement industry during the period of analysis. Therefore, it is necessary to give the industry scenario before going further with the results and discussion.

CEMENT INDUSTRY DURING 2001 TO 2005

Cement industry has shown a mind puzzling performance during 2001 to 2005, if gazed by the eye of finance. However, an analyst must keep the market and economy in mind before making a final opinion about a sector. As far as financing the assets is concerned, cement firms have resorted to use equity financing for the most part. In only one instance of the sample data, debt has increased past the total assets also. One reason for this equity financing is the reluctance of lenders to give debt to these firms.

We also have to consider the Afghanistan's high demand of cement, which boosted the sales of the cement sector in Pakistan, tremendously. Around 50% of the firms have been able to come up with normal non-negative returns. Other

times, firms have also sustained heavy losses. The increase in revenue may be supported as even when the equity has been increasing over the five years, the Return on Equity shows a progressive trend. Given the increase in denominator the firms have surely made huge revenues. Not only had the sales prices increased as per the Law of Demand, but also unit if Sales had increased. Hence, revenues showed a two fold increase.

VARIABLE WISE DISCUSSION OF RESULTS

Capital structure

Capital structure which is my independent variable, only 25% of the sample firms used more equity than debt as funding source. Another 25% used excessively high amounts of debt, which even surpassed the total assets, resulting in a negative equity. 37.5% of the sample firms use normally high debt if not too much. And only 12.5% of the firms use a mix of debt to asset ratio moving from above 50% debt to below 50%.

However, on the average, the industry Capital Structure in terms of debt to asset ratio has been found to decline over the five years of analysis (See the preceding graph). Only one sample firm is prominently at odds with this finding. Another observation is that in general the firms have stared to raise debt in the later period of analysis, mostly 2005. (See Appendix IV)

It is this variable with which all other variables have been sorted out for correlation. No value of the capital structure gives a biased opinion as revaluation has been subtracted before calculations. This is in order to maintain comparability, with other firms which may not have practiced revaluation in a particular year or the whole period of analysis.

Summary of results

n df r r2 t value P value H0

MVA 40 38 -0.0066 0.00004356 -0.041 0.4838795 A

ROE 29 27 -0.0767 0.00588 -0.4 0.346253 A

Share price 40 38 -0.3311 0.1096 -2.163 0.0184475 A

Total Returns 40 38 -0.1558 0.02427 -0.972 0.168528 A

OFA 40 38 -0.3909 0.1528 -2.618 0.0063155 A

NAB 40 38 -0.2170 0.04708 -1.37 0.0893125 A

Tax 36 34 -0.3903 0.1523 -2.472 0.0093025 A

Explanation for Symbols used in the Table of Results

In the above table

n is the number of elements of the sample data, df is the degree of freedom,

r is the coefficient of correlation,

r2 is the coefficient of determination,

t value is the critical value,

P value is the area of rejection of null hypothesis and

A or R is the symbol for acceptance of rejection of the null hypothesis respectively.

DISCUSSION OF RESULTS WITH DEPENDENT VARIABLES

Capital Structure and Market value Addition

Market Value Added is the main variable that represents the shareholder's wealth. My main objective was to study the relation between capital structure and market value added. Other variables will merely verify the relation hence found. I do not need to explain each variable at this point as they have already been discussed in detail in the first chapter.

The correlation coefficient between capital Structure and MVA comes out to be a negative value, i.e. -0.0066. However, this value merely indicates the inverse relation between the two variables. It does not show a strong inverse relation. With such an insignificant correlation it is naive, according to statistical rules, to expect a linear relation and more so head for a regression analysis. However, we may determine the Coefficient of Determination r2, to explain the degree to which the capital structure determines the MVA. The r2 comes out to be 0.0044%, which indicates that only 0.0044% of the MVA is determined by the Capital Structure. The value is clearly insignificant. The idea will be clearer by looking at the scatter diagram below.

The application of t-test reveals the acceptance of my null hypothesis. The value of r lies to right of t value, which is the region of acceptance. The region of rejection lies to the left of t value, as it is less than 50% and t is negative. This is to say that there is no correlation between Capital Structure and Market value added.

The results can be explained on the basis of the basic market rules. In Pakistan, the Stock Market is highly vulnerable to the sentiments of the market players. The variable of stock price in MVA is much more affected by the market forces than by the capital structure. For the most part, the players' tendency to anticipate overrides the impact of capital structure. This is evident form my study as well, with such a low correlation coefficient, more so with the acceptance of H0.

We will later see that the inverse relation of capital structure