Importance and Analysis of Financial Modeling

Table of Contents

Introduction
Statistics in clients survey
Determinants of Chief Executive Officer Salary
Determinants of chief executive salary
Testing hypothesis
Correlation analysis between salary and excess return
Summary of findings
Data analysis
List of References

Introduction

The purpose of this paper is to find out determinants of CEO SALARY. The main findings of the primary the data were interpreted and turned into information by using SPSS and excel quantitative methods software system jointly.

Statistics in clients survey

A total of 300 questionnaires were sent to respondents where convenience sampling was used to reflect the personal opinion of respondents.

Determinants of Chief Executive Officer Salary

The Chief Executive Officer salary including bonuses in £ of the respondents lead us to the next research question to be answered, which concerns the determinants or elements that contributed to the Chief Executive Officer salary. In evaluating the measure of Chief Executive Officer Salary, the paradigm for developing better measures of better salary scale was used. The multiitem measure was examined in terms of dimensionality and internal consistency ( Siciliano, 2003).

Nine items from the survey instrument were factor analyzed using the principal component method and reduced to nine factors with values greater than 1.0 which were retained for subsequent analysis. An in common practice, a VARIMAX rotation was performed to achieve a simpler and theoretically more meaningful factor solution. The resultant factor structure explained 79.912% of total variance. The factor coefficients indicate that all the eight factors were clear, in that each item was captured mainly by one factor alone.

Factor analysis results with varimax rotation of Chief Executive Officer Determinants and all the items show very high factor coefficients under their respective factors. Hence, the six factors are independent of each other, and there is no multi co linearity among the factors. The Cranachs alphas (ç) for the eight factors were 0.927 for factor six, and 0.884 for factor seven these exceed the cutoff point of 0.8 which is generally considered to be the criterion for demonstrating internal consistency for new scales in basic research (OSullivan, Sheffrin and Perez, 2009).

From the items under the each eight factors, the factors can be interpreted as meaningful underlying dimensions. To facilitate the interpretation of the data, it is clear from the factor loadings as highlighted that eight factors emerged. These factors represented different elements of Chief Executive Officer Salary determinants that from the underlying factors from the original scale response item given.

Factor 1 (number of directors in the board) represents determinants relating to Chief Executive Officer Salary providing procedure convenience and efficiency encompassing three subelements (Diamond and Jefferies, 2001).

Determinants of chief executive salary

While factor analysis identified the eight dimensions of chief executive salary determinants levels was not indicated. To fulfill research question regression analysis was carried out to determine the relative importance of these factors. The dependent variable is the salary, while the independent variables are factor scores of the other factors in the scale. A stepwise regression method with significance levels of 0.05 was used. The results are shown in the following table:

Model summary.

Model	R	R square	Adjusted R Square	Std. Error of the estimate
1	0.455	0.207	0.198	0.68375
2	0.561	0.314	0.298	0.63962
3	0.590	0.348	0.324	0.62779

ANOVA.

Model		Sum of squares	Df	Mean square	F	Sig.
1	Regression	10.264	1	10.264	21.954	0.000
	Residual	39.271	298	0.468
	Total	49.535	299
2	Regression	15.578	2	7.789	19.039	0.000
	Residual	33.957	297	0.409
	Total	49.535	299
3	Regression	17.217	3	5.739	14.562	0.000
	Residual	32.318	296	0.394
	Total	49.535	299

Coefficients.

Model		Unstandardized Coefficients		Standardized Coefficients	T	Sig.
1		B	Std. Error	Beta	T	Sig.
	Constant	1.349	0.074		18.294	0.000
	Regr factor score 6 for analysis 1	.364	0.077	0.455	4.685	0.000
2	Constant	1.349	0.069		19.556	0.000
	Regr factor score 6 for analysis 1	.355	0.072	0.449	4.936	0.000
	Regr factor score 2 for analysis 1	.259	0.072	0.328	3.604	0.001
	Constant	1.349	0.068		19.925	0.000
	Regr factor score 6 for analysis 1	0.356	0.071	0.450	5.042	0.000
	Regr factor score 2 for analysis 1	0.256	0.070	0.324	3.633	0.000
	Regr factor score 3 for analysis 1	0.144	0.071	0.182	2.039	0.045

The results of the multiple regression analysis are presented in above. The multiple R or multiple correlation coefficients indicates the degree of linearity of the relationship the dependent variable are independent variables. The value R square indicates the proportion of the total variation in salary determination that is accounted for by the variations in the factors. The f ration explains whether the estimated regression model could have occurred by chance. All these statistical values represent the measure of goodnessoffit of the estimated regression model. The full model was significant, as indicated by the overall F value of 14. 562 (p0.001 which indicates that the significant level and the variation explained by the model is not due to chance. The model explained 34.8% of variation in the dependent variable as indicated by the adjusted R square value.

Testing hypothesis

In this study, gourd hypotheses were developed, in terms of chief executive salary and determining factors.

Hypothesis 1 states that there is a relationship between chief executive officer salary and book value of assets.

Correlation analysis book value of assets and chief executive salary

		salary	Book value of assets
salary	Pearson correlation	1	0.002
	Sig. (2 tailed)		0.984
	N	300	300
Book value of assets	Pearson correlation	0.002	1
	Sig. (2 tailed)	0.984
	N	300	300

This hypothesis is supported by the data as the correlation coefficient was 0.002 (p=0.984). The correlation coefficient between salary and book value of assets is 0.002, which is positive significant at the 0.05 level, but the strength of the association is weak. This shows that there is relationship between the two variables, which means that a high level of chief executive officer leads to a high level of book value of assets

Hypothesis 2: states that chief executive officer salary is positively correlated with excess return generated. To test the above hypothesis the two variables were correlation. The test used to see if there was any significant correlation between the variables was a non parametric test with related samples (Bryman, 1992).

Correlation analysis between salary and excess return.

		Salary	Excess return
salary	Pearson correlation	1	0.485(**)
	Sig. (2 tailed)		0.000
	Sum of squares	13.023	5.953
	Covariance	0.153	0.070
	N	300	300
	Pearson correlation	0.485(**)	1
Excess return	Sig. (2 tailed)	0.000	1
	Sum of squares	5.953	11.593
	Covariance	0.070	0.136
	N	300	300

Correlation is significant at the 0.01 level (2 tailed).

The findings indicate that there was a positive correlation (0.325) between salary and excess return which was that significant at 0.01 confidence level as hypothesized. To further confirm the relationship, the Chi Squared test was used to examine if there were significant difference in the salary compared to excess return.

Correlation analysis between salary and excess return

Chisquare tests.

	Value	Df	Asymp. Sig. (2 sided)
Pearson chi square	32.862(a)	2	0.000
Likelihood ratio	25.209	2	0.000
Linear by linear association	19.955	1	0.000
N of Valid Cases	300

3 cells (50%) have expected count less than 5. the minimum expected count is.19

as the Pearson Chi squared value 32.862 is greater than the critical

Value (9.21), hence that test statistics value is located outside the body of X2 distribution. Therefore, at the 1% level of significance, it indicates there is a positively correlation between the two variables.

Hypothesis 3, state that there is a positive relationship between chief executive officer and sales growth of a company.

Correlation analysis between salary and sales growth.

		Salary	Sales growth
Salary	Pearson correlation	1	0.535(**)
	Sig. (2 tailed)		0.000
	N	300	300
Sales growth	Pearson correlation	0.535(**)	1
	Sig. (2 tailed)	0.000
	N	300	300

** Correlation is significant at the 0.01 level (2 tailed).

According to the Pearson correlation coefficient (0.535), the correlation was positive and significant at the 0.01 confidence level as hypothesized. The chisquared test above was also used to examine if there was a significant difference in the chief executive officer salary compared to sales growth (Ayers & Collinge, 2005).

Chisquare tests.

	Value	Df	Asymp. Sig. (2 sided)
Pearson chi square	56.539(a)	4	0.000
Likelihood ratio	33.663	4	0.000
Linear by linear association	24.294	1	0.000
N of Valid Cases	300

As the Pearson chisquared value 56.539 is less than the critical value (13.3), hence the test statistics value is located outside the body of the X2 distribution. Therefore, at the 1% level of significance, the H3 is accepted. Sales growth has a strong positive relationship with a salary of the chief executive.

Summary of findings

This study was examined the salary of a Chief Executive Officer in business setting. Eight factors were identified and then examined in terms of their impact on the determinant of a salary of a Chief Executive and their future intentions.

The analysis of the data has resulted in some findings. Firstly, most survey respondents have reported in majority that they the Chief Executive Officer were compensated according to his company. Eight salary determinants were extracted using factor analysis. Of these determinants via using regression analysis, sales growth factor was indicated as having the highest influence on Chief Executive Officer salary, whereas book value of assets and excess return, number of board of directors and natural logarithm of firm assets: had less influence on Chief Executive Officer salary

Proportionate growth but not sales only were found to have significant determinant on level of salary. Surprisingly, frequency of number of directors did not influence setting of salary in a company. Correlation analysis was used to test a hypothesis about the relationships between level of Chief Executive Officer Salary and sales growth. The results revealed that there is evidence that the salary of a Chief Executive Officer and sales growth are related. In testing hypotheses 2 and 3, correlation analysis and chisquared techniques were used, and both variables had strong correlations with determinants of Chief Executive Officer.

Data analysis

One of the main findings arising from this study is that Chief Executive Officer salary is high for those companies that there is high growth in sales and high return. This contradicts with the findings of a study which shows that the salary can be determined by number of directors, which reported that the Chief Executive Officer salary was low in companies less directors and low Chief Executive Officer Salary was largely due to the amount of assets and overestimating their effects in determining the salary.

This study showed Chief Executive Officer Salary to be associated with book value of assets and the growth in sales. Moreover, the results of this study also provide strong support for the notion of the direct effect of Chief Executive Officer salary on the eight determinants of levels of salary.

Compensation for achieving executive officers diver greatly, however compensation program for achievement. The compensation to these Chief Executive Officers is not as high as is portrayed if the companys are performing well and there are no questions as to their performance.

A company can make the case for the level of compensation it provides for its Chief Executive Officers citing the levels pain comparable companies and the need to be competitive to retain top executive talent. But such arguments count for little when the public and employees make the comparison that matters to them comparing the Chief Executive Officers salary with their own. The sense that something is wrong has been aggravated by evidence that there is a lack of correlation between high levels of pay and company performance. Institutional investors on both sides of the Atlantic are using their clout to press for reform (Meigs and Robert, 1970).

There is now considerable investor pressure for greater moderation in the allocation of executive options and stronger links between remuneration and performance. Companies are urged to establish their ratio of Chief Executive Officer pay to shop floor pay, to explain the rationale for it to shareholders and to justify further any drift from this ratio subsequently.

A report to the international corporate governance network by its subcommittee on executive remuneration recommended the following:

Transparency. Salary, incentives and all other payments and benefits for directors should be published.
Remuneration committees should publish the expected outcomes of the remuneration structures.
Options should be issued at regular intervals rather than in one large batch
The true cost of options should be shown as a charge on the revenue account.
The remuneration committees should control the appointment of remuneration consultants.
Companies should not make loans to their directors.
Cash transaction bonuses on the completion of acquisitions or mergers should not be payable.
Fund managers should increase the resources allocated to the analysis of remuneration structures.

Normally, the compensations policies are meant to achieve an outstanding success by helping to attain about a particular concern within the company or techniques that would be used to solve that particular problem within the company. Not only that, US CEOs also go a step further to pressurize the necessary quarters of the decision-making group to include compensated well. When certain level of success has been achieved, CEO always use this accomplishment to publicize their organization, raising public awareness about their services/activities and thereby receiving more pay.

List of References

Ayers & Collinge 2005, Economics: Explore and Apply, Enhanced Edition, Pearson Prentice Hall, New Jersey. Barrons Finance, 4th edn, 2000, p.578.

Bryman, A 1992, Quantity and quality in social research 2nd Ed.

Diamond, I. & Jefferies, J 2001, Beginning Statistics: An Introduction for Social Scientists. CA: Sage Publications

Meigs, WB & Robert F 1970, Financial Accounting. McGraw-Hill Book Company, New York.

OSullivan, A, Sheffrin, SM & Perez, SJ 2009, Economics: Principles, Applications and Tools, 6^th Ed. Pearson Prentice Hall, New Jersey

Siciliano, G 2003, Finance for the Non- Financial Manager, McGraw-Hill, New York.

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