Financial Modelling Report

Financial Modelling Report. Financial Modelling Report. ASSESSMENT

The assignment question is shown below. Course members will be assessed on their analysis, presentation and discussion as well as any calculations presented. Please note that details of the deadline and submission arrangements will be conveyed to you by the Campus Based Office.

Maximum length: 2000 words


Read carefully the case notes overleaf. Consider the information shown in the appendix.

Determine how this information can be used to identify the factors that influence chief executive remuneration.

Then write a report for the Directors of Prudent A which addresses these issues.

Remember, the Directors of Prudent A are not experts in statistical analysis. Hence you will need to explain what you are and why, as well as the meaning of your results.

In structuring your report you may wish to consider the following framework. This does not mean that you simply respond to (a) to (d) below, but rather that you formulate headings and sub-headings for your report using the framework as a starting point.

(a) A graphical representation of the data and a discussion of any issues or patterns which arise from this exercise. It would be for you to decide upon the exact data to use and the appropriate graph(s).

(b) Univariate and bivariate analysis and discussion which looks at the possible determinants of CEO remuneration.

(c) Multivariate analysis and associated discussion which makes use of the data provided in Financial Modelling Assignment 2016 – 2017.xls (see appendix I).

(d) Any other issues, problems or additional complications which you feel should be conveyed to the Directors of Prudent A with respect to your analysis.

Chief Executive Remuneration

You have recently been appointed as an analyst within PMC Inc. PMC is a UK consultancy company that undertakes independent research for client organisations.

Your first client is a large pension company (Prudent A) that has most of its assets invested in the UK stock market. Prudent A has a well diversified portfolio of UK shares that covers most industries, although the emphasis tends to be on medium and large sized firms.

The Directors of Prudent A are concerned about the remuneration (salary plus bonuses) that the CEOs of these firms receive and want to commission some independent research which looks at whether this remuneration is in some way related to the performance of the firm or results from the position and power of the chief executive.

You have been asked to undertake some quantitative analysis looking at this issue. While you are familiar with various different aspects of statistics and a number of statistical packages you have not undertaken a project of this nature before. Hence you start by conducting a literature search.

This search proves beneficial and you find that there are a number of existing studies which look at the factors that determine chief executive remuneration, although none specifically in a UK context.

Most of these studies consider whether CEO pay is determined by the performance of the company they manage or whether it simply relates to the influence which the CEO has over the board of directors.

Various measures of company performance have been used including accounting profit, shareholder wealth and growth in sales. It has also been suggested that the size of the firm can have an impact on CEO remuneration and hence this has frequently been incorporated in empirical work. CEO influence over the board of directors has usually been measured by the proportion of executive directors on the board. Executive directors are individuals who work for the company in question (usually senior managers). Hence their position in the organisation is dependent at least to some extent on decisions made by the chief executive. In contrast, non-executive directors are not employees of the company nor are they affiliated to the company in any way. Some studies have further suggested that if the CEO is also the chairman of the board of directors then this person will have even more influence over decisions made by the board. Hence a measure designed to incorporate this effect has sometimes been included in empirical work.

From the material you have identified you draw up a list of specific variables which can be used to measure the possible influences on CEO remuneration and collect numerical data on each of these (details of the data can be found in Appendix I). You also calculate the natural logarithm of some of these variables since it has been suggested by some authors that the use of such transformed data can reduce the impact of outliers.

You now need to consider how you will analyse this information. In addition you need to consider how you will explain the approach(es) you have adopted and the implication of your analysis given that the Directors of prudent A are not experts in quantitative or statistical methods.

Appendix I

The data for to this assignment can be found in Financial Modelling Assignment 2016 – 2017.xls. This information relates to a large sample of medium and large sized firms. In total there are 280 observations. The first 5 observations are shown below. All data is annual.

Variable definitions:

Salary: CEO remuneration (salary, bonuses, etc.) �’s.
Dir: Number of directors on the board, No.
Exec: Number of executive directors on the board, No.
Assets: Book value of firms assets, �m. Measure of firm size.
Exec1: Proportion of executive directors on the board.
Dummy: 1 if the CEO and chairman of the board is the same person,
0 otherwise.
Sgrow: Sales growth, proportionate growth.
Exret: Excess return, proportion. Calculated as the return on the companies
shares on and above the industry average (company return
minus industry average). Measure of shareholder wealth.
Lassets: Natural logarithm of firm assets figures.
Lsalary: Natural logarithm of CEO remuneration figures.

Salary Dir Exec Assets Exec1 Dummy Sgrow Exret Lassets Lsalary
830632 18 7 22065 0.39 1 0.25 0.24 10.00 13.63
1571662 14 5 19597 0.36 1 0.76 0.32 9.88 14.27
428640 15 2 32123 0.13 1 -0.09 -0.05 10.38 12.97
970376 12 11 17788 0.92 1 -0.01 0.24 9.79 13.79
1024938 14 3 34710 0.21 1 0.13 0.23 10.45 13.84


Criteria for a good assignment:
� good understanding of key concepts and ideas
� some imagination and originality
� development of argument so that the whole assignment hangs together.

When you write your assignment, consider the following:
� Before you begin, work out on paper a detailed outline of the structure of your assignment and the arguments you will develop.
� In the introduction, you should set out your main themes and intentions: describe the issue you are addressing, identify its main components, and indicate what you are going to do in the body of your essay.
� Break down your arguments into main parts – use this as a basis of your assignment that will then be divided up into several sections (you may want to have section title for each section).
� Build up your argument point-by-point, section-by-section, so that you develop a picture that slowly develops in the reader�s mind.
� Always try to put yourself in the position of a critical reader, ask yourself how s/he would react to your assignment, how s/he would understand it, be persuaded by it.
� Do not simply describe the ideas you�re dealing with, provide a critical evaluation.
� Summarise your arguments in a conclusion. What is the main significance of what you have been saying?

Important points to note:
� You are required to provide explanation and discussion. Hence explain what you are doing, why, and the meaning of your results.
� Do not produce graphs if you cannot provide related discussion.
� Do not produce tables if you cannot provide related discussion.
� Do not cut and paste Excel, SPSS, etc. tables. Produce your own summary tables. If you think appropriate you can provide an appendix with the Excel, SPSS, etc. information.

An example of �what you are doing, why, and the meaning of results�:
�The analysis consists of cross-tabulations and a logistic regression. The cross-tabulations, which make up the brunt of the report, allow one to see the interrelationship between two or more variables. For example, what percentage of whites and African-Americans play lottery games? Or, who spends more per month on lotteries, those younger than fifty or those older than fifty? The investigators use cross-tabulations to illustrate the relationship among demographic characteristics (e.g., age, education, income), attitudes toward lotteries, how frequently residents play lottery games, and how much they spend on them.� (Piliavin and Entner Wright, 1992: 2)
�As shown in column four, several demographic characteristics of the 1991 respondents significantly predict lottery play. These include gender, age, marital status, and education. First, men are more likely to play the lottery than women, with an estimated regression coefficient of .3242 (top of column four). Being positive, this coefficient indicates that male respondents (coded as 1) have higher probabilities of lottery play than female respondents (coded as 0).� (Piliavin and Entner Wright, 1992: 58)

Two more example reports:

Financial Modelling Report

Financial Modelling Report

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