2019-01-25T09:56:11+00:00 Assignments

extent to which country specific factors seem to influence capital structure in small and medium sized enterprises (SMEs)

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extent to which country specific factors seem to influence capital structure in small and medium sized enterprises (SMEs)

extent to which country specific factors seem to influence capital structure in small and medium sized enterprises (SMEs)
Assignment Question:

Read carefully the case notes below. Consider the information shown in the appendix.
Determine how this information can be used to shed light on the determinants of capital
structure in small and medium sized enterprises (SMEs) and the extent to which country
specific factors seem to influence this relationship.
Then write a report to the Directors of National Bank which addresses these issues.
The Determinants of Capital Structure
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 bank (National Bank) that specialises in lending to UK
companies. National Bank customers come from all sectors of the UK economy, although
the emphasis tends to be on medium and large sized firms.
The Directors of National Bank are considering whether the company should expand its
business activities by also lending to smaller companies both in the UK and in Europe.
Before making such an important decision the Directors decide to commission some
independent research looking at the factors which determine SME borrowing and the
extent to which this differs across countries.
In particular the Directors of National Bank are interested in making a comparison of the
extent of SME borrowing across a selection of different countries (some in Northern
Europe and some in the South) and determining the variables which impact on SME debt.
In addition, they wish to know whether any differences in the level of borrowing across
countries results from country specific factors or as a consequence of differing values
for the variables which appear to impact on SME debt.
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.
Page 2 of 4
This search proves beneficial and you find that there are a number of existing studies
which look at how companies use a combination of debt (borrowing) and equity (sale of
stocks/shares) to finance their activities. In the literature this balance of debt and
equity is frequently referred to as the �capital structure decision of the firm�. Most of
the studies you identify on the determinants of capital structure focus on large
organisations, however, with only a few looking specifically at SMEs and none explicitly
examining the group of countries of interest to national bank.
Various measures have been used in the literature to represent the balance of debt
versus equity funding, but by far the most common is the gearing ratio. A number of
factors have also been identified as possible determinants of the gearing ratio, the most
common being profitability, growth, asset structure, firm size and firm age. You also
find that there is no universal definition of an SME, but that the most commonly used
measure is a company employing less than 250 employees.
From this material you draw up a list of specific variables which can be used to measure
the possible determinants of SME capital structure and collect numerical information
related to each (details of the data can be found in Appendix I).
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 National Bank are not experts in quantitative or
statistical methods.Appendix I
The data for to this assignment can be found in MN7542 Financial Modelling Assignment
August – October 2016.xls. All data is annual. This information relates to a large sample
of small and medium sized firms (less than 250 employees) taken from five European
countries. In total there are 589 observations. The first 5 observations are shown below.
Variable definitions:
Gearing: Debt divided by (debt plus equity), (%).
Profit: Net margin, operating profit divided by sales, (%).
Growth: Growth in sales over previous 3 years, (%).
Assets: Asset structure, fixed assets divided by total assets, (%).
Size: Total assets, (� equivalent).
Age: Age of company, (years).
Country: Country in which company is based:
1 if Germany,
2 if Italy,
3 if Spain,
4 if UK,
5 if Portugal.
Page 3 of 4
Gearing Profit Growth Assets Size Age Country
24.59 6.20 15.36 11.67 2328424 31.08 1
32.07 5.87 14.16 20.95 4482675 24.47 1
32.22 5.01 18.53 20.14 4005674 30.47 1
24.07 5.86 24.03 15.31 4766368 28.73 1
32.44 6.81 13.16 11.39 4990534 23.75 1
Further Guidance
Students are reminded that whilst some questions may very evidently refer to a particular
unit they are all designed to span issues across the entire module. A full answer to the
question will require reflection on the issues that you have encountered throughout the
module. It is important to bear in mind that the content of your answer will depend on the
argument that you wish to put forward in answer to the question and not solely on the
concepts that the question explicitly identifies.
Remember, the Directors of National Bank are not experts in statistical analysis. Hence you
will need to explain what you are doing 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 considers the determinants of
capital structure.
(c) Multivariate analysis and associated discussion which makes use of the data given in
MN7542 Financial Modelling Assignment August – October 2016.xls.
(d) Any other issues, problems or additional complications which you feel should be
conveyed to the Directors of National Bank with respect to your analysis.
Important points to note:
? You are required to provide explanation and discussion.
? 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 into your report. Produce your own
summary tables in the main body of your report. 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�:
Page 4 of 4
�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)
Source: https://www.irp.wisc.edu/publications/sr/pdfs/sr54.pdf
Two more example reports:
https://www.ktc.uky.edu/files/2012/06/KTC_05_39_TA19_05_1F.pdf
https://www.manhattan-institute.org/html/cr_22.htm
References
Please ensure that you have read the advice on assignment writing and referencing which
is available at:
https://www2.le.ac.uk/departments/management/documents/pdfs/assignwritingguidelin
es.pdf
A literature search/review is not a requirement because the main objective of this
assignment is for you to think through the issues that can be addressed using the data
provided. However, good use of secondary material in introduction or conclusion will be
rewarded. If you use such material it should be referenced in your report and the full
citation must be provided in a bibliography.

Word limit: 3500 words


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