Business Data Analytics Instructions

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CORPFIN 2503 – Business Data Analytics
The assignment can be done in groups of one to three students. All team members are expected to contribute approximately equally to a group assign-ment. A group can eliminate an underperforming member who then will need to do the assignment individually or join another group. All group members will get the same mark for the assignment.
The maximum score is 30 points.
All numerical analysis, all tables and figures need to be done using SAS.
Please retain your SAS code and make sure that it is user-friendly (use com-ments where necessary). Using your submitted code and data set, one should be able to produce all your results, tables, and figures.
The presentation of your write-up is important. Poorly presented work may result in loss of marks (up to 10 marks out of 30).
Please retain a copy of the problem set that is submitted.
Only one member of a group submits:
a SAS code,
a data file (e.g., in txt, csv, xls, xslx formats), and
the report (in doc, docx, or pdf format) with ‘Group Assignment Cover Sheet’, which must be signed (electronic signature is okay) and dated by all group members before submission; the report should be properly formatted and be similar to business report.
Lecturer can refuse to accept assignments, which do not have a signed ac-knowledgment of the University’s policy on plagiarism.
Any suspected plagiarism will be severely punished. This includes any student that submits copied work or any student that allows their work to be copied.
You must acknowledge any external material you use in your answers, e.g., material from websites, textbooks, academic journals and newspaper articles.
All queries for this project should be directed to Lecturer.
The submission deadline for the problem set is 9pm, Friday the 25th of Octo-ber, 2019.
The submission must be done through MyUni.
Late submission will be penalized 1 mark (out of 30 marks) per day.
The assignment is based to some extent on Workshop 6. Suppose you are a bond analyst and you have been asked to look at the US corporate bond market. You decided to consider bonds with the following characteristics:
Sector: Consumer Goods, Manufacturing, Telephone, Transportations
Domicile: United States
Amount Outstanding: > 100,000,000
Coupon: > 1%
Maturity: > 1-October-2024.
You should end up with around 2,000 bonds; thus, you will probably need to make several attempts in order to download the data to Excel. You have been assigned 3 tasks. Each task caries the same weight, 10 points.
Which bonds are more likely to include “a call” feature?
A bond issuer can repurchase its bonds before their maturity if they include a call feature. Your task is to identify characteristics of callable bonds. You may consider, issue size, maturity, industry, credit rating, and other variables available on Eikon. You are expected to use t-tests and regression analysis.
The comparison of bond yields across industries
Next, you should analyze whether bond yields are statistically different in Con-sumer Goods (it includes Beverage/Bottling, Consumer Products, Food Processors, and Tobacco) and Manufacturing (it includes Aerospace, Automotive Manufacturer, Building Products, Chemicals, Conglomerate/Diversified Mf, Home Builders, and Information/Data Technology) sectors. You are expected to use t-tests and regres-sion analysis.
Estimating yield for a hypothetical bond
Lastly, you need to estimate the yield for a bond with the following characteristics:
maturity: 10 years
coupon: 3%
amount: $250,000,000
currency: US dollars
seniority: senior unsecured
S&P credit rating: A
sector: Electronics
domicile: USA
convertible: no
callable: yes
puttable: no.
Your client also would like you to compute three additional yields:
if the amount is $500,000,000, other bond characteristics the same as above
if S&P credit rating is AA, other bond characteristics the same as above (i.e., amount: $250,000,000 etc.)
if the bond is non-callable (i.e., callable: no), other bond characteristics the
same as above (i.e., amount: $250,000,000 etc.).
Are the results the same as the main estimate? Why?
Additional information
Before implementing statistical and regression analysis, check whether your sample includes any outliers. If needed, take necessary actions to deal with them.
To ensure that regression residuals “behave well,” you may need to scale or transform one or more variables. For example, to use a natural logarithm value of the variable instead of its raw value.
In the analysis, you should only use the data that can be downloaded from Eikon.

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