BUSANA 7001 - Predictive and Visual Analytics
for Business
2022 S1, Individual Assignment
Sigitas Karpavičius
Instructions
1. This is an individual assignment.
2. The maximum score is 25 points.
3. The presentation of your write-up is important.
4. All numerical analysis, all tables and figures need to be done using SAS or SAS
Visual Analytics (however, you may use Excel or Word etc. to make tables for
regressions as the standard SAS output for regressions is not very nice).
5. Please retain your SAS code and make sure that it is user-friendly (use com-
ments where necessary). Using your submitted code, one should be able to
produce all your results, tables, and figures.
6. Please retain a copy of the problem set that is submitted.
7. You should submit 3 files (feel free to combine them into a single file):
• ‘Assignment Cover Sheet’, which must be signed (electronic signature is
okay) and dated
• the report (in pdf format) for Task 1
• the report (in doc, docx, or pdf format) for Tasks 2 and 3; the report
should be properly formatted and be similar to a business report; font:
12 pt Times New Roman; maximum number of pages: 10 (no penalty for
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exceeding this limit); at the end of the report (in the appendix) include
your SAS code.
8. Lecturer can refuse to accept assignments, which do not have a signed ac-
knowledgment of the University’s policy on plagiarism.
9. 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.
10. You must acknowledge any external material you use in your answers, e.g.,
material from websites, textbooks, academic journals and newspaper articles.
11. All queries (including deadline extensions) for this project should be directed
to Lecturer.
12. The submission deadline for the problem set is 6pm, Friday the 1st of April,
2022 Sunday, the 3rd of April, 2022.
13. The submission must be done through MyUni.
14. Late submission will be penalized 2.5 points per day.
1 Visual Analytics (8 points)
Assume that you are a real estate analyst. Your goal is to analyze the recent
sales of residential property in the area. Using the dataset ‘AMESHOUSING3’
which is available in SAS Viya for Learners, create a report with various figures and
tables (around 6 objects). Briefly describe your results (using ‘Text’ object which
is available under ‘Content’ group).
Estimate an OLS regression model where the dependent variable is the natural
logarithm of sale price (you will need to generate this variable). Motivate your choice
of the independent variables and discuss the results (using ‘Text’ object which is
available under ‘Content’ group).
2 Sample and description statistics (8 points)
Assume that you are a bond analyst and you have been asked to focus on the
U.S. corporate bond market. You have been provided 2 files with the bond data
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(Sample_a.csv and Sample_b.csv). First, you should prepare your data for the
analysis:
• remove duplicates
• merge the files using ‘bond_id’ variable
• remove observations with missing values of any variable
• check for outliers and take necessary actions to deal with them
• remove bonds not denominated in US dollars (i.e., your sample should include
bonds which currency is US dollar)
• remove putable bonds from the sample
• remove convertible bonds from the sample.
Then create the following variables:
1. years to maturity:
• SAS code: maturity2=(maturity-today())/365;
2. amount outstanding in billions of USD (amount2)
3. a natural logarithm of amount outstanding (ln_amount)
4. a dummy if a bond is putable callable
5. a dummy if ‘seniority’ is ‘Senior Unsecured’
6. the following dummy variables:
• aaa_d=1 if credit rating is Aaa; SAS code:
aaa_d=0;
if Moodys_cred_rat="Aaa" then aaa_d=1;
• aa_d=1 if credit rating is Aa1, Aa2, Aa3; SAS code:
aa_d=0;
if Moodys_cred_rat in ("Aa1" "Aa2" "Aa3") then aa_d=1;
• a_d=1 if credit rating is A1, A2, A3
• baa_d=1 if credit rating is Baa1, Baa2, Baa3
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• ba_d=1 if credit rating is Ba1, Ba2, Ba3
• b_d=1 if credit rating is B1, B2, B3
• c_d=1 for all other values of ‘Moodys_cred_rat’.
Discuss briefly your sample, including the number of observations, outliers. Pro-
vide the descriptive statistics of the sample. How you choose to do this is entirely at
your discretion. However, it is recommended that you consider using both summary
statistic and graphical methods (this task should include at least one properly for-
matted table, one pie chart, one histogram, and one scatter plot) while also noting
any peculiarities within the data set. You should put more emphasis on variables
that are the dependent variables in the regressions estimated in the next task.
3 Estimating yield for a hypothetical bond (9 points)
Lastly, you need to estimate the yield for a bond with the following characteristics:
• maturity: 10 years
• coupon: 2.5%
• amount outstanding: $750,000,000
• seniority: senior unsecured
• Moody’s (Issue) credit rating (‘Moodys_cred_rat’): Aa2
• sector: Electronics
• callable: yes
• market of issue: global.
Use the sample from the previous task. To ensure that the results are robust,
estimate at least 3 regression models (e.g., in the first regression model, one includes
amount in $, in the second model, one uses the natural logarithm of amount in $, and
the third model features something else). 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. Do not forget to
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include credit rating dummies in the regression models as the independent variables
(i.e., credit rating fixed effects).
Briefly discuss the determinants of yield.
Using one of the regression models, compute two additional yields:
1. the amount is $1,000,000,000, other bond characteristics the same as above
2. Moody’s (Issue) credit rating is A2, other bond characteristics the same as
above (i.e., amount: $750,000,000 etc.).
Are the results the same as the main estimate? Why?
Good luck!