Business Data Analytics of Bonds

Business Data Analytics of Bonds

 

Business Data Analytics of Bonds

From Eikon download bond issues with the following characteristics:
1. Sector: Consumer Goods, Manufacturing, Service Company, Telephone, Transportation
2. Domicile: United States
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3. Amount Outstanding: ⩾ 100,000,000
4. Coupon: > 0%
5. Maturity: ⩾ 1-October-2026
6. Moody’s (Issue) Credit Rating: ⩾ C
7. Putable: No
8. Market of Issue: Domestic, Global, or Eurobond
9. make sure that the following lters are visible on the left hand side:
(a) Currency
(b) Convertible
(c) Callable
(d) Seniority.
You should end up with more than 4,000 bonds; thus, you will need to download
the data to Excel in batches (e.g., rst, download small bond issues, then medium,
and lastly with large bond issues). From the remaining issues, drop the convertible
bonds.
Discuss brieflyy your sample, including the number of observations, outliers. Provide 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 formatted 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 other tasks.
2 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. Firstly, identify statistically signicant characteristics of callable bonds.

Business Data Analytics of Bonds

You may consider, issue size, maturity, industry, credit rating, and other variables
available in Eikon. Secondly, compute the average of the individual marginal eects
of the amount outstanding on the probability that a bond includes a call feature.
Thirdly, estimate the probability that a bond with the following characteristics
includes a call feature:
ˆ maturity: 10 years
ˆ coupon: 2.5%
ˆ amount: $750,000,000
ˆ currency: US dollars
ˆ seniority: senior unsecured
ˆ Moody’s (Issue) credit rating: Aa2
ˆ sector: Electronics
ˆ domicile: USA
ˆ convertible: no
ˆ market of issue: global
ˆ puttable: no.
Do the results suggest that this bond is callable or not?
In this task, you are expected to use a logit regression analysis. To ensure that
the results are robust, estimate at least two regression models (e.g., in the rst
regression model, one includes amount in $ and in the second model, one uses the
natural logarithm of amount in $).
3 Which bonds are more likely to be issued in the
domestic and foreign markets? (6 points)
Market of Issue can take the following values:
ˆ Domestic: a bond is issued in the US
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ˆ Global: a bond is issued in the US and foreign markets
ˆ Eurobond: a bond denominated in USD is issued in the foreign market.
You need to estimate the probabilities that a bond with the following characteristics is issued in each market:
ˆ maturity: 10 years
ˆ coupon: 2.5%
ˆ amount: $750,000,000
ˆ currency: US dollars
ˆ seniority: senior unsecured
ˆ Moody’s (Issue) credit rating: Aa2
ˆ sector: Electronics
ˆ domicile: USA
ˆ convertible: no
ˆ callable: yes
ˆ puttable: no.

 

In the analysis, estimate multinomial logit regression model and briefly discuss the
determinants of Market of Issue.

 

According to the analysis, what is the most likely
market of issue of this bond?
4 Estimating yield for a hypothetical bond (7 points)
Lastly, you need to estimate the yield for a bond with the following characteristics:
ˆ maturity: 10 years
ˆ coupon: 2.5%
ˆ amount: $750,000,000
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ˆ currency: US dollars
ˆ seniority: senior unsecured
ˆ Moody’s (Issue) credit rating: Aa2
ˆ sector: Electronics
ˆ domicile: USA
ˆ convertible: no
ˆ callable: yes
ˆ puttable: no
ˆ market of issue: global.
To ensure that the results are robust, estimate at least 3 regression models (e.g.,
in the rst 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). Briey 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?
Additional information
1. Before implementing statistical and regression analysis, check whether your
sample includes any outliers and duplicate observations. If needed, take necessary actions to deal with them.
2. 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.
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3. In the analysis, you should only use the data that can be downloaded from
Eikon.
4. You may move technical calculations to Appendix if you think it helps your
report to look more professional.