CORPFIN 2503 - Business Data Analytics 2021 S2, Group Assignment Sigitas Karpavi£ius Instructions 1. The assignment can be done in groups of one to three students. All team mem- bers are expected to contribute approximately equally to a group assignment. A group can eliminate an underperforming member who then will need to do the assignment individually or join another group. Similarly, one can quit an underperforming group to do the assignment individually or join another group. All group members will get the same mark for the assignment. 2. The maximum score is 30 points. 3. The presentation of your write-up is important. Up to 5 bonus points will be given for a properly formatted report. 4. All numerical analysis, all tables and figures need to be done using SAS (how- ever, 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 and data set, 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. Only one member of a group submits: a SAS code, a data file (e.g., in txt, csv, xls, xslx formats), and 1 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 a business report; font: 12 pt Times New Roman; maximum number of pages: 15. 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, Monday the 18th of October, 2021. 13. The submission must be done through MyUni. 14. Late submission will be penalized 3 points per day. Agenda The assignment is based to some extent on Workshop 4. Assume that you are a bond analyst and you have been asked to focus on the U.S. corporate bond market. 1 Sample and description statistics (5 points) From Eikon download bond issues with the following characteristics: 1. Sector: Consumer Goods, Manufacturing, Service Company, Telephone, Trans- portation 2. Domicile: United States 2 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 filters 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., first, download small bond issues, then medium, and lastly with large bond issues). From the remaining issues, drop the convertible bonds. 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 other tasks. 2 Which bonds are more likely to include a call feature? (7 points) A bond issuer can repurchase its bonds before their maturity if they include a call feature. Firstly, identify statistically significant characteristics of callable bonds. 3 You may consider, issue size, maturity, industry, credit rating, and other variables available in Eikon. Secondly, compute the average of the individual marginal effects 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: maturity: 10 years coupon: 2.5% amount: $750,000,000 currency: US dollars seniority: senior unsecured Moody's (Issue) credit rating: Aa2 sector: Electronics domicile: USA convertible: no market of issue: global 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 first 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: Domestic: a bond is issued in the US 4 Global: a bond is issued in the US and foreign markets Eurobond: a bond denominated in USD is issued in the foreign market. You need to estimate the probabilities that a bond with the following character- istics is issued in each market: maturity: 10 years coupon: 2.5% amount: $750,000,000 currency: US dollars seniority: senior unsecured Moody's (Issue) credit rating: Aa2 sector: Electronics domicile: USA convertible: no callable: yes 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: maturity: 10 years coupon: 2.5% amount: $750,000,000 5 currency: US dollars seniority: senior unsecured Moody's (Issue) credit rating: Aa2 sector: Electronics domicile: USA convertible: no callable: yes puttable: no market of issue: global. 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). 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? Additional information 1. Before implementing statistical and regression analysis, check whether your sample includes any outliers and duplicate observations. If needed, take nec- essary 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. 6 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. Good luck! 7
欢迎咨询51作业君
