investment management class assignment

Assignment 1
1. The dataset ISE30.xls contains historical monthly USD returns from January 2008 to December 2012 for the 30 stock funds in the ISE-30 index. You may assume that the (US) risk-free interest rate is 4% per annum or 33 basis points (0.33%) per month.
2. Use the sample averages and covariance terms as ‘naïve’ estimates for the future expected returns and covariance terms. (Clearly, the historical averages of 60 monthly observations are inaccurate even under the ideal assumption that the observations are i.i.d. normal random variables.)
3. Apply the security-selection model of Treynor and Black (see Chapter 8 and 27.1) to find the optimal active portfolio and optimal risky portfolio. The benchmark index portfolio is the ISE-30 index.
4. Make sure that the optimal portfolio weights, Jensen’s alpha, market beta, information ratio and Sharpe ratio are reported for the two portfolios.
5. You can make the assignments in any statistical software package or simply in Excel. For using Excel spreadsheet software, it may be useful to install the add-ins ‘Analysis ToolPak’ and ‘Solver Add-in’.
6. You can chose to work individually or work together in a small group of two or three students.
7. Hand-in: Post your .xls file on the F: drive. Please do not send your work by email, because it may then be difficult for me to find your work in the daily flow of emails. If you have any problems accessing the F: drive, please contact the CIT office.
8. Deadline: Sunday October 20, 2013, 23:59. Submissions after the deadline can be corrected at request but will not be graded. You may want to set your own ‘soft’ deadline one or two days before the ‘hard’ deadline in order to avoid last-minute planning problems. You may also want to check before the deadline whether you can access the F: drive.
9. Make sure that your names and student numbers (for all team members) are clearly stated. Also make sure that your steps and possible assumptions are clearly explained, so that I can understand and replicate your results.
10. The University code of honor applies for all graded work.

investment management class assignment

Assignment 1
1. The dataset ISE30.xls contains historical monthly USD returns from January 2008 to December 2012 for the 30 stock funds in the ISE-30 index. You may assume that the (US) risk-free interest rate is 4% per annum or 33 basis points (0.33%) per month.
2. Use the sample averages and covariance terms as ‘naïve’ estimates for the future expected returns and covariance terms. (Clearly, the historical averages of 60 monthly observations are inaccurate even under the ideal assumption that the observations are i.i.d. normal random variables.)
3. Apply the security-selection model of Treynor and Black (see Chapter 8 and 27.1) to find the optimal active portfolio and optimal risky portfolio. The benchmark index portfolio is the ISE-30 index.
4. Make sure that the optimal portfolio weights, Jensen’s alpha, market beta, information ratio and Sharpe ratio are reported for the two portfolios.
5. You can make the assignments in any statistical software package or simply in Excel. For using Excel spreadsheet software, it may be useful to install the add-ins ‘Analysis ToolPak’ and ‘Solver Add-in’.
6. You can chose to work individually or work together in a small group of two or three students.
7. Hand-in: Post your .xls file on the F: drive. Please do not send your work by email, because it may then be difficult for me to find your work in the daily flow of emails. If you have any problems accessing the F: drive, please contact the CIT office.
8. Deadline: Sunday October 20, 2013, 23:59. Submissions after the deadline can be corrected at request but will not be graded. You may want to set your own ‘soft’ deadline one or two days before the ‘hard’ deadline in order to avoid last-minute planning problems. You may also want to check before the deadline whether you can access the F: drive.
9. Make sure that your names and student numbers (for all team members) are clearly stated. Also make sure that your steps and possible assumptions are clearly explained, so that I can understand and replicate your results.
10. The University code of honor applies for all graded work.