Syllabus

Title
0554 Y2E Portfolio Management - Applications
Instructors
Univ.Prof. Dr. Otto Randl
Contact details
otto.randl@wu.ac.at
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/01/20 to 09/25/20
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Elective - Portfolio Management - Applications
Dates
Day Date Time Room
Friday 11/20/20 02:00 PM - 05:30 PM Online-Einheit
Friday 12/04/20 02:00 PM - 05:30 PM Online-Einheit
Friday 12/11/20 02:00 PM - 05:30 PM Online-Einheit
Friday 12/18/20 02:00 PM - 05:30 PM Online-Einheit
Friday 01/15/21 02:00 PM - 05:30 PM Online-Einheit
Friday 01/22/21 02:00 PM - 05:30 PM Online-Einheit
Friday 01/29/21 02:00 PM - 05:30 PM Online-Einheit

On this page:

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Procedure for the course when limited activity on campus

Distance mode: Classes will take place online.Students have to upload the solutions to the assignments before class. Students will be be asked to present their solution via MS Teams. The grading components and deadlines for uploading student solutions are as described above.  

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Contents

This course deals with modern investment theory and its application to portfolio management. This course will focus on practical applications and corresponds closely to course Portfolio Management - Foundations taught by Josef Zechner where the focus is on foundations.

       

Topics include asset allocation, market efficiency, return predictability, and performance evaluation. Following an introduction, we will review the basic concepts of portfolio theory and discuss issues of practical implementability. Active portfolio management requires return predictability. Thus, we will deal with the question whether return predictability exists and how it may be related to market efficiency. We will explore return predictability with a particular emphasis on the most important asset classes, namely stocks and bonds. Our discussion of asset allocation will cover the Black-Litterman model that utilizes return forecasts as a basis for active asset allocation. Further, we will discuss volatility-managed portfolios. Finally, we will deal with (risk-ajdusted) performance measures that allow to evaluate the success of a portfolio management strategy.

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Learning outcomes

Students who have successfully completed this course will have acquired the following skills:

  • Understand the role and possibility of portfolio management within the framework of modern capital market theory
After completing this class the student will have the ability to:
  • distinguish between asset allocation and security selection in passive and active portfolio management
  • appreciate the interplay between risk aversion and optimal capital allocation
  • construct passive and active optimal risky portfolios
  • master the Black-Litterman method to view-based optimal asset class allocation
  • evaluate the performance of portfolios
After completing this class the student will also have the ability to:
  • Analyze and solve complex portfolio problems individually and as a member of a group and to develop solutions by functioning as a valuable and cooperative team member
  • Summarize and professionally present solutions in class
  • Adequately communicate and participate in in-class discussions
  • Solve and present a case study in small groups
  • Find the necessary literature and data to solve complex portfolio problems using, e.g., the Internet, Reuters, Bloomberg.
  • Master reasonably complex problems in R, eg. to solve linear programming and regression tasks.
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Attendance requirements

75% attendance is required for a pass grade.

 
 
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Teaching/learning method(s)
The course will focus on class room discussions and analyses of assignments. To review the theory and methods necessary to analyze the assignments, lecture notes by Prof. Josef Zechner will be used. The assignments will involve quantitative analyses using R. Students should work in small groups, consisting of 2 students per group. Solutions to these assignments must be uploaded before class. The solution will be presented and discussed in class by the students.
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Assessment

 

  • Home assignments (75%): There will be 7 assignments to be solved in (changing) teams of 2 students. For every student, I will ignore his/her worst assignment. 
  • Presentations (25%): Each student has to indicate individually before class whether he/she would like to present the solution of the home assignment. Both the number of exercises indicated and the quality of presentations count for the grade.

Students need at least 50% of the total marks to pass the course.

Note that while you are allowed to miss 2 classes to pass the course, missing classes might impact your grade as you cannot earn credits for presentations when being absent from class.

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Availability of lecturer(s)

otto.randl@wu.ac.at

Last edited: 2020-09-15



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