0450 Y2E Portfolio Management - Foundations
em.o.Univ.Prof. Dr. Josef Zechner
Contact details
Weekly hours
Language of instruction
09/01/22 to 09/23/22
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Day Date Time Room
Tuesday 11/29/22 09:00 AM - 12:30 PM D4.0.039
Tuesday 12/06/22 09:00 AM - 12:30 PM D4.0.039
Tuesday 12/13/22 09:00 AM - 12:30 PM D4.0.039
Tuesday 12/20/22 09:00 AM - 12:30 PM D4.0.039
Tuesday 01/10/23 09:00 AM - 12:30 PM D4.0.039
Tuesday 01/17/23 09:00 AM - 12:30 PM D4.0.039
Tuesday 01/24/23 09:00 AM - 11:00 AM D4.0.022

This course deals with modern investment theory and its application to portfolio management. Topics include asset allocation, security selection, and inclusion of socially responsible investment objectives. The course should be taken concurrently with the course “Portfolio
Management: Applications”. In the applications course the concepts introduced in the foundations course will be used in a number of case studies and examples.

Students are expected to review the basic concepts of portfolio theory, such as identifying the minimum variance portfolio and the tangency portfolio as well as factor models and the APT. The relevant notes for the review are available online.  Active portfolio management requires return predictability. Thus, the main part of the course will deal with the question whether return predictability exists and how it may be related to market efficiency. Thus, we will explore return predictability with a particular emphasis on the most important asset classes, namely stocks and bonds. Foreign exchange and commodities will be covered in the "Applications" course.

In the next part of the course we will develop concepts to utilize return forecasts as a basis for active asset allocation and security selection. In particular we will cover the Black‐Litterman Model as well as the model by Brandt Santa‐Clara and Valkanov.

We will also discuss agency problems associated with delegated portfolio management. Here we will cover the various main vehicles used in delegated portfolio management, such as mutual funds, exchange traded funds (ETFs), target funds, hedge funds and private equity funds. We will hereby place particular emphasis on how agency problems may manifest themselves in these investment vehicles.  

Finally, we will discuss the role of socially responsible investment strategies (SRI), how they may affect risk and return characteristics of portfolios, and how they may affect corporate social responsibility.

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
  • 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
  • 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 MS Excel: Use matrix formulas to solve linear programming and regression tasks. Employ the Solver tool to implement optimization constraints
  • Develop an Excel-based model to incorporate consensus and individual forecasts into a mean-variance optimal portfolio
Attendance requirements

Full attendance is compulsory. This means that students should attend at least 80% of all lectures, at most one lecture can be missed.

Teaching/learning method(s)

The course will be taught in units of 3.5 hours each. It will consist of a mix of regular lectures, class room discussions and analyses of assignments. The lectures will be largely based on the instructor’s lecture notes, on the main textbook and additional readings and aim to communicate students the theoretical framework. There will be assignments to practice the concepts developed during the lectures. The assignments will involve quantitative analyses using Excel or programming language R. Students will be allowed to work in small groups, consisting of a maximum of  5 students per group. Solutions to these assignments must be sent to the instructor electronically. The solution will be presented and discussed in class by the students.


▶ Assignments [45 points]

▶ Assignment 1 [25 points], Deadline for upload to Learn: January 5th, 2023, 9 a.m.

▶ Assignment 2 [20 points] Deadline for upload to Learn: January 31st, 2023, 9 a.m.

▶ Final exam [45 points] (on campus). 

▶ In-class participation [15 points]: Credit of 0 to 3 points per class for answering questions, contributing to the discussion, and for raising good questions. I will sometimes ask specific students to reply to a question. 2 points per class will be deducted for not leaving the camera on during class.

▶ Minimum of 50 points in total (exam + case study + assignment + in-class participation) to pass.

▶ Mandatory attendance. You may miss one class.


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Availability of lecturer(s)
Last edited: 2022-10-24