Distance mode: With the possible exception of the exam, classes will take place online. Students are expected to actively contribute and present the assignments via MS Teams. The exam will either take place online or on campus.The grading components and deadlines for uploading student solutions are as described below.  

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.

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 is mandatory! Students may miss no more than one class.

The course will be taught in seven 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.

• Final exam (45%): There will be a 90‐minute,  final exam. Depending on whether it will be held online or on campus, it will be open book (if online) or closed book (if on campus).
• Assignments (15% for Assignment 1, 20% for Assignment 2): Students have to hand in solutions to case studies and/or problem sets.
• Presentation of Assignment 1 (10%): Students present the solutions to Assignment 1 in class
• In‐class participation (10%): Students can earn up to 10% by participating actively in the class discussion by, for example, raising good questions, or giving good answers to questions raised by the instructor or other students in class.
• Students need at least 50% of the total marks (exam + assignment +presentation + inclass participation) to pass the course.

Readings:

• Bodie, Z., Kane, A., & Marcus, A. (2014). Investments (10th global edition). McGraw-Hill
• Ang, Andrew, Asset Management (2014). A Systematic Approach to Factor Investing (1^st edition). Oxford University Press

Additional readings:

• Cochrane, John, H., (2005). Asset Pricing (revised edition). Princeton University Press
• Grinold, R., & Kahn, R. (2000). Active Portfolio Management (2nd edtion). McGraw-Hill
• Solnik, B., & McLeavey, D. (2005). International Investments (5th edtion). Pearson, Addison-Wesley

Additional material and papers will be uploaded to Learn@WU.