The courses “Active Portfolio Management I (APMI)” and “Active Portfolio Management II (APM II)” are closely linked and students should take both. Both courses together cover the main concepts underlying modern active portfolio management. Active portfolio management can be delivered via two dimensions: (i) via active asset allocation and (ii) via security selection. The course APM I mainly covers active asset allocation. The main focus will be on stocks, bonds and commodities. After a review of the concept of market efficiency, we will develop models to analyze and forecast the risk premia for each of the above asset classes. In a further step, we will develop a framework allowing the portfolio manager to include his/her return and risk expectations into an optimization framework. The basic model we will use was originally developed by Black and Litterman. We will apply this approach empirically, using Excel or R. We will then discuss issues that arise when investors delegate the management of their portfolios to external managers. Finally, we will cover concepts for responsible investing.

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

• understand the role and possibility of active 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 active portfolio management
• know about return and risk from historical records and its implications for forecasts
• appreciate the interplay between risk aversion and optimal capital allocation
• know how to construct optimal risky portfolios
• classify financial assets into major groups and grasp their importance in a unified asset management framework
• associate primary theories of return drivers and asset pricing models with the different asset classes
• master the Black-Litterman method to view based optimal asset class allocation.

Moreover, this course will contribute to the students’ 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

After completing this class the student will also have the ability to:

• 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 2 classes.

This course consists 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 on additional readings. The aim is to introduce the theoretical framework to the students. There will be assignments to practice the concepts developed during the lectures. The assignments will involve quantitative analyses using Excel or R. Students will be allowed to work in small groups. Solutions to these assignments must be uploaded to Learn@WU. The solution will be presented and discussed in class by the students.

• Case study (30%): Students have to hand in a solution to a case study and prepare a presentation in groups; deadline: to be uploaded to Learn@WU including the names of each group member no later than November 11, 9:00 am; presentations: November 15^th.
• Computational assignment (20%): Students have to solve a portfolio problem with Microsoft Excel or R; deadline: to be uploaded to Learn@WU including the names of each group by November 30, 11:59 pm. 
• Final Exam (40%): The final exam will be held on November 24, 2022.
• In-class participation (10% ): Students can earn up to 10% by actively participating in the class discussion by, for example, raising good questions, or giving a good answer to questions raised by the instructor or other students in class.

Students need at least 50% in total (exam + case study + assignment + in-class participation) to pass the course.

• Completion of the assessment phase

• Successful completion of 8 courses within the subject "GrundlagenFinanzwirtschaft, Rechnungswesen und Steuern" ("Basics in Finance,Accounting and Taxes")

• Allocation to the elective

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Readings:

• Bodie, Z., Kane, A., & Marcus, A. (2021). Investments (12th edition). McGraw-Hill.
  Previous (not too old) or global editions are fine.

Additional readings:

• Ang, Andrew, Asset Management (2014). A Systematic Approach to Factor Investing (1^st edition). Oxford University Press

Additional material and papers will be uploaded to Learn@WU