The courses “Active Portfolio Management I” and “Active Portfolio Management II” are closely linked, and students need to follow both. The first course has a slightly stronger focus on theory and lectures, while the second class devotes more time to develop and discuss specific applications computer models. This course covers 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. We continue covering active security selection. Here the focus is on choosing individual securities for portfolio construction. We begin with fundamental macroeconomic and industry analysis. For stock selection, we will introduce the concept of fundamental valuation. There will be some background work on financial statement analysis, while (risk) factor models will be considered in depth and arbitrage pricing theories refreshed. Besides the theory on active portfolio management, we will also derive a scoring model, which over- or under-weights individual stocks. Students will also be familiarized with the more advanced scoring approach by Brandt-Santa Clara-Valkanov (2009). Behavioural finance, as a discipline acknowledging potential irrationality due to human psychological factors, will be another point of emphasis in this course. Finally, we will develop a framework for optimally selecting corporate bonds, based on various characteristics of the bond and the issuing corporation. Regular discussions, for instance on performance evaluation of actively managed portfolios, will take place each class.

The learning outcomes of the course revolve around an:

• Understanding of the role and possibilities of active portfolio management within the framework of modern capital market theory

Students who have successfully completed this course should have the general abilities 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
• understand how active portfolio managers can select individual equity and fixed income securities based on their forecasts
• evaluate a portfolio’s performance and to attribute ex-post portfolio performance to skill, luck, security selection, market timing, asset allocation etc.
• Understand the limitations of rationality assumptions through behavioural finance

Moreover, the class should contribute to the student’s skills to:

• analyse and solve complex portfolio problems individually and as a member of a group, by functioning as a valuable and cooperative team member
• summarize and professionally present solutions in class, as a group
• adequately communicate and participate in in-class discussions, as an individual

While the Students should develop the following more technical skills 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 attribute performance to the different dimensions of active portfolio management or to implement a scoring model

Attendance is mandatory! Students may miss no more than 2 classes.

This course consists of a mix of regular lectures, structured classroom discussions, working on problem sets in-class as exam preparation, and the analyses of assignments. The lectures will be largely based on the instructor’s lecture notes, on the main textbook and additional readings of scientific papers. The aim is to communicate the theoretical framework to the students. There will be two major assignments to practically apply the concepts developed during the lectures. The assignments will involve quantitative analyses using Excel. Students will be allowed to work in small groups of a maximum of 4-5 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 (35%): There will be a 90-minute supervised final exam in an IT-room (details will follow). Milestone: 15/35 points required.
• Case study (30%): Students have to hand in the solution to a case study and prepare a presentation in groups
• Computational assignment (35%): Students have to solve a portfolio problem with Microsoft Excel, present the solutions, and peer-review another group
• In-class participation (5% extra): Students can earn up to 5%-points in bonus points by actively participating in the class discussions. This can happen at announced times during the lecture or in dedicated debate sessions.

Students need at least 50 points in total (exams + case study + computational assignment + in-class participation) to pass the course. Grade boundaries: 88 (1), 75 (2), 63 (3), 50 (4). Please contact the lecturer if you have further questions about the assessment.

• 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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