1768 S3INV2 Active Portfolio Management II
Sebastian Lutz, Ph.D.
Contact details
Weekly hours
Language of instruction
09/01/23 to 09/22/23
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Day Date Time Room
Monday 11/20/23 08:30 AM - 12:00 PM D4.0.127
Monday 11/27/23 08:30 AM - 12:00 PM D4.0.127
Monday 12/04/23 08:30 AM - 12:00 PM D4.0.127
Monday 12/11/23 08:30 AM - 12:00 PM D4.0.127
Monday 12/18/23 08:30 AM - 12:00 PM D4.0.127
Monday 01/08/24 08:30 AM - 12:00 PM Online-Einheit
Monday 01/15/24 08:30 AM - 12:00 PM D4.0.127
Wednesday 01/24/24 09:00 AM - 11:00 AM TC.-1.61
The courses “Active Portfolio Management I” and “Active Portfolio Management II” are closely linked and students need 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. For stock selection we will introduce the concept of fundamental valuation. We will then derive a scoring model, which over- or under-weights individual stocks. Students will also be familiarized with the scoring approach by Brandt-Santa Clara-Valkanov (2009). Given the recent sovereign bond crisis, we will also learn how to analyze sovereign risk and choose a portfolio of sovereign bonds. Finally, we will develop a framework for optimally selecting corporate bonds, based on various characteristics of the bond and the issuing corporation. The last chapter of the course deals with performance evaluation of actively managed portfolios. We introduce the concept of attribution analysis, which breaks down a portfolio’s relative performance into its various components, such as asset allocation and security selection.
Learning outcomes

Students who have successfully completed this course 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
  • 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.

Moreover, the class 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 attribute performance to the different dimensions of active portfolio management or to implement a scoring model
Attendance requirements

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

Teaching/learning method(s)
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 additional readings and aim to communicate students the theoretical framework. To each main section of the class there will be assignments to practice the concepts developed during the lectures. The assignments will involve quantitative analyses using Excel. Students will be allowed to work in small groups of maximum of 4 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
  • In-class participation (5% extra): Students can earn up to 5% 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 + assignment + in-class participation) to pass the course.

Please contact the lecturer if you have further questions about the assessment.

Prerequisites for participation and waiting lists
  • 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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Last edited: 2023-08-29