Portfolio Management

a) General Information

In addition to portfolio selection theory, the course covers more advanced optimization methods, which take into account higher moments of distribution. Approaches for dealing with the parameter estimation risk are also presented.  Furthermore, particular focus of this course is on dealing with the theoretical foundations and a thorough understanding of the underlying assumptions of different asset pricing models including the capital asset pricing model (CAPM), multi-factor models and arbitrage pricing theory (APT). The accompanying tutorials focus on the practical application and on coding in Python. A deeper understanding of portfolio management is fueled by student investment proposal presentations. For 20 years, a real portfolio was managed by students of our department. You have to prepare an investment or selling proposition which comprises topics of practical and/or theoretical importance to portfolio management.

b) Course Structure

1. Basics of Risk and Return
2. Performance Evaluation and Attribution
3. Optimal Risky Portfolio and Capital Allocation
4. Asset Pricing (CAPM, Factor Models, APT)
5. Parameter Estimation Risk
6. Advanced Optimization Techniques

c) Gast Lectures

This year's guest lectures will be given by Stephan Pilz (Head of Portfolio Management, Sand & Schott Vermögensverwaltung) on Methods of Equity Valuation, Thilo Berchtold (Head of Portfolio Management, Stuttgarter Lebensversicherung a.G.) on Fixed Income Portfolio Management and Anton Vorobets (CEO & Founder, Fortitudo Technologies) on Sequential Entropy Pooling. Valentin Pertschi (Junior Equity Portfolio Manager, Stuttgarter Lebensversicherung a.G.) will provide a hands-on introduction to Python and apply the concepts covered in the lecture directly with you. 

d) Requirements:

1. intermediate knowledge in descriptive statistics (calculation and interpretation of first four moments and linear dependency measures like covariance and correlation)
2. good calculus and algebra skills (solving maximization and minimization problems, taking derivatives, familiarity with sums, knowledge of matrix calculation)
3. basic knowledge in econometrics (interpretation of uni- and multivariate regression outputs, assumptions behind regression analysis)
4. basic knowledge in programming is helpful, but not necessary

e) Grading 

The grade comprises 3 components:

1. 50% Exam (60 minutes)
2. 30% Investment Proposal for the Portfolio of the Chair (presentation)
3. 20% Python coding exercise

f) Literature:

Bodie, Z.; Kane, A.; Marcus, A.J. (2024): Investments, Mc Graw Hill, New York.

Elton, E.J.; Gruber, M.J.; Brown, S.J.; Goetzmann, W.N. (2017): Modern Portfolio Theory and Investment Analysis, John Wiley & Sons, Hoboken.

Fabozzi, F.J.; Kolm, P.N.; Pachamanova, D.A.; Focardi, S.M. (2007): Robust Portfolio Optimization and Management, John Wiley & Sons, Hoboken.

Poddig, T.; Brinkmann, U.; Seiler, K. (2005): Portfolio Management: Konzepte und Strategien - Theorie und praxisorientierte Anwendungen mit Excel, Uhlenbruch Verlag, Bad Soden.

Vorobets, A. (2025): Portfolio Construction and Risk Management. Odense. Download Link: https://antonvorobets.substack.com/p/pcrm-book

g) Important Dates:

November 1, 2026 (3pm at the latest) - Deadline for registration (Investment Proposals)
January 7, 2027 (3pm at the latest) - Deadline Submission of Presentations
January 14, 2027 - Buy & Sell-Session I
January 21, 2027 - Buy & Sell-Session II
January 28, 2027 - Buy & Sell-Session III
(Attendance is compulsory for all students at the presentations given by their fellow students.)
January 31, 2027 (3 pm at the latest) - Deadline Submission of Coding Assignment
February 4, 2027 (4:15 to 5:45 pm, HS 32, 60 minutes) - exam (first examination period)
May 20, 2027 (4:15 to 5:45 pm, HS 32, 60 minutes) - exam (second examination period)