154-0579/01 – Advanced Portfolio Management (APM)

Gurantor departmentDepartment of FinanceCredits5
Subject guarantordoc. Ing. Aleš Kresta, Ph.D.Subject version guarantordoc. Ing. Aleš Kresta, Ph.D.
Study levelundergraduate or graduateRequirementChoice-compulsory type B
Year3Semesterwinter
Study languageEnglish
Year of introduction2024/2025Year of cancellation
Intended for the facultiesEKFIntended for study typesBachelor
Instruction secured by
LoginNameTuitorTeacher giving lectures
KRE330 doc. Ing. Aleš Kresta, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2

Subject aims expressed by acquired skills and competences

Graduates of the course will have the following skills and competencies. They will understand the basic of Python syntax with a focus on portfolio optimization. They will know how to calculate basic statistics and visualize financial time series data in Python. They will understand the portfolio optimization process and they will be capable of optimizing the portfolio with respect to various models. They will be able to measure and evaluate the historical performance and risk of the portfolios and compare portfolios‘ performance to each other. They will understand the basics of algorithmic trading.

Teaching methods

Lectures
Tutorials

Summary

This course is designed to provide a comprehensive understanding of portfolio management using advanced techniques and tools. The course covers the basics of Python programming language and its applications in financial analysis, including data structures using NumPy and Pandas libraries. The course then delves into portfolio optimization and efficient set models, including mean-variance portfolio optimization, mean-semivariance, mean-CVaR, mean-CDaR, and the Black-Litterman model. The course also covers the uniform portfolio, the portfolio with uniform risk contributions, and the Hierarchical Risk Parity portfolio. Additionally, the course covers measuring portfolio performance and risk and various approaches to portfolio rebalancing, including alarms. The course also covers the basics of algorithmic trading and automated trading systems, including an example of two moving averages crossover system. Overall, this course provides a solid foundation for advanced portfolio management and equips learners with the necessary skills to succeed in this field.

Compulsory literature:

BRUGIÈRE, Pierre. Quantitative portfolio management: with applications in Python. Cham, Switzerland: Springer, 2020. Springer texts in business and economics. ISBN 978-3-030-37739-7. HILPISCH, Yves J. Python for algorithmic trading: from idea to cloud deployment. Sebastopol, CA: O'Reilly, 2020. ISBN 978-1-492-05335-4. PETZEL, Todd E. Modern Portfolio Management: Moving Beyond Modern Portfolio Theory. Hoboken, New Jersey: Wiley, 2022. ISBN 978-1119818502.

Recommended literature:

CFA Institute. Quantitative Investment Analysis. Fourth edition. Hoboken, New Jersey: Wiley, 2020. CFA Institute Investment Series. ISBN 978-1-119-74362-0. PALEOLOGO, Giuseppe A. Advanced Portfolio Management: A Quant's Guide for Fundamental Investors. Hoboken, New Jersey: Wiley, 2021. ISBN 978-1119789796. POMPIAN, Michael M. Behavioral finance and your portfolio: a navigation guide for building wealth. Hoboken, New Jersey: Wiley, 2021. ISBN 978-1119801610.

Way of continuous check of knowledge in the course of semester

Credit - elaboration and defense of the project. Examination - written.

E-learning

Other requirements

There are no other requirements on the student.

Prerequisities

Subject codeAbbreviationTitleRequirement
154-0578 PRM Portfolio and Risk Management Recommended

Co-requisities

Subject has no co-requisities.

Subject syllabus:

1. Introduction to Python: introduction, installation, Anaconda environment, Jupyter Notebook, Spyder editor 2. Introduction to Python: basic concepts and syntax, basic data types and working with variables, control structures 3. Structured data types (data structures), NumPy and Pandas libraries for data science: basic principles, selected cases 4. Working with financial time series in Python, calculation of basic statistics and visualization 5. Mean-variance portfolio optimization 6. General efficient sets: mean-semivariance, mean-CVaR, mean-CDaR 7. Black-Litterman model 8. The uniform portfolio, the portfolio with uniform risk contributions, Hierarchical Risk Parity portfolio 9. Measuring portfolio performance and risk 10. Possible approaches to portfolio rebalancing, alarms 11. Introduction to algorithmic trading and automated trading systems 12. Algorithmic trading and automated trading systems - example of two moving averages 13. Introduction to Python packages yfinance, PyPortfolioOpt, empyrial, Zipline 14. Using MS Excel for portfolio optimization and calculating historical performance

Conditions for subject completion

Full-time form (validity from: 2024/2025 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)		Min. number of pointsMax. počet pokusů
Credit and Examination Credit and Examination 100 (100) 51 3
        Credit Credit 35  18 2
        Examination Examination 65  23 3
Mandatory attendence participation: Without obligatory participation.

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Conditions for subject completion and attendance at the exercises within ISP: Credit - elaboration and defense of the project. Examination - written. Without obligatory attendance.

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Occurrence in study plans

Academic yearProgrammeBranch/spec.Spec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2024/2025 (B0412EKF015) Financial and Accounting Advisory P English Ostrava 3 Choice-compulsory type B study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner

Assessment of instruction

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