154-0578/01 – Portfolio and Risk Management (PRM)
| Gurantor department | Department of Finance | Credits | 6 |
| Subject guarantor | doc. Ing. Aleš Kresta, Ph.D. | Subject version guarantor | doc. Ing. Aleš Kresta, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Compulsory |
| Year | 2 | Semester | summer |
| | Study language | English |
| Year of introduction | 2024/2025 | Year of cancellation | |
| Intended for the faculties | EKF | Intended for study types | Bachelor |
Subject aims expressed by acquired skills and competences
By the end of the course, students should be able to understand the basics of portfolio management. They should understand the concept of risk aversion and utility function, the Capital Asset Pricing Model (CAPM), risk management, coherent risk measures. They should be able to apply portfolio selection techniques, analyze different investment strategies, measure portfolio performance, and calculate the risk (in terms of selected risk measures). They should also have a basic knowledge of the fintech in investment management, including big data, machine learning, artificial intelligence, and distributed ledger technology.
Teaching methods
Lectures
Tutorials
Teaching by an expert (lecture or tutorial)
Summary
This course covers the principles and practices of portfolio and risk management in the investment industry. Overall, this course aims to equip the students with the knowledge and skills needed to succeed in various roles within the investment industry. By the end of the course, students should understand the basics of portfolio management. They should understand the concept of risk aversion and utility function, the Capital Asset Pricing Model (CAPM), risk management, coherent risk measures. They should be able to apply portfolio selection techniques, analyze different investment strategies, measure portfolio performance, and calculate the risk (in terms of selected risk measures). They should also have a basic knowledge of the fintech in investment management, including big data, machine learning, artificial intelligence, and distributed ledger technology.
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
credit - elaboration of a given assignment
examination - written
E-learning
Other requirements
There are no other requirements on the student.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1. Basics of portfolio management: risk reduction through diversification, investment clients, steps in the portfolio management process, and pooled investments.
2. Basic characteristics in portfolio management: assets and portfolio returns, variance and covariance.
3. Basic characteristics in portfolio management: the concept of risk aversion, utility function, and its application in portfolio selection.
4. Portfolio selection: the feasible and efficient set of portfolios, optimal portfolio, minimum-variance portfolio, and tangential portfolio.
5. Capital Asset Pricing Model (CAPM): a portfolio of risk-free asset and risky assets, the capital market line, systematic risk and idiosyncratic risk, calculation of beta, assumptions of CAPM and its applications, limitations, and extensions.
6. Investment strategies: value investing, growth investing, income investing, momentum investing, and their implementation in portfolio management, as well as ESG investing.
7. Portfolio planning: investment policy statement, portfolio construction, and portfolio rebalancing.
8. Basics of risk management: risk management process, enterprise view of risk governance, risk identification, and risk drivers.
9. Basics of risk management: taxonomy of risks, tools, and practices for portfolio risk management.
10. Measuring portfolio risk: coherent risk measures, risk metrics, variance, semivariance, Value at Risk (VaR), and Conditional Value at Risk (CVaR).
11. Measuring portfolio performance: compound Annual Growth Rate (CAGR), maximum drawdown, and performance ratios.
12. Fintech in investment management: definition of fintech, big data, machine learning and artificial intelligence (AI), data science, and information extraction from big data.
13. Fintech in investment management: distributed ledger technology and its potential applications.
14. Selected applications of fintech in investment management.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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