Financial Modeling
- Credits: 6
- Ending: Examination
- Range: 2P + 2C
- Semester: winter
- Year: 2
- Faculty of Economic Informatics
Teachers
Included in study programs
Teaching results
Upon successful completion of the course, students will acquire the following knowledge:
- knowledge of financial market analysis,
- knowledge of portfolio theory,
- knowledge of the application of knowledge of portfolio theory in determining investment strategies,
- knowledge of machine learnig tools usable in the management of investment strategies.
Upon successful completion of the course, students will acquire the following skills:
- ability to use portfolio theory models in setting investment strategies,
- control of adequate software for solving portfolio theory tasks.
Upon successful completion of the course, students will acquire the following competencies:
- practical skills and competencies with the application of portfolio theory models in the analysis of financial markets using adequate software.
Indicative content
1. Evaluation of investment projects with financial mathematics tools.
2. Input financial data (stock markets) and their graphical interpretation.
3. Return and risk and their measurement: Concepts of risk measurement (standard deviation, absolute deviation, VaR, CVaR, DrawDown).
4. Simulation of returns on financial assets.
5. Categories of risk and return rates.
6. The concept of portfolio. Investment risk. Systematic and non-systematic risk. The concept of diversification.
7. Markowitz's approach to portfolio selection. Expected return and portfolio risk level. Analysis of the set of all portfolios. A set of effective portfolios. Method of generating efficient portfolios.
8. Models of portfolio selection in the area of return and risk.
9. Analysis of effective portfolios: Analysis of portfolios from risk-free and risky investments. Market portfolio and its properties.
10. CAPM model - modeling the mechanism of creating the equilibrium price of capital assets.
11. Portfolio performance and portfolio selection models.
12. Machine learning tools in finance.
13. Use of Machine learning tools in portfolio selection.
Support literature
1. Paiva, Felipe & Cardoso, Rodrigo & Hanaoka, Gustavo & Duarte, Wendel. (2018). Decision-Making for Financial Trading: A Fusion Approach of Machine Learning and Portfolio Selection. Expert Systems with Applications. 115. 10.1016/j.eswa.2018.08.003.
2. X. Yuan, J. Yuan, T. Jiang and Q. U. Ain, "Integrated Long-Term Stock Selection Models Based on Feature Selection and Machine Learning Algorithms for China Stock Market," in IEEE Access, vol. 8, pp. 22672-22685, 2020, doi: 10.1109/ACCESS.2020.2969293.
3. Guan, Hao and Zhiyong An. “A local adaptive learning system for online portfolio selection.” Knowl. Based Syst. 186 (2019): n. pag.
4. Kim, J.; Shin, S.; Lee, H.S.; Oh, K.J. A Machine Learning Portfolio Allocation System for IPOs in Korean Markets Using GA-Rough Set Theory. Sustainability 2019, 11, 6803. https://doi.org/10.3390/su11236803
5. Pekár J.: Modely matematického programovania na výber portfólia. 1. vyd. - Bratislava : Vydavateľstvo EKONÓM, 2015.
Requirements to complete the course
30 % work at seminars and writing of projects
70 % combined final exam
Student workload
156 hours
26 hours lecture attendance
26 hours seminar attendance
26 hours preparation for seminars
26 hours writing a seminar paper
52 hours preparation for final exam
Language whose command is required to complete the course
Slovak, Eglish
Date of approval: 11.03.2024
Date of the latest change: 16.05.2022