Financial Analysis Software Support
- Credits: 8
- Ending: Examination
- Range: 26sP
- Semester: winter
- Faculty of Economic Informatics
Teachers
Included in study programs
Teaching results
Teaching results:
Completion of the course Financial analysis software support presupposes the development of IT skills and skills in the field of data science, financial analysis and analysis of financial markets. The graduate of the course will obtain:
Knowledge
- extension of knowledge of programming languages (R and Python), which are used worldwide for data analysis, application of statistical methods, advanced data visualization, also for machine learning and big data,
- be able to work with the raw data and subsequently process this data into a form suitable for analysis,
- the use of mathematics, statistics and econometrics techniques for further financial risk management decisions
Skills
- solving the basic problems of data analysis using the mentioned programming languages,
- use of selected advanced approaches in the field of financial market analysis and financial econometrics
Competences
- knowledge and skills that can be used to solve practical tasks in financial practice
Indicative content
Selection of programming languages for financial analysis (Python language, R language). Advantages, disadvantages of use. Object types. Data types and structures. Working with data. Additional libraries. Modules (NumPy, pandas, ...). Preparation of data for processing. Data cleaning. Missing data. Data mining. Data munging. Importing financial data from the Internet. Webscraping. Input / Output operations. Data analysis. Advanced data visualization. Simulations. Portfolio optimization. Portfolio valuation. Option strategies. Financial derivatives. Financial time series. Financial econometrics (LM, GLM, ARIMA, GARCH, ...).
Support literature
1. CIPRA, T. Riziko ve financích a pojišťovnictví: Basel III a Solvency II. Praha : Ekopress, 2015.
2. HILPISCH, Y. Derivatives Analytics with Python. Data Analysis, Models, Simulation,
Calibration and Hedging. West Sussex: John Wiley & Sons Ltd, 2015.
3. HILPISCH, Y. Python for Finance: Mastering Data-Driven Finance. 2nd Edition, O'Reilly Media, 2019.
4. HITCHNER, J. R. Financial Valuation Applications and Models, New Jersey : John Wiley & Sons, 2003.
5. HULL, J. Options, Futures, and Other Derivatives. 11th Edition. Pearson, University of
Toronto, 2021.
6. PÁLEŠ, M. Jazyk R pre aktuárov. Bratislava : Vydavateľstvo Letra Edu, 2019.
7. PÁLEŠ, M. Jazyk Python pre aktuárov. Bratislava : Vydavateľstvo Letra Edu, 2022.
8. PECINOVSKÝ, R. Python. Kompletní příručka jazyka pro verzi 3.8. Praha: Grada Publishing, 2020.
9. PERLIN, M. S. Processing and Analyzing Financial Data with R. 1st Edition. Agencia Brasileira, 2017.
10. PILGRIM, M. Python 3. Ponořme se do Python(u) 3. Praha: CZ.NIC, z. s. p. o., 2011.
11. UNPINGCO, J. Python for Probability, Statistics, and Machine Learning. Second Edition. Cham : Springer Nature Switzerland AG, 2016.
Syllabus
1. Selection of programming languages for financial analysis (Python language, R language). Advantages, disadvantages of use. Object types. Data types and structures. Working with data. Additional libraries. Modules. 2. Preparation of data for processing. Data cleaning. Missing data. Data mining. Data munging. Importing financial data from the Internet. Webscraping. Input / Output operations. Data analysis. Advanced data visualization. 3. Portfolio optimization. Portfolio valuation. Option strategies. Financial derivatives. 4. Financial time series. Financial econometrics.
Requirements to complete the course
Requirements to complete the course:
The project elaboration - 60%
The project presentation and oral exam - 40%
Student workload
Total study load (in hours):
Participation in lectures - 26
Preparation for lectures - 60
Project preparation and project presentation - 100
Preparation for the final exam - 76
Total load - 260
Language whose command is required to complete the course
Slovak
Date of approval: 11.03.2024
Date of the latest change: 15.05.2022