102-050-IO Data Science
Study program: | International Financial Management (B.Sc.) |
Academic level and semester: | Bachelor, 5th/7th semester |
ECTS credits/workload per semester: | 6 / 150 |
Contact hours per week/contact hours per semester: | 4 / 45 |
Type/Teaching method: | Lecture |
| Language of instruction: | English |
Frequency: | Every semester |
Lecturer: | Prof. Dr. Leander Geisinger, Prof. Dr. Holger Graf |
Content: | Data science in finance plays a growing role, in particular regarding sustainability. Valuation and screening of financial instruments according to ESG-criteria (ecological, social and governancerelated criteria) relies on the analysis of large and fragmented data sets, a growing effort in view of current regulatory initiatives. This module consists of the two parts: 102-050-01 Data Science in Finance: Financial Analytics: Introduction to R and importing of financial data; Introduction to basic methods of data analysis (big data analysis, clustering, classification and covariance analysis, natural language processing) and the applications of these to financial data; Development and implementation of algorithms for automated risk management, portfolio optimizing, securities selection and valuation, simulation of financial markets and trading strategies 102-050-02 Financial Econometrics: Basics on properties of financial returns; Non-predictability of financinal returns; Stylized facts, in particular: heavy-tailed distributions, stochastic volatilities, volatility clustering, averaging of volatilities, etc.; GARCH-models and extensions; Copula-models, in particular pair-copula-constructions; Simulations of financial econometrics with R; Applications to risk- and portfolio-management |
Textbooks: | Bennett, M., & Hugen, D. (2016). Financial Analytics with R: Building a Laptop Laboratory for Data Science. Cambridge: Cambridge University PressAggarwal, C. C. (2015). Data Mining: The Textbook. Cham: SpringerAggarwal, C. C. (2018). Neural Networks and Deep Learning. Cham: SpringerCarmona, R (2014). Statistical analysis of financial data in R. Second edition. New York, NY: SpringerW. N. Venables, D. M. Smith (2021). An Introduction to R. |
Recommended for: | Undergraduates, graduates |
Prerequisites: | Intermediate level in Business/Finance/Economics, Excel |
Restrictions: | None |
Assessment: | Course work project |