Semester:WS 20/21
Type:Module/Course/Examination
Language:English
ECTS-Credits:4.0
Scheduled in semester:1
Semester Hours per Week / Contact Hours:40.0 L / 30.0 h
Self-directed study time:90.0 h
Type:Module/Course/Examination
Language:English
ECTS-Credits:4.0
Scheduled in semester:1
Semester Hours per Week / Contact Hours:40.0 L / 30.0 h
Self-directed study time:90.0 h
Module coordination/Lecturers
- Ass.-Prof. Dr. Sebastian Stöckl
(Modulleitung)
Curricula
Master's degree programme in Finance (01.09.2020)Events
Description
- An Introduction to tidy statistics and programming in RSourcing and downloading Financial Data (e.g. from Refinitiv Datastream and Eikon)Supervised vs. unsupervised learningLinear and multiple regressionsClassification problemsPrincipal components and clustering
Learning Outcomes
- Students understand and can apply simple and multiple linear regressions as well as corresponding diagnostic tests.Students understand the pitfalls related to financial time series and know the corresponding methods and tools to overcome them.Students understand the concepts of supervised and unsupervised learning, can give examples and apply such methods to financial datasets.
Qualifications
Lectures Method
Interactive lectures, exercises
Literature
- DeFusco, R. A., McLeavey, D. W., Pinto, J. E., Runkle, D. E., & Anson, M. J. P. (2015). Quantitative Investment Analysis (3 ed.). Hoboken, New Jersey: Wiley.Groebner, D. F., Shannon, P. W., & Fry, P. C. (2017). Business Statistics: A Decision-Making Approach (10 ed.). Boston: Pearson.James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning: With Applications in R (1st ed.). Springer.
Exam Modalities
See lecture(s) within the module
Exams
- PWW-MA_Statistics (WS 20/21, bewertet)