Introduction to Portfolio Management with R
Exam number: 6844
Semester: From the first semester
Duration of the module: first block
Form of the module (i.e. obligatory, elective etc.): Elective
Frequency of module offer: Every winter semesters
Prerequisites: Interest in the R programming language, good knowledge of mathematics, statistics and finance on a Bachelor level.
Applicability of module for other study programmes:
Obligatory or elective in other study programmes. For further information check regulations of the study programme.
Person responsible for module: Prof. Dr. Sven Husmann
Name of the professor: Prof. Dr. Sven Husmann
Language of teaching: English
ECTS-Credits (based on the workload): 6
Workload and its composition (self-study, contact time):
Contact time 60 h; self-study: 120 h
Contact hours (per week in semester): 4
Methods and duration of examination:
Oral examination of approximately 20 minutes and one home assignment.
Emphasis of the grade for the final grade: Please check regulations of the study programs.
Aim of the module (expected learning outcomes and competencies to be acquired):
Participants receive an intensive introduction to the R programming language and learn how to compare competing portfolio strategies based on real data.
Contents of the module:
From a scientific perspective, the core of portfolio management is to provide evidence that one investment strategy is superior to another. Therefore, a portfolio manager should have a basic knowledge of at least one programming language as well as a solid background in capital market theory, data analysis and linear algebra. The contents of this course are based on an application-oriented teaching of this knowledge, which we divide into two self-contained areas: First, an introduction to the programming language R and second, a scientifically oriented introduction to the basic procedures of modern portfolio management. Specific topics will be announced in the Moodle course.
Teaching and learning methods:
Self-study, online lectures and exercises, case studies.
Special features (e.g. percentage of online-work, practice, guest speaker, etc.):
Self-enrollment in the Moodle course is required.
Literature (compulsory reading, recommended literature):
Literature will be announced in the Moodle course.
Registration in Moodle Viadrina required.