Computational Optimization in Finance (R-Module)
Exam number: 6769
Semester: From 2nd semester
Duration of the module: One block
Form of the module (i.e. obligatory, elective etc.): Elective
Frequency of module offer: Every winter semester, usually 2nd block
Prerequisites: Students must have taken at least one course that introduced data analysis, statistical analysis, and/or machine learning, such as "Introduction to Portfolio Management with R" or "Data Analysis and Visualization with R". In particular, this means that students should have a good knowledge of the R programming language and a strong interest in quantitative methods and classification models.
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, Prof. Dr. Achim Koberstein
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: 28 h; Self-study: 152 h
Contact hours (per week in semester): 4
Methods and duration of examination:
Successful completion of an approx. 10-page seminar paper and implementation of a programming project in the R language as well as presentation at the end of the 2nd block (30 minutes).
Emphasis of the grade for the final grade: Please check regulations of the study programme
Aim of the module (expected learning outcomes and competencies to be acquired):
Students enrolled in this course will gain in-depth knowledge of various classification models and their application to real-world data to predict credit defaults. At the beginning of the seminar, all students receive an introduction to the general research topic. Afterwards, seminar participants work individually or in groups of two over several weeks independently on an empirical research project. At the end of the seminar, the results have to be summarized in a scientific paper. Overall, in this seminar, students acquire competencies in literature and data research, improve their R programming skills, and are especially enabled to conduct in-depth empirical research.
Contents of the module:
Please refer to the Moodle course for details.
Teaching and learning methods:
In the first two weeks, compulsory lectures introduce the general topic. After that, project work is carried out in small groups.
Literature (compulsory reading, recommended literature):
Literature will be announced in the Moodle course.
Self-enrollment in the Moodle course is required. Students must register for the seminar by the deadline specified in Moodle.