Data-Driven Financial Models (DatFin)
Course content
The course gives the student a thorough introduction to financial
theory, financial markets and products. Besides theory, students
will be introduced to practical problems faced by Financial
Engineers through a number of real-world case studies. The course
will prepare the student to take other advanced
courses within finance. The students who are interested
in using big data in financial markets should
consider taking this course.
We will cover some of the following subjects in class:
- Introduction to finance and Matlab
- Delineating Efficient Portfolio and calculate the Efficient Frontier
- The Capital Asset Pricing Model (CAPM)
- Interest rate theory, bonds and management of bond portfolios
- Empirical tests of the CAPM
- Evaluation of portfolio performance
Knowledge of
- Financial securities and financial markets
- Basic statistical properties of financial data
- Selected financial models, e.g. Single index model (Sharpe's model), Black-Litterman model, CAPM
- The ideas behind diversification and modern portfolio theory
- Basic evaluation of financial portfolios and money managers
- The basic theory of fixed income markets
Skills in
- Using Matlab to analyse financial data
- Modeling, implementing and evaluating basic trading strategies for risk management
- Applying mean-variance portfolio theory
Competencies in
- Developing basic financial portfolios using quantitative analysis
- Performing quantitative evaluation of risk-return trade-offs
- Testing new trading strategies
- Using quantitative skills in financial markets
Mixture of lectures, study groups and project group work with hand-ins.
Suggested literature:
Introduction to Matlab by MathWorks: https://www.mathworks.com/moler/intro.pdf
E. Elton, M. Gruber, S. Brown, W. Goetzmann, Modern Portfolio Theory and Investment Analysis, Wiley
It is expected the students know how to install and use Matlab
by themselves. It is also expected that students know what matrices
and vectors are and basic statistics (such as linear regression)
and basic knowledge of programming in any language.
Academic qualifications equivalent to a BSc degree is
recommended.
As an exchange, guest and credit student - click here!
3rd Year B.Sc. students are invited to sign up as well.
Continuing Education - click here!
PhD’s can register for MSc-course by following the same procedure as credit-students, see link above.
- ECTS
- 7,5 ECTS
- Type of assessment
-
Oral examination, 20 minutes
- Type of assessment details
- The oral examination is without preparation and is primarily
based on the group project report.
The grade is based on the group project report and the oral examination. However, as the oral exam is done individually, grades may vary significantly between team members, and it is required to clearly state the individual contributions in the project report. - Exam registration requirements
-
The group project report must be submitted by the due date in order to qualify for the exam.
The group project report is written in groups of 2-4 students.
- Aid
- Only certain aids allowed
Students are allowed to bring their group project report.
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners.
- Re-exam
-
Same as the ordinary exam.
In order to qualify for the re-exam, the student must submit a revised project report with clearly stated individual contributions no later than 2 weeks prior to the re-exam.
Criteria for exam assessment
See Learning Outcome.
Single subject courses (day)
- Category
- Hours
- Lectures
- 30
- Preparation
- 60
- Exercises
- 30
- Project work
- 80
- Exam Preparation
- 5
- Exam
- 1
- English
- 206
Kursusinformation
- Language
- English
- Course number
- NDAK17001U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 block
- Placement
- Block 2
- Schedulegroup
-
B1 And D
- Capacity
- No limitation – unless you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
- Studyboard
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Computer Science
Contracting faculty
- Faculty of Science
Course Coordinator
- Omry Ross (4-7270756c43676c316e7831676e)
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Kursusinformation for indskrevne studerende