Data-Driven Financial Models (DatFin)
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
- 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
- Using Matlab to analyse financial data
- Modeling, implementing and evaluating basic trading strategies for risk management
- Applying mean-variance portfolio theory
- 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.
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.
3rd Year B.Sc. students are invited to sign up as well.
PhD’s can register for MSc-course by following the same procedure as credit-students, see link above.
- 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.
- 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.
Criteria for exam assessment
See Learning Outcome.
Single subject courses (day)
- Project work
- Exam Preparation
- Course number
- 7,5 ECTS
- Programme level
- Full Degree Master
- Block 2
B1 And D
- No limit
The number of seats may be reduced in the late registration period
- Study Board of Mathematics and Computer Science
- Department of Computer Science
- Faculty of Science
- Omry Ross (4-7472776e45696e33707a336970)
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