# 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
Learning outcome

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
• 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.

Oral
Continuous feedback during the course
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.
Aid
Only certain aids allowed

Students are allowed to bring their group project report.

Marking scale
Censorship form
No external censorship
Several internal examiners.
##### 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 limit
The number of seats may be reduced in the late registration period
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)
Saved on the 05-05-2022

### Are you BA- or KA-student?

Are you bachelor- or kandidat-student, then find the course in the course catalog for students:

Courseinformation of students