NDAK17001U Data-Driven Financial Models (DatFin)

Volume 2024/2025

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
  • Testing new trading strategies
  • Using quantitative skills in financial markets

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.
Mixture of lectures, study groups and project group work with hand-ins.
  • Category
  • Hours
  • Lectures
  • 30
  • Preparation
  • 60
  • Exercises
  • 30
  • Project work
  • 80
  • Exam Preparation
  • 5
  • Exam
  • 1
  • Total
  • 206
Continuous feedback during the course of the semester
Feedback by final exam (In addition to the grade)
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.


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.

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 assesment

See Learning Outcome.