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 analyze 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:

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.

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
Oral examination without preparation primarily based on the group report.
Only certain aids allowed

Students are allowed to bring their group project.

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)

  • Category
  • Hours
  • Exercises
  • 30
  • Lectures
  • 30
  • Project work
  • 80
  • Preparation
  • 60
  • Exam Preparation
  • 5
  • Exam
  • 1
  • English
  • 206