Financial Decision Science is concerned with computing optimal quantitative decisions in the area of Finance. The focus is set on various approaches towards modelling decision problems using certain Optimization under Uncertainty techniques, i.e. Stochastic Optimization and Robust Optimization, with the emphasis on computing a decision that can be re-integrated into a respective business process accordingly. The course aims at closing the gap between the need of decision takers and the art of modeling decision problems quantitatively.

Students are able to identify a decision problem in the area of Finance and formulate a non-trivial decision optimization model. Furthermore, students are able to formulate optimization models with various optimization modeling languages, e.g. Julia/JuMP or AMPL. Finally they are able to integrate the whole Decision Science workflow using R, i.e. from obtaining and pre-processing data to polishing results and interpreting the solution appropriately. Upon completion of the course participants will be able to:

1. Define a research problem that can be formulated as a novel and non-trivial optimization problem from from the area of Financial Decision Science.
2. Solve the research problem using R as well as modern optimization modeling languages.
3. Present results at scientific conferences.

The course offers an in-depth tutorial for modeling optimization problems as well as a combination of student presentations of group projects and joint discussion of journal articles relevant for Financial Decision Science. 

1. Preparation and active participation (30%);
2. Quality of own presentations with regard to scientific quality and presentation skills (50%);
3. Quality of contribution in reviewing other teams' scientific work (20%);

- Admission to doctoral or PhD program

Via Email.