Analytics in Finance

 

Analytics in Finance

The tools used by an engineer in Business Analytics, are often grouped in:

  • Descriptive Analytics, which can answer the question “What has happened?”
  • Predictive Analytics, that focus more on finding out “What will happen?”
  • Prescriptive Analytics, where actions are taken and thus answers the question “What should we do?”

The MSc in Business Analytics builds upon this concept and offers three optional study lines, each allowing you to become a specialist in certain areas:

  • Predictive Analytics
  • Prescriptive Analytics
  • Analytics in Finance

Analytics in Finance is all about data analysis and data-based decision making. From the performance of financial markets to political decisions impacting those markets to the financial dispositions of an individual household on their savings, loans and insurance needs, it all is reflected in large amounts of data.

Within this study line,  the students can follow courses which teach them about the workings of financial markets, financial products and risk management and optimisation in financial decision making. Combining courses in finance, machine learning and optimisation, enables the students to analyse financial data and set up mathematical models for financial problem solving, be it asset allocation, risk management or household financial advice. 

There are many companies, both within the Fintech ecosystem and the established financial sector who are interested in working with students and later hiring them to work on these topics.

Aside from the mandatory courses from the general competencies, the study line Analytics in Finance requires you select technical specialization courses in the following matter.

Select the following courses:

02417 Time Series Analysis 5 point Spring F4B (Fri 8-12)
42104 Introduction to Financial Engineering 5 point Autumn E5A (Wed 8-12)
42108 Advanced Financial Engineering 5 point Spring F5B (Wed 13-17)

and at least two of the following courses: 

02425 Diffusions and stochastic differential equations 5 point Autumn E1B (Thurs 13-17)
02427 Advanced Time Series Analysis 10 point Autumn E5 (Wed 8-17)
02435 Decision-Making Under Uncertainty 5 point Spring F4A (Tues 13-17)
42015 Energy Economics 5 point Autumn E2A (Mon 13-17)
46755 Renewables in electricity markets 5 point Spring F2A (Mon 13-17)

Students following this study line can benefit from taking elective courses at Copenhagen Business School (CBS) or the University of Copenhagen (KU). Course registration is subject to the rules and regulations for visiting students at CBS and KU, hence, you are not guaranteed access.

Beware that the deadline for course registrations is not the same as at DTU. It is also a good idea to apply early for course pre-approval.

CBS course suggestions:

KAN-CMECV1251U  Financial Engineering (Advanced)
KAN-CMECV1250U  Mathematical Finance 2: Continuous Time Finance (Advanced)
KAN-CCMVV2413U  Python for the Financial Economist (Advanced)

KU course suggestions:

NMAA05113U Continuous Time Finance (FinKont) (Advanced)
NMAK15010U Continuous Time Finance 2: (FinKont2) (Advanced)
NMAA09045U Finance 2: Dynamic Portfolio Choice (Fin2) (Advanced)
NMAK16004U Computational Finance (Advanced)

Example study plans

Analytics in Finance_skema

General competences
Technological specialization
Thesis
Electives
1.Semester
42104
Introduction to Financial Engineering
5 point
42577
Introduction to Business Analytics
5 point
42114
Integer Programming
5 point
42112
Mathematical Programming Modelling
5 point
Electives I
Electives
10 point
2.Semester
42576
From Analytics to Action
5 point
42578
Advanced Business Analytics
5 point
02417
Time Series Analysis
5 point
42108
Advanced Financial Engineering
5 point
42186
Model-based machine learning
5 point
42500
Innovation in Engineering
5 point
3.Semester
Electives II
Electives
20 point
02427
Advanced Time Series Analysis
10 point
4.Semester
Thesis
Thesis
30 point

Winter start is a bit more complex, as most introductory courses will only be available on the second semester. The following study plan is an example of how a to follow the Analytics in Finance study line with winter start.

General competences
Technological specialization
Thesis
Electives
1.Semester
02417
Time Series Analysis
5 point
42500
Innovation in Engineering
5 point
Electives I
Electives
15 point
02806
Social data analysis and visualization
5 point
2.Semester
42577
Introduction to Business Analytics
5 point
42104
Introduction to Financial Engineering
5 point
42114
Integer Programming
5 point
42112
Mathematical Programming Modelling
5 point
Electives II
Electives
10 point
3.Semester
42576
From Analytics to Action
5 point
42578
Advanced Business Analytics
5 point
42108
Advanced Financial Engineering
5 point
02443
Stochastic Simulation
5 point
Electives III
Electives
5 point
02435
Decision-Making Under Uncertainty
5 point
4.Semester
Thesis
Thesis
30 point