# GRA 6518 Data Science for Finance

## GRA 6518 Data Science for Finance

Course code:
GRA 6518
Department:
Data Science and Analytics
Credits:
6
Course coordinator:
Paolo Giordani
Course name in Norwegian:
Data Science for Finance
Product category:
Master
Portfolio:
MSc in Quantitative Finance
Semester:
2025 Spring
Active status:
Active
Level of study:
Master
Teaching language:
English
Course type:
One semester
Introduction

This course covers the fundamental statistical tools used by quantitative analysts with a focus on forecasting financial series. Several key methods used in modern data science (also known as “statistical inference” and “machine learning”) are presented and applied to financial data.

Learning outcomes - Knowledge

By the end of the course, students are expected to know:

• Key features of financial data.
• Estimation methods: Least squares, weighted least squares, penalized least squares, maximum likelihood, boosting.
• Nonlinear modelling via splines, local regression, local maximum likelihood, regression trees.
• How to evaluate and select models.
Learning outcomes - Skills

By the end of the course, the students should have developed further the following key skills:

• written communication,
• oral communication,
• ethical awareness in conducting research,
• teamwork,
• problem solving and analysis,
• using initiative, and
• computer literacy.
General Competence

The students by the end of the course are expected to be able to reflect on the workings and limitations of the different models and algorithms, particularly as applied to financial data.

Course content
• Introduction to statistical learning. Features of financial data.
• K-nearest neighbour. The bias-variance trade-off.
• Least squares and linear projections. Weighted least squares.
• Modelling nonlinearities with interaction terms and local regressions.
• Ridge regression.
• Cross-validation.
• Modelling nonlinearities with regression splines.
• Introduction to time-varying models.
• Maximum likelihood (ML).
• Penalized ML. Weighted ML. Local ML. Splines and ML.
• Bootstrap and bagging.
• Introduction to boosting and regression trees.
Teaching and learning activities

Relevant financial data will be used throughout the course, both in lectures and in assignments. Students will be given regular assignments, focusing on hands-on application of the material covered in class and on software. Some coding will be necessary, and students will receive assistance with aspects of the assignments related to software and coding. These assignments are not compulsory and are not graded, but will be most useful as preparation for the take-home exam.

Labs will be conducted in R, but students are free to use any other programming language (Matlab, Python, Julia etc).

Software tools
R

The exam for this course has been changed starting academic year 2023/2024. The course now has two exam codes instead of one. It is not possible to retake the old version of the exam. For questions regarding previous results, please contact InfoHub.

It is the student’s own responsibility to obtain any information provided in class.

Honour Code
Academic honesty and trust are important to all of us as individuals, and represent values that are encouraged and promoted by the honour code system. This is a most significant university tradition. Students are responsible for familiarizing themselves with the ideals of the honour code system, to which the faculty are also deeply committed. Any violation of the honour code will be dealt with in accordance with BI’s procedures for cheating. These issues are a serious matter to everyone associated with the programs at BI and are at the heart of the honour code and academic integrity. If you have any questions about your responsibilities under the honour code, please ask.

Qualifications

All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

### Disclaimer

Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.

Required prerequisite knowledge

All students should have taken introductory courses in statistics and calculus. Introductory-level knowledge of a programming language (ideally R) is also needed.

Assessments
Assessments
Exam category:
Submission
Form of assessment:
Submission PDF
Exam/hand-in semester:
First Semester
Weight:
30
Grouping:
Group/Individual (1 - 3)
Duration:
1 Week(s)
Comment:
Assignment
Exam code:
GRA 65182
ECTS
Resit:
Examination when next scheduled course
Exam category:
School Exam
Form of assessment:
Written School Exam - pen and paper
Exam/hand-in semester:
First Semester
Weight:
70
Grouping:
Individual
Support materials:
• BI-approved exam calculator
• Simple calculator
• Bilingual dictionary
Duration:
2 Hour(s)
Exam code:
GRA 65183
ECTS
Resit:
Examination when next scheduled course
Type of Assessment:
Ordinary examination
All exams must be passed to get a grade in this course.
Total weight:
100
ActivityDurationComment
Teaching
24 Hour(s)
Webinar
12 Hour(s)
Student's own work with learning resources
25 Hour(s)
Group work / Assignments
24 Hour(s)
Prepare for teaching
40 Hour(s)
Examination
35 Week(s)