Financial Engineering in Python (IND660)

This course will introduce students to applying statistical and empirical analysis for financial engineering and quantitative analyses.


Course description for study year 2025-2026

See course description and exam/assesment information for this semester (2024-2025)
Facts

Course code

IND660

Version

1

Credits (ECTS)

10

Semester tution start

Spring

Number of semesters

1

Exam semester

Spring

Language of instruction

English

Offered by
Time table

View course schedule

Content

This course will apply statistical and empirical analysis for financial engineering and quantitative analysis. Investment decisions have become increasingly data-driven and this course will provide tools for the student to assess investment opportunities quantitatively. The theory and methods in the course will provide an improved basis for economic decision-making.

Students will solve and discuss problems and case studies using programming and data from stocks, commodities, and fixed income. The course will expand on topics covered in the course IND500 Investment Analysis, in addition to introducing new relevant topics.

Content:

  • Statistical and empirical models for quantitative analysis

  • Programming for quantitative analysis

  • Time series analysis

  • Portfolio analysis

  • Stochastic and deterministic models

  • Gain understanding of quantitative analysis and financial engineering applied to investment opportunities

Learning outcome

Knowledge

After completing the course, the student should know:

  • Programming statistical and empirical models

  • Data analysis

  • Monte Carlo simulation and scenario analysis

  • Random numbers

  • Time Series Analysis: Characteristics in equity, commodity and bond markets.

  • Portfolio management

  • Forecasting and backtesting

Skills

After completing the course, the student should be able to:

  • Identify an investment opportunity and set up how to implement and solve the investment problem using programming and quantitative analysis

  • Utilize data analysis in order to provide unbiased investment analysis

  • Discuss different methods and their pros and cons

  • Perform stochastic and deterministic analysis

  • Discuss results and outcomes from a business/investor perspective

General competence

After completing the course, the student should be able to communicate:

  • How an investment opportunity can be assessed using quantitative analysis and financial engineering

  • What models to utilize to a given problem/opportunity

  • How to implement the models and utilize data for analysis

  • Data characteristics and limitations to a data set

Required prerequisite knowledge

None

Exam

Form of assessment Weight Duration Marks Aid
Home exam 1/1 1 Days Letter grades All

The home exam is done individually. 

Course teacher(s)

Course coordinator:

Roy Endre Holsvik Dahl

Head of Department:

Tore Markeset

Method of work

Lectures and voluntary tasks. Discussions and workshops during the lectures.

Open for

Admission to Single Courses at the Faculty of Science and Technology
Industrial Economics - Master of Science Degree Programme Industrial Economics - Master of Science Degree Programme, Five Year Mathematics and Physics - Master of Science Degree Programme Risk Analysis - Master of Science Degree Programme
Exchange programme at Faculty of Science and Technology

Admission requirements

Must meet the admission requirements of one of the study programmes the course is open for.

Course assessment

The faculty decides whether early dialogue should be conducted in all or selected groups of courses offered by the faculty. The purpose is to gather feedback from students for making changes and adjustments to the course during the current semester. In addition, a digital evaluation, students’ course evaluation, must be conducted at least once every three years. Its purpose is to collect students` experiences with the course.

Literature

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