Course
Financial Engineering in Python (IND660)
Facts
Course code IND660
Credits (ECTS) 10
Semester tution start Spring
Language of instruction English
Number of semesters 1
Exam semester Spring
Time table View course schedule
Literature Search for literature in Leganto
Introduction
This course will introduce students to applying statistical and empirical analysis for financial engineering and quantitative analyses.
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.
The course covers the following topics:
- 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
Exam
Project and home exam
Weight 1/1
Marks Letter grades
Project
Weight 6/10
Marks Letter grades
Home exam
Weight 4/10
Duration 1 Days
Marks Letter grades
Aid All
The assessment is done in two parts:
- Project assignment that can be submitted individually or in groups of up to two students (60%)
- Home exam that is done individually (40%), all aids allowed
Assignment texts for the project and take-home exam are given in English, and the deliverables may be written in English or Norwegian.
Retake options are not available for the project assignment. Students who do not pass or who wish to improve their grade will undertake the project assignment again the next time the course is taught. Deliverables in one semester may not be reused in subsequent curse iterations.
A self-declaration form for the use of AI tools in the course must be used.