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 2024-2025. Please note that changes may occur.
Course code
IND660
Version
1
Credits (ECTS)
10
Semester tution start
Spring
Number of semesters
1
Exam semester
Spring
Language of instruction
English
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
Exam
| Form of assessment | Weight | Duration | Marks | Aid |
|---|---|---|---|---|
| Home exam | 1/1 | 1 Days | Letter grades | All |
The home exam is done individually.