Current active course description (last updated 2018/19)
Data Science for Economics and Finance
BE202E
Current active course description (last updated 2018/19)
Data Science for Economics and Finance
BE202E
This course provides a thorough introduction to computational economics and finance. You will learn how to use RStudio to analyze financial data, estimating statistical models, and simulate observations. Rstudio is a free statistical software, which can be downloaded via your web browser. You will learn to use statistical models to assess whether observations are normally distributed, methods to carry out hypothesis testing, and statistical inference. Students will also learn the basics of resampling methods, and how to apply this to various economic and financial problems. Students will also be able to determine optimal portfolios and investments with factor models.
Upon successful completion of this course, the student should have the following learning outcome
Knowledge and understanding
- Be familiar with how to compute risk and returns
- Be familiar with analyzing the statistical distribution of risk and returns
- Be familiar with statistical inference of financial returns
- Be able to perform simple and multiple regression analysis
- Be familiar with autocorrelation and heteroskedasticity
- Be familiar with optimal portfolios
- Be familiar with factor models and how to apply these in a portfolio setting
Skills
- Demonstrate knowledge of how to use statistical software to import, and process economic and financial data.
- Know how to visualize statistical and other properties of economic and financial data
- Understand the basic principles of computational finance and economics
- Understand passive and active portfolio allocation
General competence
- Understand the capital asset allocation model
- Understand factor models
- Know how to apply statistical methods on economic and financial observations
- Know intermediate portfolio theory in practice
- Make market strategic decisions based on both optimal portfolio allocation, as well as factor investing
No tuition fees. Semester fees and cost of course literature apply.
Elective
Lectures with active and extensive use of computers and software. Online resources, such as courses offered through datacamp.com, is widely used throughout the course.
Evaluation using mid-term and final surveys. Students are also encouraged to participate in the central quality surveys.
Standard.
MA205E Matemathical methods in economics
BE206E Corporate Finance,
Overlap refers to a similarity between courses with the same content. Therefore, you will receive the following reduction in credits if you have taken the courses listed below:
ECO2002 - Introductory Empirical Finance and Data Science - 7.5 credits