Data Science for Finance
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.
- 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.
- 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.
- Introduction to Bayesian inference.
This is an excerpt from the complete course description for the course. If you are an active student at BI, you can find the complete course descriptions with information on eg. learning goals, learning process, curriculum and exam at portal.bi.no. We reserve the right to make changes to this description.