Data Science for Finance

Introduction

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.

Course content

• 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.
• Cross-validation.
• 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.