Data Science for Finance

Introduction

Please note that this is a preliminary course description. The final version will be published in June 2026.

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. Local regression.
• Penalization: ridge regression and lasso.
• Cross-validation.
• Modelling nonlinearities with regression splines.
• Generalized linear models and maximum likelihood.
• Penalized ML. Weighted ML. Local ML. Splines and ML.
• Regression trees and boosting.
• Nonlinear modelling of variance and quantiles.
• (Bootstrap and bagging --- if time allows).