Data Science for Finance

Introduksjon

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. The textbooks are widely adopted in machine learning courses but they do not focus on financial data; the necessary adaptations will be discussed in class and in the lecture notes. We will also learn how to move past the conditional mean and model other moments of the conditional distribution, such as quantiles. 

Kursets innhold

• 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.
• Model stacking.  
• Introduction to Bayesian statistical thinking (if time allows).

Forbehold

Dette er et utdrag fra den komplette kursbeskrivelsen for kurset. Dersom du er aktiv student på BI, kan du finne de komplette kursbeskrivelsene med informasjon om bl.a. læringsmål, læreprosess, pensum og eksamen på portal.bi.no. Vi tar forbehold om endringer i denne beskrivelsen.