ST458      Half Unit
Financial Statistics II

This information is for the 2025/26 session.

Course Convenor

Availability

Requisites

Pre-requisites:

Students must have completed ST425 and ST436 before taking this course.

The course covers a wide range of modern financial data analytics, with some built on the course ST436 Financial Statistics using the "R" environment, bridging advanced concepts in financial statistics to hands on practices.

Topics include:

• Decision Trees, Random Forests and Gradient Boosting;
• Neural Networks and deep learning in financial data analysis;
• Extension to the LSTM architecture;
• Factor model and cointegration;
• Granger Causality;
• Portfolio allocation in high frequency data;
• All refresh vs pairwise refresh times;
• Gross/Maximum exposure constraints for vast portfolios.

Consolidation of all concepts and practices will be done through case studies, on top of bi-weekly exercises.

10 hours of seminars and 20 hours of lectures in the Winter Term.

This course has a reading week in Week 6 of Winter Term.

Students will be expected to produce 5 problem sets in the WT.

• De Prado, M. L. (2018). Advances in Financial Machine Learning. John Wiley & Sons.
• Dr. Param Jeet, Prashant Vats (2017). Learning Quantitative Finance with R. Packt Publishing.
• Ruey S. Tsay (2005). Analysis of Financial Time Series, 2nd Edition. Wiley Series in Probability and Statistics.
• Trevor Hastie, Robert Tibshirani, Jerome Friedman (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. Springer Series in Statistics.
• Matthew F. Dixon, Igor Halperin, Paul Bilokon (2020). Machine Learning in Finance: From Theory to Practice. Springer.
• Stefan Jansen (2020). Machine Learning for Algorithmic Trading, 2nd Edition. Packt Publishing.

Lecture notes will be provided on Moodle.

Exam (70%), duration: 120 Minutes in the Spring exam period

Project (30%)