Advanced Financial Modelling and Machine Learning (774N1)

15 credits, Level 7 (Masters)

Spring teaching

On this module, you’ll develop advanced skills in financial econometrics and machine learning for empirical analysis.

You’ll learn to:

  • model and forecast using time series techniques (ARMA, GARCH, cointegration)
  • implement machine learning methods (LASSO, PCA, tree-based models)
  • analyse dependence with copula theory.

All concepts are explained with practical examples and applied in Python. You’ll develop your IT proficiency, financial data handling skills with Reuters data and reinforce programming skills essential for modern quantitative finance.

Teaching

67%: Lecture
33%: Seminar

Assessment

70%: Coursework (Project)
30%: Examination (Computer-based examination)

Contact hours and workload

This module is approximately 150 hours of work. This breaks down into about 33 hours of contact time and about 117 hours of independent study. The University may make minor variations to the contact hours for operational reasons, including timetabling requirements.

We regularly review our modules to incorporate student feedback, staff expertise, as well as the latest research and teaching methodology. We鈥檙e planning to run these modules in the academic year 2026/27. However, there may be changes to these modules in response to feedback, staff availability, student demand or updates to our curriculum.

We鈥檒l make sure to let you know of any material changes to modules at the earliest opportunity.