Course Details in 2024/25 Session

Module Title	LM Advanced Quantitative Methods
School	Birmingham Business School
Department	Economics
Module Code	07 40053
Module Lead	Dr Gunes Bebek
Level	Masters Level
Credits	20
Semester	Semester 2
Pre-requisites
Co-requisites
Restrictions	None
Exclusions
Description	The module gives a solid introduction to machine learning tools and applies them in financial economics. The module begins with an introduction to the problem of statistical learning and maps the various machine learning methods and their key characteristics such as supervised and unsupervised learning, prediction, classification, the bias-variance trade-off and interpretability. Then it proceeds to cover the main key techniques used in practice such as regularization and model selection, logistic regression, principal components, trees and random forests, support vector machines. These methods are demonstrated in real-world financial economics problems.
Learning Outcomes	By the end of the module students should be able to: Critically evaluate knowledge and understanding of econometric techniques employed to analyse financial data. Synthesise econometric studies in the empirical literature and critically analyse the results and the approaches adopted. Critically analyse financial databases and create models using appropriate software. Identify and critically appraise recent developments in advanced econometrics techniques used in financial economics. Develop critical thinking, judgement and ICT based problem-solving skills.
Assessment
Assessment Methods & Exceptions	Assessment: (1750 word equivalent) individual econometric project (e.g. using a simulation or digital platform) and financial report (50%). 1.5 hour exam (50%). Reassessment: Students only resit the failed assessment component with same weighting. (1750 word equivalent) individual econometric project (e.g. using a simulation or digital platform) and financial report (50%). 1.5 hour exam (50%).
Other
Reading List