Course Catalogue

Advances in Machine Learning in Finance ACCFIN5229

  • Academic Session: 2025-26
  • School: Adam Smith Business School
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 1 (Alternate Years)
  • Available to Visiting Students: No
  • Collaborative Online International Learning: No
  • Curriculum For Life: No

Short Description

The course provides an overview of the latest applications of Machine Learning in Finance.

Timetable

Course is delivered over 2 weeks, comprising of 14 hours of lectures and 2 hours of tutorials.

Requirements of Entry

Registration on the MSc Financial Technology programme

Excluded Courses

None

Co-requisites

None

Assessment

ILO being assessed

Are reassessment opportunities available for all summative assessments? No

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below.

 

The group presentation is not reassessable.

Course Aims

The overall aim of the course is to present, discuss and explain some of the latest application applications of machine learning in Finance. Initially, it will discuss algorithmic and pairs trading. Then, it will move to bootstrapping, multiple hypothesis testing and its significance in Finance. The latest applications of machine learning in variable selection and factor analysis in Finance will be presented along with their importance and how they revolutionizes research in Finance.   

Intended Learning Outcomes of Course

By the end of this course students will be able to:

 

1. Understand, explain and evaluate algorithmic and pairs trading.

2. Understand, explain and compare the Family Wise Error Rate and the False Discovery Rate in the context of Finance

3. Appraise the applications of multiple hypothesis testing in variable selection and factor analysis in Finance

4. Evaluate the importance of machine learning in Finance research and the underlying reasoning of its popularity.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components of the course's summative assessment.