Financial Technology MSc
Advances in Machine Learning in Finance ACCFIN5229
- Academic Session: 2026-27
- School: Adam Smith Business School
- Credits: 10
- Level: Level 5 (SCQF level 11)
- Typically Offered: Semester 1
- 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 10 hours of lectures and 2 hours of tutorials.
Excluded Courses
None
Co-requisites
None
Assessment
1. Written assignment, including Essay; Individual; 1300 words; 75%; ILOs 1-4.
2. Oral assessment & presentation; Group; 20 minutes; 25%; ILOs 1-4.
Are reassessment opportunities available for all summative assessments? No
It is the default expectation that all courses will offer opportunities for reassessment or deferred assessment. Where it is not possible to offer this in some assessment components, the grade achieved at the first attempt will be counted towards the final course grade, and any exceptions for this course are described below.
[No exceptions]
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.