Please note: there may be some adjustments to the teaching arrangements published in the course catalogue for 2020-21. Given current circumstances related to the Covid-19 pandemic it is anticipated that some usual arrangements for teaching on campus will be modified to ensure the safety and wellbeing of students and staff on campus; further adjustments may also be necessary, or beneficial, during the course of the academic year as national requirements relating to management of the pandemic are revised.

Advances in Machine Learning in Finance ACCFIN5229

  • Academic Session: 2022-23
  • School: Adam Smith Business School
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No
  • Available to Erasmus Students: No

Short Description

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


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





ILO being assessed

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 100% by weight of the components of the course's summative assessment.