CS984 – Evolutionary Computation For Finance 2

TIMETABLE TEACHING MATERIAL
Credits 10
Level 5
Semester 2
Prerequisites N/A
Availability Available only to MSc Quantitative Finance students
Contact Lectures: 10 | Labs: 10 | Homework / Private Study: 80
Assessment 100% coursework. The assessment will comprise a number of individual assignments.
Resit TBC
Lecturer Dr Marc Roper

General Aims

To help students understand the more advanced approaches of evolutionary computing and machine learning for financial applications and to provide practical experience in applying them to challenging financial problems such as algorithmic trading.

Learning Outcomes

On completion of this class students will:

  • gain an understanding of the more advanced evolutionary computational and machine learning techniques available;
  • gain an understanding of the relative advantages and disadvantages of each technique for different financial applications;
  • be able to evaluate the results of a financial problem investigated using evolutionary techniques
  • be able to operate applications that incorporate evolutionary approaches to computing.

Syllabus

The class will be presented through a mixture of lecture presentations, practical assignments and assessed projects and will include an introduction to evolutionary computing with special emphasis on financial applications. This class will focus on the more advanced approaches: neural networks and deep learning, their implementation in R (for example) and application to problems such as algorithmic trading.

Recommended Text/Reading

The class lecturer will recommend a mixture of specific and generic references for each part of the course. Where possible, free online material will be recommended.