CS983 – Evolutionary Computation For Finance 1

TIMETABLE TEACHING MATERIAL
Credits 10
Level 5
Semester 2
Prerequisites N/A
Availability Available only to MSc Quantitative Finance, MSc Fintech, MSc Data Analytics 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 (a) the nature of evolutionary computing (b) the suitability of evolutionary computing for financial applications and to provide practical experience in developing and operating evolutionary computing approaches for financial applications.

Learning Outcomes

On completion of this class students will:

  • gain an understanding of the basic evolutionary computational 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.

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 basic approaches: genetic algorithms and genetic programming, their implementation in R (for example) and application to problems such as forecasting (price prediction) and portfolio optimisation.

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.