|Availability||Available only to MSc Quantitative Finance students|
|Contact||Lectures: 16 | Tutorials: 0 | Labs: 20 Assignments: 80 | Self study: 84|
|Assessment||100% coursework. The assessment will comprise four individual assignments.|
|Lecturer||Dr Marc Roper|
This class aims to provide an overview of the application of evolutionary computation techniques – those which mimic natural evolutionary processes (genetic algorithms and genetic programming in particular) – to a range of financial applications such as forecasting, portfolio optimisation and algorithmic trading.
The course is very practical in its nature: it is assessed entirely by a set of small mini-projects, and students are expected to develop solutions to problems using evolutionary computation techniques, evaluate these on real data sets, and compare them with other more traditional approaches. Consequently, a large amount of self-directed study and learning is expected.
After completing this module participants will be able to:
- Understand the benefits and opportunities for evolutionary computing in the context of financial applications
- Understand the principles of evolutionary computation: genetic programming and genetic algorithms in particular
- Discuss the application of EC tools to financial problem solving and understand their limitations
- Develop and evaluate practical solutions to finance-based problems.
- Programming using R (Students will have some familiarity with R, typically from a statistic point of view, but this initial section looks at its use as a programming language)
- Principles of genetic algorithms and genetic programming
- Forecasting and Prediction – This topic looks at how it is possible to use genetic programming to generate predictive functions for time-series data.
- Portfolio Optimisation – This topic looks at how you can use evolutionary algorithms, and GAs in particular, to develop an optimal portfolio – a balanced set of investments that will yield the best return for the least risk.
- Algorithmic Trading – Algorithmic trading is a very broad area which sees the application of both GAs and GP (and sometimes both) to a wide variety of trading-related problems.
- Self-Selected Topic – The purpose of this final topic is to let you choose and explore some area of interest, carry out some practical investigations into the area, and produce a report which discusses your findings.