CS971 - Evolutionary Computation for Finance

Aims and Objectives

This class aims to provide an overview of the application of evolutionary computation techniques – those which mimic natural evolutionary processes (genetic algorithms, genetic programming and neural networks in particular) – to a range of financial applications such as forecasting, portfolio optimisation and algorithmic trading.

The course is very practical in its nature: much of the learning is achieved via a number (around 4) of assessed 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.

Learning Outcomes

After completing this class 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, and also neural networks (particularly those configurations most suited to time series data).
• Understand how the computational approaches covered in the class may be applied to financial problem solving and understand their limitations.
• Develop and evaluate practical solutions to finance-based problems.

Syllabus

Part 1

• 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
• 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
• Principles of basic neural networks
• Forecasting and Predication – This topic looks at how it is possible to use genetic programming and/or neural networks to generate predictive functions for time-series data.

Part 2

• Advanced Neural Network configurations (e.g. RNNs and LSTMs)
• Algorithmic Trading – This topic looks at the application of both Neural Networks potentially combined with GAs or GP to identify automatically at what point a trade should be executed, and evaluate the effectiveness of the strategy through backtesting.

Recommended Reading

This list is indicative only – the class lecturer may recommend alternative reading material. Please do not purchase any of the reading material listed below until you have confirmed with the class lecturer that it will be used for this class.

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.

Last updated: 2022-10-05 00:13:02