Gorse, D;
(2011)
Application of stochastic recurrent reinforcement learning to index trading.
In:
ESANN 2011 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
ESANN
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Abstract
A novel stochastic adaptation of the recurrent reinforcement learning (RRL) methodology is applied to daily, weekly, and monthly stock index data, and compared to results obtained elsewhere using genetic programming (GP). The data sets used have been a considered a challenging test for algorithmic trading. It is demonstrated that RRL can reliably outperform buy-and-hold for the higher frequency data, in contrast to GP which performed best for monthly data.
| Type: | Proceedings paper |
|---|---|
| Title: | Application of stochastic recurrent reinforcement learning to index trading |
| Event: | ESANN 2011: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
| ISBN-13: | 978-2-87419-044-5 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | http:http://www.elen.ucl.ac.be/Proceedings/esann/e... |
| Language: | English |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1339314 |
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