Utilizing artificial neural networks and genetic algorithms to build an algo-trading model for intra-day foreign exchange speculation

Evans, C. and Pappas, K. and Xhafa, F. (2013) Utilizing artificial neural networks and genetic algorithms to build an algo-trading model for intra-day foreign exchange speculation. Mathematical and Computer Modelling, 58 (5-6). pp. 1249-1266. ISSN 08957177 (ISSN)

Full text not available from this repository.

Abstract

The Foreign Exchange Market is the biggest and one of the most liquid markets in the world. This market has always been one of the most challenging markets as far as short term prediction is concerned. Due to the chaotic, noisy, and non-stationary nature of the data, the majority of the research has been focused on daily, weekly, or even monthly prediction. The literature review revealed that there is a gap for intra-day market prediction. Identifying this gap, this paper introduces a prediction and decision making model based on Artificial Neural Networks (ANN) and Genetic Algorithms. The dataset utilized for this research comprises of 70 weeks of past currency rates of the 3 most traded currency pairs: GBP{set minus}USD, EUR{set minus}GBP, and EUR{set minus}USD. The initial statistical tests confirmed with a significance of more than 95% that the daily FOREX currency rates time series are not randomly distributed. Another important result is that the proposed model achieved 72.5% prediction accuracy. Furthermore, implementing the optimal trading strategy, this model produced 23.3% Annualized Net Return. © 2013 Elsevier Ltd.

Item Type: Article
Uncontrolled Keywords: Artificial neural networks, Foreign exchange, Genetic algorithms, Technical analysis, Trading strategies, Foreign exchange, Foreign exchange markets, Neural networks and genetic algorithms, Optimal trading strategy, Prediction and decision, Short term prediction, Technical analysis, Trading strategies, Data processing, Economics, Forecasting, Genetic algorithms, Neural networks, Commerce
Subjects: G400 Computer Science
Divisions: UoA Collections > UoA11: Computer Science and Informatics
Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Hussen Farooq
Date Deposited: 04 Aug 2016 14:10
Last Modified: 04 Aug 2016 14:10
URI: http://www.open-access.bcu.ac.uk/id/eprint/2624

Actions (login required)

View Item View Item

Research

In this section...