How Good Is the Backpropogation Neural Network Using a Self-Organised Network Inspired by Immune Algorithm (SONIA) When Used for Multi-step Financial Time Series Prediction?
In this paper, a novel application of the backpropagation network using a self-organised layer inspired by immune algorithm is used for the prediction of financial time series. The simulations assess the data from two time series: Firstly the daily exchange rate between the US dollar and the Euro for the period from the 3rd January 2000 until the 4th November 2005, giving approximately 1525 data points. Secondly the IBM common stock closing price for the period from the 17th May 1961 until the 2nd November 1962, establishing 360 trading days as data points. The backpropagation network with the self-organising immune system algorithm produced an increase in profits of approximately 2% against the standard back propagation network, in the simulation, for the prediction of the IBM common stock price. However there was a slightly lower profit for the US dollar/Euro exchange rate prediction.
Unable to display preview. Download preview PDF.
- 2.Lindemann, A., Dunis, C.L., Lisboa, P.: Level Estimation, Classification and Probability Distribution Architectures for Trading the EUR/USD Exchange Rate. Neural Computing & Applications, forthcoming (2005)Google Scholar
- 4.Dunis, C., Williams, M.: Applications of Advanced Regression Analysis for Trading and Investment. In: Dunis, C., Laws, J., Naïm, P. (eds.) Applied Quantitative Methods for Trading and Investment, John Wiley, Chichester (2003)Google Scholar
- 9.Knowles, A.: Higher Order and pipelined Networks for Time Series Prediction of Currency Exchange Rates. MPhil, Liverpool John Moores University (2006)Google Scholar
- 12.Timmis, J.I.: Artificial Immune Systems: a Novel Data Analysis Technique Inspire by the Immune Network Theory. Ph.D. Dissertation, University of Wales, Aberystwyth (2001)Google Scholar
- 13.Thomason, M.: The practitioner method and tools. Journal of Computational Intelligence in Finance 7(3), 36–45 (1999)Google Scholar
- 15.Hyndman, R.J. (n.d.): Time Series Data Library, downloaded from: http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/. Original source from: McCleary & Hay, Applied Time Series Analysis for the Social Sciences, Sage Publications, Thousand Oaks (1980)