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Optimal artificial neural network topology for foreign exchange forecasting

Published: 28 March 2008 Publication History

Abstract

Foreign exchange market is one of the highest investments markets the average daily trade volume is 1.8 trillion USD. Foreign exchange rate forecasting has been always one of the most challenging subject and area of researches. Trader around the world is relying on the technical indicators which just following the price and has emerged a lag results. When the currency market has a random move (when the market is not trending) most of the indicators gets confused because of the fact that classical linear methods are unable to react with the non linearity in the data and hence with the market behavior.
This research reports empirical results that tend to confirm the applicability of a neural network model to the prediction of the foreign exchange rates market. Artificial neural networks have proven to be efficient and profitable in forecasting financial time series in particular, feed forwarded back propagation. It is important to use an optimal ANN topology that emerged great results in short term prediction and the daily predication results showed that ANN model learns well and most likely to generalize well. Weekly predication results demonstrate good results in the low prediction while failed to have a good results on the high and the close prediction while the monthly prediction did not give a satisfactory results due to a very few data samples.

References

[1]
Zahedi, F., "Intelligence Systems for Business, Expert Systems with Neural Networks", wodsworth Publishing Inc., 1993.
[2]
Jacek M. Zurada, "Introduction to Artificial Neural Systems", PWS Publishing Company, 1992, ISBN:534-95460-X
[3]
S. Kang, "An investigation of the use of feed forward neural network for forecasting", PhD Thesis, Kent State University, Kent, USA, (1991).
[4]
Phua, P. K. H. Ming, D., Lin, "Neural Network with Genetic Algorithms For Stocks Prediction", Fifth Conference of the Association of Asian-Pacific Operations Research Societies, July, Singapore, 2000.
[5]
"Forex information site". www.webtrading.com/downloads/forexmanual.pdf
[6]
"Trading forex site". http://www.easy-forex.com/en/Forex.FinancialCalendar.aspx
[7]
Yao, J. & Tan, C. L, "A case study on using neural networks to perform technical forecasting of forex. Neurocomputing", 31, 81--99, 2000
[8]
Makridakis, S., Anderson, A., Carbone, R., Fildes, R., Hibdon, M., Lewandowski, R., Newton, J., Parzen, E., & Winkler, R., "The accuracy of extrapolation (time series) methods: Results of a forecasting competition", Journal of Forecasting, 1, 111--153. (1982).
[9]
Ginzburg, I. & Horn, D., "Combined neural networks for time series analysis. Advances in Neural Information Processing Systems", pp 224--231, 1994.
[10]
"Wikipedia, free encyclopedia". http://en.wikipedia.org/wiki/Time_series.
[11]
Esfandiar Maasoumi, "Entropy and predictability of stock market returns", Journal of Econometrics, pp. 280--330, 2002
[12]
"Forex information site". http://www.webtrading.com/downloads/forexmanual.pdf
[13]
"Math Lab help". http://ww.mathworks.com/access/helpdesk/help/toolbox/nnet/nnet.html
[14]
Adya, M. & Collopy, F., "How effective are neural networks at forecasting and prediction? A review and evaluation", Journal of Forecasting, 17, 481--495. (1998).
[15]
"Trading forex site". http://www.easy-forex.com/en/Forex.FinancialCalendar.aspx
[16]
Wedding, D. K. & Cios, K. J., "Time series forecasting by combining RBF networks, certainty factors, and the Box-Jenkins model. Neuro computing, 10, 149--168, 1996.
[17]
James D Thomas, "News and Trading Rules", PhD Thesis, Graduate School of Industrial Carnegie Mellon University, January 2003.
[18]
Kaastra and M. Boyd, "Designing a neural network for forecasting financial and economic time series. Neuro computing", April 26, 2002.

Cited By

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  • (2022)BERTFOREX: Cascading Model for Forex Market Forecasting Using Fundamental and Technical Indicator Data Based on BERTIEEE Access10.1109/ACCESS.2022.315215210(23425-23437)Online publication date: 2022
  • (2019)Adaptive detection of FOREX repetitive chart patternsPattern Analysis and Applications10.1007/s10044-019-00862-823:3(1277-1292)Online publication date: 4-Dec-2019
  • (2018)Foreign currency exchange rate prediction using neuro-fuzzy systemsProcedia Computer Science10.1016/j.procs.2018.10.523144(232-238)Online publication date: 2018
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    cover image ACM Other conferences
    ACMSE '08: Proceedings of the 46th annual ACM Southeast Conference
    March 2008
    548 pages
    ISBN:9781605581057
    DOI:10.1145/1593105
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 28 March 2008

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    Author Tags

    1. artificial neural networks
    2. foreign exchange
    3. optimal topology

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    ACM SE08
    ACM SE08: ACM Southeast Regional Conference
    March 28 - 29, 2008
    Alabama, Auburn

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    Cited By

    View all
    • (2022)BERTFOREX: Cascading Model for Forex Market Forecasting Using Fundamental and Technical Indicator Data Based on BERTIEEE Access10.1109/ACCESS.2022.315215210(23425-23437)Online publication date: 2022
    • (2019)Adaptive detection of FOREX repetitive chart patternsPattern Analysis and Applications10.1007/s10044-019-00862-823:3(1277-1292)Online publication date: 4-Dec-2019
    • (2018)Foreign currency exchange rate prediction using neuro-fuzzy systemsProcedia Computer Science10.1016/j.procs.2018.10.523144(232-238)Online publication date: 2018
    • (2018)Extreme Market Prediction for Trading Signal with Deep Recurrent Neural NetworkComputational Science – ICCS 201810.1007/978-3-319-93701-4_31(410-418)Online publication date: 11-Jun-2018
    • (2015)Forecasting exchange rate using deep belief networks and conjugate gradient methodNeurocomputing10.1016/j.neucom.2015.04.071167:C(243-253)Online publication date: 1-Nov-2015
    • (2015)Technical Indicators for Forex Forecasting: A Preliminary StudyAdvances in Swarm and Computational Intelligence10.1007/978-3-319-20469-7_11(87-97)Online publication date: 2-Jun-2015
    • (2012)Linear Relationship Between The AUD/USD Exchange Rate And The Respective Stock Market Indices: A Computational Finance PerspectiveInternational Journal of Intelligent Systems in Accounting and Finance Management10.1002/isaf.33219:1(19-42)Online publication date: 1-Jan-2012
    • (2011)Forecasting exchange rate with deep belief networksThe 2011 International Joint Conference on Neural Networks10.1109/IJCNN.2011.6033368(1259-1266)Online publication date: Jul-2011

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