Open Access Open Access  Restricted Access Subscription or Fee Access

Predicting Stock Market Exchange Prices for the Reserve Bank of Australia Using Auto-Regressive Feedforward Neural Network Model

(*) Corresponding author

Authors' affiliations



Predicting foreign exchange prices is one of the challenging fields of research due to its complexity, dynamical and non-linearity. It is usually affected by many factors such as economic conditions, investors' expectations, governmental events, and of course Wars in various areas of the world. The process of predicting money exchange rate help organizations, governments and business market to make decisions; it is essential for determining information about future markets. This paper introduces the basic idea of developing predicting models for currency exchange rate using Artificial Neural Network (ANN) and Linear Regression (LR) models. The data set used in the experiments collected during January 4, 2010 to December 31, 2013. Number of criterion were used to validate the developed model's performance. The ANN model show promising results.
Copyright © 2015 Praise Worthy Prize - All rights reserved.


Foreign Exchange Prices; Australian Dollar; Regression; Neural Network

Full Text:



B. Premanode and C. Toumazou, “Improving prediction of exchange rates using differential EMD,” Expert Syst. Appl., vol. 40, pp. 377–384, Jan. 2013.

M. Alizadeh, R. Rada, A. K. G. Balagh, and M. M. S. Esfahani, “Forecasting exchange rates: A neuro-fuzzy approach.,” in The 16th World Congress of the International Fuzzy Systems Association (IFSA) and the 9th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT) (J. a. P. Carvalho, D. Dubois, U. Kaymak, and J. M. da Costa Sousa, eds.), pp. 1745–1750, 2009.

A. K. Nag and A. Mitra, “Forecasting daily foreign exchange rates using genetically optimized neural networks,” Journal of Forecasting, vol. 21, no. 7, pp. 501–511, 2002.

W. Huang, K. K. Lai, Y. Nakamori, and S. Wang, “Forecasting foreign exchange rates with artificial neural networks: a review,” International Journal of Information Technology & Decision Making, vol. 3, no. 01, pp. 145–165, 2004.

D.-z. Cao, S.-L. Pang, and Y.-H. Bai, “Forecasting exchange rate using support vector machines,” in Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on, vol. 6, pp. 3448–3452, IEEE, 2005.

L. Ljung, ed., System Identification (2Nd Ed.): Theory for the User. Upper Saddle River, NJ, USA: Prentice Hall PTR, 1999.

M. Salimifard, M. Jafari, and M. Dehghani, “Identification of nonlinear mimo block-oriented systems with moving average noises using gradient based and least squares based iterative algorithms,” Neurocomput., vol. 94, pp. 22–31, Oct. 2012.

B. Majhi, M. Rout, R. Majhi, G. Panda, and P. J. Fleming, “New robust forecasting models for exchange rates prediction,” Expert Syst. Appl., vol. 39, pp. 12658–12670, Nov. 2012.

M. Khashei, M. Bijari, and G. A. RaissiArdali, “Improvement of auto-regressive integrated moving average models using fuzzy logic and artificial neural networks (anns),” Neurocomput., vol. 72, pp. 956–967, Jan. 2009.

C.-F. Liu, C.-Y. Yeh, and S.-J. Lee, “Application of type-2 neurofuzzymodeling in stock price prediction,” Appl. Soft Comput., vol. 12, pp. 1348–1358, Apr. 2012.

A. Sheta and K. De Jong, “Time-series forecasting using GA-tuned radial basis functions,” in Information Science Journal, pp. 221–228, 2001.

K. Lukoseviciute and M. Ragulskis, “Evolutionary algorithms for the selection of time lags for time series forecasting by fuzzy inference systems,” Neurocomput., vol. 73, pp. 2077–2088, June 2010.

M. Khashei, M. Bijari, and G. A. RaissiArdali, “Hybridization of autoregressive integrated moving average (ARIMA) with probabilistic neural networks (PNNS),” Comput. Ind. Eng., vol. 63, pp. 37–45, Aug. 2012.

E. Guresen, G. Kayakutlu, and T. U. Daim, “Using artificial neural network models in stock market index prediction,” Expert Syst. Appl., vol. 38, pp. 10389–10397, Aug. 2011.

J. Kamruzzaman and R. A. Sarker, “ANN-based forecasting of foreign currency exchange rates,” Neural Information Processing-Letters and Reviews, vol. 3, no. 2, pp. 49–58, 2004.

C.-T. Lye, T.-H. Chan, and C.-W. Hooy, “Forecasting chinese foreign exchange with monetary fundamentals using artificial neural networks,” in 3rd IntConfInf Finance Eng, vol. 12, pp. 560–564, 2011.

J. Kamruzzaman, R. Sarker, et al., “Forecasting of currency exchange rates using ANN: A case study,” in Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on, vol. 1, pp. 793–797, IEEE, 2003.

M. A. Hernandez Medina and C. M. Mendez, “Modelling and prediction of the MXNUSD exchange rate using interval singleton type-2 fuzzy logic systems [application notes],” Comp. Intell. Mag., vol. 2, pp. 5–8, Feb. 2007.

A. F. Sheta, S. E. M. Ahmed, and H. Faris, “A comparison between regression, artificial neural networks and support vector machines for predicting stock market index,” International Journal of Advanced Research in Artificial Intelligence (IJARAI), 2015.

A. F. Sheta, S. E. M. Ahmed, and H. Faris, “Evolving stock market prediction models using multigene symbolic regression genetic programming,” Artificial Intelligence and Machine Learning (AIML), vol. 15, pp. 11–20, 6 2015.

A. Sheta, H. Faris, and M. Alkasassbeh, “A genetic programming model for S&P 500 stock market prediction,” International Journal of Control and Automation, vol. 6, no. 5, pp. 303–314, 2013.

D. E. Gustafson and W. C. Kessel, “Fuzzy clustering with a fuzzy covariance matrix,” in Proceedings of the IEEE CDC, San Diego, CA, USA, p. 761766, 1979.

R. Babu˜ska, Fuzzy Modeling and Identification. PhD thesis, Delft Univesrsity of Technology, 1996.

L.Ljung, System Identification - Theory for The User. Prentice Hall, Upper SaddleRiver, N.J., 2nd edition, 1999.

M.Norgaard, O.Ravn, Poulsen, and L.K.Hansen, Neural Networks for Modelling and Control of Dynamic Systems. Springer, London, 2000.

H. Hiary, “Identification and model predictive controller design of nonlinear systems using artificial neural networks,” Master’s thesis, Balqa Applied University, Salt, Jordan, 2005.

A. Sheta, H. Al-Hiary, and M. Braik, “Identification and model predictive controller design of the Tennessee Eastman chemical process using ANN,” in Proceedings of the 2009 International Conference on Artificial Intelligence (ICAI’09), July 13-16, 2009, USA, vol. 1, pp. 25–31, 2009.

C. Busch, Whole-Arm Grasping with Hyper-Redundant Planer ManipulatorsUsing Neural Networks. Vorarlberg University of Applied Sciences, Diplomathesis, 2002.

N.Chiras, C.Evans, and D.Rees, “Non-linear gas turbine modeling using feedforward neural networks,” Proceedings of ASME TURBO EXPO June 3-6, Amsterdam, The Netherlands GT-30035, University of Glamorgan, publisher of Electronics, Pontypridd, CF37 1DL, Wales, UK, 2002.

H. Al-Hiary, A. Sheta, and A. Ayesh, “Identification of a chemical process reactor using soft computing techniques,” in Proceedings of the 2008 International Conference on Fuzzy Systems (FUZZ2008) within the 2008 IEEE World Congress on Computational Intelligence (WCCI2008), Hong Kong, 1-6 June, pp. 845–653, 2008.

M. Gibson, E.Ferreira, X.Cheng, T.Knight, D.Greve, and B.Krogh, “System identification methods for plasma enhanced chemical vapor deposition,” ECE Department Carnegie Mellon University, 1997.

M. Norgaard, “Neural network based system identification toolbox,” Department of Automation. Technical University of Denmark, 2000.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2023 Praise Worthy Prize